From 3106480d1578852dac46838162cd326e7cc49192 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Mon, 30 Aug 2021 15:21:42 +0900 Subject: [PATCH 01/47] Fix database.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit database.py 설정이 오류가 나지않도록 수정하였습니다. 줄바꿈을 적용하다가 실수가 있어 제대로 작동하지않았습니다. --- app/database.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/app/database.py b/app/database.py index 0c04917..6264aa8 100644 --- a/app/database.py +++ b/app/database.py @@ -15,9 +15,10 @@ def connect(db): POSTGRES_PASSWORD = os.getenv("POSTGRES_PASSWORD") POSTGRES_PORT = os.getenv("POSTGRES_PORT") POSTGRES_SERVER = os.getenv("POSTGRES_SERVER") - POSTGRES_DB = db - SQLALCHEMY_DATABASE_URL = f"postgresql://{POSTGRES_USER}:{POSTGRES_PASSWORD}\ - @{POSTGRES_SERVER}:{POSTGRES_PORT}/{POSTGRES_DB}" + + SQLALCHEMY_DATABASE_URL = \ + f'postgresql://{POSTGRES_USER}:{POSTGRES_PASSWORD}@' +\ + f'{POSTGRES_SERVER}:{POSTGRES_PORT}/{db}' connection = create_engine(SQLALCHEMY_DATABASE_URL) From 01e0bd7e71bb625e5cd87cc937757b62a9ae70cf Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Mon, 30 Aug 2021 18:39:09 +0900 Subject: [PATCH 02/47] Add upload feature MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit upload_file 을 구현하였습니다. 파일을 받아서 file_name(path) 와 db에 저장되는 형식인 pickle 이라는 dictionary를 리턴합니다. 임시적인 구현입니다. --- .pre-commit-config.yaml | 6 ++--- app/crud.py | 17 +++++++++++++ app/models.py | 19 +++++++++++++++ app/router/predict.py | 16 ++++++++++++ app/router/test.py | 54 +++++++++++++++++++++++++++++++++++++++++ app/router/upload.py | 49 +++++++++++++++++++++++++++++++++++++ app/schemas.py | 28 +++++++++++++++++++++ main.py | 15 +++++++++++- 8 files changed, 200 insertions(+), 4 deletions(-) create mode 100644 app/crud.py create mode 100644 app/models.py create mode 100644 app/router/test.py create mode 100644 app/router/upload.py create mode 100644 app/schemas.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 84a6bde..ec4bac1 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,9 +1,9 @@ repos: - - repo: https://github.com/PyCQA/flake8 - rev: 3.9.2 + - repo: https://github.com/pre-commit/mirrors-autopep8 + rev: 'v1.5.7' hooks: - - id: flake8 + - id: autopep8 diff --git a/app/crud.py b/app/crud.py new file mode 100644 index 0000000..195f5ac --- /dev/null +++ b/app/crud.py @@ -0,0 +1,17 @@ +from sqlalchemy.orm import Session + +from . import models, schemas + + +def get_pickle(db: Session, version=1): + return db.query(models.Pickle).filter( + models.Pickle.version == version + ).first() + + +def create_pickle(db: Session, pickle: schemas.PickleCreate): + db_pickle = models.Pickle(**pickle) + db.add(db_pickle) + db.commit() + db.refresh(db_pickle) + return db_pickle diff --git a/app/models.py b/app/models.py new file mode 100644 index 0000000..3388638 --- /dev/null +++ b/app/models.py @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +from sqlalchemy import Column, Integer, String + +from .database import Base + + +class Item(Base): + __tablename__ = 'items' + + id = Column(Integer, primary_key=True, index=True, autoincrement=True) + title = Column(String, index=True, default='test') + + +class Pickle(Base): + __tablename__ = 'pickle' + + id = Column(Integer, primary_key=True, index=True, autoincrement=True) + path = Column(String, index=True) + version = Column(Integer, index=True, autoincrement=True) diff --git a/app/router/predict.py b/app/router/predict.py index f60a49e..75b56d1 100644 --- a/app/router/predict.py +++ b/app/router/predict.py @@ -1,5 +1,21 @@ +# -*- coding: utf-8 -*- from fastapi import APIRouter +from .. import models +from ..database import SessionLocal, engine + + +models.Base.metadata.create_all(bind=engine) + + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() + + router = APIRouter( prefix="/predict", tags=["predict"], diff --git a/app/router/test.py b/app/router/test.py new file mode 100644 index 0000000..26d2c14 --- /dev/null +++ b/app/router/test.py @@ -0,0 +1,54 @@ +# -*- coding: utf-8 -*- +from app import crud + +from fastapi import Depends, APIRouter +import numpy as np +from sqlalchemy.orm import Session + +from .. import models +from ..database import SessionLocal, engine + + +models.Base.metadata.create_all(bind=engine) + +# Dependency + + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() + + +router = APIRouter( + prefix="/test", + tags=["test"], + responses={404: {"description": "Not Found"}} +) + + +@router.get('/file') +def read_file(column=0, row=5, db: Session = Depends(get_db)): + """ + Upload file Temporary + + param + (validation 진행 필요) + column: int (default=0) + row: int (deafult=5) + return + (임시적으로 구성) + file_name: str (path of filename) + pickle: + path: str + version: int + """ + pkl = crud.get_pickle(db, version=1) + try: + data = np.load(pkl.path, allow_pickle=True) + return {'data': data[:int(row), 0:int(column)+1].tolist()} + except Exception as e: + print(e) + return 0 diff --git a/app/router/upload.py b/app/router/upload.py new file mode 100644 index 0000000..11c318f --- /dev/null +++ b/app/router/upload.py @@ -0,0 +1,49 @@ +# -*- coding: utf-8 -*- +import shutil + +from fastapi import APIRouter, Depends, File, UploadFile +from sqlalchemy.orm import Session + +from app import crud +from .. import models +from ..database import SessionLocal, engine + + +models.Base.metadata.create_all(bind=engine) + + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() + + +router = APIRouter( + prefix="/upload", + tags=["upload"], + responses={404: {"description": "Not Found"}} +) + + +@router.post('/file') +async def upload_file(file: UploadFile = File(...), db: Session = Depends(get_db)): + """ + Upload file Temporary + + param + file: File + return (임시적으로 구성) + file_name: str (path of filename) + pickle: + path: str + version: int + """ + with open(f'{file.filename}', 'wb') as buffer: + shutil.copyfileobj(file.file, buffer) + pickle = crud.create_pickle(db, pickle={ + 'path': file.filename, + 'version': 1 + }) + return {'file_name': file.filename, 'pickle': pickle} diff --git a/app/schemas.py b/app/schemas.py new file mode 100644 index 0000000..d5fbc8e --- /dev/null +++ b/app/schemas.py @@ -0,0 +1,28 @@ +from pydantic import BaseModel + + +class ItemBase(BaseModel): + title: str + + +class ItemCreate(ItemBase): + pass + + +class Item(ItemBase): + class Config: + orm_mode = True + + +class PickleBase(BaseModel): + path: str + version: int + + +class PickleCreate(PickleBase): + pass + + +class Pickle(PickleBase): + class config: + orm_mode = True diff --git a/main.py b/main.py index 454a233..f05c01d 100644 --- a/main.py +++ b/main.py @@ -1,11 +1,24 @@ from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware -from app.router import predict +from app.router import test, predict, upload from app.database import SessionLocal app = FastAPI() +origins = ["*"] + +app.add_middleware( + CORSMiddleware, + allow_origins=origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +app.include_router(test.router) app.include_router(predict.router) +app.include_router(upload.router) def get_db(): From 662b6145b89ede4abdc26d3f62c35eb15d993949 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Tue, 31 Aug 2021 18:17:48 +0900 Subject: [PATCH 03/47] Add training feature MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 임시로 /train/mnist 로 post request 하면 model을 training 하고 model을 file로 저장한 후 path를 얻어서 그 정보들로 db에 저장합니다. /test/model 로 get request를 보내면 저장된 model을 불러와서 임시적으로 있는 test_mnist 에 대해 예측하여 값을 반환합니다. --- app/crud.py | 15 ++++++++++ app/models.py | 10 +++++++ app/router/test.py | 36 ++++++++++++++++++++++-- app/router/train.py | 67 ++++++++++++++++++++++++++++++++++++++++++++ app/schemas.py | 16 +++++++++++ app/util.py | 15 ++++++++++ main.py | 3 +- requirements.txt | Bin 0 -> 1596 bytes test_mnist.pkl | Bin 0 -> 3287 bytes 9 files changed, 158 insertions(+), 4 deletions(-) create mode 100644 app/router/train.py create mode 100644 app/util.py create mode 100644 requirements.txt create mode 100644 test_mnist.pkl diff --git a/app/crud.py b/app/crud.py index 195f5ac..f7ee5f9 100644 --- a/app/crud.py +++ b/app/crud.py @@ -15,3 +15,18 @@ def create_pickle(db: Session, pickle: schemas.PickleCreate): db.commit() db.refresh(db_pickle) return db_pickle + + +def get_clf_model(db: Session, version=1, name='random_forest'): + return db.query(models.ClfModel).filter( + models.ClfModel.version == version and + models.ClfModel.name == name + ).first() + + +def create_clf_model(db: Session, clf_model: schemas.ClfModelCreate): + db_cf_model = models.ClfModel(**clf_model) + db.add(db_cf_model) + db.commit() + db.refresh(db_cf_model) + return db_cf_model diff --git a/app/models.py b/app/models.py index 3388638..8d455b8 100644 --- a/app/models.py +++ b/app/models.py @@ -17,3 +17,13 @@ class Pickle(Base): id = Column(Integer, primary_key=True, index=True, autoincrement=True) path = Column(String, index=True) version = Column(Integer, index=True, autoincrement=True) + + +class ClfModel(Base): + __tablename__ = 'clf_model' + + id = Column(Integer, primary_key=True, index=True, autoincrement=True) + path = Column(String, index=True) + version = Column(Integer, index=True, autoincrement=True) + name = Column(String, index=True) + classes = Column(Integer) diff --git a/app/router/test.py b/app/router/test.py index 26d2c14..fd42cd6 100644 --- a/app/router/test.py +++ b/app/router/test.py @@ -1,4 +1,5 @@ # -*- coding: utf-8 -*- +import pickle from app import crud from fastapi import Depends, APIRouter @@ -30,9 +31,9 @@ def get_db(): @router.get('/file') -def read_file(column=0, row=5, db: Session = Depends(get_db)): +def read_file(column: int = 0, row: int = 5, db: Session = Depends(get_db)): """ - Upload file Temporary + Read file Temporary param (validation 진행 필요) @@ -48,7 +49,36 @@ def read_file(column=0, row=5, db: Session = Depends(get_db)): pkl = crud.get_pickle(db, version=1) try: data = np.load(pkl.path, allow_pickle=True) - return {'data': data[:int(row), 0:int(column)+1].tolist()} + return {'data': data[:row, 0:column+1].tolist()} except Exception as e: print(e) return 0 + + +@router.get('/model') +def read_model(version=1, name='random_forest', db: Session = Depends(get_db)): + """ + Read Model Temporary + + param + version: int + name: str + return + path: str + version: int + name: str + classes: int + """ + clf_model = crud.get_clf_model(db, version=version, name=name) + print(clf_model.path) + try: + loaded_model = pickle.load(open(clf_model.path, 'rb')) + test = pickle.load(open('test_mnist.pkl', 'rb')).reshape(1, -1) + + pred = loaded_model.predict(test) + + return pred.tolist() + + except Exception as e: + print(e) + return 11 diff --git a/app/router/train.py b/app/router/train.py new file mode 100644 index 0000000..63ce873 --- /dev/null +++ b/app/router/train.py @@ -0,0 +1,67 @@ +# -*- coding: utf-8 -*- +from fastapi import Depends, APIRouter +import numpy as np +import pickle +from sklearn.ensemble import RandomForestClassifier +from sqlalchemy.orm import Session + +from app.util import preprocessing +from app import crud + +import app.models as models +from app.database import SessionLocal, engine + + +models.Base.metadata.create_all(bind=engine) + +# Dependency + + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() + + +router = APIRouter( + prefix="/train", + tags=["train"], + responses={404: {"description": "Not Found"}} +) + + +@router.post('/mnist') +def train_mnist_rf(version: int = 1, db: Session = Depends(get_db)): + """ + param + version: int + return + path: str + version: int + name: str + classes: int + """ + + pkl = crud.get_pickle(db, version=version) + data = np.load(pkl.path, allow_pickle=True) + + X_train, X_valid, y_train, y_test = preprocessing(data) + + clf_model = RandomForestClassifier(n_estimators=500, + max_depth=3, + random_state=0) + + clf_model.fit(X_train, y_train) + + pickle_md = crud.create_clf_model(db, clf_model={ + 'path': 'model.pkl', + 'version': version, + 'name': 'random_forest', + 'classes': len(np.unique(y_train)) + }) + + pickle.dump(clf_model, open('model.pkl', 'wb')) + + return pickle_md diff --git a/app/schemas.py b/app/schemas.py index d5fbc8e..42ffb52 100644 --- a/app/schemas.py +++ b/app/schemas.py @@ -26,3 +26,19 @@ class PickleCreate(PickleBase): class Pickle(PickleBase): class config: orm_mode = True + + +class ClfModelBase(BaseModel): + path: str + version: int + name: str + classes: int + + +class ClfModelCreate(ClfModelBase): + pass + + +class ClfModel(ClfModelBase): + class Config: + orm_mode = True diff --git a/app/util.py b/app/util.py new file mode 100644 index 0000000..e24f4a8 --- /dev/null +++ b/app/util.py @@ -0,0 +1,15 @@ +import numpy as np +from sklearn.model_selection import train_test_split + + +def preprocessing(data: np.ndarray, + TEST_SIZE: float = 0.2): + """ + return train_test_split result + """ + + X, y = data[:, 1:], data[:, 0] + X_train, X_valid, y_train, y_valid = \ + train_test_split(X, y, test_size=TEST_SIZE) + + return X_train, X_valid, y_train, y_valid diff --git a/main.py b/main.py index f05c01d..250b4a9 100644 --- a/main.py +++ b/main.py @@ -1,7 +1,7 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware -from app.router import test, predict, upload +from app.router import test, predict, train, upload from app.database import SessionLocal app = FastAPI() @@ -19,6 +19,7 @@ app.include_router(test.router) app.include_router(predict.router) app.include_router(upload.router) +app.include_router(train.router) def get_db(): diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..b407b19e10ac6bdc2c4ed9448a352d647f225f37 GIT binary patch literal 1596 zcmZXU&2HO341{?u&_{6)$&J$-ddRhCfVMgH=*a((Wkr&cx)0y>8&WGd2@KhWI3#C= z-2L}=Zd*Ipito~v*4h_7mv&)SJeT&-KG@tUqAjS;Hsf99m}`%ZnAX;y8fvUOJ|+H% zC+lmpcA`#U6A-^acKmDkp7ZX}GqS$o`;0X?6;A`YgGV#qvz}w&(T=)OA`Ll_obI7y zI^Z_cXz09j29;Oa^R6WCk5`FoDU$*@P+4sL16QZXC)*Uo)Uf9+qY9TTm2cVg2H)Lx(QzacxE)dqzN+Vf&i zpqWSa^BtRjJ>ZW|^yThA^!&-$Prk{QP}ZVmi}P_SXm7+I&WXC`-X$~!R@_saIFH(_mI`D2 z?5yv-{ebf=3JX6G`?Gv0$oq2@XQ~0G-IaUl63zuqeRPhcc8f literal 0 HcmV?d00001 diff --git a/test_mnist.pkl b/test_mnist.pkl new file mode 100644 index 0000000000000000000000000000000000000000..72b0f57ebf58b8a23129a6f78e90b607efd83b06 GIT binary patch literal 3287 zcmeH~Pe>F|9LINEH4q`gy41m}gC1fD^yoqI5-n}uL9h;@y9-IV$~YVBAzBMDVIa}h z!ERnU6x1>LLnw%N2tvB(sX*|cXF(GGe7?IgAj=Ty?g)lo_{^Jq?>FD?n>X(rHdp!j zM{OuC=TbT|FqDlZ2CYp= zp;6Nj?lWVi(=^Azck3df=3&ICO4`|>l%A>RZ#CmD3&Rm7+-BnO_?zuT!_26?@Q7Vy&zI#r{6 zb6i#P-~`C$6EuLIw5HEMbt`@ahwSTiJ^r;1u2SQDSJfrqxO&Hfn)6d1g9v?3;18%~ z8}36X^75V^FuBIPauz{3VgnvQsj@K8IrC%B?f*jC4CT;8Os2tmkAtn|@EIzhoYHr? z;Btj_0hId_bS{IE&PEQDrSo_bg4Auym8E^y6J!wHTF+mgyb%amV+1H`6Pf}JtHFO3 hl=mBg)(ZlZB_0GFhX0J4^?|nj1oBREgyEk=bRQyyne_kw literal 0 HcmV?d00001 From 856cc42eb7b4a43e6eaefe5bbb86c4836a23ea0e Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Tue, 31 Aug 2021 19:12:50 +0900 Subject: [PATCH 04/47] Modify name Pickle to Dataset MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 원래 Pickle이라 model, schema name등을 지정했는데 적절하지 않은 이름으로 보여서 Dataset으로 변경하였습니다. 추후에 저장방법이 통일되거나? 하면 적절한 이름을 찾아 다시 수정하겠습니다. --- app/crud.py | 16 ++++++++-------- app/models.py | 4 ++-- app/router/test.py | 3 +-- app/router/train.py | 2 +- app/router/upload.py | 7 ++++--- app/schemas.py | 6 +++--- 6 files changed, 19 insertions(+), 19 deletions(-) diff --git a/app/crud.py b/app/crud.py index f7ee5f9..3f9639e 100644 --- a/app/crud.py +++ b/app/crud.py @@ -3,18 +3,18 @@ from . import models, schemas -def get_pickle(db: Session, version=1): - return db.query(models.Pickle).filter( - models.Pickle.version == version +def get_dataset(db: Session, version=1): + return db.query(models.Dataset).filter( + models.Dataset.version == version ).first() -def create_pickle(db: Session, pickle: schemas.PickleCreate): - db_pickle = models.Pickle(**pickle) - db.add(db_pickle) +def create_dataset(db: Session, Dataset: schemas.DatasetCreate): + db_dataset = models.Dataset(**Dataset) + db.add(db_dataset) db.commit() - db.refresh(db_pickle) - return db_pickle + db.refresh(db_dataset) + return db_dataset def get_clf_model(db: Session, version=1, name='random_forest'): diff --git a/app/models.py b/app/models.py index 8d455b8..a970377 100644 --- a/app/models.py +++ b/app/models.py @@ -11,8 +11,8 @@ class Item(Base): title = Column(String, index=True, default='test') -class Pickle(Base): - __tablename__ = 'pickle' +class Dataset(Base): + __tablename__ = 'dataset' id = Column(Integer, primary_key=True, index=True, autoincrement=True) path = Column(String, index=True) diff --git a/app/router/test.py b/app/router/test.py index fd42cd6..241e161 100644 --- a/app/router/test.py +++ b/app/router/test.py @@ -46,7 +46,7 @@ def read_file(column: int = 0, row: int = 5, db: Session = Depends(get_db)): path: str version: int """ - pkl = crud.get_pickle(db, version=1) + pkl = crud.get_dataset(db, version=1) try: data = np.load(pkl.path, allow_pickle=True) return {'data': data[:row, 0:column+1].tolist()} @@ -70,7 +70,6 @@ def read_model(version=1, name='random_forest', db: Session = Depends(get_db)): classes: int """ clf_model = crud.get_clf_model(db, version=version, name=name) - print(clf_model.path) try: loaded_model = pickle.load(open(clf_model.path, 'rb')) test = pickle.load(open('test_mnist.pkl', 'rb')).reshape(1, -1) diff --git a/app/router/train.py b/app/router/train.py index 63ce873..6cd76e4 100644 --- a/app/router/train.py +++ b/app/router/train.py @@ -44,7 +44,7 @@ def train_mnist_rf(version: int = 1, db: Session = Depends(get_db)): classes: int """ - pkl = crud.get_pickle(db, version=version) + pkl = crud.get_dataset(db, version=version) data = np.load(pkl.path, allow_pickle=True) X_train, X_valid, y_train, y_test = preprocessing(data) diff --git a/app/router/upload.py b/app/router/upload.py index 11c318f..957e895 100644 --- a/app/router/upload.py +++ b/app/router/upload.py @@ -1,4 +1,5 @@ # -*- coding: utf-8 -*- +from app.schemas import Dataset import shutil from fastapi import APIRouter, Depends, File, UploadFile @@ -36,14 +37,14 @@ async def upload_file(file: UploadFile = File(...), db: Session = Depends(get_db file: File return (임시적으로 구성) file_name: str (path of filename) - pickle: + dataset: path: str version: int """ with open(f'{file.filename}', 'wb') as buffer: shutil.copyfileobj(file.file, buffer) - pickle = crud.create_pickle(db, pickle={ + dataset = crud.create_dataset(db, Dataset={ 'path': file.filename, 'version': 1 }) - return {'file_name': file.filename, 'pickle': pickle} + return {'file_name': file.filename, 'dataset': dataset} diff --git a/app/schemas.py b/app/schemas.py index 42ffb52..cd5e820 100644 --- a/app/schemas.py +++ b/app/schemas.py @@ -14,16 +14,16 @@ class Config: orm_mode = True -class PickleBase(BaseModel): +class DatasetBase(BaseModel): path: str version: int -class PickleCreate(PickleBase): +class DatasetCreate(DatasetBase): pass -class Pickle(PickleBase): +class Dataset(DatasetBase): class config: orm_mode = True From b624f4ef6711ea76669c7bfe44103a986831e977 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Wed, 1 Sep 2021 13:33:12 +0900 Subject: [PATCH 05/47] Fix import order by convention MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit convention에 맞게 import 를 수정하였습니다. --- .gitignore | 3 ++- app/crud.py | 2 +- app/database.py | 3 +-- app/models.py | 2 +- app/router/predict.py | 5 +++-- app/router/test.py | 13 +++++++------ app/router/train.py | 21 +++++++++++++-------- app/router/upload.py | 12 +++++++----- app/util.py | 4 ++-- 9 files changed, 37 insertions(+), 28 deletions(-) diff --git a/.gitignore b/.gitignore index 2eea525..3545ad1 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,2 @@ -.env \ No newline at end of file +.env +*.pkl \ No newline at end of file diff --git a/app/crud.py b/app/crud.py index 3f9639e..03d6dbe 100644 --- a/app/crud.py +++ b/app/crud.py @@ -1,6 +1,6 @@ from sqlalchemy.orm import Session -from . import models, schemas +from app import models, schemas def get_dataset(db: Session, version=1): diff --git a/app/database.py b/app/database.py index 6264aa8..08d8e96 100644 --- a/app/database.py +++ b/app/database.py @@ -1,11 +1,10 @@ import os +from dotenv import load_dotenv from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker from sqlalchemy.ext.declarative import declarative_base -from dotenv import load_dotenv - def connect(db): diff --git a/app/models.py b/app/models.py index a970377..3e27286 100644 --- a/app/models.py +++ b/app/models.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- from sqlalchemy import Column, Integer, String -from .database import Base +from app.database import Base class Item(Base): diff --git a/app/router/predict.py b/app/router/predict.py index 75b56d1..d82bff6 100644 --- a/app/router/predict.py +++ b/app/router/predict.py @@ -1,8 +1,9 @@ # -*- coding: utf-8 -*- from fastapi import APIRouter -from .. import models -from ..database import SessionLocal, engine +from app import models +from app.database import engine +from app.database import SessionLocal models.Base.metadata.create_all(bind=engine) diff --git a/app/router/test.py b/app/router/test.py index 241e161..84a5217 100644 --- a/app/router/test.py +++ b/app/router/test.py @@ -1,13 +1,14 @@ # -*- coding: utf-8 -*- -import pickle -from app import crud - -from fastapi import Depends, APIRouter import numpy as np +import pickle +from fastapi import APIRouter +from fastapi import Depends from sqlalchemy.orm import Session -from .. import models -from ..database import SessionLocal, engine +from app import crud +from app import models +from app.database import engine +from app.database import SessionLocal models.Base.metadata.create_all(bind=engine) diff --git a/app/router/train.py b/app/router/train.py index 6cd76e4..40be369 100644 --- a/app/router/train.py +++ b/app/router/train.py @@ -1,15 +1,16 @@ # -*- coding: utf-8 -*- -from fastapi import Depends, APIRouter +from fastapi import APIRouter +from fastapi import Depends import numpy as np import pickle from sklearn.ensemble import RandomForestClassifier from sqlalchemy.orm import Session -from app.util import preprocessing from app import crud - +from app.database import engine +from app.database import SessionLocal import app.models as models -from app.database import SessionLocal, engine +from app.util import mnist_preprocessing models.Base.metadata.create_all(bind=engine) @@ -33,7 +34,11 @@ def get_db(): @router.post('/mnist') -def train_mnist_rf(version: int = 1, db: Session = Depends(get_db)): +def train_mnist_rf( + model_name: str = 'model.pkl', + version: int = 1, + db: Session = Depends(get_db) +): """ param version: int @@ -44,10 +49,10 @@ def train_mnist_rf(version: int = 1, db: Session = Depends(get_db)): classes: int """ - pkl = crud.get_dataset(db, version=version) - data = np.load(pkl.path, allow_pickle=True) + dataset = crud.get_dataset(db, version=version) + data = np.load(dataset.path, allow_pickle=True) - X_train, X_valid, y_train, y_test = preprocessing(data) + X_train, X_valid, y_train, y_test = mnist_preprocessing(data) clf_model = RandomForestClassifier(n_estimators=500, max_depth=3, diff --git a/app/router/upload.py b/app/router/upload.py index 957e895..021405c 100644 --- a/app/router/upload.py +++ b/app/router/upload.py @@ -1,14 +1,16 @@ # -*- coding: utf-8 -*- -from app.schemas import Dataset import shutil -from fastapi import APIRouter, Depends, File, UploadFile +from fastapi import APIRouter +from fastapi import Depends +from fastapi import File +from fastapi import UploadFile from sqlalchemy.orm import Session from app import crud -from .. import models -from ..database import SessionLocal, engine - +from app import models +from app.database import engine +from app.database import SessionLocal models.Base.metadata.create_all(bind=engine) diff --git a/app/util.py b/app/util.py index e24f4a8..2406b38 100644 --- a/app/util.py +++ b/app/util.py @@ -2,8 +2,8 @@ from sklearn.model_selection import train_test_split -def preprocessing(data: np.ndarray, - TEST_SIZE: float = 0.2): +def mnist_preprocessing(data: np.ndarray, + TEST_SIZE: float = 0.2): """ return train_test_split result """ From 3de74ab9b38d7731f33b41dba39fed37ff7ae9a3 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Thu, 2 Sep 2021 21:06:28 +0900 Subject: [PATCH 06/47] Add local NNI tunning --- module/config.yml | 21 ++++++++ module/search_space.json | 6 +++ module/trial.py | 113 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 140 insertions(+) create mode 100644 module/config.yml create mode 100644 module/search_space.json create mode 100644 module/trial.py diff --git a/module/config.yml b/module/config.yml new file mode 100644 index 0000000..4a22ca2 --- /dev/null +++ b/module/config.yml @@ -0,0 +1,21 @@ +authorName: ehddnr +experimentName: Lab04 +trialConcurrency: 1 +maxExecDuration: 1h +maxTrialNum: 10 +#choice: local, remote, pai +trainingServicePlatform: local +#nniManagerIp: +#choice: true, false +searchSpacePath: search_space.json +useAnnotation: false +tuner: + #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner + #SMAC (SMAC should be installed through nnictl) + builtinTunerName: Anneal + classArgs: + #choice: maximize, minimize + optimize_mode: minimize +trial: + command: python trial.py + codeDir: . \ No newline at end of file diff --git a/module/search_space.json b/module/search_space.json new file mode 100644 index 0000000..774f778 --- /dev/null +++ b/module/search_space.json @@ -0,0 +1,6 @@ +{ + "max_depth": {"_type":"randint", "_value":[2, 10]}, + "colsample_bytree": {"_type":"uniform", "_value":[0.3, 1.0]}, + "learning_rate": {"_type":"uniform", "_value":[0.001, 0.1]}, + "n_estimators": {"_type":"randint", "_value":[100, 200]} +} \ No newline at end of file diff --git a/module/trial.py b/module/trial.py new file mode 100644 index 0000000..43c0e23 --- /dev/null +++ b/module/trial.py @@ -0,0 +1,113 @@ +from sklearn.model_selection import train_test_split +from xgboost import XGBClassifier +from sklearn.metrics import mean_absolute_error, mean_squared_error +from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler +from sklearn.model_selection import KFold +import pandas as pd +import numpy as np +import nni +from os.path import join +import multiprocessing +from sqlalchemy.engine import create_engine +from xgboost.sklearn import XGBRegressor + +n_cpus = multiprocessing.cpu_count() + + +def preprocess(x_train, x_valid, col_list): + tmp_x_train = x_train.copy() + tmp_x_valid = x_valid.copy() + + tmp_x_train.reset_index(drop=True, inplace=True) + tmp_x_valid.reset_index(drop=True, inplace=True) + + # 전처리 함수 작성 + encoder = LabelEncoder() + + for col in col_list: + tmp_x_train.loc[:, col] = encoder.fit_transform( + tmp_x_train.loc[:, col]) + tmp_x_valid.loc[:, col] = encoder.transform(tmp_x_valid.loc[:, col]) + + return tmp_x_train.values, tmp_x_valid.values + + +def main(params, df): + global best_model, best_mae + + # df = pd.read_csv(join('.', 'insurance.csv')) + + label_col = ['sex', 'smoker', 'region'] + + y = df.charges.to_frame() + x = df.iloc[:, :-1] + + x_train, x_valid, y_train, y_valid = train_test_split( + x, y, test_size=0.2, random_state=42) + + kf = KFold(n_splits=5, shuffle=True, random_state=42) + + cv_mse = [] + cv_mae = [] + + best_model = None + best_mae = 99999999 + + for trn_idx, val_idx in kf.split(x, y): + x_train, y_train = x.iloc[trn_idx], y.iloc[trn_idx] + x_valid, y_valid = x.iloc[val_idx], y.iloc[val_idx] + + # 전처리 + x_tra, x_val = preprocess(x_train, x_valid, label_col) + + # 모델 정의 및 파라미터 전달 + model = XGBRegressor(**params) + + # 모델 학습 및 Early Stopping 적용 + model.fit(x_tra, y_train) + + y_train_pred = model.predict(x_tra) + y_valid_pred = model.predict(x_val) + # Loss 계산 + train_mse = mean_squared_error(y_train, y_train_pred) + valid_mse = mean_squared_error(y_valid, y_valid_pred) + train_mae = mean_absolute_error(y_train, y_train_pred) + valid_mae = mean_absolute_error(y_valid, y_valid_pred) + + cv_mse.append(valid_mse) + cv_mae.append(valid_mae) + + if best_model: + temp = best_mae + best_mae = min(best_mae, valid_mae) + print('best_mae::', best_mae) + if temp != best_mae: + best_model = model + print('best_model::', best_model) + else: + best_mae = valid_mae + best_model = model + print(best_mae) + + cv_mse_mean = np.mean(cv_mse) + cv_mae_mean = np.mean(cv_mae) + + print(cv_mse_mean, cv_mae_mean) + # 학습 결과 리포팅 + print(best_model) + nni.report_final_result(cv_mae_mean) + print('Final result is %g', cv_mae_mean) + print('Send final result done.') + + +if __name__ == '__main__': + params = nni.get_next_parameter() + engine = create_engine( + "postgresql://postgres:0000@localhost:5432/postgres") + + df = pd.read_sql(""" + SELECT * + FROM insurance + """, engine) + + main(params, df) From 8af94e6ba50f13772e0b94ddf5bb1abf167e72f4 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Fri, 3 Sep 2021 19:16:55 +0900 Subject: [PATCH 07/47] Add model save in NNi tunning preocess MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit NNi 로 튜닝되는 과정에서 DB와 통신합니다. DB의 기록과 비교해서 성능이 더 잘나온다면 덮어씁니다. --- .pre-commit-config.yaml | 6 +-- app/models.py | 21 +++++++- app/schemas.py | 1 + module/__init__.py | 0 module/trial.py | 116 ++++++++++++++++++++++++++++------------ 5 files changed, 104 insertions(+), 40 deletions(-) create mode 100644 module/__init__.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ec4bac1..8557ba5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,8 +3,4 @@ repos: - repo: https://github.com/pre-commit/mirrors-autopep8 rev: 'v1.5.7' hooks: - - id: autopep8 - - - - + - id: autopep8 \ No newline at end of file diff --git a/app/models.py b/app/models.py index 3e27286..275d996 100644 --- a/app/models.py +++ b/app/models.py @@ -1,8 +1,12 @@ # -*- coding: utf-8 -*- -from sqlalchemy import Column, Integer, String +import datetime +from sqlalchemy import Column, Integer, String, FLOAT, DateTime +from sqlalchemy.sql.functions import now from app.database import Base +KST = datetime.timezone(datetime.timedelta(hours=9)) + class Item(Base): __tablename__ = 'items' @@ -27,3 +31,18 @@ class ClfModel(Base): version = Column(Integer, index=True, autoincrement=True) name = Column(String, index=True) classes = Column(Integer) + score = Column(FLOAT) + + +class RegModel(Base): + __tablename__ = 'reg_model' + + id = Column(Integer, primary_key=True, index=True, autoincrement=True) + path = Column(String, nullable=False) + experimenter = Column(String, nullable=False) + experiment_name = Column(String, nullable=False) + model_name = Column(String, nullable=False) + version = Column(FLOAT, autoincrement=True) + metric = Column(String, nullable=False) + score = Column(FLOAT, nullable=False) + created_at = Column(DateTime(timezone=True), server_default=now()) diff --git a/app/schemas.py b/app/schemas.py index cd5e820..57d95dc 100644 --- a/app/schemas.py +++ b/app/schemas.py @@ -33,6 +33,7 @@ class ClfModelBase(BaseModel): version: int name: str classes: int + score: float class ClfModelCreate(ClfModelBase): diff --git a/module/__init__.py b/module/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/module/trial.py b/module/trial.py index 43c0e23..6ce2072 100644 --- a/module/trial.py +++ b/module/trial.py @@ -1,17 +1,18 @@ -from sklearn.model_selection import train_test_split -from xgboost import XGBClassifier -from sklearn.metrics import mean_absolute_error, mean_squared_error -from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler -from sklearn.model_selection import KFold +import os +import pickle + +from dotenv import load_dotenv import pandas as pd import numpy as np import nni -from os.path import join -import multiprocessing +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_absolute_error, mean_squared_error +from sklearn.preprocessing import LabelEncoder +from sklearn.model_selection import KFold from sqlalchemy.engine import create_engine from xgboost.sklearn import XGBRegressor -n_cpus = multiprocessing.cpu_count() +load_dotenv(verbose=True) def preprocess(x_train, x_valid, col_list): @@ -21,7 +22,6 @@ def preprocess(x_train, x_valid, col_list): tmp_x_train.reset_index(drop=True, inplace=True) tmp_x_valid.reset_index(drop=True, inplace=True) - # 전처리 함수 작성 encoder = LabelEncoder() for col in col_list: @@ -32,10 +32,18 @@ def preprocess(x_train, x_valid, col_list): return tmp_x_train.values, tmp_x_valid.values -def main(params, df): - global best_model, best_mae +def main(params, df, engine, experiment_info, connection): + """ + + """ - # df = pd.read_csv(join('.', 'insurance.csv')) + path = experiment_info['path'] + experimenter = experiment_info['experimenter'] + experiment_name = experiment_info['experiment_name'] + model_name = experiment_info['model_name'] + version = experiment_info['version'] + + global best_model, best_mae label_col = ['sex', 'smoker', 'region'] @@ -50,9 +58,6 @@ def main(params, df): cv_mse = [] cv_mae = [] - best_model = None - best_mae = 99999999 - for trn_idx, val_idx in kf.split(x, y): x_train, y_train = x.iloc[trn_idx], y.iloc[trn_idx] x_valid, y_valid = x.iloc[val_idx], y.iloc[val_idx] @@ -64,7 +69,8 @@ def main(params, df): model = XGBRegressor(**params) # 모델 학습 및 Early Stopping 적용 - model.fit(x_tra, y_train) + model.fit(x_tra, y_train, eval_set=[ + (x_val, y_valid)], early_stopping_rounds=10) y_train_pred = model.predict(x_tra) y_valid_pred = model.predict(x_val) @@ -77,24 +83,49 @@ def main(params, df): cv_mse.append(valid_mse) cv_mae.append(valid_mae) - if best_model: - temp = best_mae - best_mae = min(best_mae, valid_mae) - print('best_mae::', best_mae) - if temp != best_mae: - best_model = model - print('best_model::', best_model) - else: - best_mae = valid_mae - best_model = model - print(best_mae) - cv_mse_mean = np.mean(cv_mse) cv_mae_mean = np.mean(cv_mae) - print(cv_mse_mean, cv_mae_mean) - # 학습 결과 리포팅 - print(best_model) + best_model = pd.read_sql(f""" + SELECT * + FROM reg_model + WHERE experiment_name = {experiment_name} + """, engine) + + if len(best_model) == 0: + + with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f: + pickle.dump(model, f) + connection.execute(f""" + INSERT INTO reg_model ( + path, + experimenter, + experiment_name, + model_name, + version, + metric, + score + ) VALUES ( + {path}, + {experimenter}, + {experiment_name}, + {model_name}, + {version}, + 'mae', + {cv_mae_mean} + ) + """) + else: + with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f: + pickle.dump(model, f) + saved_score = best_model['score'].values[0] + if saved_score > valid_mae: + connection.execute(f""" + UPDATE reg_model + SET score = {cv_mae_mean} + WHERE experiment_name = {experiment_name} + """) + nni.report_final_result(cv_mae_mean) print('Final result is %g', cv_mae_mean) print('Send final result done.') @@ -102,12 +133,29 @@ def main(params, df): if __name__ == '__main__': params = nni.get_next_parameter() - engine = create_engine( - "postgresql://postgres:0000@localhost:5432/postgres") + POSTGRES_USER = os.getenv("POSTGRES_USER") + POSTGRES_PASSWORD = os.getenv("POSTGRES_PASSWORD") + POSTGRES_PORT = os.getenv("POSTGRES_PORT") + POSTGRES_SERVER = os.getenv("POSTGRES_SERVER") + POSTGRES_DB = os.getenv("POSTGRES_DB") + SQLALCHEMY_DATABASE_URL = \ + f'postgresql://{POSTGRES_USER}:{POSTGRES_PASSWORD}@' +\ + f'{POSTGRES_SERVER}:{POSTGRES_PORT}/{POSTGRES_DB}' + engine = create_engine(SQLALCHEMY_DATABASE_URL) + + experiment_info = { + 'path': "'C:\\Users\\TFG5076XG\\Documents\\MLOps'", + 'experimenter': "'DongUk'", + 'experiment_name': "'insurance0903'", + 'model_name': "'keep_update_model'", + 'version': 0.1 + } df = pd.read_sql(""" SELECT * FROM insurance """, engine) - main(params, df) + with engine.connect() as connection: + with connection.begin(): + main(params, df, engine, experiment_info, connection) From dcaafeb4a7036a66c6734941b0b06b49ad02b45a Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Sat, 4 Sep 2021 10:14:37 +0900 Subject: [PATCH 08/47] Modify RegModel Column MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit RegModel 에 대해서 원래는 validation mae만 저장하던것을 train, validation 에 대해 MAE와 MSE모두 저장합니다. --- app/models.py | 6 ++++-- module/trial.py | 42 +++++++++++++++++++++++++++++++++++------- 2 files changed, 39 insertions(+), 9 deletions(-) diff --git a/app/models.py b/app/models.py index 275d996..889a91a 100644 --- a/app/models.py +++ b/app/models.py @@ -43,6 +43,8 @@ class RegModel(Base): experiment_name = Column(String, nullable=False) model_name = Column(String, nullable=False) version = Column(FLOAT, autoincrement=True) - metric = Column(String, nullable=False) - score = Column(FLOAT, nullable=False) + train_mae = Column(FLOAT, nullable=False) + val_mae = Column(FLOAT, nullable=False) + train_mse = Column(FLOAT, nullable=False) + val_mse = Column(FLOAT, nullable=False) created_at = Column(DateTime(timezone=True), server_default=now()) diff --git a/module/trial.py b/module/trial.py index 6ce2072..bf883f2 100644 --- a/module/trial.py +++ b/module/trial.py @@ -16,6 +16,15 @@ def preprocess(x_train, x_valid, col_list): + """ + param: + x_train : train dataset dataframe + x_valid : validation dataset dataframe + col_list : columns that required for LabelEncoding + return: + tmp_x_train.values : numpy.ndarray + tmp_x_valid.values : numpy.ndarray + """ tmp_x_train = x_train.copy() tmp_x_valid = x_valid.copy() @@ -34,7 +43,12 @@ def preprocess(x_train, x_valid, col_list): def main(params, df, engine, experiment_info, connection): """ - + param: + params: Parameters determined by NNi + df: Dataframe read from DB + engine: sqlalchemy engine + experiment_info: information of experiment [dict] + connection: connection used to communicate with DB """ path = experiment_info['path'] @@ -57,6 +71,8 @@ def main(params, df, engine, experiment_info, connection): cv_mse = [] cv_mae = [] + tr_mse = [] + tr_mae = [] for trn_idx, val_idx in kf.split(x, y): x_train, y_train = x.iloc[trn_idx], y.iloc[trn_idx] @@ -82,9 +98,13 @@ def main(params, df, engine, experiment_info, connection): cv_mse.append(valid_mse) cv_mae.append(valid_mae) + tr_mse.append(train_mse) + tr_mae.append(train_mae) cv_mse_mean = np.mean(cv_mse) cv_mae_mean = np.mean(cv_mae) + tr_mse_mean = np.mean(tr_mse) + tr_mae_mean = np.mean(tr_mae) best_model = pd.read_sql(f""" SELECT * @@ -103,26 +123,34 @@ def main(params, df, engine, experiment_info, connection): experiment_name, model_name, version, - metric, - score + train_mae, + val_mae, + train_mse, + val_mse ) VALUES ( {path}, {experimenter}, {experiment_name}, {model_name}, {version}, - 'mae', - {cv_mae_mean} + {tr_mae_mean}, + {tr_mse_mean}, + {cv_mae_mean}, + {cv_mse_mean} ) """) else: with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f: pickle.dump(model, f) - saved_score = best_model['score'].values[0] + saved_score = best_model['val_mae'].values[0] if saved_score > valid_mae: connection.execute(f""" UPDATE reg_model - SET score = {cv_mae_mean} + SET + train_mae = {tr_mae_mean}, + val_mae = {cv_mae_mean}, + train_mse = {tr_mse_mean}, + val_mse = {cv_mse_mean} WHERE experiment_name = {experiment_name} """) From 87c41457ba87a25bac7cd3addd9469437247d50c Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Sat, 4 Sep 2021 10:38:54 +0900 Subject: [PATCH 09/47] Modify Project Structure MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 프로젝트 구조를 변경했습니다. 기존에 app 밑에 router가 바로있었는데 api에 사용되는 부분을 구분하기 위해서 api폴더를 따로 만들어서 router 폴더와 schema.py 파일을 이동하였습니다. --- app/api/__init__.py | 0 app/api/router/__init__.py | 0 app/api/router/predict.py | 29 +++++++++++++ app/api/router/test.py | 84 ++++++++++++++++++++++++++++++++++++++ app/api/router/train.py | 72 ++++++++++++++++++++++++++++++++ app/api/router/upload.py | 52 +++++++++++++++++++++++ app/{ => api}/schemas.py | 0 app/crud.py | 3 +- main.py | 2 +- 9 files changed, 240 insertions(+), 2 deletions(-) create mode 100644 app/api/__init__.py create mode 100644 app/api/router/__init__.py create mode 100644 app/api/router/predict.py create mode 100644 app/api/router/test.py create mode 100644 app/api/router/train.py create mode 100644 app/api/router/upload.py rename app/{ => api}/schemas.py (100%) diff --git a/app/api/__init__.py b/app/api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/api/router/__init__.py b/app/api/router/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/api/router/predict.py b/app/api/router/predict.py new file mode 100644 index 0000000..d82bff6 --- /dev/null +++ b/app/api/router/predict.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +from fastapi import APIRouter + +from app import models +from app.database import engine +from app.database import SessionLocal + + +models.Base.metadata.create_all(bind=engine) + + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() + + +router = APIRouter( + prefix="/predict", + tags=["predict"], + responses={404: {"description": "Not Found"}} +) + + +@router.get("/") +def hello_world(): + return {"message": "Hello predict"} diff --git a/app/api/router/test.py b/app/api/router/test.py new file mode 100644 index 0000000..84a5217 --- /dev/null +++ b/app/api/router/test.py @@ -0,0 +1,84 @@ +# -*- coding: utf-8 -*- +import numpy as np +import pickle +from fastapi import APIRouter +from fastapi import Depends +from sqlalchemy.orm import Session + +from app import crud +from app import models +from app.database import engine +from app.database import SessionLocal + + +models.Base.metadata.create_all(bind=engine) + +# Dependency + + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() + + +router = APIRouter( + prefix="/test", + tags=["test"], + responses={404: {"description": "Not Found"}} +) + + +@router.get('/file') +def read_file(column: int = 0, row: int = 5, db: Session = Depends(get_db)): + """ + Read file Temporary + + param + (validation 진행 필요) + column: int (default=0) + row: int (deafult=5) + return + (임시적으로 구성) + file_name: str (path of filename) + pickle: + path: str + version: int + """ + pkl = crud.get_dataset(db, version=1) + try: + data = np.load(pkl.path, allow_pickle=True) + return {'data': data[:row, 0:column+1].tolist()} + except Exception as e: + print(e) + return 0 + + +@router.get('/model') +def read_model(version=1, name='random_forest', db: Session = Depends(get_db)): + """ + Read Model Temporary + + param + version: int + name: str + return + path: str + version: int + name: str + classes: int + """ + clf_model = crud.get_clf_model(db, version=version, name=name) + try: + loaded_model = pickle.load(open(clf_model.path, 'rb')) + test = pickle.load(open('test_mnist.pkl', 'rb')).reshape(1, -1) + + pred = loaded_model.predict(test) + + return pred.tolist() + + except Exception as e: + print(e) + return 11 diff --git a/app/api/router/train.py b/app/api/router/train.py new file mode 100644 index 0000000..40be369 --- /dev/null +++ b/app/api/router/train.py @@ -0,0 +1,72 @@ +# -*- coding: utf-8 -*- +from fastapi import APIRouter +from fastapi import Depends +import numpy as np +import pickle +from sklearn.ensemble import RandomForestClassifier +from sqlalchemy.orm import Session + +from app import crud +from app.database import engine +from app.database import SessionLocal +import app.models as models +from app.util import mnist_preprocessing + + +models.Base.metadata.create_all(bind=engine) + +# Dependency + + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() + + +router = APIRouter( + prefix="/train", + tags=["train"], + responses={404: {"description": "Not Found"}} +) + + +@router.post('/mnist') +def train_mnist_rf( + model_name: str = 'model.pkl', + version: int = 1, + db: Session = Depends(get_db) +): + """ + param + version: int + return + path: str + version: int + name: str + classes: int + """ + + dataset = crud.get_dataset(db, version=version) + data = np.load(dataset.path, allow_pickle=True) + + X_train, X_valid, y_train, y_test = mnist_preprocessing(data) + + clf_model = RandomForestClassifier(n_estimators=500, + max_depth=3, + random_state=0) + + clf_model.fit(X_train, y_train) + + pickle_md = crud.create_clf_model(db, clf_model={ + 'path': 'model.pkl', + 'version': version, + 'name': 'random_forest', + 'classes': len(np.unique(y_train)) + }) + + pickle.dump(clf_model, open('model.pkl', 'wb')) + + return pickle_md diff --git a/app/api/router/upload.py b/app/api/router/upload.py new file mode 100644 index 0000000..021405c --- /dev/null +++ b/app/api/router/upload.py @@ -0,0 +1,52 @@ +# -*- coding: utf-8 -*- +import shutil + +from fastapi import APIRouter +from fastapi import Depends +from fastapi import File +from fastapi import UploadFile +from sqlalchemy.orm import Session + +from app import crud +from app import models +from app.database import engine +from app.database import SessionLocal + +models.Base.metadata.create_all(bind=engine) + + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() + + +router = APIRouter( + prefix="/upload", + tags=["upload"], + responses={404: {"description": "Not Found"}} +) + + +@router.post('/file') +async def upload_file(file: UploadFile = File(...), db: Session = Depends(get_db)): + """ + Upload file Temporary + + param + file: File + return (임시적으로 구성) + file_name: str (path of filename) + dataset: + path: str + version: int + """ + with open(f'{file.filename}', 'wb') as buffer: + shutil.copyfileobj(file.file, buffer) + dataset = crud.create_dataset(db, Dataset={ + 'path': file.filename, + 'version': 1 + }) + return {'file_name': file.filename, 'dataset': dataset} diff --git a/app/schemas.py b/app/api/schemas.py similarity index 100% rename from app/schemas.py rename to app/api/schemas.py diff --git a/app/crud.py b/app/crud.py index 03d6dbe..c9181b4 100644 --- a/app/crud.py +++ b/app/crud.py @@ -1,6 +1,7 @@ from sqlalchemy.orm import Session -from app import models, schemas +from app import models +from app.api import schemas def get_dataset(db: Session, version=1): diff --git a/main.py b/main.py index 250b4a9..0179e37 100644 --- a/main.py +++ b/main.py @@ -1,7 +1,7 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware -from app.router import test, predict, train, upload +from app.api.router import test, predict, train, upload from app.database import SessionLocal app = FastAPI() From e2173aefb1f29416c0775d38868ccb78d6f77313 Mon Sep 17 00:00:00 2001 From: hlchoi Date: Sat, 4 Sep 2021 18:03:10 +0900 Subject: [PATCH 10/47] delete duplicated folder --- app/router/__init__.py | 0 app/router/predict.py | 29 --------------- app/router/test.py | 84 ------------------------------------------ app/router/train.py | 72 ------------------------------------ app/router/upload.py | 52 -------------------------- 5 files changed, 237 deletions(-) delete mode 100644 app/router/__init__.py delete mode 100644 app/router/predict.py delete mode 100644 app/router/test.py delete mode 100644 app/router/train.py delete mode 100644 app/router/upload.py diff --git a/app/router/__init__.py b/app/router/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/app/router/predict.py b/app/router/predict.py deleted file mode 100644 index d82bff6..0000000 --- a/app/router/predict.py +++ /dev/null @@ -1,29 +0,0 @@ -# -*- coding: utf-8 -*- -from fastapi import APIRouter - -from app import models -from app.database import engine -from app.database import SessionLocal - - -models.Base.metadata.create_all(bind=engine) - - -def get_db(): - db = SessionLocal() - try: - yield db - finally: - db.close() - - -router = APIRouter( - prefix="/predict", - tags=["predict"], - responses={404: {"description": "Not Found"}} -) - - -@router.get("/") -def hello_world(): - return {"message": "Hello predict"} diff --git a/app/router/test.py b/app/router/test.py deleted file mode 100644 index 84a5217..0000000 --- a/app/router/test.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -import numpy as np -import pickle -from fastapi import APIRouter -from fastapi import Depends -from sqlalchemy.orm import Session - -from app import crud -from app import models -from app.database import engine -from app.database import SessionLocal - - -models.Base.metadata.create_all(bind=engine) - -# Dependency - - -def get_db(): - db = SessionLocal() - try: - yield db - finally: - db.close() - - -router = APIRouter( - prefix="/test", - tags=["test"], - responses={404: {"description": "Not Found"}} -) - - -@router.get('/file') -def read_file(column: int = 0, row: int = 5, db: Session = Depends(get_db)): - """ - Read file Temporary - - param - (validation 진행 필요) - column: int (default=0) - row: int (deafult=5) - return - (임시적으로 구성) - file_name: str (path of filename) - pickle: - path: str - version: int - """ - pkl = crud.get_dataset(db, version=1) - try: - data = np.load(pkl.path, allow_pickle=True) - return {'data': data[:row, 0:column+1].tolist()} - except Exception as e: - print(e) - return 0 - - -@router.get('/model') -def read_model(version=1, name='random_forest', db: Session = Depends(get_db)): - """ - Read Model Temporary - - param - version: int - name: str - return - path: str - version: int - name: str - classes: int - """ - clf_model = crud.get_clf_model(db, version=version, name=name) - try: - loaded_model = pickle.load(open(clf_model.path, 'rb')) - test = pickle.load(open('test_mnist.pkl', 'rb')).reshape(1, -1) - - pred = loaded_model.predict(test) - - return pred.tolist() - - except Exception as e: - print(e) - return 11 diff --git a/app/router/train.py b/app/router/train.py deleted file mode 100644 index 40be369..0000000 --- a/app/router/train.py +++ /dev/null @@ -1,72 +0,0 @@ -# -*- coding: utf-8 -*- -from fastapi import APIRouter -from fastapi import Depends -import numpy as np -import pickle -from sklearn.ensemble import RandomForestClassifier -from sqlalchemy.orm import Session - -from app import crud -from app.database import engine -from app.database import SessionLocal -import app.models as models -from app.util import mnist_preprocessing - - -models.Base.metadata.create_all(bind=engine) - -# Dependency - - -def get_db(): - db = SessionLocal() - try: - yield db - finally: - db.close() - - -router = APIRouter( - prefix="/train", - tags=["train"], - responses={404: {"description": "Not Found"}} -) - - -@router.post('/mnist') -def train_mnist_rf( - model_name: str = 'model.pkl', - version: int = 1, - db: Session = Depends(get_db) -): - """ - param - version: int - return - path: str - version: int - name: str - classes: int - """ - - dataset = crud.get_dataset(db, version=version) - data = np.load(dataset.path, allow_pickle=True) - - X_train, X_valid, y_train, y_test = mnist_preprocessing(data) - - clf_model = RandomForestClassifier(n_estimators=500, - max_depth=3, - random_state=0) - - clf_model.fit(X_train, y_train) - - pickle_md = crud.create_clf_model(db, clf_model={ - 'path': 'model.pkl', - 'version': version, - 'name': 'random_forest', - 'classes': len(np.unique(y_train)) - }) - - pickle.dump(clf_model, open('model.pkl', 'wb')) - - return pickle_md diff --git a/app/router/upload.py b/app/router/upload.py deleted file mode 100644 index 021405c..0000000 --- a/app/router/upload.py +++ /dev/null @@ -1,52 +0,0 @@ -# -*- coding: utf-8 -*- -import shutil - -from fastapi import APIRouter -from fastapi import Depends -from fastapi import File -from fastapi import UploadFile -from sqlalchemy.orm import Session - -from app import crud -from app import models -from app.database import engine -from app.database import SessionLocal - -models.Base.metadata.create_all(bind=engine) - - -def get_db(): - db = SessionLocal() - try: - yield db - finally: - db.close() - - -router = APIRouter( - prefix="/upload", - tags=["upload"], - responses={404: {"description": "Not Found"}} -) - - -@router.post('/file') -async def upload_file(file: UploadFile = File(...), db: Session = Depends(get_db)): - """ - Upload file Temporary - - param - file: File - return (임시적으로 구성) - file_name: str (path of filename) - dataset: - path: str - version: int - """ - with open(f'{file.filename}', 'wb') as buffer: - shutil.copyfileobj(file.file, buffer) - dataset = crud.create_dataset(db, Dataset={ - 'path': file.filename, - 'version': 1 - }) - return {'file_name': file.filename, 'dataset': dataset} From 425a9f756915b60cf4e1aa460b011cd6c8258f27 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Mon, 6 Sep 2021 16:31:39 +0900 Subject: [PATCH 11/47] Modify Table structure MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ML model과 metadata정보를 구분하여 테이블을 생성하였습니다. --- app/models.py | 20 +++++++++++++++----- module/trial.py | 32 ++++++++++++++++++++++---------- 2 files changed, 37 insertions(+), 15 deletions(-) diff --git a/app/models.py b/app/models.py index 889a91a..3c85211 100644 --- a/app/models.py +++ b/app/models.py @@ -1,7 +1,8 @@ # -*- coding: utf-8 -*- import datetime -from sqlalchemy import Column, Integer, String, FLOAT, DateTime +from sqlalchemy import Column, Integer, String, FLOAT, DateTime, ForeignKey from sqlalchemy.sql.functions import now +from sqlalchemy.orm import relationship from app.database import Base @@ -37,12 +38,21 @@ class ClfModel(Base): class RegModel(Base): __tablename__ = 'reg_model' - id = Column(Integer, primary_key=True, index=True, autoincrement=True) + model_name = Column(String, primary_key=True) path = Column(String, nullable=False) + + model_metadata = relationship( + "reg_model_metadata", backref="reg_model.model_name") + + +class RegModelMetadata(Base): + __tablename__ = 'reg_model_metadata' + + experiment_name = Column(String, primary_key=True) + reg_model_name = Column(String, ForeignKey( + 'reg_model.model_name'), nullable=False) experimenter = Column(String, nullable=False) - experiment_name = Column(String, nullable=False) - model_name = Column(String, nullable=False) - version = Column(FLOAT, autoincrement=True) + version = Column(FLOAT) train_mae = Column(FLOAT, nullable=False) val_mae = Column(FLOAT, nullable=False) train_mse = Column(FLOAT, nullable=False) diff --git a/module/trial.py b/module/trial.py index bf883f2..43ec239 100644 --- a/module/trial.py +++ b/module/trial.py @@ -109,7 +109,7 @@ def main(params, df, engine, experiment_info, connection): best_model = pd.read_sql(f""" SELECT * FROM reg_model - WHERE experiment_name = {experiment_name} + WHERE model_name = {model_name} """, engine) if len(best_model) == 0: @@ -118,20 +118,27 @@ def main(params, df, engine, experiment_info, connection): pickle.dump(model, f) connection.execute(f""" INSERT INTO reg_model ( - path, - experimenter, - experiment_name, model_name, + path + ) VALUES( + {model_name}, + {path} + ) + """) + connection.execute(f""" + INSERT INTO reg_model_metadata ( + experiment_name, + reg_model_name, + experimenter, version, train_mae, val_mae, train_mse, val_mse ) VALUES ( - {path}, - {experimenter}, {experiment_name}, {model_name}, + {experimenter}, {version}, {tr_mae_mean}, {tr_mse_mean}, @@ -140,12 +147,17 @@ def main(params, df, engine, experiment_info, connection): ) """) else: - with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f: - pickle.dump(model, f) - saved_score = best_model['val_mae'].values[0] + best_model_metadata = pd.read_sql(f""" + SELECT val_mae + FROM reg_model_metadata + WHERE reg_model_name = {model_name} + """, engine) + saved_score = best_model_metadata.values[0] if saved_score > valid_mae: + with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f: + pickle.dump(model, f) connection.execute(f""" - UPDATE reg_model + UPDATE reg_model_metadata SET train_mae = {tr_mae_mean}, val_mae = {cv_mae_mean}, From 091f9513de37ae63e0a81cc67dc653ab85cce40e Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Tue, 7 Sep 2021 10:10:33 +0900 Subject: [PATCH 12/47] Add query.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit query 문을 별도로 관리하기위해 query.py 파일을 추가하였습니다. --- .gitignore | 3 +- module/query.py | 61 +++++++++++++++++++++++++++++++++++++++ module/trial.py | 77 +++++++++++++++---------------------------------- 3 files changed, 87 insertions(+), 54 deletions(-) create mode 100644 module/query.py diff --git a/.gitignore b/.gitignore index 3545ad1..d823241 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,3 @@ .env -*.pkl \ No newline at end of file +*.pkl +__pycache__ \ No newline at end of file diff --git a/module/query.py b/module/query.py new file mode 100644 index 0000000..15a81bf --- /dev/null +++ b/module/query.py @@ -0,0 +1,61 @@ +# INSERT +INSERT_REG_MODEL = """ + INSERT INTO reg_model ( + model_name, + path + ) VALUES( + {}, + {} + ) + """ + +INSERT_REG_MODEL_METADATA = """ + INSERT INTO reg_model_metadata ( + experiment_name, + reg_model_name, + experimenter, + version, + train_mae, + val_mae, + train_mse, + val_mse + ) VALUES ( + {}, + {}, + {}, + {}, + {}, + {}, + {}, + {} + ) + """ + +# UPDATE +UPDATE_REG_MODEL_METADATA = """ + UPDATE reg_model_metadata + SET + train_mae = {}, + val_mae = {}, + train_mse = {}, + val_mse = {} + WHERE experiment_name = {} + """ + +# pd READ_SQL +ALL_INSURANCE = """ + SELECT * + FROM insurance + """ + +VAL_MAE = """ + SELECT val_mae + FROM reg_model_metadata + WHERE reg_model_name = %s + """ + +MODEL = """ + SELECT * + FROM reg_model + WHERE model_name = %s + """ diff --git a/module/trial.py b/module/trial.py index 43ec239..6b5919b 100644 --- a/module/trial.py +++ b/module/trial.py @@ -12,6 +12,8 @@ from sqlalchemy.engine import create_engine from xgboost.sklearn import XGBRegressor +from query import * + load_dotenv(verbose=True) @@ -106,65 +108,37 @@ def main(params, df, engine, experiment_info, connection): tr_mse_mean = np.mean(tr_mse) tr_mae_mean = np.mean(tr_mae) - best_model = pd.read_sql(f""" - SELECT * - FROM reg_model - WHERE model_name = {model_name} - """, engine) + best_model = pd.read_sql(MODEL % (model_name), engine) if len(best_model) == 0: with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f: pickle.dump(model, f) - connection.execute(f""" - INSERT INTO reg_model ( - model_name, - path - ) VALUES( - {model_name}, - {path} - ) - """) - connection.execute(f""" - INSERT INTO reg_model_metadata ( - experiment_name, - reg_model_name, - experimenter, - version, - train_mae, - val_mae, - train_mse, - val_mse - ) VALUES ( - {experiment_name}, - {model_name}, - {experimenter}, - {version}, - {tr_mae_mean}, - {tr_mse_mean}, - {cv_mae_mean}, - {cv_mse_mean} - ) - """) + connection.execute(INSERT_REG_MODEL % (model_name, path)) + connection.execute(INSERT_REG_MODEL_METADATA % ( + experiment_name, + model_name, + experimenter, + version, + tr_mae_mean, + tr_mse_mean, + cv_mae_mean, + cv_mse_mean) + ) + else: - best_model_metadata = pd.read_sql(f""" - SELECT val_mae - FROM reg_model_metadata - WHERE reg_model_name = {model_name} - """, engine) + best_model_metadata = pd.read_sql(VAL_MAE % (model_name), engine) saved_score = best_model_metadata.values[0] if saved_score > valid_mae: with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f: pickle.dump(model, f) - connection.execute(f""" - UPDATE reg_model_metadata - SET - train_mae = {tr_mae_mean}, - val_mae = {cv_mae_mean}, - train_mse = {tr_mse_mean}, - val_mse = {cv_mse_mean} - WHERE experiment_name = {experiment_name} - """) + connection.execute(UPDATE_REG_MODEL_METADATA % ( + tr_mae_mean, + cv_mae_mean, + tr_mse_mean, + cv_mse_mean, + experiment_name) + ) nni.report_final_result(cv_mae_mean) print('Final result is %g', cv_mae_mean) @@ -191,10 +165,7 @@ def main(params, df, engine, experiment_info, connection): 'version': 0.1 } - df = pd.read_sql(""" - SELECT * - FROM insurance - """, engine) + df = pd.read_sql(ALL_INSURANCE, engine) with engine.connect() as connection: with connection.begin(): From f769af890e978b3923df18364d67911c56c5d89f Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Tue, 7 Sep 2021 13:24:19 +0900 Subject: [PATCH 13/47] Add predict insurance fee api MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit parameter를 받아서 predict 하는 api입니다. 임시로 preprocess 과정을 추가하지 않고 진행하기 위해 프론트에서 전처리하여 값을 넘긴다는 가정으로 미리 전처리된 값을 넣도록 파라미터를 세팅했습니다. 추후 수정될 예정입니다. --- app/api/router/predict.py | 54 +++++++++++++++++++++++++++++++++++++-- app/api/schemas.py | 18 +++++++++++++ app/crud.py | 6 +++++ app/models.py | 2 +- 4 files changed, 77 insertions(+), 3 deletions(-) diff --git a/app/api/router/predict.py b/app/api/router/predict.py index d82bff6..09c986c 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -1,7 +1,13 @@ # -*- coding: utf-8 -*- -from fastapi import APIRouter +import os +import pickle +import numpy as np +from fastapi.param_functions import Depends +from app.api.schemas import RegModelPrediction +from fastapi import APIRouter, HTTPException +from sqlalchemy.orm import Session -from app import models +from app import crud, models from app.database import engine from app.database import SessionLocal @@ -27,3 +33,47 @@ def get_db(): @router.get("/") def hello_world(): return {"message": "Hello predict"} + + +@router.put("/insurance") +def predict_insurance(info: RegModelPrediction, db: Session = Depends(get_db)): + """ + Get information and predict insurance fee + param: + info: + # 임시로 int형태를 받도록 제작 + # preprocess 단계를 거치도록 만들 예정 + age: int + sex: int + bmi: float + children: int + smoker: int + region: int + return: + insurance_fee: float + """ + reg_model = crud.get_reg_model(db, model_name=info.model_name) + + if reg_model: + loaded_model = pickle.load(open( + os.path.join(reg_model.path, f'{reg_model.model_name}.pkl'), + 'rb') + ) + test_set = np.array([ + info.age, + info.sex, + info.bmi, + info.children, + info.smoker, + info.region + ]).reshape(1, -1) + + pred = loaded_model.predict(test_set) + + return {"result": pred.tolist()[0]} + else: + raise HTTPException( + status_code=404, + detail="Model Not Found", + headers={"X-Error": "Model Not Found"}, + ) diff --git a/app/api/schemas.py b/app/api/schemas.py index 57d95dc..4506481 100644 --- a/app/api/schemas.py +++ b/app/api/schemas.py @@ -43,3 +43,21 @@ class ClfModelCreate(ClfModelBase): class ClfModel(ClfModelBase): class Config: orm_mode = True + + +class RegModelBase(BaseModel): + model_name: str + + +class RegModelPrediction(RegModelBase): + age: int + sex: int + bmi: float + children: int + smoker: int + region: int + + +class RegModel(RegModelBase): + class Config: + orm_mode = True diff --git a/app/crud.py b/app/crud.py index c9181b4..3d91ed6 100644 --- a/app/crud.py +++ b/app/crud.py @@ -31,3 +31,9 @@ def create_clf_model(db: Session, clf_model: schemas.ClfModelCreate): db.commit() db.refresh(db_cf_model) return db_cf_model + + +def get_reg_model(db: Session, model_name: schemas.RegModelBase): + return db.query(models.RegModel).filter( + models.RegModel.model_name == model_name + ).first() diff --git a/app/models.py b/app/models.py index 3c85211..d99b57b 100644 --- a/app/models.py +++ b/app/models.py @@ -42,7 +42,7 @@ class RegModel(Base): path = Column(String, nullable=False) model_metadata = relationship( - "reg_model_metadata", backref="reg_model.model_name") + "RegModelMetadata", backref="reg_model.model_name") class RegModelMetadata(Base): From fb64ae7e73ce1b6aad6fb9cafd56e45061386345 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Tue, 7 Sep 2021 20:20:45 +0900 Subject: [PATCH 14/47] Clean up file & code MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 사용하지 않는 코드와 파일을 정리하였습니다. 추가로 query.py 안에있는 포맷도 통일하였습니다. --- app/api/router/predict.py | 5 --- app/api/router/test.py | 84 --------------------------------------- app/api/router/train.py | 72 --------------------------------- app/api/router/upload.py | 52 ------------------------ app/api/schemas.py | 44 -------------------- app/crud.py | 29 -------------- app/models.py | 28 +------------ app/util.py | 15 ------- main.py | 14 +------ module/query.py | 36 ++++++++--------- module/trial.py | 7 ++-- 11 files changed, 24 insertions(+), 362 deletions(-) delete mode 100644 app/api/router/test.py delete mode 100644 app/api/router/train.py delete mode 100644 app/api/router/upload.py delete mode 100644 app/util.py diff --git a/app/api/router/predict.py b/app/api/router/predict.py index 09c986c..08f4a5f 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -30,11 +30,6 @@ def get_db(): ) -@router.get("/") -def hello_world(): - return {"message": "Hello predict"} - - @router.put("/insurance") def predict_insurance(info: RegModelPrediction, db: Session = Depends(get_db)): """ diff --git a/app/api/router/test.py b/app/api/router/test.py deleted file mode 100644 index 84a5217..0000000 --- a/app/api/router/test.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -import numpy as np -import pickle -from fastapi import APIRouter -from fastapi import Depends -from sqlalchemy.orm import Session - -from app import crud -from app import models -from app.database import engine -from app.database import SessionLocal - - -models.Base.metadata.create_all(bind=engine) - -# Dependency - - -def get_db(): - db = SessionLocal() - try: - yield db - finally: - db.close() - - -router = APIRouter( - prefix="/test", - tags=["test"], - responses={404: {"description": "Not Found"}} -) - - -@router.get('/file') -def read_file(column: int = 0, row: int = 5, db: Session = Depends(get_db)): - """ - Read file Temporary - - param - (validation 진행 필요) - column: int (default=0) - row: int (deafult=5) - return - (임시적으로 구성) - file_name: str (path of filename) - pickle: - path: str - version: int - """ - pkl = crud.get_dataset(db, version=1) - try: - data = np.load(pkl.path, allow_pickle=True) - return {'data': data[:row, 0:column+1].tolist()} - except Exception as e: - print(e) - return 0 - - -@router.get('/model') -def read_model(version=1, name='random_forest', db: Session = Depends(get_db)): - """ - Read Model Temporary - - param - version: int - name: str - return - path: str - version: int - name: str - classes: int - """ - clf_model = crud.get_clf_model(db, version=version, name=name) - try: - loaded_model = pickle.load(open(clf_model.path, 'rb')) - test = pickle.load(open('test_mnist.pkl', 'rb')).reshape(1, -1) - - pred = loaded_model.predict(test) - - return pred.tolist() - - except Exception as e: - print(e) - return 11 diff --git a/app/api/router/train.py b/app/api/router/train.py deleted file mode 100644 index 40be369..0000000 --- a/app/api/router/train.py +++ /dev/null @@ -1,72 +0,0 @@ -# -*- coding: utf-8 -*- -from fastapi import APIRouter -from fastapi import Depends -import numpy as np -import pickle -from sklearn.ensemble import RandomForestClassifier -from sqlalchemy.orm import Session - -from app import crud -from app.database import engine -from app.database import SessionLocal -import app.models as models -from app.util import mnist_preprocessing - - -models.Base.metadata.create_all(bind=engine) - -# Dependency - - -def get_db(): - db = SessionLocal() - try: - yield db - finally: - db.close() - - -router = APIRouter( - prefix="/train", - tags=["train"], - responses={404: {"description": "Not Found"}} -) - - -@router.post('/mnist') -def train_mnist_rf( - model_name: str = 'model.pkl', - version: int = 1, - db: Session = Depends(get_db) -): - """ - param - version: int - return - path: str - version: int - name: str - classes: int - """ - - dataset = crud.get_dataset(db, version=version) - data = np.load(dataset.path, allow_pickle=True) - - X_train, X_valid, y_train, y_test = mnist_preprocessing(data) - - clf_model = RandomForestClassifier(n_estimators=500, - max_depth=3, - random_state=0) - - clf_model.fit(X_train, y_train) - - pickle_md = crud.create_clf_model(db, clf_model={ - 'path': 'model.pkl', - 'version': version, - 'name': 'random_forest', - 'classes': len(np.unique(y_train)) - }) - - pickle.dump(clf_model, open('model.pkl', 'wb')) - - return pickle_md diff --git a/app/api/router/upload.py b/app/api/router/upload.py deleted file mode 100644 index 021405c..0000000 --- a/app/api/router/upload.py +++ /dev/null @@ -1,52 +0,0 @@ -# -*- coding: utf-8 -*- -import shutil - -from fastapi import APIRouter -from fastapi import Depends -from fastapi import File -from fastapi import UploadFile -from sqlalchemy.orm import Session - -from app import crud -from app import models -from app.database import engine -from app.database import SessionLocal - -models.Base.metadata.create_all(bind=engine) - - -def get_db(): - db = SessionLocal() - try: - yield db - finally: - db.close() - - -router = APIRouter( - prefix="/upload", - tags=["upload"], - responses={404: {"description": "Not Found"}} -) - - -@router.post('/file') -async def upload_file(file: UploadFile = File(...), db: Session = Depends(get_db)): - """ - Upload file Temporary - - param - file: File - return (임시적으로 구성) - file_name: str (path of filename) - dataset: - path: str - version: int - """ - with open(f'{file.filename}', 'wb') as buffer: - shutil.copyfileobj(file.file, buffer) - dataset = crud.create_dataset(db, Dataset={ - 'path': file.filename, - 'version': 1 - }) - return {'file_name': file.filename, 'dataset': dataset} diff --git a/app/api/schemas.py b/app/api/schemas.py index 4506481..0df4913 100644 --- a/app/api/schemas.py +++ b/app/api/schemas.py @@ -1,50 +1,6 @@ from pydantic import BaseModel -class ItemBase(BaseModel): - title: str - - -class ItemCreate(ItemBase): - pass - - -class Item(ItemBase): - class Config: - orm_mode = True - - -class DatasetBase(BaseModel): - path: str - version: int - - -class DatasetCreate(DatasetBase): - pass - - -class Dataset(DatasetBase): - class config: - orm_mode = True - - -class ClfModelBase(BaseModel): - path: str - version: int - name: str - classes: int - score: float - - -class ClfModelCreate(ClfModelBase): - pass - - -class ClfModel(ClfModelBase): - class Config: - orm_mode = True - - class RegModelBase(BaseModel): model_name: str diff --git a/app/crud.py b/app/crud.py index 3d91ed6..a00180a 100644 --- a/app/crud.py +++ b/app/crud.py @@ -4,35 +4,6 @@ from app.api import schemas -def get_dataset(db: Session, version=1): - return db.query(models.Dataset).filter( - models.Dataset.version == version - ).first() - - -def create_dataset(db: Session, Dataset: schemas.DatasetCreate): - db_dataset = models.Dataset(**Dataset) - db.add(db_dataset) - db.commit() - db.refresh(db_dataset) - return db_dataset - - -def get_clf_model(db: Session, version=1, name='random_forest'): - return db.query(models.ClfModel).filter( - models.ClfModel.version == version and - models.ClfModel.name == name - ).first() - - -def create_clf_model(db: Session, clf_model: schemas.ClfModelCreate): - db_cf_model = models.ClfModel(**clf_model) - db.add(db_cf_model) - db.commit() - db.refresh(db_cf_model) - return db_cf_model - - def get_reg_model(db: Session, model_name: schemas.RegModelBase): return db.query(models.RegModel).filter( models.RegModel.model_name == model_name diff --git a/app/models.py b/app/models.py index d99b57b..41ddaf2 100644 --- a/app/models.py +++ b/app/models.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- import datetime -from sqlalchemy import Column, Integer, String, FLOAT, DateTime, ForeignKey +from sqlalchemy import Column, String, FLOAT, DateTime, ForeignKey from sqlalchemy.sql.functions import now from sqlalchemy.orm import relationship @@ -9,32 +9,6 @@ KST = datetime.timezone(datetime.timedelta(hours=9)) -class Item(Base): - __tablename__ = 'items' - - id = Column(Integer, primary_key=True, index=True, autoincrement=True) - title = Column(String, index=True, default='test') - - -class Dataset(Base): - __tablename__ = 'dataset' - - id = Column(Integer, primary_key=True, index=True, autoincrement=True) - path = Column(String, index=True) - version = Column(Integer, index=True, autoincrement=True) - - -class ClfModel(Base): - __tablename__ = 'clf_model' - - id = Column(Integer, primary_key=True, index=True, autoincrement=True) - path = Column(String, index=True) - version = Column(Integer, index=True, autoincrement=True) - name = Column(String, index=True) - classes = Column(Integer) - score = Column(FLOAT) - - class RegModel(Base): __tablename__ = 'reg_model' diff --git a/app/util.py b/app/util.py deleted file mode 100644 index 2406b38..0000000 --- a/app/util.py +++ /dev/null @@ -1,15 +0,0 @@ -import numpy as np -from sklearn.model_selection import train_test_split - - -def mnist_preprocessing(data: np.ndarray, - TEST_SIZE: float = 0.2): - """ - return train_test_split result - """ - - X, y = data[:, 1:], data[:, 0] - X_train, X_valid, y_train, y_valid = \ - train_test_split(X, y, test_size=TEST_SIZE) - - return X_train, X_valid, y_train, y_valid diff --git a/main.py b/main.py index 0179e37..2ed0b60 100644 --- a/main.py +++ b/main.py @@ -1,8 +1,7 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware -from app.api.router import test, predict, train, upload -from app.database import SessionLocal +from app.api.router import predict app = FastAPI() @@ -16,18 +15,7 @@ allow_headers=["*"], ) -app.include_router(test.router) app.include_router(predict.router) -app.include_router(upload.router) -app.include_router(train.router) - - -def get_db(): - db = SessionLocal() - try: - yield db - finally: - db.close() @app.get("/") diff --git a/module/query.py b/module/query.py index 15a81bf..992cde5 100644 --- a/module/query.py +++ b/module/query.py @@ -4,8 +4,8 @@ model_name, path ) VALUES( - {}, - {} + %s, + %s ) """ @@ -20,14 +20,14 @@ train_mse, val_mse ) VALUES ( - {}, - {}, - {}, - {}, - {}, - {}, - {}, - {} + %s, + %s, + %s, + %s, + %s, + %s, + %s, + %s ) """ @@ -35,26 +35,26 @@ UPDATE_REG_MODEL_METADATA = """ UPDATE reg_model_metadata SET - train_mae = {}, - val_mae = {}, - train_mse = {}, - val_mse = {} - WHERE experiment_name = {} + train_mae = %s, + val_mae = %s, + train_mse = %s, + val_mse = %s + WHERE experiment_name = %s """ # pd READ_SQL -ALL_INSURANCE = """ +SELECT_ALL_INSURANCE = """ SELECT * FROM insurance """ -VAL_MAE = """ +SELECT_VAL_MAE = """ SELECT val_mae FROM reg_model_metadata WHERE reg_model_name = %s """ -MODEL = """ +SELECT_REG_MODEL = """ SELECT * FROM reg_model WHERE model_name = %s diff --git a/module/trial.py b/module/trial.py index 6b5919b..edc2fee 100644 --- a/module/trial.py +++ b/module/trial.py @@ -108,7 +108,7 @@ def main(params, df, engine, experiment_info, connection): tr_mse_mean = np.mean(tr_mse) tr_mae_mean = np.mean(tr_mae) - best_model = pd.read_sql(MODEL % (model_name), engine) + best_model = pd.read_sql(SELECT_REG_MODEL % (model_name), engine) if len(best_model) == 0: @@ -127,7 +127,8 @@ def main(params, df, engine, experiment_info, connection): ) else: - best_model_metadata = pd.read_sql(VAL_MAE % (model_name), engine) + best_model_metadata = pd.read_sql( + SELECT_VAL_MAE % (model_name), engine) saved_score = best_model_metadata.values[0] if saved_score > valid_mae: with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f: @@ -165,7 +166,7 @@ def main(params, df, engine, experiment_info, connection): 'version': 0.1 } - df = pd.read_sql(ALL_INSURANCE, engine) + df = pd.read_sql(SELECT_ALL_INSURANCE, engine) with engine.connect() as connection: with connection.begin(): From 5dcb2bf84666cadf38a0fc73451aa5fbba1751df Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Wed, 8 Sep 2021 15:46:23 +0900 Subject: [PATCH 15/47] Modify the way of model save MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 모델 저장방식을 파일저장방식에서 DB에 바로 넣는 방식으로 저장했습니다. 이에따라 변경된 Table명과 Column명이 존재합니다. ModelCore부분에서는 LargeBinary 형태로 DB에 넣고 model_name으로 구분합니다. 저장방식이 바뀜에따라 수정된 부분 ---- predict.py : 파일을 읽어오는 방식에서 DB에서 읽는것으로 수정하였습니다. query.py : 읽어오는 table명과 column명 변경이 있어서 수정하였습니다. 그외 수정 ---- schemas.py : model명은 따로 받기위해 Prediction부분에서는 제외하였습니다. crud.py : filter -> filter_by --- app/api/router/predict.py | 14 +++++++------- app/api/schemas.py | 6 +++--- app/crud.py | 6 ++---- app/models.py | 20 ++++++++++---------- module/query.py | 33 ++++++++++++++++++++------------- module/trial.py | 19 +++++++++++-------- 6 files changed, 53 insertions(+), 45 deletions(-) diff --git a/app/api/router/predict.py b/app/api/router/predict.py index 08f4a5f..e9aabe1 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -1,9 +1,10 @@ # -*- coding: utf-8 -*- import os +import codecs import pickle import numpy as np from fastapi.param_functions import Depends -from app.api.schemas import RegModelPrediction +from app.api.schemas import ModelCorePrediction from fastapi import APIRouter, HTTPException from sqlalchemy.orm import Session @@ -31,7 +32,7 @@ def get_db(): @router.put("/insurance") -def predict_insurance(info: RegModelPrediction, db: Session = Depends(get_db)): +def predict_insurance(info: ModelCorePrediction, model_name: str, db: Session = Depends(get_db)): """ Get information and predict insurance fee param: @@ -47,13 +48,12 @@ def predict_insurance(info: RegModelPrediction, db: Session = Depends(get_db)): return: insurance_fee: float """ - reg_model = crud.get_reg_model(db, model_name=info.model_name) + reg_model = crud.get_reg_model(db, model_name=model_name) if reg_model: - loaded_model = pickle.load(open( - os.path.join(reg_model.path, f'{reg_model.model_name}.pkl'), - 'rb') - ) + loaded_model = pickle.loads( + codecs.decode(reg_model.model_file, 'base64')) + test_set = np.array([ info.age, info.sex, diff --git a/app/api/schemas.py b/app/api/schemas.py index 0df4913..87698ac 100644 --- a/app/api/schemas.py +++ b/app/api/schemas.py @@ -1,11 +1,11 @@ from pydantic import BaseModel -class RegModelBase(BaseModel): +class ModelCoreBase(BaseModel): model_name: str -class RegModelPrediction(RegModelBase): +class ModelCorePrediction(BaseModel): age: int sex: int bmi: float @@ -14,6 +14,6 @@ class RegModelPrediction(RegModelBase): region: int -class RegModel(RegModelBase): +class ModelCore(ModelCoreBase): class Config: orm_mode = True diff --git a/app/crud.py b/app/crud.py index a00180a..640f9d1 100644 --- a/app/crud.py +++ b/app/crud.py @@ -4,7 +4,5 @@ from app.api import schemas -def get_reg_model(db: Session, model_name: schemas.RegModelBase): - return db.query(models.RegModel).filter( - models.RegModel.model_name == model_name - ).first() +def get_reg_model(db: Session, model_name: schemas.ModelCoreBase): + return db.query(models.ModelCore).filter_by(model_name=model_name).first() diff --git a/app/models.py b/app/models.py index 41ddaf2..65b58d1 100644 --- a/app/models.py +++ b/app/models.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- import datetime -from sqlalchemy import Column, String, FLOAT, DateTime, ForeignKey +from sqlalchemy import Column, Integer, String, FLOAT, DateTime, ForeignKey, LargeBinary from sqlalchemy.sql.functions import now from sqlalchemy.orm import relationship @@ -9,22 +9,22 @@ KST = datetime.timezone(datetime.timedelta(hours=9)) -class RegModel(Base): - __tablename__ = 'reg_model' +class ModelCore(Base): + __tablename__ = 'model_core' model_name = Column(String, primary_key=True) - path = Column(String, nullable=False) + model_file = Column(LargeBinary, nullable=False) - model_metadata = relationship( - "RegModelMetadata", backref="reg_model.model_name") + model_metadata_relation = relationship( + "ModelMetadata", backref="model_core.model_name") -class RegModelMetadata(Base): - __tablename__ = 'reg_model_metadata' +class ModelMetadata(Base): + __tablename__ = 'model_metadata' experiment_name = Column(String, primary_key=True) - reg_model_name = Column(String, ForeignKey( - 'reg_model.model_name'), nullable=False) + model_core_name = Column(String, ForeignKey( + 'model_core.model_name'), nullable=False) experimenter = Column(String, nullable=False) version = Column(FLOAT) train_mae = Column(FLOAT, nullable=False) diff --git a/module/query.py b/module/query.py index 992cde5..f1fb600 100644 --- a/module/query.py +++ b/module/query.py @@ -1,18 +1,18 @@ # INSERT -INSERT_REG_MODEL = """ - INSERT INTO reg_model ( +INSERT_MODEL_CORE = """ + INSERT INTO model_core ( model_name, - path + model_file ) VALUES( %s, - %s + '%s' ) """ -INSERT_REG_MODEL_METADATA = """ - INSERT INTO reg_model_metadata ( +INSERT_MODEL_METADATA = """ + INSERT INTO model_metadata ( experiment_name, - reg_model_name, + model_core_name, experimenter, version, train_mae, @@ -32,8 +32,8 @@ """ # UPDATE -UPDATE_REG_MODEL_METADATA = """ - UPDATE reg_model_metadata +UPDATE_MODEL_METADATA = """ + UPDATE model_metadata SET train_mae = %s, val_mae = %s, @@ -41,6 +41,13 @@ val_mse = %s WHERE experiment_name = %s """ +UPDATE_MODEL_CORE = """ + UPDATE model_core + SET + model_file = '%s' + WHERE + model_name = %s + """ # pd READ_SQL SELECT_ALL_INSURANCE = """ @@ -50,12 +57,12 @@ SELECT_VAL_MAE = """ SELECT val_mae - FROM reg_model_metadata - WHERE reg_model_name = %s + FROM model_metadata + WHERE model_core_name = %s """ -SELECT_REG_MODEL = """ +SELECT_MODEL_CORE = """ SELECT * - FROM reg_model + FROM model_core WHERE model_name = %s """ diff --git a/module/trial.py b/module/trial.py index edc2fee..81eeef4 100644 --- a/module/trial.py +++ b/module/trial.py @@ -1,4 +1,5 @@ import os +import codecs import pickle from dotenv import load_dotenv @@ -108,14 +109,13 @@ def main(params, df, engine, experiment_info, connection): tr_mse_mean = np.mean(tr_mse) tr_mae_mean = np.mean(tr_mae) - best_model = pd.read_sql(SELECT_REG_MODEL % (model_name), engine) + best_model = pd.read_sql(SELECT_MODEL_CORE % (model_name), engine) if len(best_model) == 0: - with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f: - pickle.dump(model, f) - connection.execute(INSERT_REG_MODEL % (model_name, path)) - connection.execute(INSERT_REG_MODEL_METADATA % ( + pickled_model = codecs.encode(pickle.dumps(model), "base64").decode() + connection.execute(INSERT_MODEL_CORE % (model_name, pickled_model)) + connection.execute(INSERT_MODEL_METADATA % ( experiment_name, model_name, experimenter, @@ -130,10 +130,13 @@ def main(params, df, engine, experiment_info, connection): best_model_metadata = pd.read_sql( SELECT_VAL_MAE % (model_name), engine) saved_score = best_model_metadata.values[0] + if saved_score > valid_mae: - with open(f"{os.path.join(path, model_name)}.pkl".replace("'", ""), "wb") as f: - pickle.dump(model, f) - connection.execute(UPDATE_REG_MODEL_METADATA % ( + pickled_model = codecs.encode( + pickle.dumps(model), "base64").decode() + + connection.execute(UPDATE_MODEL_CORE % (pickled_model, model_name)) + connection.execute(UPDATE_MODEL_METADATA % ( tr_mae_mean, cv_mae_mean, tr_mse_mean, From be510a196767699bafe99305326a8f5d62ee8172 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Wed, 8 Sep 2021 16:09:19 +0900 Subject: [PATCH 16/47] Modify folder structure MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 다른 학습도 고려해서 폴더로 분리하였습니다. + 임시로 넣어두었던 test_mnist는 제거하였습니다. --- {module => experiments/insurance}/config.yml | 0 {module => experiments/insurance}/query.py | 0 .../insurance}/search_space.json | 0 {module => experiments/insurance}/trial.py | 0 module/__init__.py | 0 test_mnist.pkl | Bin 3287 -> 0 bytes 6 files changed, 0 insertions(+), 0 deletions(-) rename {module => experiments/insurance}/config.yml (100%) rename {module => experiments/insurance}/query.py (100%) rename {module => experiments/insurance}/search_space.json (100%) rename {module => experiments/insurance}/trial.py (100%) delete mode 100644 module/__init__.py delete mode 100644 test_mnist.pkl diff --git a/module/config.yml b/experiments/insurance/config.yml similarity index 100% rename from module/config.yml rename to experiments/insurance/config.yml diff --git a/module/query.py b/experiments/insurance/query.py similarity index 100% rename from module/query.py rename to experiments/insurance/query.py diff --git a/module/search_space.json b/experiments/insurance/search_space.json similarity index 100% rename from module/search_space.json rename to experiments/insurance/search_space.json diff --git a/module/trial.py b/experiments/insurance/trial.py similarity index 100% rename from module/trial.py rename to experiments/insurance/trial.py diff --git a/module/__init__.py b/module/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/test_mnist.pkl b/test_mnist.pkl deleted file mode 100644 index 72b0f57ebf58b8a23129a6f78e90b607efd83b06..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3287 zcmeH~Pe>F|9LINEH4q`gy41m}gC1fD^yoqI5-n}uL9h;@y9-IV$~YVBAzBMDVIa}h z!ERnU6x1>LLnw%N2tvB(sX*|cXF(GGe7?IgAj=Ty?g)lo_{^Jq?>FD?n>X(rHdp!j zM{OuC=TbT|FqDlZ2CYp= zp;6Nj?lWVi(=^Azck3df=3&ICO4`|>l%A>RZ#CmD3&Rm7+-BnO_?zuT!_26?@Q7Vy&zI#r{6 zb6i#P-~`C$6EuLIw5HEMbt`@ahwSTiJ^r;1u2SQDSJfrqxO&Hfn)6d1g9v?3;18%~ z8}36X^75V^FuBIPauz{3VgnvQsj@K8IrC%B?f*jC4CT;8Os2tmkAtn|@EIzhoYHr? z;Btj_0hId_bS{IE&PEQDrSo_bg4Auym8E^y6J!wHTF+mgyb%amV+1H`6Pf}JtHFO3 hl=mBg)(ZlZB_0GFhX0J4^?|nj1oBREgyEk=bRQyyne_kw From d78cc8d368c77d6b5c0ee2bf06adf8db099db1a7 Mon Sep 17 00:00:00 2001 From: hlchoi Date: Thu, 9 Sep 2021 17:24:13 +0900 Subject: [PATCH 17/47] =?UTF-8?q?=EC=9E=84=EC=8B=9C=EB=A1=9C=20=ED=91=B8?= =?UTF-8?q?=EC=8B=9C...?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/api/router/predict.py | 54 ++++++++ app/database.py | 5 +- app/utils.py | 38 +++++ experiments/atmos_tmp_01/config.yml | 19 +++ experiments/atmos_tmp_01/expr_db.py | 20 +++ experiments/atmos_tmp_01/preprocessing.py | 11 ++ experiments/atmos_tmp_01/search_space.json | 4 + experiments/atmos_tmp_01/train.py | 131 ++++++++++++++++++ experiments/atmos_tmp_01/train72_pred24.h5 | Bin 0 -> 112108 bytes tf_model/test_model/saved_model.pb | Bin 0 -> 3458347 bytes .../variables/variables.data-00000-of-00001 | Bin 0 -> 958248 bytes tf_model/test_model/variables/variables.index | Bin 0 -> 3922 bytes 12 files changed, 279 insertions(+), 3 deletions(-) create mode 100644 app/utils.py create mode 100644 experiments/atmos_tmp_01/config.yml create mode 100644 experiments/atmos_tmp_01/expr_db.py create mode 100644 experiments/atmos_tmp_01/preprocessing.py create mode 100644 experiments/atmos_tmp_01/search_space.json create mode 100644 experiments/atmos_tmp_01/train.py create mode 100644 experiments/atmos_tmp_01/train72_pred24.h5 create mode 100644 tf_model/test_model/saved_model.pb create mode 100644 tf_model/test_model/variables/variables.data-00000-of-00001 create mode 100644 tf_model/test_model/variables/variables.index diff --git a/app/api/router/predict.py b/app/api/router/predict.py index e9aabe1..609901f 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -7,6 +7,9 @@ from app.api.schemas import ModelCorePrediction from fastapi import APIRouter, HTTPException from sqlalchemy.orm import Session +from typing import List +from app.utils import * +import tensorflow as tf from app import crud, models from app.database import engine @@ -72,3 +75,54 @@ def predict_insurance(info: ModelCorePrediction, model_name: str, db: Session = detail="Model Not Found", headers={"X-Error": "Model Not Found"}, ) + + +# import tensorflow as tf +# import zipfile +# physical_devices = tf.config.list_physical_devices('GPU') +# tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) + +# def load_tf_model(model_name): +# """ +# * DB에 있는 텐서플로우 모델을 불러옵니다. +# * 모델은 zip형식으로 압축되어 binary로 저장되어 있습니다. +# * 모델의 이름을 받아 압축 해제 및 tf_model폴더 아래에 저장한 후 로드하여 +# 텐서플로우 모델 객체를 반환합니다. +# """ +# query = f"""SELECT model_file +# FROM model_core +# WHERE model_name='{model_name}';""" +# bin_data = engine.execute(query).fetchone()[0] +# model_buffer = pickle.loads(codecs.decode(bin_data, "base64")) +# with zipfile.ZipFile(model_buffer, "r") as bf: +# bf.extractall(f"../../tf_model/{model_name}") +# tf_model = tf.keras.models.load_model(f"./tf_model/{model_name}") +# print("tf_model loaded") + +# return tf_model + +#---------------------------------------------------------------- + +# physical_devices = tf.config.list_physical_devices('GPU') +# tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) + +# model_path = load_tf_model("test_model") +# tf_model = tf.keras.models.load_model(model_path) + +#---------------------------------------------------------------- + +@router.put("/atmos") +async def predict_temperature(time_series: List[float]): + + try: + tf_model = load_tf_model("test_model") + time_series = np.array(time_series).reshape(1, -1, 1) + result = tf_model.predict(time_series) + return result.tolist() + + except Exception as e: + print(e) # 나중에 로그남기기용 + return {"result":str(e)} + + + diff --git a/app/database.py b/app/database.py index 08d8e96..62f0cf0 100644 --- a/app/database.py +++ b/app/database.py @@ -5,10 +5,10 @@ from sqlalchemy.orm import sessionmaker from sqlalchemy.ext.declarative import declarative_base +load_dotenv(verbose=True) def connect(db): - - load_dotenv(verbose=True) + print(db) POSTGRES_USER = os.getenv("POSTGRES_USER") POSTGRES_PASSWORD = os.getenv("POSTGRES_PASSWORD") @@ -25,7 +25,6 @@ def connect(db): POSTGRES_DB = os.getenv("POSTGRES_DB") - engine = connect(POSTGRES_DB) SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) Base = declarative_base() diff --git a/app/utils.py b/app/utils.py new file mode 100644 index 0000000..ac99f5a --- /dev/null +++ b/app/utils.py @@ -0,0 +1,38 @@ +from app.database import engine +import codecs +import pickle +import zipfile +import os +import tensorflow as tf + +base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + +def load_tf_model(model_name): + """ + * DB에 있는 텐서플로우 모델을 불러옵니다. + * 모델은 zip형식으로 압축되어 binary로 저장되어 있습니다. + * 모델의 이름을 받아 압축 해제 및 tf_model폴더 아래에 저장한 후 로드하여 + 텐서플로우 모델 객체를 반환합니다. + """ + query = f"""SELECT model_file + FROM model_core + WHERE model_name='{model_name}';""" + + bin_data = engine.execute(query).fetchone()[0] + print("query") + + model_buffer = pickle.loads(codecs.decode(bin_data, "base64")) + print("buffer") + model_path = os.path.join(base_dir, "tf_model", model_name) + print("model path:", model_path) + with zipfile.ZipFile(model_buffer, "r") as bf: + bf.extractall(model_path) + print("unzip") + tf_model = tf.keras.models.load_model(model_path) + print("tf_model") + + return tf_model + + # return model_path + diff --git a/experiments/atmos_tmp_01/config.yml b/experiments/atmos_tmp_01/config.yml new file mode 100644 index 0000000..c79bbea --- /dev/null +++ b/experiments/atmos_tmp_01/config.yml @@ -0,0 +1,19 @@ +experimentName: GRU +searchSpaceFile: search_space.json +trialCommand: python train.py +trialCodeDirectory: . +trialConcurrency: 1 +maxExperimentDuration: 48h +maxTrialNumber: 1000 +tuner: + # choice: + # TPE, Anneal, Evolution, SMAC, BatchTuner, GridSearch, Hyperband + # NetworkMorphism, MetisTuner, BOHB, GPTuner, PBTTuner, DNGOTuner + # SMAC need to be installed (pip install nni[SMAC]) + # https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Evolution + name: Anneal + classArgs: + optimize_mode: minimize # maximize or minimize +trainingService: + platform: local + useActiveGpu: True \ No newline at end of file diff --git a/experiments/atmos_tmp_01/expr_db.py b/experiments/atmos_tmp_01/expr_db.py new file mode 100644 index 0000000..4519068 --- /dev/null +++ b/experiments/atmos_tmp_01/expr_db.py @@ -0,0 +1,20 @@ +import os +from dotenv import load_dotenv +import sqlalchemy + +def connect(db): + """Returns a connection and a metadata object""" + + load_dotenv(verbose=True) + + POSTGRES_USER = os.getenv("POSTGRES_USER") + POSTGRES_PASSWORD = os.getenv("POSTGRES_PASSWORD") + POSTGRES_SERVER = os.getenv("POSTGRES_SERVER") + POSTGRES_PORT = os.getenv("POSTGRES_PORT") + POSTGRES_DB = db + + url = f"postgresql://{POSTGRES_USER}:{POSTGRES_PASSWORD}@{POSTGRES_SERVER}:{POSTGRES_PORT}/{POSTGRES_DB}" + + connection = sqlalchemy.create_engine(url) + + return connection \ No newline at end of file diff --git a/experiments/atmos_tmp_01/preprocessing.py b/experiments/atmos_tmp_01/preprocessing.py new file mode 100644 index 0000000..981f638 --- /dev/null +++ b/experiments/atmos_tmp_01/preprocessing.py @@ -0,0 +1,11 @@ +import pandas as pd + + +def preprocess(data): + + # missing data + data = data.fillna(method="ffill") + + # etc. + + return data diff --git a/experiments/atmos_tmp_01/search_space.json b/experiments/atmos_tmp_01/search_space.json new file mode 100644 index 0000000..43282c1 --- /dev/null +++ b/experiments/atmos_tmp_01/search_space.json @@ -0,0 +1,4 @@ +{ + "layer_n": {"_type":"randint", "_value":[2, 8]}, + "cell": {"_type":"randint", "_value":[24, 80]} +} \ No newline at end of file diff --git a/experiments/atmos_tmp_01/train.py b/experiments/atmos_tmp_01/train.py new file mode 100644 index 0000000..9d6a58b --- /dev/null +++ b/experiments/atmos_tmp_01/train.py @@ -0,0 +1,131 @@ +import pandas as pd +import numpy as np +import time +import os +import nni +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense +from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint +from tensorflow.keras.layers import GRU + +from preprocessing import preprocess +from expr_db import connect + +physical_devices = tf.config.list_physical_devices('GPU') +tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) + + +def make_dataset(data, label, window_size=365, predsize=None): + feature_list = [] + label_list = [] + + if isinstance(predsize, int): + for i in range(len(data) - (window_size + predsize)): + feature_list.append(np.array(data.iloc[i : i + window_size])) + label_list.append( + np.array(label.iloc[i + window_size : i + window_size + predsize]) + ) + else: + for i in range(len(data) - window_size): + feature_list.append(np.array(data.iloc[i : i + window_size])) + label_list.append(np.array(label.iloc[i + window_size])) + + return np.array(feature_list), np.array(label_list) + + +def split_data(data_length, ratio): + """ + return index based on ratio + -------------------------------------------------- + example + + >>> split_data(data_length = 20, ratio = [1,2,3]) + [3, 10] + -------------------------------------------------- + """ + ratio = np.cumsum(np.array(ratio) / np.sum(ratio)) + + idx = [] + for i in ratio[:-1]: + idx.append(round(data_length * i)) + + return idx + + +def main(params): + con = connect("atmos_data") + data = pd.read_sql("select tmp from stn108;", con) + + data = preprocess(data) + + base_dir = os.path.dirname(os.path.abspath(__file__)) + parent_dir = base_dir.split(os.path.sep)[-1] + + train_feature, train_label = make_dataset(data, data, 72, 24) + + idx = split_data(train_feature.shape[0], [6, 3, 1]) + X_train, X_valid, X_test = ( + train_feature[: idx[0]], + train_feature[idx[0] : idx[1]], + train_feature[idx[1] :], + ) + y_train, y_valid, y_test = ( + train_label[: idx[0]], + train_label[idx[0] : idx[1]], + train_label[idx[1] :], + ) + + model = Sequential() + for layer in range(params["layer_n"]): + if layer == params["layer_n"] - 1: + model.add(GRU(params["cell"])) + else: + model.add( + GRU( + params["cell"], + return_sequences=True, + input_shape=[None, train_feature.shape[2]], + ) + ) + model.add(Dense(24)) + + model_path = "./" + model.compile(loss="mae", optimizer=keras.optimizers.Adam(lr=0.001)) + early_stop = EarlyStopping(monitor="val_loss", patience=30) + expr_time = time.strftime("%y%m%d_%H%M%S") + filename = os.path.join(model_path, + f"./temp/{expr_time}_{parent_dir}") + checkpoint = ModelCheckpoint( + filename, + monitor="val_loss", + verbose=1, + save_best_only=True, + mode="auto", + ) + + model.fit( + X_train, + y_train, + epochs=10, + batch_size=128, + validation_data=(X_valid, y_valid), + callbacks=[early_stop, checkpoint], + ) + + result = ( + np.sum( + np.abs( + y_test.reshape(y_test.shape[0], y_test.shape[1]) - model.predict(X_test) + ) + ) + / y_test.shape[0] + ) + + nni.report_final_result(result) + + +if __name__ == "__main__": + params = nni.get_next_parameter() + main(params) diff --git a/experiments/atmos_tmp_01/train72_pred24.h5 b/experiments/atmos_tmp_01/train72_pred24.h5 new file mode 100644 index 0000000000000000000000000000000000000000..579e26be3d6e9cbe93a4a4498becf7c31606c8f4 GIT binary patch literal 112108 zcmeFZ2UHeMlP`>dBn1&r6a+z05D}Cl!gM!^D53-jl0<@tl9eD5RFIq`M+u5V6G1WY 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+ print('heloo') try: - tf_model = load_tf_model("test_model") - time_series = np.array(time_series).reshape(1, -1, 1) - result = tf_model.predict(time_series) - return result.tolist() - + # tf_model = load_tf_model("test_model") + tf_model = my_model.my_model + # time_series = np.array(time_series).reshape(1, -1, 1) + # result = tf_model.predict(time_series) + # return result.tolist() + return 1 except Exception as e: - print(e) # 나중에 로그남기기용 - return {"result":str(e)} - - - + print(e) # 나중에 로그남기기용 + return {"result": str(e)} diff --git a/app/utils.py b/app/utils.py index ac99f5a..85b52e5 100644 --- a/app/utils.py +++ b/app/utils.py @@ -8,31 +8,46 @@ base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -def load_tf_model(model_name): - """ - * DB에 있는 텐서플로우 모델을 불러옵니다. - * 모델은 zip형식으로 압축되어 binary로 저장되어 있습니다. - * 모델의 이름을 받아 압축 해제 및 tf_model폴더 아래에 저장한 후 로드하여 - 텐서플로우 모델 객체를 반환합니다. - """ - query = f"""SELECT model_file - FROM model_core - WHERE model_name='{model_name}';""" - - bin_data = engine.execute(query).fetchone()[0] - print("query") - - model_buffer = pickle.loads(codecs.decode(bin_data, "base64")) - print("buffer") - model_path = os.path.join(base_dir, "tf_model", model_name) - print("model path:", model_path) - with zipfile.ZipFile(model_buffer, "r") as bf: - bf.extractall(model_path) - print("unzip") - tf_model = tf.keras.models.load_model(model_path) - print("tf_model") - - return tf_model - - # return model_path +class MyModel: + def __init__(self): + self._my_model = None + def load_tf_model(self, model_name): + """ + * DB에 있는 텐서플로우 모델을 불러옵니다. + * 모델은 zip형식으로 압축되어 binary로 저장되어 있습니다. + * 모델의 이름을 받아 압축 해제 및 tf_model폴더 아래에 저장한 후 로드하여 + 텐서플로우 모델 객체를 반환합니다. + """ + print('load_tftf') + # query = f"""SELECT model_file + # FROM model_core + # WHERE model_name='{model_name}';""" + + # bin_data = engine.execute(query).fetchone()[0] + # print("query") + + # model_buffer = pickle.loads(codecs.decode(bin_data, "base64")) + # print("buffer") + # model_path = os.path.join(base_dir, "tf_model", model_name) + # print("model path:", model_path) + # with zipfile.ZipFile(model_buffer, "r") as bf: + # bf.extractall(model_path) + # print("unzip") + # tf_model = tf.keras.models.load_model(model_path) + # print("tf_model") + + return + + # return model_path + + def load_model(self): + self._my_model = self.load_tf_model('keep_update_model') + + @property + def my_model(self): + return self._my_model + + +my_model = MyModel() +my_model.load_model() From 0e4a0ee7d76e8c7549fed5ca14e0b298d22aa691 Mon Sep 17 00:00:00 2001 From: hlchoi Date: Fri, 10 Sep 2021 03:51:56 +0900 Subject: [PATCH 19/47] =?UTF-8?q?Add=20temperature=20predict=20API=2072?= =?UTF-8?q?=EC=8B=9C=EA=B0=84=EC=9D=98=20=EC=98=A8=EB=8F=84=EB=8D=B0?= =?UTF-8?q?=EC=9D=B4=ED=84=B0=EB=A5=BC=20=EB=B0=9B=EC=95=84=EC=84=9C=2024?= =?UTF-8?q?=EC=8B=9C=EA=B0=84=20=EC=98=88=EC=B8=A1=EC=9D=84=20=EC=88=98?= =?UTF-8?q?=ED=96=89=ED=95=98=EB=8A=94=20api=EC=9E=85=EB=8B=88=EB=8B=A4.?= =?UTF-8?q?=20=EB=AA=A8=EB=8D=B8=EC=9D=80=20tensorflow=EB=A1=9C=20?= =?UTF-8?q?=EB=A7=8C=EB=93=A4=EC=96=B4=EC=A0=B8=20=EC=9E=88=EC=9C=BC?= =?UTF-8?q?=EB=A9=B0=20DB=EC=97=90=EB=8A=94=20=EC=95=95=EC=B6=95=ED=95=98?= =?UTF-8?q?=EC=97=AC=20binary=EB=A1=9C=20=EC=A0=80=EC=9E=A5=ED=95=98?= =?UTF-8?q?=EC=98=80=EC=8A=B5=EB=8B=88=EB=8B=A4.=20=EC=B2=AB=201=ED=9A=8C?= =?UTF-8?q?=EB=A7=8C=20DB=EC=97=90=EC=84=9C=20=EC=9D=BD=EC=96=B4=EC=99=80?= =?UTF-8?q?=20=EB=AA=A8=EB=8D=B8=EC=9D=84=20=EB=A1=9C=EB=93=9C=ED=95=9C=20?= =?UTF-8?q?=ED=9B=84=20=EA=B3=84=EC=86=8D=20=EB=A9=94=EB=AA=A8=EB=A6=AC?= =?UTF-8?q?=EC=97=90=20=EB=82=A8=EC=95=84=20=EC=9A=94=EC=B2=AD=EC=9D=B4=20?= =?UTF-8?q?=EB=93=A4=EC=96=B4=EC=98=AC=20=EB=95=8C=20=EB=A7=88=EB=8B=A4=20?= =?UTF-8?q?=EC=98=88=EC=B8=A1=EC=9D=84=20=EC=88=98=ED=96=89=ED=95=A9?= =?UTF-8?q?=EB=8B=88=EB=8B=A4.=20-----------------------------------------?= =?UTF-8?q?-------------------------------------=20=ED=8E=B8=EC=9D=98?= =?UTF-8?q?=EB=A5=BC=20=EC=9C=84=ED=95=B4=20main.py=EB=A5=BC=20=EC=8B=A4?= =?UTF-8?q?=ED=96=89=ED=95=98=EB=A9=B4=20=EB=B0=94=EB=A1=9C=20uvicorn=20se?= =?UTF-8?q?rver=EA=B0=80=20=EC=8B=A4=ED=96=89=EB=90=98=EB=8F=84=EB=A1=9D?= =?UTF-8?q?=20=EB=B3=80=EA=B2=BD=ED=95=98=EC=98=80=EC=8A=B5=EB=8B=88?= =?UTF-8?q?=EB=8B=A4.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/api/router/predict.py | 56 +++++++-------------------------------- app/utils.py | 33 +++++++++++------------ main.py | 8 ++++++ 3 files changed, 33 insertions(+), 64 deletions(-) diff --git a/app/api/router/predict.py b/app/api/router/predict.py index 1563d03..a45ec36 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -1,15 +1,12 @@ # -*- coding: utf-8 -*- -import os import codecs import pickle import numpy as np -from fastapi.param_functions import Depends +from fastapi.param_functions import Depends, Query from app.api.schemas import ModelCorePrediction from fastapi import APIRouter, HTTPException from sqlalchemy.orm import Session from typing import List -from app.utils import * -import tensorflow as tf from app.utils import my_model from app import crud, models @@ -78,50 +75,17 @@ def predict_insurance(info: ModelCorePrediction, model_name: str, db: Session = ) -# import tensorflow as tf -# import zipfile -# physical_devices = tf.config.list_physical_devices('GPU') -# tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) - -# def load_tf_model(model_name): -# """ -# * DB에 있는 텐서플로우 모델을 불러옵니다. -# * 모델은 zip형식으로 압축되어 binary로 저장되어 있습니다. -# * 모델의 이름을 받아 압축 해제 및 tf_model폴더 아래에 저장한 후 로드하여 -# 텐서플로우 모델 객체를 반환합니다. -# """ -# query = f"""SELECT model_file -# FROM model_core -# WHERE model_name='{model_name}';""" -# bin_data = engine.execute(query).fetchone()[0] -# model_buffer = pickle.loads(codecs.decode(bin_data, "base64")) -# with zipfile.ZipFile(model_buffer, "r") as bf: -# bf.extractall(f"../../tf_model/{model_name}") -# tf_model = tf.keras.models.load_model(f"./tf_model/{model_name}") -# print("tf_model loaded") - -# return tf_model - -# ---------------------------------------------------------------- - -# physical_devices = tf.config.list_physical_devices('GPU') -# tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) - -# model_path = load_tf_model("test_model") -# tf_model = tf.keras.models.load_model(model_path) - -# ---------------------------------------------------------------- - @router.put("/atmos") async def predict_temperature(time_series: List[float]): - print('heloo') + if len(time_series) != 72: + return "time series must have 72 values" + try: - # tf_model = load_tf_model("test_model") tf_model = my_model.my_model - # time_series = np.array(time_series).reshape(1, -1, 1) - # result = tf_model.predict(time_series) - # return result.tolist() - return 1 + time_series = np.array(time_series).reshape(1, -1, 1) + result = tf_model.predict(time_series) + return result.tolist() + except Exception as e: - print(e) # 나중에 로그남기기용 - return {"result": str(e)} + print(e) + return {"error": str(e)} diff --git a/app/utils.py b/app/utils.py index 85b52e5..1da705e 100644 --- a/app/utils.py +++ b/app/utils.py @@ -7,6 +7,8 @@ base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +physical_devices = tf.config.list_physical_devices('GPU') +tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) class MyModel: def __init__(self): @@ -19,30 +21,25 @@ def load_tf_model(self, model_name): * 모델의 이름을 받아 압축 해제 및 tf_model폴더 아래에 저장한 후 로드하여 텐서플로우 모델 객체를 반환합니다. """ - print('load_tftf') - # query = f"""SELECT model_file - # FROM model_core - # WHERE model_name='{model_name}';""" - # bin_data = engine.execute(query).fetchone()[0] - # print("query") + query = f"""SELECT model_file + FROM model_core + WHERE model_name='{model_name}';""" - # model_buffer = pickle.loads(codecs.decode(bin_data, "base64")) - # print("buffer") - # model_path = os.path.join(base_dir, "tf_model", model_name) - # print("model path:", model_path) - # with zipfile.ZipFile(model_buffer, "r") as bf: - # bf.extractall(model_path) - # print("unzip") - # tf_model = tf.keras.models.load_model(model_path) - # print("tf_model") + bin_data = engine.execute(query).fetchone()[0] - return + model_buffer = pickle.loads(codecs.decode(bin_data, "base64")) + model_path = os.path.join(base_dir, "tf_model", model_name) + + with zipfile.ZipFile(model_buffer, "r") as bf: + bf.extractall(model_path) + tf_model = tf.keras.models.load_model(model_path) + + return tf_model - # return model_path def load_model(self): - self._my_model = self.load_tf_model('keep_update_model') + self._my_model = self.load_tf_model('test_model') @property def my_model(self): diff --git a/main.py b/main.py index 2ed0b60..0adecc5 100644 --- a/main.py +++ b/main.py @@ -1,5 +1,6 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware +import uvicorn from app.api.router import predict @@ -21,3 +22,10 @@ @app.get("/") def hello_world(): return {"message": "Hello World"} + +if __name__ == "__main__": + uvicorn.run("main:app", + host="0.0.0.0", + port=8000, + reload=True, + reload_dirs=['app/']) \ No newline at end of file From fc05692b9fed1cfa75e37229ae8a5a317bcc28fd Mon Sep 17 00:00:00 2001 From: hlchoi Date: Fri, 10 Sep 2021 09:27:08 +0900 Subject: [PATCH 20/47] =?UTF-8?q?Delete=20model=20file=20=EB=AA=A8?= =?UTF-8?q?=EB=8D=B8=ED=8C=8C=EC=9D=BC=EB=93=A4=20=EC=82=AD=EC=A0=9C?= =?UTF-8?q?=ED=96=88=EC=8A=B5=EB=8B=88=EB=8B=A4.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 3 ++- experiments/atmos_tmp_01/train72_pred24.h5 | Bin 112108 -> 0 bytes tf_model/test_model/saved_model.pb | Bin 3458347 -> 0 bytes .../variables/variables.data-00000-of-00001 | Bin 958248 -> 0 bytes tf_model/test_model/variables/variables.index | Bin 3922 -> 0 bytes 5 files changed, 2 insertions(+), 1 deletion(-) delete mode 100644 experiments/atmos_tmp_01/train72_pred24.h5 delete mode 100644 tf_model/test_model/saved_model.pb delete mode 100644 tf_model/test_model/variables/variables.data-00000-of-00001 delete mode 100644 tf_model/test_model/variables/variables.index diff --git a/.gitignore b/.gitignore index d823241..623f1d9 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ .env *.pkl -__pycache__ \ No newline at end of file +__pycache__ +tf_model/**/* \ No newline at end of file diff --git a/experiments/atmos_tmp_01/train72_pred24.h5 b/experiments/atmos_tmp_01/train72_pred24.h5 deleted file mode 100644 index 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Qbn~ zM+(jcA1PF1Oz`nR=O-SfiKhHCTI>DCC-~a~Z8yf)vsPd6kC|w@w(h%rTrc*pZ(AJ z>HYfq4X*)Q3*nvq?#yJK-0#^iQ8eKv6aIe1YnbpK#;e2rb~jeri1Bx)Qp2zMi}LEg zzulYt3+FYE|Aq74?%C?*yoL$2aQ@r9+5hGgA^dlz2;u+r6e0ZgrU-Qc|Ielf;nfG} zx*AoTn7=zksDWNr+o}`&e=uN}K;{MBsRRj2^#$3aM zx*AoTCe%G*)v(}4Tdsjx*A$^n)Vc|;F;4!c`>cVBb&M8u;{FTaHGu0TysjxiohH;x zcnu4FCcLgGLY=5}6JBGOtc~!xrU-T7{>yMx1GpB#s}GsK-J8`tT-7k)Ka5w0{q1h- me?CPBH?9u++r8Ppa9(44{uj=FyJxGL^BN}9!ujv^X8#L5nvzKX diff --git a/tf_model/test_model/variables/variables.index b/tf_model/test_model/variables/variables.index deleted file mode 100644 index 6147a35cd1d07d04543c395d7e011d3bbfbd40a3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3922 zcmb`Je@q)?9LC=kgi@qHJ2F}dD}M(P5jv@PKVSzE zu-@ZDW_8J*ASLt^X}8pyEp-%LLptnKWj%fnudHpbIq(B|lc~sqavQxNDm6aJiZpNw+Q3HoSY5ZyozhDnH|*Fq+wn9NFA>^90) zZMIt+q%ROwcLu(Qx>J%2w2_S#J7uY`lCbwa-Y1d=(GiN|ev&Sc=4TAs^e5KFA0~n< z<$na8G}m3?uG!L06A64gyz#{$1detUCkJziLA)Kq@*^3x9z?{2?2VNX%BP8e5$trwLBYUR9)%=u z>Jh^Yju4O=9T=XcLtr`OyLz|Zz=3n znV3CqD`3o*+H|YJ^lf2K3k4hUIt>tb8HQ6}Y6w)J5#imod5Oqei}*X@l!itjwrV&pY>J{}~;kM_W!Ma+S@HtLVSk>@W=3*%wpkG)9o+SL()mxslc{RniIt>2@IL~uR{Q>ikr;Jz<~N!t2mzY?ZWW9MU_;Nfqp4)UthLT?h}4h)co=@2s54KPLOT$Uo| z^B?3?2or**Jq9VhTw0#WD+KRnNeFB{-E|Srm(ve`o->32a6`zPEUBbhSA~(i#&AZ~4ATH(VFu8;<<)FL3@Uq5uE@ From a28ee89266aaabd54301b677701e88861a6d6a2a Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Fri, 10 Sep 2021 13:17:33 +0900 Subject: [PATCH 21/47] Add training API MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit training 하는것을 API로 만들어서 요청을 받습니다. 요청받은 값으로 config.yml 을 만들고 그 파일로 training을 진행합니다. --- app/api/router/train.py | 54 ++++++++++++++++++++++++++++++++++ app/utils.py | 48 ++++++++++++++++++++++++++++-- experiments/insurance/trial.py | 35 ++++++++++++++++++---- main.py | 14 +++++---- 4 files changed, 137 insertions(+), 14 deletions(-) create mode 100644 app/api/router/train.py diff --git a/app/api/router/train.py b/app/api/router/train.py new file mode 100644 index 0000000..1be02b5 --- /dev/null +++ b/app/api/router/train.py @@ -0,0 +1,54 @@ +# -*- coding: utf-8 -*- +from app.utils import write_yml +import subprocess + +from fastapi import APIRouter + + +router = APIRouter( + prefix="/train", + tags=["train"], + responses={404: {"description": "Not Found"}} +) + + +@router.put("/") +def train_insurance( + PORT: int = 8080, + experiment_sec: int = 20, + experiment_name: str = 'exp1', + experimenter: str = 'DongUk', + model_name: str = 'insurance_fee_model', + version: float = 0.1 +): + """ + Args: + PORT (int): PORT to run NNi. Defaults to 8080 + experiment_sec (int): Express the experiment time in seconds Defaults to 20 + experiment_name (str): experiment name Defaults to exp1 + experimeter (str): experimenter (author) Defaults to DongUk + model_name (str): model name Defaults to insurance_fee_model + version (float): version of experiment Defaults to 0.1 + Returns: + msg: Regardless of success or not, return address values including PORT. + """ + path = 'experiments/insurance/' + try: + write_yml( + path, + experiment_name, + experimenter, + model_name, + version + ) + subprocess.Popen( + "nnictl create --port {} --config {}/{}.yml && timeout {} && nnictl stop --port {}".format( + PORT, path, model_name, experiment_sec, PORT), + shell=True, + ) + + except Exception as e: + print('error') + print(e) + + return {"msg": f'Check out http://127.0.0.1:{PORT}'} diff --git a/app/utils.py b/app/utils.py index 1da705e..460b24e 100644 --- a/app/utils.py +++ b/app/utils.py @@ -1,15 +1,20 @@ -from app.database import engine import codecs import pickle -import zipfile import os +import yaml +import zipfile + import tensorflow as tf +from app.database import engine + + base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) physical_devices = tf.config.list_physical_devices('GPU') tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) + class MyModel: def __init__(self): self._my_model = None @@ -37,7 +42,6 @@ def load_tf_model(self, model_name): return tf_model - def load_model(self): self._my_model = self.load_tf_model('test_model') @@ -48,3 +52,41 @@ def my_model(self): my_model = MyModel() my_model.load_model() + + +def write_yml( + path, + experiment_name, + experimenter, + model_name, + version +): + print(type(version)) + with open('{}/{}.yml'.format(path, model_name), 'w') as yml_config_file: + yaml.dump({ + 'authorName': f'{experimenter}', + 'experimentName': f'{experiment_name}', + 'trialConcurrency': 1, + 'maxExecDuration': '1h', + 'maxTrialNum': 10, + 'trainingServicePlatform': 'local', + 'searchSpacePath': 'search_space.json', + 'useAnnotation': False, + 'tuner': { + 'builtinTunerName': 'Anneal', + 'classArgs': { + 'optimize_mode': 'minimize' + }}, + 'trial': { + 'command': 'python -e %s -en %s -mn %s -v %f trial.py' % ( + experimenter, + experiment_name, + model_name, + version + ), + 'codeDir': '.' + }}, yml_config_file, default_flow_style=False) + + yml_config_file.close() + + return diff --git a/experiments/insurance/trial.py b/experiments/insurance/trial.py index 81eeef4..b027a3d 100644 --- a/experiments/insurance/trial.py +++ b/experiments/insurance/trial.py @@ -1,6 +1,8 @@ import os import codecs import pickle +import sys +import getopt from dotenv import load_dotenv import pandas as pd @@ -161,14 +163,37 @@ def main(params, df, engine, experiment_info, connection): f'{POSTGRES_SERVER}:{POSTGRES_PORT}/{POSTGRES_DB}' engine = create_engine(SQLALCHEMY_DATABASE_URL) + argv = sys.argv experiment_info = { - 'path': "'C:\\Users\\TFG5076XG\\Documents\\MLOps'", - 'experimenter': "'DongUk'", - 'experiment_name': "'insurance0903'", - 'model_name': "'keep_update_model'", - 'version': 0.1 + "experimenter": '', + "experiment_name": '', + "model_name": '', + "version": 0.1 } + try: + opts, etc_args = getopt.getopt( + argv[1:], + "e:en:mn:v:", + [ + "experimenter=", + "experiment_name=", + "model_name=", + "version" + ]) + for opt, arg in opts: + if opt in ('-e', "--experimenter"): + experiment_info['experimenter'] = arg + elif opt in ("-en", "--experiment_name"): + experiment_info['experiment_name'] = arg + elif opt in ("-mn", "--model_name"): + experiment_info['model_name'] = arg + elif opt in ("-v", "--version"): + experiment_info['version'] = arg + + except getopt.GetoptError: + sys.exit(2) + df = pd.read_sql(SELECT_ALL_INSURANCE, engine) with engine.connect() as connection: diff --git a/main.py b/main.py index 0adecc5..68d6f21 100644 --- a/main.py +++ b/main.py @@ -2,7 +2,7 @@ from fastapi.middleware.cors import CORSMiddleware import uvicorn -from app.api.router import predict +from app.api.router import predict, train app = FastAPI() @@ -17,15 +17,17 @@ ) app.include_router(predict.router) +app.include_router(train.router) @app.get("/") def hello_world(): return {"message": "Hello World"} + if __name__ == "__main__": - uvicorn.run("main:app", - host="0.0.0.0", - port=8000, - reload=True, - reload_dirs=['app/']) \ No newline at end of file + uvicorn.run("main:app", + host="0.0.0.0", + port=8000, + reload=True, + reload_dirs=['app/']) From 9483ab5a3ed8e635f70189a5765dad3ead737ffb Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Fri, 10 Sep 2021 15:43:04 +0900 Subject: [PATCH 22/47] Fix argument MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit argument를 받는 부분에서 오류가 있어 수정하였습니다. --- app/utils.py | 7 +++---- experiments/insurance/trial.py | 26 +++++++++++++++----------- 2 files changed, 18 insertions(+), 15 deletions(-) diff --git a/app/utils.py b/app/utils.py index 460b24e..45d2ced 100644 --- a/app/utils.py +++ b/app/utils.py @@ -11,8 +11,8 @@ base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -physical_devices = tf.config.list_physical_devices('GPU') -tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) +# physical_devices = tf.config.list_physical_devices('GPU') +# tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) class MyModel: @@ -61,7 +61,6 @@ def write_yml( model_name, version ): - print(type(version)) with open('{}/{}.yml'.format(path, model_name), 'w') as yml_config_file: yaml.dump({ 'authorName': f'{experimenter}', @@ -78,7 +77,7 @@ def write_yml( 'optimize_mode': 'minimize' }}, 'trial': { - 'command': 'python -e %s -en %s -mn %s -v %f trial.py' % ( + 'command': 'python trial.py -e {} -n {} -m {} -v {}'.format( experimenter, experiment_name, model_name, diff --git a/experiments/insurance/trial.py b/experiments/insurance/trial.py index b027a3d..029c64c 100644 --- a/experiments/insurance/trial.py +++ b/experiments/insurance/trial.py @@ -56,14 +56,11 @@ def main(params, df, engine, experiment_info, connection): connection: connection used to communicate with DB """ - path = experiment_info['path'] experimenter = experiment_info['experimenter'] experiment_name = experiment_info['experiment_name'] model_name = experiment_info['model_name'] version = experiment_info['version'] - global best_model, best_mae - label_col = ['sex', 'smoker', 'region'] y = df.charges.to_frame() @@ -78,7 +75,8 @@ def main(params, df, engine, experiment_info, connection): cv_mae = [] tr_mse = [] tr_mae = [] - + fold_mae = 1e10 + fold_model = None for trn_idx, val_idx in kf.split(x, y): x_train, y_train = x.iloc[trn_idx], y.iloc[trn_idx] x_valid, y_valid = x.iloc[val_idx], y.iloc[val_idx] @@ -106,6 +104,10 @@ def main(params, df, engine, experiment_info, connection): tr_mse.append(train_mse) tr_mae.append(train_mae) + new_mae = min(fold_mae, valid_mae) + if new_mae != fold_mae: + fold_model = model + cv_mse_mean = np.mean(cv_mse) cv_mae_mean = np.mean(cv_mae) tr_mse_mean = np.mean(tr_mse) @@ -135,7 +137,7 @@ def main(params, df, engine, experiment_info, connection): if saved_score > valid_mae: pickled_model = codecs.encode( - pickle.dumps(model), "base64").decode() + pickle.dumps(fold_model), "base64").decode() connection.execute(UPDATE_MODEL_CORE % (pickled_model, model_name)) connection.execute(UPDATE_MODEL_METADATA % ( @@ -174,20 +176,22 @@ def main(params, df, engine, experiment_info, connection): try: opts, etc_args = getopt.getopt( argv[1:], - "e:en:mn:v:", + "e:n:m:v:", [ "experimenter=", "experiment_name=", "model_name=", "version" ]) + print(opts) for opt, arg in opts: + print(opt, arg) if opt in ('-e', "--experimenter"): - experiment_info['experimenter'] = arg - elif opt in ("-en", "--experiment_name"): - experiment_info['experiment_name'] = arg - elif opt in ("-mn", "--model_name"): - experiment_info['model_name'] = arg + experiment_info['experimenter'] = f"'{arg}'" + elif opt in ("-n", "--experiment_name"): + experiment_info['experiment_name'] = f"'{arg}'" + elif opt in ("-m", "--model_name"): + experiment_info['model_name'] = f"'{arg}'" elif opt in ("-v", "--version"): experiment_info['version'] = arg From c5db149562668d45bd8a5522c5bd08e386ff313a Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Fri, 10 Sep 2021 17:45:22 +0900 Subject: [PATCH 23/47] Remove ORM MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ORM 을 사용하지 않고 query문을 작성해서 DB내용을 가져옵니다. --- app/api/router/predict.py | 22 ++++++++++------------ app/crud.py | 8 -------- 2 files changed, 10 insertions(+), 20 deletions(-) delete mode 100644 app/crud.py diff --git a/app/api/router/predict.py b/app/api/router/predict.py index a45ec36..b1abbbd 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -9,7 +9,7 @@ from typing import List from app.utils import my_model -from app import crud, models +from app import models from app.database import engine from app.database import SessionLocal @@ -17,14 +17,6 @@ models.Base.metadata.create_all(bind=engine) -def get_db(): - db = SessionLocal() - try: - yield db - finally: - db.close() - - router = APIRouter( prefix="/predict", tags=["predict"], @@ -33,7 +25,7 @@ def get_db(): @router.put("/insurance") -def predict_insurance(info: ModelCorePrediction, model_name: str, db: Session = Depends(get_db)): +def predict_insurance(info: ModelCorePrediction, model_name: str): """ Get information and predict insurance fee param: @@ -49,11 +41,17 @@ def predict_insurance(info: ModelCorePrediction, model_name: str, db: Session = return: insurance_fee: float """ - reg_model = crud.get_reg_model(db, model_name=model_name) + query = """ + SELECT model_file + FROM model_core + WHERE model_name='{}'; + """.format(model_name) + + reg_model = engine.execute(query).fetchone()[0] if reg_model: loaded_model = pickle.loads( - codecs.decode(reg_model.model_file, 'base64')) + codecs.decode(reg_model, 'base64')) test_set = np.array([ info.age, diff --git a/app/crud.py b/app/crud.py deleted file mode 100644 index 640f9d1..0000000 --- a/app/crud.py +++ /dev/null @@ -1,8 +0,0 @@ -from sqlalchemy.orm import Session - -from app import models -from app.api import schemas - - -def get_reg_model(db: Session, model_name: schemas.ModelCoreBase): - return db.query(models.ModelCore).filter_by(model_name=model_name).first() From 248e7304556d83cc66784e506179f214e851672b Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Sat, 11 Sep 2021 11:53:04 +0900 Subject: [PATCH 24/47] Clean the code MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit NNi train에서 중복되게 사용하던 db connect 부분 공통으로 사용하게 하였습니다. predict.py insurance 예측부분 if else 문을 if문으로만 동작하게 대체했습니다. 일부 import 순서 조정되었습니다. --- app/api/router/predict.py | 42 +++++++++------------- app/api/router/train.py | 3 +- app/models.py | 1 + experiments/atmos_tmp_01/train.py | 11 +++--- experiments/{atmos_tmp_01 => }/expr_db.py | 5 +-- experiments/insurance/trial.py | 43 ++++++----------------- 6 files changed, 41 insertions(+), 64 deletions(-) rename experiments/{atmos_tmp_01 => }/expr_db.py (91%) diff --git a/app/api/router/predict.py b/app/api/router/predict.py index b1abbbd..136c5a0 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -1,17 +1,15 @@ # -*- coding: utf-8 -*- import codecs -import pickle import numpy as np -from fastapi.param_functions import Depends, Query -from app.api.schemas import ModelCorePrediction -from fastapi import APIRouter, HTTPException -from sqlalchemy.orm import Session +import pickle from typing import List -from app.utils import my_model + +from fastapi import APIRouter, HTTPException from app import models +from app.api.schemas import ModelCorePrediction from app.database import engine -from app.database import SessionLocal +from app.utils import my_model models.Base.metadata.create_all(bind=engine) @@ -47,31 +45,25 @@ def predict_insurance(info: ModelCorePrediction, model_name: str): WHERE model_name='{}'; """.format(model_name) - reg_model = engine.execute(query).fetchone()[0] - - if reg_model: - loaded_model = pickle.loads( - codecs.decode(reg_model, 'base64')) - - test_set = np.array([ - info.age, - info.sex, - info.bmi, - info.children, - info.smoker, - info.region - ]).reshape(1, -1) - - pred = loaded_model.predict(test_set) + reg_model = engine.execute(query).fetchone() - return {"result": pred.tolist()[0]} - else: + if reg_model is None: raise HTTPException( status_code=404, detail="Model Not Found", headers={"X-Error": "Model Not Found"}, ) + loaded_model = pickle.loads( + codecs.decode(reg_model[0], 'base64')) + + info = info.dict() + test_set = np.array([*info.values()]).reshape(1, -1) + + pred = loaded_model.predict(test_set) + + return {"result": pred.tolist()[0]} + @router.put("/atmos") async def predict_temperature(time_series: List[float]): diff --git a/app/api/router/train.py b/app/api/router/train.py index 1be02b5..9df8968 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -1,9 +1,10 @@ # -*- coding: utf-8 -*- -from app.utils import write_yml import subprocess from fastapi import APIRouter +from app.utils import write_yml + router = APIRouter( prefix="/train", diff --git a/app/models.py b/app/models.py index 65b58d1..a84e491 100644 --- a/app/models.py +++ b/app/models.py @@ -1,5 +1,6 @@ # -*- coding: utf-8 -*- import datetime + from sqlalchemy import Column, Integer, String, FLOAT, DateTime, ForeignKey, LargeBinary from sqlalchemy.sql.functions import now from sqlalchemy.orm import relationship diff --git a/experiments/atmos_tmp_01/train.py b/experiments/atmos_tmp_01/train.py index 9d6a58b..01741e2 100644 --- a/experiments/atmos_tmp_01/train.py +++ b/experiments/atmos_tmp_01/train.py @@ -1,8 +1,12 @@ -import pandas as pd -import numpy as np -import time import os +import sys +import time +from preprocessing import preprocess +sys.path.append(os.path.dirname( os.path.dirname( os.path.abspath(__file__) ) )) + +import numpy as np import nni +import pandas as pd import tensorflow as tf from tensorflow import keras from tensorflow.keras.models import Sequential @@ -10,7 +14,6 @@ from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint from tensorflow.keras.layers import GRU -from preprocessing import preprocess from expr_db import connect physical_devices = tf.config.list_physical_devices('GPU') diff --git a/experiments/atmos_tmp_01/expr_db.py b/experiments/expr_db.py similarity index 91% rename from experiments/atmos_tmp_01/expr_db.py rename to experiments/expr_db.py index 4519068..9c5ad81 100644 --- a/experiments/atmos_tmp_01/expr_db.py +++ b/experiments/expr_db.py @@ -2,7 +2,8 @@ from dotenv import load_dotenv import sqlalchemy -def connect(db): + +def connect(db='postgres'): """Returns a connection and a metadata object""" load_dotenv(verbose=True) @@ -17,4 +18,4 @@ def connect(db): connection = sqlalchemy.create_engine(url) - return connection \ No newline at end of file + return connection diff --git a/experiments/insurance/trial.py b/experiments/insurance/trial.py index 029c64c..c66239c 100644 --- a/experiments/insurance/trial.py +++ b/experiments/insurance/trial.py @@ -3,6 +3,7 @@ import pickle import sys import getopt +sys.path.append(os.path.dirname( os.path.dirname( os.path.abspath(__file__) ) )) from dotenv import load_dotenv import pandas as pd @@ -12,14 +13,13 @@ from sklearn.metrics import mean_absolute_error, mean_squared_error from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import KFold -from sqlalchemy.engine import create_engine from xgboost.sklearn import XGBRegressor +from expr_db import connect from query import * load_dotenv(verbose=True) - def preprocess(x_train, x_valid, col_list): """ param: @@ -46,16 +46,16 @@ def preprocess(x_train, x_valid, col_list): return tmp_x_train.values, tmp_x_valid.values -def main(params, df, engine, experiment_info, connection): +def main(params, engine, experiment_info, connection): """ param: params: Parameters determined by NNi - df: Dataframe read from DB engine: sqlalchemy engine experiment_info: information of experiment [dict] connection: connection used to communicate with DB """ + df = pd.read_sql(SELECT_ALL_INSURANCE, engine) experimenter = experiment_info['experimenter'] experiment_name = experiment_info['experiment_name'] model_name = experiment_info['model_name'] @@ -71,12 +71,9 @@ def main(params, df, engine, experiment_info, connection): kf = KFold(n_splits=5, shuffle=True, random_state=42) - cv_mse = [] - cv_mae = [] - tr_mse = [] - tr_mae = [] - fold_mae = 1e10 - fold_model = None + cv_mse, cv_mae, tr_mse, tr_mae = [], [], [], [] + fold_mae, fold_model = 1e10, None + for trn_idx, val_idx in kf.split(x, y): x_train, y_train = x.iloc[trn_idx], y.iloc[trn_idx] x_valid, y_valid = x.iloc[val_idx], y.iloc[val_idx] @@ -155,23 +152,9 @@ def main(params, df, engine, experiment_info, connection): if __name__ == '__main__': params = nni.get_next_parameter() - POSTGRES_USER = os.getenv("POSTGRES_USER") - POSTGRES_PASSWORD = os.getenv("POSTGRES_PASSWORD") - POSTGRES_PORT = os.getenv("POSTGRES_PORT") - POSTGRES_SERVER = os.getenv("POSTGRES_SERVER") - POSTGRES_DB = os.getenv("POSTGRES_DB") - SQLALCHEMY_DATABASE_URL = \ - f'postgresql://{POSTGRES_USER}:{POSTGRES_PASSWORD}@' +\ - f'{POSTGRES_SERVER}:{POSTGRES_PORT}/{POSTGRES_DB}' - engine = create_engine(SQLALCHEMY_DATABASE_URL) - + engine = connect() argv = sys.argv - experiment_info = { - "experimenter": '', - "experiment_name": '', - "model_name": '', - "version": 0.1 - } + experiment_info = {} try: opts, etc_args = getopt.getopt( @@ -181,11 +164,9 @@ def main(params, df, engine, experiment_info, connection): "experimenter=", "experiment_name=", "model_name=", - "version" + "version=" ]) - print(opts) for opt, arg in opts: - print(opt, arg) if opt in ('-e', "--experimenter"): experiment_info['experimenter'] = f"'{arg}'" elif opt in ("-n", "--experiment_name"): @@ -198,8 +179,6 @@ def main(params, df, engine, experiment_info, connection): except getopt.GetoptError: sys.exit(2) - df = pd.read_sql(SELECT_ALL_INSURANCE, engine) - with engine.connect() as connection: with connection.begin(): - main(params, df, engine, experiment_info, connection) + main(params, engine, experiment_info, connection) From c9a501ae5492df248778bf66f330cd36a0887f60 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Mon, 13 Sep 2021 10:59:28 +0900 Subject: [PATCH 25/47] Change modeling OOP MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 모델을 load하는것과 predict하는 부분을 class로 만들어서 좀더 객체지향적 프로그래밍을 하고자 하였습니다. --- app/api/router/predict.py | 38 ++++++++-------------- app/utils.py | 68 +++++++++++++++++++++++---------------- 2 files changed, 55 insertions(+), 51 deletions(-) diff --git a/app/api/router/predict.py b/app/api/router/predict.py index 136c5a0..9c72462 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -1,15 +1,14 @@ # -*- coding: utf-8 -*- -import codecs import numpy as np -import pickle from typing import List -from fastapi import APIRouter, HTTPException +from fastapi import APIRouter +from starlette.concurrency import run_in_threadpool from app import models from app.api.schemas import ModelCorePrediction from app.database import engine -from app.utils import my_model +from app.utils import ScikitLearnModel, my_model models.Base.metadata.create_all(bind=engine) @@ -23,7 +22,7 @@ @router.put("/insurance") -def predict_insurance(info: ModelCorePrediction, model_name: str): +async def predict_insurance(info: ModelCorePrediction, model_name: str): """ Get information and predict insurance fee param: @@ -39,30 +38,21 @@ def predict_insurance(info: ModelCorePrediction, model_name: str): return: insurance_fee: float """ - query = """ - SELECT model_file - FROM model_core - WHERE model_name='{}'; - """.format(model_name) - reg_model = engine.execute(query).fetchone() + def sync_call(info, model_name): + model = ScikitLearnModel(model_name) + model.load_model() - if reg_model is None: - raise HTTPException( - status_code=404, - detail="Model Not Found", - headers={"X-Error": "Model Not Found"}, - ) + info = info.dict() + test_set = np.array([*info.values()]).reshape(1, -1) - loaded_model = pickle.loads( - codecs.decode(reg_model[0], 'base64')) + pred = model.predict_target(test_set) - info = info.dict() - test_set = np.array([*info.values()]).reshape(1, -1) + return {"result": pred.tolist()[0]} - pred = loaded_model.predict(test_set) + result = await run_in_threadpool(sync_call, info, model_name) - return {"result": pred.tolist()[0]} + return result @router.put("/atmos") @@ -71,7 +61,7 @@ async def predict_temperature(time_series: List[float]): return "time series must have 72 values" try: - tf_model = my_model.my_model + tf_model = my_model.model time_series = np.array(time_series).reshape(1, -1, 1) result = tf_model.predict(time_series) return result.tolist() diff --git a/app/utils.py b/app/utils.py index 45d2ced..1f9b2e7 100644 --- a/app/utils.py +++ b/app/utils.py @@ -11,46 +11,60 @@ base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -# physical_devices = tf.config.list_physical_devices('GPU') -# tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) +physical_devices = tf.config.list_physical_devices('GPU') +if physical_devices: + tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) -class MyModel: - def __init__(self): - self._my_model = None +class CoreModel: - def load_tf_model(self, model_name): - """ - * DB에 있는 텐서플로우 모델을 불러옵니다. - * 모델은 zip형식으로 압축되어 binary로 저장되어 있습니다. - * 모델의 이름을 받아 압축 해제 및 tf_model폴더 아래에 저장한 후 로드하여 - 텐서플로우 모델 객체를 반환합니다. - """ + def __init__(self, model_name): + self.model_name = model_name + self.model = None + self.query = """ + SELECT model_file + FROM model_core + WHERE model_name='{}'; + """.format(self.model_name) - query = f"""SELECT model_file - FROM model_core - WHERE model_name='{model_name}';""" + def load_model(self): + raise Exception - bin_data = engine.execute(query).fetchone()[0] + def predict_target(self, target_data): + return self.model.predict(target_data) - model_buffer = pickle.loads(codecs.decode(bin_data, "base64")) - model_path = os.path.join(base_dir, "tf_model", model_name) - with zipfile.ZipFile(model_buffer, "r") as bf: - bf.extractall(model_path) - tf_model = tf.keras.models.load_model(model_path) +class ScikitLearnModel(CoreModel): + def __init__(self, *args): + super().__init__(*args) + + def load_model(self): + _model = engine.execute(self.query).fetchone() + if _model is None: + raise ValueError('Model Not Found!') - return tf_model + self.model = pickle.loads( + codecs.decode(_model[0], 'base64') + ) + + +class TensorFlowModel(CoreModel): + def __init__(self, *args): + super().__init__(*args) def load_model(self): - self._my_model = self.load_tf_model('test_model') + _model = engine.execute(self.query).fetchone() + if _model is None: + raise ValueError('Model Not Found!') + model_buffer = pickle.loads(codecs.decode(_model[0], "base64")) + model_path = os.path.join(base_dir, "tf_model", self.model_name) - @property - def my_model(self): - return self._my_model + with zipfile.ZipFile(model_buffer, "r") as bf: + bf.extractall(model_path) + self.model = tf.keras.models.load_model(model_path) -my_model = MyModel() +my_model = TensorFlowModel('test_model') my_model.load_model() From 5c2fc325a452f9eff9322fdbb87b9cfdca52741d Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Mon, 13 Sep 2021 16:24:38 +0900 Subject: [PATCH 26/47] Add logger (temp version) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit log를 남기기위해 Logger를 추가하였습니다. 임시로 동작확인만 하였으며 자세한 형식은 통일할 필요가 있습니다. --- .gitignore | 3 ++- app/api/router/predict.py | 22 +++++++++++++++------- app/api/router/train.py | 8 ++++++-- logger.py | 19 +++++++++++++++++++ 4 files changed, 42 insertions(+), 10 deletions(-) create mode 100644 logger.py diff --git a/.gitignore b/.gitignore index 623f1d9..d00ddd1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ .env *.pkl __pycache__ -tf_model/**/* \ No newline at end of file +tf_model/**/* +log.txt \ No newline at end of file diff --git a/app/api/router/predict.py b/app/api/router/predict.py index 9c72462..f8d29ca 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- -import numpy as np from typing import List +import numpy as np from fastapi import APIRouter from starlette.concurrency import run_in_threadpool @@ -9,6 +9,7 @@ from app.api.schemas import ModelCorePrediction from app.database import engine from app.utils import ScikitLearnModel, my_model +from logger import L models.Base.metadata.create_all(bind=engine) @@ -38,7 +39,6 @@ async def predict_insurance(info: ModelCorePrediction, model_name: str): return: insurance_fee: float """ - def sync_call(info, model_name): model = ScikitLearnModel(model_name) model.load_model() @@ -47,25 +47,33 @@ def sync_call(info, model_name): test_set = np.array([*info.values()]).reshape(1, -1) pred = model.predict_target(test_set) - return {"result": pred.tolist()[0]} + try: + result = await run_in_threadpool(sync_call, info, model_name) + L.info( + f"Predict Args info:{info}\n\tmodel_name is {model_name}\n\tPrediction Result: {result}") + return result - result = await run_in_threadpool(sync_call, info, model_name) - - return result + except Exception as e: + L.error(e) + return {'error': str(e)} @router.put("/atmos") async def predict_temperature(time_series: List[float]): if len(time_series) != 72: + L.error(f'input time_series: {time_series} is not valid') return "time series must have 72 values" try: tf_model = my_model.model time_series = np.array(time_series).reshape(1, -1, 1) result = tf_model.predict(time_series) + + L.info( + f"Predict Args info:{time_series}\n\tmodel_name is {tf_model}\n\tPrediction Result: {result.tolist()}") return result.tolist() except Exception as e: - print(e) + L.error(e) return {"error": str(e)} diff --git a/app/api/router/train.py b/app/api/router/train.py index 9df8968..423e9e9 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -4,6 +4,7 @@ from fastapi import APIRouter from app.utils import write_yml +from logger import L router = APIRouter( @@ -33,8 +34,11 @@ def train_insurance( Returns: msg: Regardless of success or not, return address values including PORT. """ + L.info( + f"Train Args info\n\texperiment_sec: {experiment_sec}\n\texperiment_name: {experiment_name}\n\texperimenter: {experimenter}\n\tmodel_name: {model_name}\n\tversion: {version}") path = 'experiments/insurance/' try: + L.info("Start NNi") write_yml( path, experiment_name, @@ -49,7 +53,7 @@ def train_insurance( ) except Exception as e: - print('error') - print(e) + L.error(e) + return {'error': str(e)} return {"msg": f'Check out http://127.0.0.1:{PORT}'} diff --git a/logger.py b/logger.py new file mode 100644 index 0000000..5d5ed85 --- /dev/null +++ b/logger.py @@ -0,0 +1,19 @@ +import logging +import logging.handlers + +L = logging.getLogger('snowdeer_log') +L.setLevel(logging.DEBUG) + +formatter = logging.Formatter( + fmt="[%(levelname)s] [%(filename)s:%(lineno)d - %(funcName)20s()]\n\t%(message)s", + datefmt='%Y-%m-%d %H:%M:%S' +) + +fileHandler = logging.FileHandler('./log.txt') +streamHandler = logging.StreamHandler() + +fileHandler.setFormatter(formatter) +streamHandler.setFormatter(formatter) + +L.addHandler(fileHandler) +L.addHandler(streamHandler) From 32417259d02a462694a938878a1074600fc3289a Mon Sep 17 00:00:00 2001 From: hlchoi Date: Mon, 13 Sep 2021 17:20:04 +0900 Subject: [PATCH 27/47] =?UTF-8?q?modify=20log=20message=20=EB=A1=9C?= =?UTF-8?q?=EA=B7=B8=EB=A9=94=EC=84=B8=EC=A7=80=20=EC=95=9E=EB=B6=80?= =?UTF-8?q?=EB=B6=84=EC=97=90=20=EC=8B=9C=EA=B0=84=EC=9D=84=20=EC=B6=94?= =?UTF-8?q?=EA=B0=80=EC=A0=81=EC=9C=BC=EB=A1=9C=20=EB=82=A8=EA=B8=B0?= =?UTF-8?q?=EB=8F=84=EB=A1=9D=20=ED=95=98=EC=98=80=EC=8A=B5=EB=8B=88?= =?UTF-8?q?=EB=8B=A4.=20=EB=A1=9C=EA=B7=B8=EB=8D=B0=EC=9D=B4=ED=84=B0?= =?UTF-8?q?=EC=9D=98=20=ED=98=95=EC=8B=9D=EC=9D=84=20=EC=A7=81=EA=B4=80?= =?UTF-8?q?=EC=A0=81=EC=9D=B4=EA=B3=A0=20=EC=9D=BC=EA=B4=80=EB=90=98?= =?UTF-8?q?=EA=B2=8C=20=EB=B3=80=EA=B2=BD=ED=95=98=EC=98=80=EC=8A=B5?= =?UTF-8?q?=EB=8B=88=EB=8B=A4.=20ex=201)=20[[1],=20=20[2],=20=20=20=20-->?= =?UTF-8?q?=20=20=20[1,=202,=203]=20=20[3],]?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ex 2) input: ~~~ input: ~~~ model name is ~~~ --> model name: ~~~ output: ~~~~ output: ~~~ --- app/api/router/predict.py | 4 ++-- logger.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/app/api/router/predict.py b/app/api/router/predict.py index f8d29ca..46d1efd 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -51,7 +51,7 @@ def sync_call(info, model_name): try: result = await run_in_threadpool(sync_call, info, model_name) L.info( - f"Predict Args info:{info}\n\tmodel_name is {model_name}\n\tPrediction Result: {result}") + f"Predict Args info: {info}\n\tmodel_name: {model_name}\n\tPrediction Result: {result}") return result except Exception as e: @@ -71,7 +71,7 @@ async def predict_temperature(time_series: List[float]): result = tf_model.predict(time_series) L.info( - f"Predict Args info:{time_series}\n\tmodel_name is {tf_model}\n\tPrediction Result: {result.tolist()}") + f"Predict Args info: {time_series.flatten().tolist()}\n\tmodel_name: {tf_model}\n\tPrediction Result: {result.tolist()[0]}") return result.tolist() except Exception as e: diff --git a/logger.py b/logger.py index 5d5ed85..a994ecd 100644 --- a/logger.py +++ b/logger.py @@ -5,8 +5,8 @@ L.setLevel(logging.DEBUG) formatter = logging.Formatter( - fmt="[%(levelname)s] [%(filename)s:%(lineno)d - %(funcName)20s()]\n\t%(message)s", - datefmt='%Y-%m-%d %H:%M:%S' + fmt="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d - %(funcName)20s()]\n\t%(message)s", + datefmt='%y-%m-%d %H:%M:%S' ) fileHandler = logging.FileHandler('./log.txt') From 8bf4eacdf0728c367dc9d63e671421fb1fb3d742 Mon Sep 17 00:00:00 2001 From: chl8469 Date: Wed, 15 Sep 2021 09:32:17 +0900 Subject: [PATCH 28/47] =?UTF-8?q?add=20train=20api(train=20atmosphere=20mo?= =?UTF-8?q?del)=20=ED=85=90=EC=84=9C=ED=94=8C=EB=A1=9C=EC=9A=B0=EB=8A=94?= =?UTF-8?q?=20=EA=B0=81=20=EC=B2=B4=ED=81=AC=ED=8F=AC=EC=9D=B8=ED=8A=B8?= 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=?UTF-8?q?=EA=B8=B0=20=EC=9C=84=ED=95=98=EC=97=AC=20=ED=95=B4=EB=8B=B9=20?= =?UTF-8?q?=EC=97=AD=ED=95=A0=EC=9D=84=20=ED=95=98=EB=8A=94=20=EC=9E=90?= =?UTF-8?q?=EC=8B=9D=ED=94=84=EB=A1=9C=EC=84=B8=EC=8A=A4=EB=A5=BC=20?= =?UTF-8?q?=EB=A7=8C=EB=93=A4=EB=8F=84=EB=A1=9D=20=ED=95=98=EC=98=80?= =?UTF-8?q?=EC=8A=B5=EB=8B=88=EB=8B=A4.=20*=20=EC=9E=90=EC=8B=9D=20?= =?UTF-8?q?=ED=94=84=EB=A1=9C=EC=84=B8=EC=8A=A4=EB=8A=94=20=EB=B6=80?= =?UTF-8?q?=EB=AA=A8=20=ED=94=84=EB=A1=9C=EC=84=B8=EC=8A=A4=EA=B0=80=20nni?= =?UTF-8?q?=EC=8B=A4=ED=97=98=EC=9D=84=20=ED=98=B8=EC=B6=9C=ED=95=98?= =?UTF-8?q?=EA=B3=A0=20=EC=A2=85=EB=A3=8C=EB=90=9C=20=EC=9D=B4=ED=9B=84?= =?UTF-8?q?=EC=97=90=EB=8F=84=20=EB=82=A8=EC=95=84=EC=9E=88=EC=8A=B5?= =?UTF-8?q?=EB=8B=88=EB=8B=A4.=20*=20=EB=AA=A8=EB=8D=B8=EC=9D=B4=20?= =?UTF-8?q?=EA=B3=BC=EB=8F=84=ED=95=98=EA=B2=8C=20=EB=A7=8E=EC=9D=B4=20?= =?UTF-8?q?=EC=83=9D=EC=84=B1=EB=90=98=EB=8A=94=20=EA=B2=83=EC=9D=84=20?= =?UTF-8?q?=EB=B0=A9=EC=A7=80=ED=95=98=EA=B8=B0=20=EC=9C=84=ED=95=98?= =?UTF-8?q?=EC=97=AC=20=ED=85=90=EC=84=9C=ED=94=8C=EB=A1=9C=EC=9A=B0?= =?UTF-8?q?=EA=B0=80=20=EB=AA=A8=EB=8D=B8=20=EC=9D=B4=EB=A6=84=EC=97=90=20?= =?UTF-8?q?=EC=84=B1=EB=8A=A5=EC=9D=84=20=EC=A0=81=EC=96=B4=EB=91=90?= =?UTF-8?q?=EB=A9=B4=20=EA=B7=B8=EA=B2=83=EC=9D=84=20=EA=B8=B0=EB=B0=98?= =?UTF-8?q?=EC=9C=BC=EB=A1=9C=20=EC=9E=90=EC=8B=9D=20=ED=94=84=EB=A1=9C?= =?UTF-8?q?=EC=84=B8=EC=8A=A4=EA=B0=80=20=EC=A3=BC=EA=B8=B0=EC=A0=81?= =?UTF-8?q?=EC=9C=BC=EB=A1=9C=20=EC=A0=80=EC=9E=A5=EB=90=9C=20=EB=AA=A8?= =?UTF-8?q?=EB=8D=B8=EC=9D=98=20=EA=B0=9C=EC=88=98=EB=A5=BC=20=ED=8C=8C?= =?UTF-8?q?=EC=95=85=ED=95=98=EA=B3=A0=203=EA=B0=9C=EB=A7=8C=20=EC=9C=A0?= =?UTF-8?q?=EC=A7=80=ED=95=98=EB=8F=84=EB=A1=9D=20=ED=95=A9=EB=8B=88?= =?UTF-8?q?=EB=8B=A4.=20*=20nni=20=EC=8B=A4=ED=97=98=EC=9D=B4=20=EB=AA=A8?= =?UTF-8?q?=EB=91=90=20=EC=A2=85=EB=A3=8C=EB=90=98=EB=A9=B4=20best=20score?= =?UTF-8?q?=20model=EC=9D=84=20DB=EC=97=90=20=EC=A0=80=EC=9E=A5=ED=95=98?= =?UTF-8?q?=EA=B3=A0=20nnictl=20stop=20(experiment=20ID)=EB=A5=BC=20?= =?UTF-8?q?=ED=98=B8=EC=B6=9C=ED=95=98=EC=97=AC=20=EC=8B=A4=ED=97=98?= =?UTF-8?q?=EC=9D=84=20=EC=A2=85=EB=A3=8C=ED=95=98=EA=B3=A0=20=EC=9E=90?= =?UTF-8?q?=EC=8B=A0=EB=8F=84=20=EC=A2=85=EB=A3=8C=ED=95=A9=EB=8B=88?= =?UTF-8?q?=EB=8B=A4.=20*=20multiprocessing=20=EB=9D=BC=EC=9D=B4=EB=B8=8C?= =?UTF-8?q?=EB=9F=AC=EB=A6=AC=EB=A5=BC=20=EC=9D=B4=EC=9A=A9=ED=95=98?= =?UTF-8?q?=EC=98=80=EC=8A=B5=EB=8B=88=EB=8B=A4.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 3 +- app/api/router/predict.py | 8 +- app/api/router/train.py | 40 ++++++++- app/utils.py | 99 +++++++++++++++++++++- experiments/atmos_tmp_01/config.yml | 4 +- experiments/atmos_tmp_01/search_space.json | 4 +- experiments/atmos_tmp_01/train.py | 54 +++++++----- 7 files changed, 181 insertions(+), 31 deletions(-) diff --git a/.gitignore b/.gitignore index d00ddd1..867b6d8 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,5 @@ *.pkl __pycache__ tf_model/**/* -log.txt \ No newline at end of file +log.txt +experiments/**/temp/ diff --git a/app/api/router/predict.py b/app/api/router/predict.py index 46d1efd..219e1d5 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -65,13 +65,17 @@ async def predict_temperature(time_series: List[float]): L.error(f'input time_series: {time_series} is not valid') return "time series must have 72 values" - try: + def sync_pred_ts(time_series): tf_model = my_model.model time_series = np.array(time_series).reshape(1, -1, 1) result = tf_model.predict(time_series) - L.info( f"Predict Args info: {time_series.flatten().tolist()}\n\tmodel_name: {tf_model}\n\tPrediction Result: {result.tolist()[0]}") + + return result + + try: + result = await run_in_threadpool(sync_pred_ts, time_series) return result.tolist() except Exception as e: diff --git a/app/api/router/train.py b/app/api/router/train.py index 423e9e9..61970da 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -1,9 +1,12 @@ # -*- coding: utf-8 -*- import subprocess +import multiprocessing +import re +import os from fastapi import APIRouter -from app.utils import write_yml +from app.utils import write_yml, get_free_port, base_dir, check_expr_over from logger import L @@ -57,3 +60,38 @@ def train_insurance( return {'error': str(e)} return {"msg": f'Check out http://127.0.0.1:{PORT}'} + + +@router.put("/atmos") +def train_atmos(expr_name: str): + nni_port = get_free_port() + expr_path = os.path.join(base_dir, 'experiments', expr_name) + + # subprocess로 nni실행 + try: + nni_create_result = subprocess.getoutput( + "nnictl create --port {} --config {}/config.yml".format( + nni_port, expr_path)) + sucs_msg = "Successfully started experiment!" + + if sucs_msg in nni_create_result: + p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") + expr_id = p.findall(nni_create_result)[0] + m_process = multiprocessing.Process( + target = check_expr_over, + args = (expr_id, expr_name, expr_path) + ) + m_process.start()# 자식 프로세스 분리(nni 실험 진행상황 감시 및 모델 저장) + + L.info(nni_create_result) + return nni_create_result + + else: + L.error(nni_create_result) + return {"error": nni_create_result} + + except Exception as e: + L.error(e) + return {'error': str(e)} + + # 코드는 바이너리로 저장하는건 별로인가?(버전관리 차원에서 score랑 같은 행에...) \ No newline at end of file diff --git a/app/utils.py b/app/utils.py index 1f9b2e7..da367cb 100644 --- a/app/utils.py +++ b/app/utils.py @@ -3,11 +3,18 @@ import os import yaml import zipfile +import socketserver +import time +import glob +import shutil +import subprocess +import io import tensorflow as tf from app.database import engine - +from logger import L +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) @@ -103,3 +110,93 @@ def write_yml( yml_config_file.close() return + +def zip_model(model_path): + model_buffer = io.BytesIO() + + basedir = os.path.basename(model_path) + + with zipfile.ZipFile(model_buffer, "w") as zf: + for root, dirs, files in os.walk(model_path): + make_arcname = lambda x: os.path.join(root.split(basedir)[-1], x) + for dr in dirs: + dir_path = os.path.join(root, dr) + zf.write(filename = dir_path, arcname = make_arcname(dr)) + for file in files: + file_path = os.path.join(root, file) + zf.write(filename = file_path, arcname = make_arcname(file)) + + return model_buffer + +def get_free_port(): + with socketserver.TCPServer(("localhost", 0), None) as s: + free_port = s.server_address[1] + return free_port + +def check_expr_over(experiment_id, experiment_name, experiment_path): + minute = 60 + + while True: + time.sleep(1*minute) + + # 실험이 끝났는지 확인 + expr_list = subprocess.getoutput("nnictl experiment list") + + running_expr = [expr for expr in expr_list.split('\n') if experiment_id in expr] + print(running_expr) + if running_expr and ("DONE" in running_expr[0]): + stop_expr = subprocess.getoutput("nnictl stop {}".format( + experiment_id)) + L.info(stop_expr) + break # 실험이 끝나면 무한루프 종료 + + elif experiment_id not in expr_list: + L.info(expr_list) + break # 갑자기 누군가가 nnictl stop으로 다 꺼버렸을 상황에 대비 + + else: + model_path = os.path.join(experiment_path, + "temp", + "*_{}*".format(experiment_name)) + exprs = glob.glob(model_path) + if len(exprs) > 3: # 모델파일이 너무 많아지지 않게 3개 넘으면 삭제 + exprs.sort() + [shutil.rmtree(_) for _ in exprs[3:]] + + # 모델저장 + model_path = os.path.join(experiment_path, + "temp", + "*_{}*".format(experiment_name)) + exprs = glob.glob(model_path) + if not exprs: # 모델파일이 하나도 없을 경우 그냥 종료 + return 0 + + exprs.sort() + exprs = exprs[0] + metrics = os.path.basename(exprs).split("_")[:2] # metric 개수 + metrics = [float(metric) for metric in metrics] + + score_sql = """SELECT mae + FROM atmos_model_metadata + ORDER BY mae;""" + saved_score = engine.execute(score_sql).fetchone() + + if not saved_score or (metrics < saved_score[0]): + winner_model = os.path.join(os.path.join(experiment_path, + "temp", + experiment_name)) + os.rename(exprs, winner_model) + m_buffer = zip_model(winner_model) + encode_model = codecs.encode(pickle.dumps(m_buffer), "base64").decode() + sql_save_model = "INSERT INTO model_core VALUES ('%s', '%s')" + engine.execute(sql_save_model%(experiment_name, + encode_model)) + + sql_save_score = "INSERT INTO atmos_model_metadata VALUES ('%s', '%s', '%s', '%s')" + engine.execute(sql_save_score%(experiment_name, + experiment_id, + metrics[0], + metrics[1])) + L.info("saved model %s %s"%(experiment_id, experiment_name)) + + diff --git a/experiments/atmos_tmp_01/config.yml b/experiments/atmos_tmp_01/config.yml index c79bbea..031af06 100644 --- a/experiments/atmos_tmp_01/config.yml +++ b/experiments/atmos_tmp_01/config.yml @@ -3,8 +3,8 @@ searchSpaceFile: search_space.json trialCommand: python train.py trialCodeDirectory: . trialConcurrency: 1 -maxExperimentDuration: 48h -maxTrialNumber: 1000 +maxExperimentDuration: 2h +maxTrialNumber: 2 tuner: # choice: # TPE, Anneal, Evolution, SMAC, BatchTuner, GridSearch, Hyperband diff --git a/experiments/atmos_tmp_01/search_space.json b/experiments/atmos_tmp_01/search_space.json index 43282c1..40fdad8 100644 --- a/experiments/atmos_tmp_01/search_space.json +++ b/experiments/atmos_tmp_01/search_space.json @@ -1,4 +1,4 @@ { - "layer_n": {"_type":"randint", "_value":[2, 8]}, - "cell": {"_type":"randint", "_value":[24, 80]} + "layer_n": {"_type":"randint", "_value":[2, 3]}, + "cell": {"_type":"randint", "_value":[24, 30]} } \ No newline at end of file diff --git a/experiments/atmos_tmp_01/train.py b/experiments/atmos_tmp_01/train.py index 01741e2..1945af6 100644 --- a/experiments/atmos_tmp_01/train.py +++ b/experiments/atmos_tmp_01/train.py @@ -2,7 +2,7 @@ import sys import time from preprocessing import preprocess -sys.path.append(os.path.dirname( os.path.dirname( os.path.abspath(__file__) ) )) +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import numpy as np import nni @@ -13,11 +13,13 @@ from tensorflow.keras.layers import Dense from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint from tensorflow.keras.layers import GRU +from sklearn.metrics import mean_absolute_error, mean_squared_error from expr_db import connect -physical_devices = tf.config.list_physical_devices('GPU') -tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) +physical_devices = tf.config.list_physical_devices('GPU') +if physical_devices: + tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) def make_dataset(data, label, window_size=365, predsize=None): @@ -58,14 +60,11 @@ def split_data(data_length, ratio): def main(params): - con = connect("atmos_data") - data = pd.read_sql("select tmp from stn108;", con) + con = connect("postgres") + data = pd.read_sql("select tmp from atmos_stn108;", con) data = preprocess(data) - base_dir = os.path.dirname(os.path.abspath(__file__)) - parent_dir = base_dir.split(os.path.sep)[-1] - train_feature, train_label = make_dataset(data, data, 72, 24) idx = split_data(train_feature.shape[0], [6, 3, 1]) @@ -94,12 +93,21 @@ def main(params): ) model.add(Dense(24)) + base_dir = os.path.dirname(os.path.abspath(__file__)) + parent_dir = base_dir.split(os.path.sep)[-1] + model_path = "./" model.compile(loss="mae", optimizer=keras.optimizers.Adam(lr=0.001)) - early_stop = EarlyStopping(monitor="val_loss", patience=30) + early_stop = EarlyStopping(monitor="val_loss", patience=5) expr_time = time.strftime("%y%m%d_%H%M%S") - filename = os.path.join(model_path, - f"./temp/{expr_time}_{parent_dir}") + model_path = os.path.join(model_path, f"./temp") + if not os.path.exists(model_path): + os.makedirs(model_path) + + # 실험시작시간은 여러 모델간의 구분을 위해 임시로 넣었지만 + # 여러 워커를 동시에 실행시킬 경우 겹칠 수 있음. 추후 변경 필요!! + filename = os.path.join(model_path, f'./{parent_dir}_{expr_time}') + print(filename) checkpoint = ModelCheckpoint( filename, monitor="val_loss", @@ -111,24 +119,26 @@ def main(params): model.fit( X_train, y_train, - epochs=10, + epochs=2, batch_size=128, validation_data=(X_valid, y_valid), callbacks=[early_stop, checkpoint], ) - result = ( - np.sum( - np.abs( - y_test.reshape(y_test.shape[0], y_test.shape[1]) - model.predict(X_test) - ) - ) - / y_test.shape[0] - ) - nni.report_final_result(result) + y_true = y_test.reshape(y_test.shape[0], y_test.shape[1]) + y_hat = model.predict(X_test) + + mae = mean_absolute_error(y_true, y_hat) + mse = mean_squared_error(y_true, y_hat) + + src_f = os.path.join(model_path, f'./{parent_dir}_{expr_time}') + dst_f = os.path.join(model_path, f'./{mae:.03f}_{mse:.03f}_{parent_dir}_{expr_time}') + os.rename(src_f, dst_f) + + nni.report_final_result(mae) if __name__ == "__main__": params = nni.get_next_parameter() - main(params) + main(params) \ No newline at end of file From eced4d4e4168219798d4009423a2973bf8812e36 Mon Sep 17 00:00:00 2001 From: chl8469 Date: Wed, 15 Sep 2021 18:55:30 +0900 Subject: [PATCH 29/47] =?UTF-8?q?fix=20list=20index=20=EC=9D=B8=EB=8D=B1?= =?UTF-8?q?=EC=8B=B1=20=EC=9E=98=EB=AA=BB=EB=8F=BC=EC=84=9C=20=EC=98=A4?= =?UTF-8?q?=EB=A5=98=EB=82=98=EB=8D=98=EA=B2=83=20=EC=88=98=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/app/utils.py b/app/utils.py index da367cb..6b47265 100644 --- a/app/utils.py +++ b/app/utils.py @@ -181,7 +181,7 @@ def check_expr_over(experiment_id, experiment_name, experiment_path): ORDER BY mae;""" saved_score = engine.execute(score_sql).fetchone() - if not saved_score or (metrics < saved_score[0]): + if not saved_score or (metrics[0] < saved_score[0]): winner_model = os.path.join(os.path.join(experiment_path, "temp", experiment_name)) From 93bf6940d9e04245945a12f84315dd78ff9d4ad3 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Wed, 15 Sep 2021 19:59:01 +0900 Subject: [PATCH 30/47] Update README.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit README에 API 문서화를 진행했습니다. >> 추가적으로 return message 통일이 필요합니다. --- README.md | 6 +- app/api/router/train.py | 14 ++-- docs/api-list.md | 145 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 157 insertions(+), 8 deletions(-) create mode 100644 docs/api-list.md diff --git a/README.md b/README.md index 8ffc8f4..f70e538 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,6 @@ # MLOps -👊 Build MLOps system step by step 👊 \ No newline at end of file +👊 Build MLOps system step by step 👊 + +## 문서 + +- [API DOCS](./docs/api-list.md) \ No newline at end of file diff --git a/app/api/router/train.py b/app/api/router/train.py index 61970da..fceaa15 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -17,7 +17,7 @@ ) -@router.put("/") +@router.put("/insurance") def train_insurance( PORT: int = 8080, experiment_sec: int = 20, @@ -73,15 +73,15 @@ def train_atmos(expr_name: str): "nnictl create --port {} --config {}/config.yml".format( nni_port, expr_path)) sucs_msg = "Successfully started experiment!" - + if sucs_msg in nni_create_result: p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") expr_id = p.findall(nni_create_result)[0] m_process = multiprocessing.Process( - target = check_expr_over, - args = (expr_id, expr_name, expr_path) - ) - m_process.start()# 자식 프로세스 분리(nni 실험 진행상황 감시 및 모델 저장) + target=check_expr_over, + args=(expr_id, expr_name, expr_path) + ) + m_process.start() # 자식 프로세스 분리(nni 실험 진행상황 감시 및 모델 저장) L.info(nni_create_result) return nni_create_result @@ -94,4 +94,4 @@ def train_atmos(expr_name: str): L.error(e) return {'error': str(e)} - # 코드는 바이너리로 저장하는건 별로인가?(버전관리 차원에서 score랑 같은 행에...) \ No newline at end of file + # 코드는 바이너리로 저장하는건 별로인가?(버전관리 차원에서 score랑 같은 행에...) diff --git a/docs/api-list.md b/docs/api-list.md new file mode 100644 index 0000000..64a592a --- /dev/null +++ b/docs/api-list.md @@ -0,0 +1,145 @@ +# API List + +API_URL : 127.0.0.1 + +- [API List](#api-list) + - [Train](#train) + - [Insurance](#insurance훈련) + - [Prediction](#predict) + - [Insurance](#insurance예측) + - [Temperature](#Temperature예측) + + +## Train + +### Insurance훈련 + +#### 요청 + +``` +PUT {{API_URL}}/train/insurance +``` + +| 파라미터 | 파라미터 유형 | 데이터 타입 | 필수 여부 | 설명 | +| --------------- | ------------- | ----------- | --------- | ----------------------- | +| `PORT` | `body` | `int` | `(default) 8080` | NNi 포트번호 | +| `experiment_sec` | `body` | `int` | `(default) 20` | 학습시간(초) | +| `experiment_name` | `body` | `(default) exp1` | ✅ | 학습이름 | +| `experimenter` | `body` | `str` | `(default) DongUk` | 연구자 이름 | +| `model_name` | `body` | `str` | `(default) insurance_fee_model` | 학습 모델 이름 | +| `version` | `body` | `float` | `(default) 0.1` | 모델 버전 | + + +
+ +#### 응답 + +| 키 | 데이터 타입 | 설명 | +| -------------- | ----------- | ------------- | +| `msg` | `string` | NNi실험을 확인할 수 있는 링크주소 | +| `error` | `string` | 에러내용 | + + +```jsonc +{ + "result": 'Check out {{API_URL}}:{PORT}', +} +{ + "error": "Error info" +} +``` + + +### Temperature훈련 + +#### 요청 + +| 파라미터 | 파라미터 유형 | 데이터 타입 | 필수 여부 | 설명 | +| --------------- | ------------- | ----------- | --------- | ----------------------- | +| `expr_name` | `body` | `string` | ✅ | 학습이름 | + + +
+ +#### 응답 + +| 키 | 데이터 타입 | 설명 | +| -------------- | ----------- | ------------- | +| `error` | `string` | 에러내용 | + +```jsonc +{ + "error": "Error info" +} +``` + +## Predict + +### Insurance예측 + +#### 요청 + +``` +PUT {{API_URL}}/predict/insurance +``` + +| 파라미터 | 파라미터 유형 | 데이터 타입 | 필수 여부 | 설명 | +| --------------- | ------------- | ----------- | --------- | ----------------------- | +| `age` | `body` | `int` | ✅ | 나이 | +| `sex` | `body` | `int` | ✅ | 성별 | +| `bmi` | `body` | `float` | ✅ | bmi수치 | +| `children` | `body` | `int` | ✅ | 자녀 수 | +| `smoker` | `body` | `int` | ✅ | 흡연여부 | +| `region` | `body` | `int` | ✅ | 거주지역 | + +
+ +#### 응답 + +| 키 | 데이터 타입 | 설명 | +| -------------- | ----------- | ------------- | +| `result` | `float` | 예측된 보험료 값 | +| `error` | `string` | 에러내용 | + + +```jsonc +{ + "result": 3213.123, +} +{ + "error": "Error info" +} +``` + + +### Temperature예측 + +#### 요청 + +``` +PUT {{API_URL}}/predict/atmos +``` + +| 파라미터 | 파라미터 유형 | 데이터 타입 | 필수 여부 | 설명 | +| --------------- | ------------- | ----------- | --------- | ----------------------- | +| `time_series` | `body` | `List[float]` | ✅ | 72일간의 온도데이터 | + + +
+ +#### 응답 + +| 키 | 데이터 타입 | 설명 | +| -------------- | ----------- | ------------- | +| x | `List[float]` | 예측된 향후 24일간 온도값 | +| `error` | `string` | 에러내용 | + + +```jsonc +{ + [32.32, 33.32, 34.11...] +} +{ + "error": "Error info" +} +``` From 8c34395ad7766a6edf2a7566dbb411dd4b5e35bd Mon Sep 17 00:00:00 2001 From: hlchoi Date: Wed, 15 Sep 2021 20:13:17 +0900 Subject: [PATCH 31/47] =?UTF-8?q?Add=20Docstring=20=ED=95=A8=EC=88=98?= =?UTF-8?q?=EC=99=80=20=ED=81=B4=EB=9E=98=EC=8A=A4=EC=9D=98=20=EB=8F=85?= =?UTF-8?q?=EC=8A=A4=ED=8A=B8=EB=A7=81=EC=9D=84=20=EC=9E=91=EC=84=B1?= =?UTF-8?q?=ED=95=98=EC=98=80=EC=8A=B5=EB=8B=88=EB=8B=A4.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/api/router/predict.py | 35 +++++++++------ app/api/router/train.py | 27 ++++++++---- app/api/schemas.py | 11 +++++ app/database.py | 9 ++++ app/utils.py | 91 +++++++++++++++++++++++++++++++++++---- 5 files changed, 144 insertions(+), 29 deletions(-) diff --git a/app/api/router/predict.py b/app/api/router/predict.py index 219e1d5..aa8a908 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -25,21 +25,18 @@ @router.put("/insurance") async def predict_insurance(info: ModelCorePrediction, model_name: str): """ - Get information and predict insurance fee - param: - info: - # 임시로 int형태를 받도록 제작 - # preprocess 단계를 거치도록 만들 예정 - age: int - sex: int - bmi: float - children: int - smoker: int - region: int - return: - insurance_fee: float + 정보를 입력받아 보험료를 예측하여 반환합니다. + + Args: + info(dict): 다음의 값들을 입력받습니다. age(int), sex(int), bmi(float), children(int), smoker(int), region(int) + + Returns: + insurance_fee(float): 보험료 예측값입니다. """ def sync_call(info, model_name): + """ + none sync 함수를 sync로 만들어 주기 위한 함수이며 입출력은 부모 함수와 같습니다. + """ model = ScikitLearnModel(model_name) model.load_model() @@ -61,11 +58,23 @@ def sync_call(info, model_name): @router.put("/atmos") async def predict_temperature(time_series: List[float]): + """ + 온도 1시간 간격 시계열을 입력받아 이후 24시간 동안의 온도를 1시간 간격의 시계열로 예측합니다. + + Args: + time_series(List): 72시간 동안의 1시간 간격 온도 시계열 입니다. 72개의 원소를 가져야 합니다. + + Returns: + List[float]: 입력받은 시간 이후 24시간 동안의 1시간 간격 온도 예측 시계열 입니다. + """ if len(time_series) != 72: L.error(f'input time_series: {time_series} is not valid') return "time series must have 72 values" def sync_pred_ts(time_series): + """ + none sync 함수를 sync로 만들어 주기 위한 함수이며 입출력은 부모 함수와 같습니다. + """ tf_model = my_model.model time_series = np.array(time_series).reshape(1, -1, 1) result = tf_model.predict(time_series) diff --git a/app/api/router/train.py b/app/api/router/train.py index 61970da..8e34177 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -27,15 +27,18 @@ def train_insurance( version: float = 0.1 ): """ + insurance와 관련된 학습을 실행하기 위한 API입니다. + Args: - PORT (int): PORT to run NNi. Defaults to 8080 - experiment_sec (int): Express the experiment time in seconds Defaults to 20 - experiment_name (str): experiment name Defaults to exp1 - experimeter (str): experimenter (author) Defaults to DongUk - model_name (str): model name Defaults to insurance_fee_model - version (float): version of experiment Defaults to 0.1 + PORT (int): NNI가 실행될 포트번호. 기본 값: 8080 + experiment_sec (int): 최대 실험시간(초단위). 기본 값: 20 + experiment_name (str): 실험이름. 기본 값: exp1 + experimeter (str): 실험자의 이름. 기본 값: DongUk + model_name (str): 모델의 이름. 기본 값: insurance_fee_model + version (float): 실험의 버전. 기본 값: 0.1 + Returns: - msg: Regardless of success or not, return address values including PORT. + msg: 실험 실행의 성공과 상관없이 포트번호를 포함한 NNI Dashboard의 주소를 반환합니다. 실험의 최종 결과를 반환하지 않습니다. """ L.info( f"Train Args info\n\texperiment_sec: {experiment_sec}\n\texperiment_name: {experiment_name}\n\texperimenter: {experimenter}\n\tmodel_name: {model_name}\n\tversion: {version}") @@ -64,10 +67,18 @@ def train_insurance( @router.put("/atmos") def train_atmos(expr_name: str): + """ + 온도 시계열과 관련된 학습을 실행하기 위한 API입니다. + + Args: + expr_name(str): NNI가 실행할 실험의 이름 입니다. 이 파라미터를 기반으로 project_dir/experiments/[expr_name] 경로로 찾아가 config.yml을 이용하여 NNI를 실행합니다. + + Returns: + str: NNI실험이 실행된 결과값을 반환하거나 실행과정에서 발생한 에러 메세지를 반환합니다. 실험의 최종 결과를 반환하지 않습니다. + """ nni_port = get_free_port() expr_path = os.path.join(base_dir, 'experiments', expr_name) - # subprocess로 nni실행 try: nni_create_result = subprocess.getoutput( "nnictl create --port {} --config {}/config.yml".format( diff --git a/app/api/schemas.py b/app/api/schemas.py index 87698ac..dffbee3 100644 --- a/app/api/schemas.py +++ b/app/api/schemas.py @@ -6,6 +6,17 @@ class ModelCoreBase(BaseModel): class ModelCorePrediction(BaseModel): + """ + predict_insurance API의 입력 값 검증을 위한 pydantic 클래스입니다. + + Attributes: + age(int) + sex(int) + bmi(float) + children(int) + smoker(int) + region(int) + """ age: int sex: int bmi: float diff --git a/app/database.py b/app/database.py index 62f0cf0..b6eb15b 100644 --- a/app/database.py +++ b/app/database.py @@ -8,6 +8,15 @@ load_dotenv(verbose=True) def connect(db): + """ + database와의 연결을 위한 함수 입니다. + + Args: + db(str): 사용할 데이터베이스의 이름을 전달받습니다. + + Returns: + created database engine: 데이터베이스에 연결된 객체를 반환합니다. + """ print(db) POSTGRES_USER = os.getenv("POSTGRES_USER") diff --git a/app/utils.py b/app/utils.py index 6b47265..8448da6 100644 --- a/app/utils.py +++ b/app/utils.py @@ -24,7 +24,14 @@ class CoreModel: - + """ + predict API 호출을 받았을 때 사용될 ML 모델을 로드하는 클래스입니다. + + Attributes: + model_name(str): 예측을 실행할 모델의 이름 + model(obj): 모델이 저장될 인스턴스 변수 + query(str): 입력받은 모델이름을 기반으로 데이터베이스에서 모델을 불러오는 SQL query입니다. + """ def __init__(self, model_name): self.model_name = model_name self.model = None @@ -35,17 +42,35 @@ def __init__(self, model_name): """.format(self.model_name) def load_model(self): + """ + 본 클래스를 상속받았을 때 이 함수를 구현하지 않으면 예외를 발생시킵니다. + """ raise Exception def predict_target(self, target_data): + """ + 데이터베이스에서 불러와 인스턴스 변수에 저장된 모델을 기반으로 예측을 수행합니다. + + Args: + target_data: predict API 호출 시 입력받은 값입니다. 자료형은 모델에 따라 다릅니다. + + Returns: + 예측된 값을 반환 합니다. 자료형은 모델에 따라 다릅니다. + """ return self.model.predict(target_data) class ScikitLearnModel(CoreModel): + """ + Scikit learn 라이브러리 기반의 모델을 불러오기 위한 클래스입니다. + """ def __init__(self, *args): super().__init__(*args) def load_model(self): + """ + 모델을 데이터베이스에서 불러와 인스턴스 변수에 저장하는 함수 입니다. 상속받은 부모클래스의 인스턴스 변수를 이용하며, 반환 값은 없습니다. + """ _model = engine.execute(self.query).fetchone() if _model is None: raise ValueError('Model Not Found!') @@ -56,10 +81,16 @@ def load_model(self): class TensorFlowModel(CoreModel): + """ + Tensorflow 라이브러리 기반의 모델을 불러오기 위한 클래스입니다. + """ def __init__(self, *args): super().__init__(*args) def load_model(self): + """ + 모델을 데이터베이스에서 불러와 인스턴스 변수에 저장하는 함수 입니다. 상속받은 부모클래스의 인스턴스 변수를 이용하며, 반환 값은 없습니다. + """ _model = engine.execute(self.query).fetchone() if _model is None: raise ValueError('Model Not Found!') @@ -82,6 +113,19 @@ def write_yml( model_name, version ): + """ + NNI 실험을 시작하기 위한 config.yml파일을 작성하는 함수 입니다. + + Args: + path(str): 실험의 경로 + experiment_name(str): 실험의 이름 + experimenter(str): 실험자의 이름 + model_name(str): 모델의 이름 + version(float): 버전 + + Returns: + 반환 값은 없으며 입력받은 경로로 yml파일이 작성됩니다. + """ with open('{}/{}.yml'.format(path, model_name), 'w') as yml_config_file: yaml.dump({ 'authorName': f'{experimenter}', @@ -111,7 +155,20 @@ def write_yml( return + def zip_model(model_path): + """ + 입력받은 모델의 경로를 찾아가 모델을 압축하여 메모리 버퍼를 반환합니다. + + Args: + model_path(str): 모델이 있는 경로입니다. + + Returns: + memory buffer: 모델을 압축한 메모리 버퍼를 반환합니다. + + Note: + 모델을 보조기억장치에 파일로 저장하지 않습니다. + """ model_buffer = io.BytesIO() basedir = os.path.basename(model_path) @@ -128,18 +185,36 @@ def zip_model(model_path): return model_buffer + def get_free_port(): + """ + 호출 즉시 사용가능한 포트번호를 반환합니다. + + Returns: + 현재 사용가능한 포트번호 + """ with socketserver.TCPServer(("localhost", 0), None) as s: free_port = s.server_address[1] return free_port + def check_expr_over(experiment_id, experiment_name, experiment_path): + """ + train API에서 사용되기 위하여 만들어 졌습니다. experiment_id를 입력받아 해당 id를 가진 nni 실험을 모니터링 합니다. 현재 추상화되어있지 않아 코드 재사용성이 부족하며 개선이 필요합니다. + * 파일로 생성되는 모델이 너무 많아지지 않도록 유지합니다.(3개 이상 모델이 생성되면 성능순으로 3위 미만은 삭제) + * nnictl experiment list(shell command)를 주기적으로 호출하여 실험이 현제 진행중인지 파악합니다. + * 실험의 상태가 DONE으로 변경되면 최고점수 모델을 데이터베이스에 저장하고 nnictl stop experiment_id를 실행하여 실험을 종료한 후 프로세스가 종료됩니다. + + Args: + experiment_id(str) + experiment_name(str) + experiment_path(str) + """ minute = 60 while True: time.sleep(1*minute) - # 실험이 끝났는지 확인 expr_list = subprocess.getoutput("nnictl experiment list") running_expr = [expr for expr in expr_list.split('\n') if experiment_id in expr] @@ -148,32 +223,32 @@ def check_expr_over(experiment_id, experiment_name, experiment_path): stop_expr = subprocess.getoutput("nnictl stop {}".format( experiment_id)) L.info(stop_expr) - break # 실험이 끝나면 무한루프 종료 + break elif experiment_id not in expr_list: L.info(expr_list) - break # 갑자기 누군가가 nnictl stop으로 다 꺼버렸을 상황에 대비 + break else: model_path = os.path.join(experiment_path, "temp", "*_{}*".format(experiment_name)) exprs = glob.glob(model_path) - if len(exprs) > 3: # 모델파일이 너무 많아지지 않게 3개 넘으면 삭제 + if len(exprs) > 3: exprs.sort() [shutil.rmtree(_) for _ in exprs[3:]] - # 모델저장 + model_path = os.path.join(experiment_path, "temp", "*_{}*".format(experiment_name)) exprs = glob.glob(model_path) - if not exprs: # 모델파일이 하나도 없을 경우 그냥 종료 + if not exprs: return 0 exprs.sort() exprs = exprs[0] - metrics = os.path.basename(exprs).split("_")[:2] # metric 개수 + metrics = os.path.basename(exprs).split("_")[:2] metrics = [float(metric) for metric in metrics] score_sql = """SELECT mae From b7fccc53f3c376422a7d6d3d24466730ae73ef6b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EB=8F=99=EC=9A=B1?= Date: Wed, 15 Sep 2021 20:18:11 +0900 Subject: [PATCH 32/47] Update api-list.md --- docs/api-list.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/api-list.md b/docs/api-list.md index 64a592a..fc7c794 100644 --- a/docs/api-list.md +++ b/docs/api-list.md @@ -24,7 +24,7 @@ PUT {{API_URL}}/train/insurance | --------------- | ------------- | ----------- | --------- | ----------------------- | | `PORT` | `body` | `int` | `(default) 8080` | NNi 포트번호 | | `experiment_sec` | `body` | `int` | `(default) 20` | 학습시간(초) | -| `experiment_name` | `body` | `(default) exp1` | ✅ | 학습이름 | +| `experiment_name` | `body` | `str` | `(default) exp1` | 학습이름 | | `experimenter` | `body` | `str` | `(default) DongUk` | 연구자 이름 | | `model_name` | `body` | `str` | `(default) insurance_fee_model` | 학습 모델 이름 | | `version` | `body` | `float` | `(default) 0.1` | 모델 버전 | From ad512544f70c86e1849124f5f4698e4fca967553 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EB=8F=99=EC=9A=B1?= Date: Wed, 15 Sep 2021 20:33:08 +0900 Subject: [PATCH 33/47] Update api-list.md --- docs/api-list.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/api-list.md b/docs/api-list.md index fc7c794..87cf666 100644 --- a/docs/api-list.md +++ b/docs/api-list.md @@ -5,6 +5,7 @@ API_URL : 127.0.0.1 - [API List](#api-list) - [Train](#train) - [Insurance](#insurance훈련) + - [Temperature](#Temperature훈련) - [Prediction](#predict) - [Insurance](#insurance예측) - [Temperature](#Temperature예측) From d26c17df9e258464873f0cdf3480cab2706aac0d Mon Sep 17 00:00:00 2001 From: hlchoi Date: Wed, 15 Sep 2021 21:40:24 +0900 Subject: [PATCH 34/47] =?UTF-8?q?Add=20docs=20Examples=20*=20=EB=AA=87?= =?UTF-8?q?=EB=AA=87=20=ED=95=A8=EC=88=98=EC=9D=98=20=EC=82=AC=EC=9A=A9?= =?UTF-8?q?=EC=98=88=EC=8B=9C=EB=A5=BC=20=EC=B6=94=EA=B0=80=ED=95=98?= =?UTF-8?q?=EC=98=80=EC=8A=B5=EB=8B=88=EB=8B=A4.=20*=20train=20=ED=95=A8?= =?UTF-8?q?=EC=88=98=EA=B0=80=20=ED=95=99=EC=8A=B5=EC=9D=98=20=EC=B5=9C?= =?UTF-8?q?=EC=A2=85=EA=B2=B0=EA=B3=BC=EB=A5=BC=20=EB=B0=98=ED=99=98?= =?UTF-8?q?=ED=95=98=EC=A7=80=20=EC=95=8A=EB=8A=94=EB=8B=A4=EB=8A=94=20?= =?UTF-8?q?=EB=82=B4=EC=9A=A9=EC=9D=84=20Returns=EC=97=90=EC=84=9C=20Note?= =?UTF-8?q?=EB=A1=9C=20=EC=9D=B4=EB=8F=99=EC=8B=9C=EC=BC=B0=EC=8A=B5?= =?UTF-8?q?=EB=8B=88=EB=8B=A4.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/api/router/predict.py | 7 +++---- app/api/router/train.py | 16 ++++++++++------ app/database.py | 8 ++++++++ app/utils.py | 19 +++++++++++++++++-- 4 files changed, 38 insertions(+), 12 deletions(-) diff --git a/app/api/router/predict.py b/app/api/router/predict.py index aa8a908..c294ba4 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -29,7 +29,7 @@ async def predict_insurance(info: ModelCorePrediction, model_name: str): Args: info(dict): 다음의 값들을 입력받습니다. age(int), sex(int), bmi(float), children(int), smoker(int), region(int) - + Returns: insurance_fee(float): 보험료 예측값입니다. """ @@ -75,11 +75,10 @@ def sync_pred_ts(time_series): """ none sync 함수를 sync로 만들어 주기 위한 함수이며 입출력은 부모 함수와 같습니다. """ - tf_model = my_model.model time_series = np.array(time_series).reshape(1, -1, 1) - result = tf_model.predict(time_series) + result = my_model.predict_target(time_series) L.info( - f"Predict Args info: {time_series.flatten().tolist()}\n\tmodel_name: {tf_model}\n\tPrediction Result: {result.tolist()[0]}") + f"Predict Args info: {time_series.flatten().tolist()}\n\tmodel_name: {my_model.model_name}\n\tPrediction Result: {result.tolist()[0]}") return result diff --git a/app/api/router/train.py b/app/api/router/train.py index 8e34177..5c3d2b2 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -38,7 +38,10 @@ def train_insurance( version (float): 실험의 버전. 기본 값: 0.1 Returns: - msg: 실험 실행의 성공과 상관없이 포트번호를 포함한 NNI Dashboard의 주소를 반환합니다. 실험의 최종 결과를 반환하지 않습니다. + msg: 실험 실행의 성공과 상관없이 포트번호를 포함한 NNI Dashboard의 주소를 반환합니다. + + Note: + 실험의 최종 결과를 반환하지 않습니다. """ L.info( f"Train Args info\n\texperiment_sec: {experiment_sec}\n\texperiment_name: {experiment_name}\n\texperimenter: {experimenter}\n\tmodel_name: {model_name}\n\tversion: {version}") @@ -74,7 +77,10 @@ def train_atmos(expr_name: str): expr_name(str): NNI가 실행할 실험의 이름 입니다. 이 파라미터를 기반으로 project_dir/experiments/[expr_name] 경로로 찾아가 config.yml을 이용하여 NNI를 실행합니다. Returns: - str: NNI실험이 실행된 결과값을 반환하거나 실행과정에서 발생한 에러 메세지를 반환합니다. 실험의 최종 결과를 반환하지 않습니다. + str: NNI실험이 실행된 결과값을 반환하거나 실행과정에서 발생한 에러 메세지를 반환합니다. + + Note: + 실험의 최종 결과를 반환하지 않습니다. """ nni_port = get_free_port() expr_path = os.path.join(base_dir, 'experiments', expr_name) @@ -92,7 +98,7 @@ def train_atmos(expr_name: str): target = check_expr_over, args = (expr_id, expr_name, expr_path) ) - m_process.start()# 자식 프로세스 분리(nni 실험 진행상황 감시 및 모델 저장) + m_process.start() L.info(nni_create_result) return nni_create_result @@ -103,6 +109,4 @@ def train_atmos(expr_name: str): except Exception as e: L.error(e) - return {'error': str(e)} - - # 코드는 바이너리로 저장하는건 별로인가?(버전관리 차원에서 score랑 같은 행에...) \ No newline at end of file + return {'error': str(e)} \ No newline at end of file diff --git a/app/database.py b/app/database.py index b6eb15b..60be38b 100644 --- a/app/database.py +++ b/app/database.py @@ -16,6 +16,14 @@ def connect(db): Returns: created database engine: 데이터베이스에 연결된 객체를 반환합니다. + + Examples: + >>> engine = connect("my_db") + >>> query = "SHOW timezone;" + >>> engine.execute(query).fetchall() + [('Asia/Seoul',)] + >>> print(engine) + Engine(postgresql://postgres:***@127.0.0.1:5432/my_db) """ print(db) diff --git a/app/utils.py b/app/utils.py index 8448da6..5f51b87 100644 --- a/app/utils.py +++ b/app/utils.py @@ -63,6 +63,11 @@ def predict_target(self, target_data): class ScikitLearnModel(CoreModel): """ Scikit learn 라이브러리 기반의 모델을 불러오기 위한 클래스입니다. + Examples: + >>> sk_model = ScikitLearnModel("my_model") + >>> sk_model.load_model() + >>> sk_model.predict_target(target) + predict result """ def __init__(self, *args): super().__init__(*args) @@ -83,6 +88,11 @@ def load_model(self): class TensorFlowModel(CoreModel): """ Tensorflow 라이브러리 기반의 모델을 불러오기 위한 클래스입니다. + Examples: + >>> tf_model = TensorflowModel("my_model") + >>> tf_model.load_model() + >>> tf_model.predict_target(target) + predict result """ def __init__(self, *args): super().__init__(*args) @@ -159,7 +169,7 @@ def write_yml( def zip_model(model_path): """ 입력받은 모델의 경로를 찾아가 모델을 압축하여 메모리 버퍼를 반환합니다. - + Args: model_path(str): 모델이 있는 경로입니다. @@ -192,6 +202,11 @@ def get_free_port(): Returns: 현재 사용가능한 포트번호 + + Examples: + >>> avail_port = get_free_port() # 사용 가능한 포트, 그때그때 다름 + >>> print(avail_port) + 45675 """ with socketserver.TCPServer(("localhost", 0), None) as s: free_port = s.server_address[1] @@ -237,7 +252,7 @@ def check_expr_over(experiment_id, experiment_name, experiment_path): if len(exprs) > 3: exprs.sort() [shutil.rmtree(_) for _ in exprs[3:]] - + model_path = os.path.join(experiment_path, "temp", From 25908c55f740e2e9d609b6a901095e01a82a86d5 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Thu, 16 Sep 2021 16:00:37 +0900 Subject: [PATCH 35/47] Temp commit for communication MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 클래스화 하는 도중에 잘 진행되지 않아 임의로 커밋해서 공유합니다. --- app/api/router/train.py | 66 +++++++------- app/utils.py | 190 +++++++++++++++++++++++++++++++++++----- 2 files changed, 200 insertions(+), 56 deletions(-) diff --git a/app/api/router/train.py b/app/api/router/train.py index 9d22192..74e1889 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -6,7 +6,7 @@ from fastapi import APIRouter -from app.utils import write_yml, get_free_port, base_dir, check_expr_over +from app.utils import TensorflowNniExperiments, write_yml, get_free_port, base_dir, check_expr_over from logger import L @@ -39,7 +39,7 @@ def train_insurance( Returns: msg: 실험 실행의 성공과 상관없이 포트번호를 포함한 NNI Dashboard의 주소를 반환합니다. - + Note: 실험의 최종 결과를 반환하지 않습니다. """ @@ -75,38 +75,40 @@ def train_atmos(expr_name: str): Args: expr_name(str): NNI가 실행할 실험의 이름 입니다. 이 파라미터를 기반으로 project_dir/experiments/[expr_name] 경로로 찾아가 config.yml을 이용하여 NNI를 실행합니다. - + Returns: str: NNI실험이 실행된 결과값을 반환하거나 실행과정에서 발생한 에러 메세지를 반환합니다. - + Note: 실험의 최종 결과를 반환하지 않습니다. """ - nni_port = get_free_port() - expr_path = os.path.join(base_dir, 'experiments', expr_name) - - try: - nni_create_result = subprocess.getoutput( - "nnictl create --port {} --config {}/config.yml".format( - nni_port, expr_path)) - sucs_msg = "Successfully started experiment!" - - if sucs_msg in nni_create_result: - p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") - expr_id = p.findall(nni_create_result)[0] - m_process = multiprocessing.Process( - target = check_expr_over, - args = (expr_id, expr_name, expr_path) - ) - m_process.start() - - L.info(nni_create_result) - return nni_create_result - - else: - L.error(nni_create_result) - return {"error": nni_create_result} - - except Exception as e: - L.error(e) - return {'error': str(e)} + a = TensorflowNniExperiments('atmos_tmp_01', 'DongUk') + a.execute() + # nni_port = get_free_port() + # expr_path = os.path.join(base_dir, 'experiments', expr_name) + + # try: + # nni_create_result = subprocess.getoutput( + # "nnictl create --port {} --config {}/config.yml".format( + # nni_port, expr_path)) + # sucs_msg = "Successfully started experiment!" + + # if sucs_msg in nni_create_result: + # p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") + # expr_id = p.findall(nni_create_result)[0] + # m_process = multiprocessing.Process( + # target = check_expr_over, + # args = (expr_id, expr_name, expr_path) + # ) + # m_process.start() + + # L.info(nni_create_result) + # return nni_create_result + + # else: + # L.error(nni_create_result) + # return {"error": nni_create_result} + + # except Exception as e: + # L.error(e) + # return {'error': str(e)} diff --git a/app/utils.py b/app/utils.py index 5f51b87..80483bf 100644 --- a/app/utils.py +++ b/app/utils.py @@ -9,6 +9,9 @@ import shutil import subprocess import io +import re +import multiprocessing + import tensorflow as tf @@ -32,6 +35,7 @@ class CoreModel: model(obj): 모델이 저장될 인스턴스 변수 query(str): 입력받은 모델이름을 기반으로 데이터베이스에서 모델을 불러오는 SQL query입니다. """ + def __init__(self, model_name): self.model_name = model_name self.model = None @@ -69,6 +73,7 @@ class ScikitLearnModel(CoreModel): >>> sk_model.predict_target(target) predict result """ + def __init__(self, *args): super().__init__(*args) @@ -94,6 +99,7 @@ class TensorFlowModel(CoreModel): >>> tf_model.predict_target(target) predict result """ + def __init__(self, *args): super().__init__(*args) @@ -166,6 +172,143 @@ def write_yml( return +class CoreNniExperiments: + + def __init__(self, expr_name, author): + self.expr_name = expr_name + self.author = author + self.expr_path = os.path.join(base_dir, 'experiments', expr_name) + self.expr_id = None + self.nni_port = None + self.nni_create_result = None + self.server = 'localhost' + self.success_msg = "Successfully started experiment!" + + def get_free_port(self): + """ + 호출 즉시 사용가능한 포트번호를 반환합니다. + + Returns: + 현재 사용가능한 포트번호 + + Examples: + >>> avail_port = get_free_port() # 사용 가능한 포트, 그때그때 다름 + >>> print(avail_port) + 45675 + """ + with socketserver.TCPServer((self.server, 0), None) as s: + self.nni_port = s.server_address[1] + + def init_nni_process(self): + self.nni_create_result = subprocess.getoutput( + "nnictl create --port {} --config {}/config.yml".format( + self.nni_port, self.expr_path)) + + def check_nni_process(self): + if self.success_msg in self.nni_create_result: + p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") + self.expr_id = p.findall(self.nni_create_result)[0] + m_process = multiprocessing.Process( + target=self.control_nni_function + ) + m_process.start() + L.info(self.nni_create_result) + return self.nni_create_result + + else: + L.error(self.nni_create_result) + return self.nni_create_result + + def control_nni_function(self): # check_nni_process 안에서 실행되는 method + raise Exception + + def check_function(self): + raise Exception + + def execute(self): + self.check_function() + + +class TensorflowNniExperiments(CoreNniExperiments): + def __init__(self, *args): + super().__init__(*args) + self.minute = 60 + + def check_function(self): + # 논리흐름대로 실행시키기 + super().get_free_port() + super().init_nni_process() # nni process 실행시키기 + super().check_nni_process() # control_nni_function을 쓰는 check_nni_process 실행 + + def control_nni_function(self): + while True: + time.sleep(1 * self.minute) + expr_list = subprocess.getoutput('nnictl experiment list') + running_expr = [expr for expr in expr_list.split( + '\n') if self.expr_id in expr] + + if running_expr and ("DONE" in running_expr[0]): + self.nni_stop() + break + + elif self.expr_id not in expr_list: + L.info(expr_list) + break + + else: + self.left_3_models() + + model_path = os.path.join(self.expr_path, + "temp", + "*_{}*".format(self.expr_name)) + exprs = glob.glob(model_path) + if not exprs: + return 0 + + exprs.sort() + exprs = exprs[0] + metrics = os.path.basename(exprs).split("_")[:2] + metrics = [float(metric) for metric in metrics] + + score_sql = """SELECT mae + FROM atmos_model_metadata + ORDER BY mae;""" + saved_score = engine.execute(score_sql).fetchone() + + if not saved_score or (metrics[0] < saved_score[0]): + winner_model = os.path.join(os.path.join(self.expr_path, + "temp", + self.expr_name)) + os.rename(exprs, winner_model) + m_buffer = zip_model(winner_model) + encode_model = codecs.encode( + pickle.dumps(m_buffer), "base64").decode() + sql_save_model = "INSERT INTO model_core VALUES ('%s', '%s')" + engine.execute(sql_save_model % (self.expr_name, + encode_model)) + + sql_save_score = "INSERT INTO atmos_model_metadata VALUES ('%s', '%s', '%s', '%s')" + engine.execute(sql_save_score % (self.expr_name, + self.expr_id, + metrics[0], + metrics[1])) + L.info("saved model %s %s" % (self.expr_id, self.expr_name)) + + def nni_stop(self): + stop_expr = subprocess.getoutput("nnictl stop {}".format( + self.expr_id)) + L.info(stop_expr) + + def left_3_models(self): + model_path = os.path.join(self.expr_path, + "temp", + "*_{}*".format(self.expr_name)) + exprs = glob.glob(model_path) + if len(exprs) > 3: + exprs.sort() + [shutil.rmtree(_) for _ in exprs[3:]] + + def zip_model(model_path): """ 입력받은 모델의 경로를 찾아가 모델을 압축하여 메모리 버퍼를 반환합니다. @@ -180,19 +323,20 @@ def zip_model(model_path): 모델을 보조기억장치에 파일로 저장하지 않습니다. """ model_buffer = io.BytesIO() - + basedir = os.path.basename(model_path) - + with zipfile.ZipFile(model_buffer, "w") as zf: for root, dirs, files in os.walk(model_path): - make_arcname = lambda x: os.path.join(root.split(basedir)[-1], x) + def make_arcname(x): return os.path.join( + root.split(basedir)[-1], x) for dr in dirs: dir_path = os.path.join(root, dr) - zf.write(filename = dir_path, arcname = make_arcname(dr)) + zf.write(filename=dir_path, arcname=make_arcname(dr)) for file in files: file_path = os.path.join(root, file) - zf.write(filename = file_path, arcname = make_arcname(file)) - + zf.write(filename=file_path, arcname=make_arcname(file)) + return model_buffer @@ -231,8 +375,9 @@ def check_expr_over(experiment_id, experiment_name, experiment_path): time.sleep(1*minute) expr_list = subprocess.getoutput("nnictl experiment list") - - running_expr = [expr for expr in expr_list.split('\n') if experiment_id in expr] + + running_expr = [expr for expr in expr_list.split( + '\n') if experiment_id in expr] print(running_expr) if running_expr and ("DONE" in running_expr[0]): stop_expr = subprocess.getoutput("nnictl stop {}".format( @@ -253,19 +398,18 @@ def check_expr_over(experiment_id, experiment_name, experiment_path): exprs.sort() [shutil.rmtree(_) for _ in exprs[3:]] - model_path = os.path.join(experiment_path, - "temp", - "*_{}*".format(experiment_name)) + "temp", + "*_{}*".format(experiment_name)) exprs = glob.glob(model_path) if not exprs: return 0 - + exprs.sort() exprs = exprs[0] metrics = os.path.basename(exprs).split("_")[:2] metrics = [float(metric) for metric in metrics] - + score_sql = """SELECT mae FROM atmos_model_metadata ORDER BY mae;""" @@ -273,20 +417,18 @@ def check_expr_over(experiment_id, experiment_name, experiment_path): if not saved_score or (metrics[0] < saved_score[0]): winner_model = os.path.join(os.path.join(experiment_path, - "temp", - experiment_name)) + "temp", + experiment_name)) os.rename(exprs, winner_model) m_buffer = zip_model(winner_model) encode_model = codecs.encode(pickle.dumps(m_buffer), "base64").decode() sql_save_model = "INSERT INTO model_core VALUES ('%s', '%s')" - engine.execute(sql_save_model%(experiment_name, - encode_model)) + engine.execute(sql_save_model % (experiment_name, + encode_model)) sql_save_score = "INSERT INTO atmos_model_metadata VALUES ('%s', '%s', '%s', '%s')" - engine.execute(sql_save_score%(experiment_name, - experiment_id, - metrics[0], - metrics[1])) - L.info("saved model %s %s"%(experiment_id, experiment_name)) - - + engine.execute(sql_save_score % (experiment_name, + experiment_id, + metrics[0], + metrics[1])) + L.info("saved model %s %s" % (experiment_id, experiment_name)) From cd0a7bd2f0b88fde77b8f4b2962dd37b15baf042 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Thu, 16 Sep 2021 22:04:05 +0900 Subject: [PATCH 36/47] Add NniWatcher MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 작동확인하였습니다. 지금 깔끔하게 코드를 작성하지 않았고 추후에 깔끔하게 작성할 예정입니다. --- app/api/router/train.py | 93 ++++++------ app/models.py | 15 ++ app/utils.py | 265 +++++++++++++++++---------------- experiments/insurance/query.py | 27 ++++ experiments/insurance/trial.py | 49 ++---- 5 files changed, 241 insertions(+), 208 deletions(-) diff --git a/app/api/router/train.py b/app/api/router/train.py index 74e1889..852fdd6 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -6,7 +6,7 @@ from fastapi import APIRouter -from app.utils import TensorflowNniExperiments, write_yml, get_free_port, base_dir, check_expr_over +from app.utils import NniWatcher, write_yml, get_free_port, base_dir, check_expr_over from logger import L @@ -19,8 +19,6 @@ @router.put("/insurance") def train_insurance( - PORT: int = 8080, - experiment_sec: int = 20, experiment_name: str = 'exp1', experimenter: str = 'DongUk', model_name: str = 'insurance_fee_model', @@ -30,8 +28,6 @@ def train_insurance( insurance와 관련된 학습을 실행하기 위한 API입니다. Args: - PORT (int): NNI가 실행될 포트번호. 기본 값: 8080 - experiment_sec (int): 최대 실험시간(초단위). 기본 값: 20 experiment_name (str): 실험이름. 기본 값: exp1 experimeter (str): 실험자의 이름. 기본 값: DongUk model_name (str): 모델의 이름. 기본 값: insurance_fee_model @@ -43,11 +39,11 @@ def train_insurance( Note: 실험의 최종 결과를 반환하지 않습니다. """ + PORT = get_free_port() L.info( - f"Train Args info\n\texperiment_sec: {experiment_sec}\n\texperiment_name: {experiment_name}\n\texperimenter: {experimenter}\n\tmodel_name: {model_name}\n\tversion: {version}") + f"Train Args info\n\texperiment_name: {experiment_name}\n\texperimenter: {experimenter}\n\tmodel_name: {model_name}\n\tversion: {version}") path = 'experiments/insurance/' try: - L.info("Start NNi") write_yml( path, experiment_name, @@ -55,18 +51,30 @@ def train_insurance( model_name, version ) - subprocess.Popen( - "nnictl create --port {} --config {}/{}.yml && timeout {} && nnictl stop --port {}".format( - PORT, path, model_name, experiment_sec, PORT), - shell=True, + nni_create_result = subprocess.getoutput( + "nnictl create --port {} --config {}/{}.yml".format( + PORT, path, model_name) ) + sucs_msg = "Successfully started experiment!" + + if sucs_msg in nni_create_result: + p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") + expr_id = p.findall(nni_create_result)[0] + # expr id 랑 expr name 주고 instance 만들어서 + # a.excute를 target으로 넘겨주자. + nni_watcher = NniWatcher(expr_id, experiment_name) + m_process = multiprocessing.Process( + target=nni_watcher.excute + ) + m_process.start() + + L.info(nni_create_result) + return nni_create_result except Exception as e: L.error(e) return {'error': str(e)} - return {"msg": f'Check out http://127.0.0.1:{PORT}'} - @router.put("/atmos") def train_atmos(expr_name: str): @@ -82,33 +90,32 @@ def train_atmos(expr_name: str): Note: 실험의 최종 결과를 반환하지 않습니다. """ - a = TensorflowNniExperiments('atmos_tmp_01', 'DongUk') - a.execute() - # nni_port = get_free_port() - # expr_path = os.path.join(base_dir, 'experiments', expr_name) - - # try: - # nni_create_result = subprocess.getoutput( - # "nnictl create --port {} --config {}/config.yml".format( - # nni_port, expr_path)) - # sucs_msg = "Successfully started experiment!" - - # if sucs_msg in nni_create_result: - # p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") - # expr_id = p.findall(nni_create_result)[0] - # m_process = multiprocessing.Process( - # target = check_expr_over, - # args = (expr_id, expr_name, expr_path) - # ) - # m_process.start() - - # L.info(nni_create_result) - # return nni_create_result - - # else: - # L.error(nni_create_result) - # return {"error": nni_create_result} - - # except Exception as e: - # L.error(e) - # return {'error': str(e)} + + nni_port = get_free_port() + expr_path = os.path.join(base_dir, 'experiments', expr_name) + + try: + nni_create_result = subprocess.getoutput( + "nnictl create --port {} --config {}/config.yml".format( + nni_port, expr_path)) + sucs_msg = "Successfully started experiment!" + + if sucs_msg in nni_create_result: + p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") + expr_id = p.findall(nni_create_result)[0] + m_process = multiprocessing.Process( + target=check_expr_over, + args=(expr_id, expr_name, expr_path) + ) + m_process.start() + + L.info(nni_create_result) + return nni_create_result + + else: + L.error(nni_create_result) + return {"error": nni_create_result} + + except Exception as e: + L.error(e) + return {'error': str(e)} diff --git a/app/models.py b/app/models.py index a84e491..5ba2f33 100644 --- a/app/models.py +++ b/app/models.py @@ -33,3 +33,18 @@ class ModelMetadata(Base): train_mse = Column(FLOAT, nullable=False) val_mse = Column(FLOAT, nullable=False) created_at = Column(DateTime(timezone=True), server_default=now()) + + +class TempModelData(Base): + __tablename__ = 'temp_model_data' + + id = Column(Integer, primary_key=True, autoincrement=True) + model_name = Column(String, nullable=False) + model_file = Column(LargeBinary, nullable=False) + experiment_name = Column(String, nullable=False) + experimenter = Column(String, nullable=False) + version = Column(FLOAT, nullable=False) + train_mae = Column(FLOAT, nullable=False) + val_mae = Column(FLOAT, nullable=False) + train_mse = Column(FLOAT, nullable=False) + val_mse = Column(FLOAT, nullable=False) diff --git a/app/utils.py b/app/utils.py index 80483bf..5427d19 100644 --- a/app/utils.py +++ b/app/utils.py @@ -148,7 +148,7 @@ def write_yml( 'experimentName': f'{experiment_name}', 'trialConcurrency': 1, 'maxExecDuration': '1h', - 'maxTrialNum': 10, + 'maxTrialNum': 1, 'trainingServicePlatform': 'local', 'searchSpacePath': 'search_space.json', 'useAnnotation': False, @@ -172,141 +172,146 @@ def write_yml( return -class CoreNniExperiments: - - def __init__(self, expr_name, author): - self.expr_name = expr_name - self.author = author - self.expr_path = os.path.join(base_dir, 'experiments', expr_name) - self.expr_id = None - self.nni_port = None - self.nni_create_result = None - self.server = 'localhost' - self.success_msg = "Successfully started experiment!" - - def get_free_port(self): +# class NniExperiments: +# def __init__(self, expriment_name, author, model_name, version, waiting=None): +# self.experiment_name = expriment_name +# self.author = author +# self.model_name = model_name +# self.version = version +# self.waiting = waiting + + +class NniWatcher: + def __init__( + self, + experiment_id, + experiment_name, + minute=1, + is_kill=True, + is_update=True, + top_cnt=3, + evaluation_criteria='val_mae' + ): + self.experiment_id = experiment_id + self.experiment_name = experiment_name + self.is_kill = is_kill + self.is_update = is_update + self.top_cnt = top_cnt + self.evaluation_criteria = evaluation_criteria + self._wait_minute = minute * 60 + self._experiment_list = None + self._running_experiment = None + self._update_model_query = """ + DELETE FROM temp_model_data + WHERE id NOT IN ( + SELECT id + FROM temp_model_data + WHERE experiment_name = '{}' + ORDER BY {} + LIMIT {} + ) """ - 호출 즉시 사용가능한 포트번호를 반환합니다. - Returns: - 현재 사용가능한 포트번호 + def excute(self): + self.watch_process() + self.model_final_update() + + def get_running_experiment(self): + self._experiment_list = subprocess.getoutput('nnictl experiment list') + self._running_experiment = [expr for expr in self._experiment_list.split( + '\n') if self.experiment_id in expr] + L.info(self._running_experiment) + + def watch_process(self): + if self.is_kill: + while True: + self.get_running_experiment() + time.sleep(self._wait_minute) + if self._running_experiment and ("DONE" in self._running_experiment[0]): + _stop_expr = subprocess.getoutput("nnictl stop {}".format( + self.experiment_id + )) + L.info(_stop_expr) + break + + elif self.experiment_id not in self._experiment_list: + L.error('Experiment ID not in Current Experiment List') + L.info(self._experiment_list) + break + + else: + if self.is_update: + self.model_keep_update() + + def model_keep_update(self): + engine.execute( + self._update_model_query.format( + self.experiment_name, + self.evaluation_criteria, + self.top_cnt) + ) - Examples: - >>> avail_port = get_free_port() # 사용 가능한 포트, 그때그때 다름 - >>> print(avail_port) - 45675 + def model_final_update(self): + final_model = """ + SELECT * + FROM temp_model_data + WHERE experiment_name = '{}' + ORDER BY {}; + """.format(self.experiment_name, self.evaluation_criteria) + final_result = engine.execute(final_model).fetchone() + + saved_model = """ + SELECT * + FROM model_metadata + WHERE experiment_name = '{}' + """.format(self.experiment_name) + saved_result = engine.execute(saved_model).fetchone() + INSERT_MODEL_CORE = """ + INSERT INTO model_core ( + model_name, + model_file + ) VALUES( + '{}', + '{}' + ) + """ + + UPDATE_MODEL_CORE = """ + UPDATE model_core + SET + model_file = '{}' + WHERE + model_name = '{}' + """ + UPDATE_MODEL_METADATA = """ + UPDATE model_metadata + SET + train_mae = {}, + val_mae = {}, + train_mse = {}, + val_mse = {} + WHERE experiment_name = '{}' """ - with socketserver.TCPServer((self.server, 0), None) as s: - self.nni_port = s.server_address[1] - - def init_nni_process(self): - self.nni_create_result = subprocess.getoutput( - "nnictl create --port {} --config {}/config.yml".format( - self.nni_port, self.expr_path)) - - def check_nni_process(self): - if self.success_msg in self.nni_create_result: - p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") - self.expr_id = p.findall(self.nni_create_result)[0] - m_process = multiprocessing.Process( - target=self.control_nni_function + print(saved_result) + if saved_result is None: + engine.execute(INSERT_MODEL_CORE.format( + final_result.model_name, final_result.model_file)) + elif float(saved_result[self.evaluation_criteria]) > float(final_result[self.evaluation_criteria]): + engine.execute(UPDATE_MODEL_CORE.format( + final_result.model_file, final_result.model_name)) + engine.execute(UPDATE_MODEL_METADATA.format( + final_result.train_mae, + final_result.val_mae, + final_result.train_mse, + final_result.val_mse, + self.experiment_name) ) - m_process.start() - L.info(self.nni_create_result) - return self.nni_create_result - - else: - L.error(self.nni_create_result) - return self.nni_create_result - def control_nni_function(self): # check_nni_process 안에서 실행되는 method - raise Exception - - def check_function(self): - raise Exception - - def execute(self): - self.check_function() - - -class TensorflowNniExperiments(CoreNniExperiments): - def __init__(self, *args): - super().__init__(*args) - self.minute = 60 - - def check_function(self): - # 논리흐름대로 실행시키기 - super().get_free_port() - super().init_nni_process() # nni process 실행시키기 - super().check_nni_process() # control_nni_function을 쓰는 check_nni_process 실행 - - def control_nni_function(self): - while True: - time.sleep(1 * self.minute) - expr_list = subprocess.getoutput('nnictl experiment list') - running_expr = [expr for expr in expr_list.split( - '\n') if self.expr_id in expr] - - if running_expr and ("DONE" in running_expr[0]): - self.nni_stop() - break - - elif self.expr_id not in expr_list: - L.info(expr_list) - break - - else: - self.left_3_models() - - model_path = os.path.join(self.expr_path, - "temp", - "*_{}*".format(self.expr_name)) - exprs = glob.glob(model_path) - if not exprs: - return 0 - - exprs.sort() - exprs = exprs[0] - metrics = os.path.basename(exprs).split("_")[:2] - metrics = [float(metric) for metric in metrics] - - score_sql = """SELECT mae - FROM atmos_model_metadata - ORDER BY mae;""" - saved_score = engine.execute(score_sql).fetchone() - - if not saved_score or (metrics[0] < saved_score[0]): - winner_model = os.path.join(os.path.join(self.expr_path, - "temp", - self.expr_name)) - os.rename(exprs, winner_model) - m_buffer = zip_model(winner_model) - encode_model = codecs.encode( - pickle.dumps(m_buffer), "base64").decode() - sql_save_model = "INSERT INTO model_core VALUES ('%s', '%s')" - engine.execute(sql_save_model % (self.expr_name, - encode_model)) - - sql_save_score = "INSERT INTO atmos_model_metadata VALUES ('%s', '%s', '%s', '%s')" - engine.execute(sql_save_score % (self.expr_name, - self.expr_id, - metrics[0], - metrics[1])) - L.info("saved model %s %s" % (self.expr_id, self.expr_name)) - - def nni_stop(self): - stop_expr = subprocess.getoutput("nnictl stop {}".format( - self.expr_id)) - L.info(stop_expr) - - def left_3_models(self): - model_path = os.path.join(self.expr_path, - "temp", - "*_{}*".format(self.expr_name)) - exprs = glob.glob(model_path) - if len(exprs) > 3: - exprs.sort() - [shutil.rmtree(_) for _ in exprs[3:]] + DELETE_EXPERIMENTS = """ + DELETE FROM temp_model_data + WHERE experiment_name = '{}' + """ + engine.execute(DELETE_EXPERIMENTS.format(self.experiment_name)) def zip_model(model_path): diff --git a/experiments/insurance/query.py b/experiments/insurance/query.py index f1fb600..8476f9e 100644 --- a/experiments/insurance/query.py +++ b/experiments/insurance/query.py @@ -1,3 +1,30 @@ +# insert temp + +INSERT_TEMP_MODEL = """ + INSERT INTO temp_model_data ( + model_name, + model_file, + experiment_name, + experimenter, + version, + train_mae, + val_mae, + train_mse, + val_mse + ) VALUES ( + {}, + '{}', + {}, + {}, + {}, + {}, + {}, + {}, + {} + ) +""" + + # INSERT INSERT_MODEL_CORE = """ INSERT INTO model_core ( diff --git a/experiments/insurance/trial.py b/experiments/insurance/trial.py index c66239c..f6769b2 100644 --- a/experiments/insurance/trial.py +++ b/experiments/insurance/trial.py @@ -109,41 +109,20 @@ def main(params, engine, experiment_info, connection): cv_mae_mean = np.mean(cv_mae) tr_mse_mean = np.mean(tr_mse) tr_mae_mean = np.mean(tr_mae) - - best_model = pd.read_sql(SELECT_MODEL_CORE % (model_name), engine) - - if len(best_model) == 0: - - pickled_model = codecs.encode(pickle.dumps(model), "base64").decode() - connection.execute(INSERT_MODEL_CORE % (model_name, pickled_model)) - connection.execute(INSERT_MODEL_METADATA % ( - experiment_name, - model_name, - experimenter, - version, - tr_mae_mean, - tr_mse_mean, - cv_mae_mean, - cv_mse_mean) - ) - - else: - best_model_metadata = pd.read_sql( - SELECT_VAL_MAE % (model_name), engine) - saved_score = best_model_metadata.values[0] - - if saved_score > valid_mae: - pickled_model = codecs.encode( - pickle.dumps(fold_model), "base64").decode() - - connection.execute(UPDATE_MODEL_CORE % (pickled_model, model_name)) - connection.execute(UPDATE_MODEL_METADATA % ( - tr_mae_mean, - cv_mae_mean, - tr_mse_mean, - cv_mse_mean, - experiment_name) - ) + pickled_model = codecs.encode( + pickle.dumps(fold_model), "base64").decode() + + connection.execute(INSERT_TEMP_MODEL.format( + model_name, + pickled_model, + experiment_name, + experimenter, + version, + tr_mae_mean, + cv_mae_mean, + tr_mse_mean, + cv_mse_mean + )) nni.report_final_result(cv_mae_mean) print('Final result is %g', cv_mae_mean) From 36a465f25905f1ef23c973dee90158172d1b8730 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Sat, 18 Sep 2021 11:29:13 +0900 Subject: [PATCH 37/47] Seperate code and query MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 코드부분과 쿼리문을 분리했습니다. --- app/api/router/train.py | 4 +- app/query.py | 58 +++++++++++++++++++++++++++ app/utils.py | 88 ++++++++++------------------------------- 3 files changed, 79 insertions(+), 71 deletions(-) create mode 100644 app/query.py diff --git a/app/api/router/train.py b/app/api/router/train.py index 852fdd6..33ab418 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -29,7 +29,7 @@ def train_insurance( Args: experiment_name (str): 실험이름. 기본 값: exp1 - experimeter (str): 실험자의 이름. 기본 값: DongUk + experimenter (str): 실험자의 이름. 기본 값: DongUk model_name (str): 모델의 이름. 기본 값: insurance_fee_model version (float): 실험의 버전. 기본 값: 0.1 @@ -60,8 +60,6 @@ def train_insurance( if sucs_msg in nni_create_result: p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") expr_id = p.findall(nni_create_result)[0] - # expr id 랑 expr name 주고 instance 만들어서 - # a.excute를 target으로 넘겨주자. nni_watcher = NniWatcher(expr_id, experiment_name) m_process = multiprocessing.Process( target=nni_watcher.excute diff --git a/app/query.py b/app/query.py new file mode 100644 index 0000000..134c99f --- /dev/null +++ b/app/query.py @@ -0,0 +1,58 @@ +UPDATE_TEMP_MODEL_DATA = """ + DELETE FROM temp_model_data + WHERE id NOT IN ( + SELECT id + FROM temp_model_data + WHERE experiment_name = '{}' + ORDER BY {} + LIMIT {} + ) + """ + + +SELECT_TEMP_MODEL_BY_EXPR_NAME = """ + SELECT * + FROM temp_model_data + WHERE experiment_name = '{}' + ORDER BY {}; + """ + + +SELECT_MODEL_METADATA_BY_EXPR_NAME = """ + SELECT * + FROM model_metadata + WHERE experiment_name = '{}' + """ + +INSERT_MODEL_CORE = """ + INSERT INTO model_core ( + model_name, + model_file + ) VALUES( + '{}', + '{}' + ) + """ + +UPDATE_MODEL_CORE = """ + UPDATE model_core + SET + model_file = '{}' + WHERE + model_name = '{}' + """ + +UPDATE_MODEL_METADATA = """ + UPDATE model_metadata + SET + train_mae = {}, + val_mae = {}, + train_mse = {}, + val_mse = {} + WHERE experiment_name = '{}' + """ + +DELETE_ALL_EXPERIMENTS_BY_EXPR_NAME = """ + DELETE FROM temp_model_data + WHERE experiment_name = '{}' +""" diff --git a/app/utils.py b/app/utils.py index 5427d19..c6b8ef7 100644 --- a/app/utils.py +++ b/app/utils.py @@ -17,6 +17,8 @@ from app.database import engine from logger import L +from app.query import * + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) @@ -148,7 +150,7 @@ def write_yml( 'experimentName': f'{experiment_name}', 'trialConcurrency': 1, 'maxExecDuration': '1h', - 'maxTrialNum': 1, + 'maxTrialNum': 10, 'trainingServicePlatform': 'local', 'searchSpacePath': 'search_space.json', 'useAnnotation': False, @@ -172,15 +174,6 @@ def write_yml( return -# class NniExperiments: -# def __init__(self, expriment_name, author, model_name, version, waiting=None): -# self.experiment_name = expriment_name -# self.author = author -# self.model_name = model_name -# self.version = version -# self.waiting = waiting - - class NniWatcher: def __init__( self, @@ -201,16 +194,6 @@ def __init__( self._wait_minute = minute * 60 self._experiment_list = None self._running_experiment = None - self._update_model_query = """ - DELETE FROM temp_model_data - WHERE id NOT IN ( - SELECT id - FROM temp_model_data - WHERE experiment_name = '{}' - ORDER BY {} - LIMIT {} - ) - """ def excute(self): self.watch_process() @@ -245,58 +228,28 @@ def watch_process(self): def model_keep_update(self): engine.execute( - self._update_model_query.format( + UPDATE_TEMP_MODEL_DATA.format( self.experiment_name, self.evaluation_criteria, self.top_cnt) ) def model_final_update(self): - final_model = """ - SELECT * - FROM temp_model_data - WHERE experiment_name = '{}' - ORDER BY {}; - """.format(self.experiment_name, self.evaluation_criteria) - final_result = engine.execute(final_model).fetchone() - - saved_model = """ - SELECT * - FROM model_metadata - WHERE experiment_name = '{}' - """.format(self.experiment_name) - saved_result = engine.execute(saved_model).fetchone() - INSERT_MODEL_CORE = """ - INSERT INTO model_core ( - model_name, - model_file - ) VALUES( - '{}', - '{}' - ) - """ - - UPDATE_MODEL_CORE = """ - UPDATE model_core - SET - model_file = '{}' - WHERE - model_name = '{}' - """ - UPDATE_MODEL_METADATA = """ - UPDATE model_metadata - SET - train_mae = {}, - val_mae = {}, - train_mse = {}, - val_mse = {} - WHERE experiment_name = '{}' - """ - print(saved_result) + final_result = engine.execute( + SELECT_TEMP_MODEL_BY_EXPR_NAME.format( + self.experiment_name, + self.evaluation_criteria) + ).fetchone() + + saved_result = engine.execute( + SELECT_MODEL_METADATA_BY_EXPR_NAME.format( + self.experiment_name) + ).fetchone() + if saved_result is None: engine.execute(INSERT_MODEL_CORE.format( final_result.model_name, final_result.model_file)) - elif float(saved_result[self.evaluation_criteria]) > float(final_result[self.evaluation_criteria]): + elif saved_result[self.evaluation_criteria] > final_result[self.evaluation_criteria]: engine.execute(UPDATE_MODEL_CORE.format( final_result.model_file, final_result.model_name)) engine.execute(UPDATE_MODEL_METADATA.format( @@ -307,11 +260,10 @@ def model_final_update(self): self.experiment_name) ) - DELETE_EXPERIMENTS = """ - DELETE FROM temp_model_data - WHERE experiment_name = '{}' - """ - engine.execute(DELETE_EXPERIMENTS.format(self.experiment_name)) + engine.execute( + DELETE_ALL_EXPERIMENTS_BY_EXPR_NAME.format( + self.experiment_name) + ) def zip_model(model_path): From 648f72f08682fffc439a4caeafcdc67407c103f9 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Thu, 23 Sep 2021 20:05:12 +0900 Subject: [PATCH 38/47] Add color to logger MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit logging 할때 log level에 해당하는 부분만 색이 나오도록 변경했습니다. --- logger.py | 5 +++-- requirements.txt | Bin 1596 -> 3928 bytes 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/logger.py b/logger.py index a994ecd..6b5880a 100644 --- a/logger.py +++ b/logger.py @@ -1,11 +1,12 @@ import logging import logging.handlers +from colorlog import ColoredFormatter L = logging.getLogger('snowdeer_log') L.setLevel(logging.DEBUG) -formatter = logging.Formatter( - fmt="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d - %(funcName)20s()]\n\t%(message)s", +formatter = ColoredFormatter( + fmt="%(log_color)s [%(levelname)s] %(reset)s %(asctime)s [%(filename)s:%(lineno)d - %(funcName)20s()]\n\t%(message)s", datefmt='%y-%m-%d %H:%M:%S' ) diff --git a/requirements.txt b/requirements.txt index b407b19e10ac6bdc2c4ed9448a352d647f225f37..ea0763ca28facccfcff1607539fc835edbd7a5cd 100644 GIT binary patch literal 3928 zcmZ{nT~Av_5Qg`o^b@{%0S7zn9Y|E9tZ_1@&Gv@6e zqS0!rsPl5BUE_#(7wa`kTULtNE5}u=bHW>a#{DU6IVi*al>hwMT2~rjoGGGJ4p590 zai-7ol;Ke^3vszD4?63hf01>j-~D~sVSZ-TinDjDw5QQ$ujq<{6UMy`i^#K94C`B| zVJ+Y=i4Qp!k>jA#Q4Ew5?Vkiz6q}y1txsIQ*#!4n{cfXuUn7fmqaaE-`$iFslw_?s zu!vpB66HZY>GyyYWoL!_>~zMOH6w^~Zgj2gEcE*(7_9~#*ejzLp4Lbq95@83xp0_C z`sKR%QQ9BMIx;7wNqHZ*&336X+o;Q799omfS(e%lZ=7=tFZ8d=vh3YSrjEW01*qgv zx$m`J>gRLEv!<@J<_g;NZj$dzb)I#beSvbRzk7!WJ8-4*IAdNqX?PV_T|2I?jOrBA z1^U!u94C2w)nmyu+%YTq>LAAR(AmF#Ro;dBKgHU`Gp#}Bv(F=HCGP`sUlSHKJ@B3UCaTi`vpSYfp@JrR6 zMiPY^o^?Jmy<9~O&P0d9iNBSi|A^ShXC@+NvGOLm$;j9Gji(>n@jp4OrD0CkR6qAT zYK8jU>Zecj?&NSSZyRCe#L5iMTdjB=cz@Sdy%FcxzX)$rZWNiF2sS$ls~ep>7w7bi z6(jH}lf6-09E9;Ys@&CU9`yg)aK&ASujvC*#4LXehOCXsMd-W>#>~rBUm*14N$mCy zR4Y}&LEq=<-l{>m_F264ngC3Kqq1ZK=sZU3nm&)H_)tz8zf|+-vC6Mz$eOV*}HTR&gs= z>-|W_HBm2h_S9-(m7afkw>_;x97Cf+OcP!X^hqjNYu(P?3TxifteLCVIK|U2agH^w zY77kVViVb^Fea{9?v(XW4x!0vP|@gVwdgY{t-E^95VEI!;E?35x&ITd{S?uT0_6c{xw$WOU62r=dbdid@jGmIzH7m z1GRD|G;|V^s@6%J3eRI+zDLj-P0g1*yb4g9`l_z^)#{$-mFS5HB5KQS{n7UtK0peq~@78?lD&jXE?uOsR{UUieF<)Qqb}?+G~}U=~6i}$~EIC zoY^a`(-*TpV>m1EUdKEE%kz$RC)sJ{s%I2P#)Esj)qdFSB0KoWZiT;;-{t5B^+c*t z*_f$&+4uK8dWWgtSzweyMLJd3(`i4ITCjq(s|Mz-a^}}wJD7_5o;~$gtLEjn?5tU6 z%YAq+M1G@!sk;5G{W~$~)Wyu$k?s=wPVMXb8Sds~$E`!uw=rfE^Bc|ZZusW>|IWQ4 zm~ei#Ck8yULre$yU?zR&1R@v8?0HE(J{fzbDBqm8au#c@Rwg+wTV|L4YD#vzaOkge z$=#3>x_eS}WUbhE!EW@$YhrrRZxZ_ZCD_RsW+^_`E*f_4G4!GOn5pgnJoKCPsJ*ze z6l1?qS%a~w{wQVmt2X~K$mvsG?MyxHZ~o50@$}ENOD!I?PIuG_@N3Tf_n delta 149 zcmca1w}*%6|G$YUSraeBO%7p9n0$uOYVtfLmB}BNf+kO6nKe0pRci7w*1E~h*fhjT z8FCp47%CZTfzXgakHH8+PCm)5Iyr(vX7V-;mC0(HN|R@CCQNqXvYY&gOKP$fw;O*Z oLj_!qA%oduUY_vDJ9zabGx2F~8!{LGO# Date: Fri, 24 Sep 2021 12:55:54 +0900 Subject: [PATCH 39/47] fix for demo MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 데모를 위해 maxTrialNum 을 1로 수정했습니다. --- app/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/app/utils.py b/app/utils.py index c6b8ef7..f411566 100644 --- a/app/utils.py +++ b/app/utils.py @@ -150,7 +150,7 @@ def write_yml( 'experimentName': f'{experiment_name}', 'trialConcurrency': 1, 'maxExecDuration': '1h', - 'maxTrialNum': 10, + 'maxTrialNum': 1, 'trainingServicePlatform': 'local', 'searchSpacePath': 'search_space.json', 'useAnnotation': False, From c3eeb8508bd82c989c98a8caee6f4bddd660a039 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Fri, 24 Sep 2021 13:02:30 +0900 Subject: [PATCH 40/47] fix for demo MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 데모를 위해 대기시간을 조정하였습니다. --- app/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/app/utils.py b/app/utils.py index f411566..b870e2d 100644 --- a/app/utils.py +++ b/app/utils.py @@ -191,7 +191,7 @@ def __init__( self.is_update = is_update self.top_cnt = top_cnt self.evaluation_criteria = evaluation_criteria - self._wait_minute = minute * 60 + self._wait_minute = minute * 20 self._experiment_list = None self._running_experiment = None @@ -209,7 +209,6 @@ def watch_process(self): if self.is_kill: while True: self.get_running_experiment() - time.sleep(self._wait_minute) if self._running_experiment and ("DONE" in self._running_experiment[0]): _stop_expr = subprocess.getoutput("nnictl stop {}".format( self.experiment_id @@ -225,6 +224,7 @@ def watch_process(self): else: if self.is_update: self.model_keep_update() + time.sleep(self._wait_minute) def model_keep_update(self): engine.execute( From 5072b3bf9bc151d50b0fe42c9ac07d23ec8888b3 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Fri, 24 Sep 2021 14:14:09 +0900 Subject: [PATCH 41/47] Hotfix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 모델파일을 저장하는 과정에서 동작하지 않게 저장해서 임시로 수정하였습니다. --- .pre-commit-config.yaml | 13 ++- app/utils.py | 209 ++++++++++++++++++++-------------------- 2 files changed, 113 insertions(+), 109 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8557ba5..00629bb 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,11 @@ repos: - - - repo: https://github.com/pre-commit/mirrors-autopep8 - rev: 'v1.5.7' + - repo: https://github.com/PyCQA/isort + rev: 5.6.4 hooks: - - id: autopep8 \ No newline at end of file + - id: isort + language_version: python3 + - repo: https://github.com/psf/black + rev: 20.8b1 + hooks: + - id: black + language_version: python3 \ No newline at end of file diff --git a/app/utils.py b/app/utils.py index b870e2d..36acdd6 100644 --- a/app/utils.py +++ b/app/utils.py @@ -1,29 +1,28 @@ import codecs -import pickle -import os -import yaml -import zipfile -import socketserver -import time import glob -import shutil -import subprocess import io -import re import multiprocessing - +import os +import pickle +import re +import shutil +import socketserver +import subprocess +import time +import zipfile import tensorflow as tf +import yaml from app.database import engine -from logger import L from app.query import * +from logger import L -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -physical_devices = tf.config.list_physical_devices('GPU') +physical_devices = tf.config.list_physical_devices("GPU") if physical_devices: tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) @@ -45,7 +44,9 @@ def __init__(self, model_name): SELECT model_file FROM model_core WHERE model_name='{}'; - """.format(self.model_name) + """.format( + self.model_name + ) def load_model(self): """ @@ -85,11 +86,9 @@ def load_model(self): """ _model = engine.execute(self.query).fetchone() if _model is None: - raise ValueError('Model Not Found!') + raise ValueError("Model Not Found!") - self.model = pickle.loads( - codecs.decode(_model[0], 'base64') - ) + self.model = pickle.loads(codecs.decode(_model[0], "base64")) class TensorFlowModel(CoreModel): @@ -111,7 +110,7 @@ def load_model(self): """ _model = engine.execute(self.query).fetchone() if _model is None: - raise ValueError('Model Not Found!') + raise ValueError("Model Not Found!") model_buffer = pickle.loads(codecs.decode(_model[0], "base64")) model_path = os.path.join(base_dir, "tf_model", self.model_name) @@ -120,17 +119,11 @@ def load_model(self): self.model = tf.keras.models.load_model(model_path) -my_model = TensorFlowModel('test_model') +my_model = TensorFlowModel("test_model") my_model.load_model() -def write_yml( - path, - experiment_name, - experimenter, - model_name, - version -): +def write_yml(path, experiment_name, experimenter, model_name, version): """ NNI 실험을 시작하기 위한 config.yml파일을 작성하는 함수 입니다. @@ -144,30 +137,31 @@ def write_yml( Returns: 반환 값은 없으며 입력받은 경로로 yml파일이 작성됩니다. """ - with open('{}/{}.yml'.format(path, model_name), 'w') as yml_config_file: - yaml.dump({ - 'authorName': f'{experimenter}', - 'experimentName': f'{experiment_name}', - 'trialConcurrency': 1, - 'maxExecDuration': '1h', - 'maxTrialNum': 1, - 'trainingServicePlatform': 'local', - 'searchSpacePath': 'search_space.json', - 'useAnnotation': False, - 'tuner': { - 'builtinTunerName': 'Anneal', - 'classArgs': { - 'optimize_mode': 'minimize' - }}, - 'trial': { - 'command': 'python trial.py -e {} -n {} -m {} -v {}'.format( - experimenter, - experiment_name, - model_name, - version - ), - 'codeDir': '.' - }}, yml_config_file, default_flow_style=False) + with open("{}/{}.yml".format(path, model_name), "w") as yml_config_file: + yaml.dump( + { + "authorName": f"{experimenter}", + "experimentName": f"{experiment_name}", + "trialConcurrency": 1, + "maxExecDuration": "1h", + "maxTrialNum": 1, + "trainingServicePlatform": "local", + "searchSpacePath": "search_space.json", + "useAnnotation": False, + "tuner": { + "builtinTunerName": "Anneal", + "classArgs": {"optimize_mode": "minimize"}, + }, + "trial": { + "command": "python trial.py -e {} -n {} -m {} -v {}".format( + experimenter, experiment_name, model_name, version + ), + "codeDir": ".", + }, + }, + yml_config_file, + default_flow_style=False, + ) yml_config_file.close() @@ -183,7 +177,7 @@ def __init__( is_kill=True, is_update=True, top_cnt=3, - evaluation_criteria='val_mae' + evaluation_criteria="val_mae", ): self.experiment_id = experiment_id self.experiment_name = experiment_name @@ -200,9 +194,12 @@ def excute(self): self.model_final_update() def get_running_experiment(self): - self._experiment_list = subprocess.getoutput('nnictl experiment list') - self._running_experiment = [expr for expr in self._experiment_list.split( - '\n') if self.experiment_id in expr] + self._experiment_list = subprocess.getoutput("nnictl experiment list") + self._running_experiment = [ + expr + for expr in self._experiment_list.split("\n") + if self.experiment_id in expr + ] L.info(self._running_experiment) def watch_process(self): @@ -210,14 +207,14 @@ def watch_process(self): while True: self.get_running_experiment() if self._running_experiment and ("DONE" in self._running_experiment[0]): - _stop_expr = subprocess.getoutput("nnictl stop {}".format( - self.experiment_id - )) + _stop_expr = subprocess.getoutput( + "nnictl stop {}".format(self.experiment_id) + ) L.info(_stop_expr) break elif self.experiment_id not in self._experiment_list: - L.error('Experiment ID not in Current Experiment List') + L.error("Experiment ID not in Current Experiment List") L.info(self._experiment_list) break @@ -229,41 +226,45 @@ def watch_process(self): def model_keep_update(self): engine.execute( UPDATE_TEMP_MODEL_DATA.format( - self.experiment_name, - self.evaluation_criteria, - self.top_cnt) + self.experiment_name, self.evaluation_criteria, self.top_cnt + ) ) def model_final_update(self): final_result = engine.execute( SELECT_TEMP_MODEL_BY_EXPR_NAME.format( - self.experiment_name, - self.evaluation_criteria) + self.experiment_name, self.evaluation_criteria + ) ).fetchone() saved_result = engine.execute( - SELECT_MODEL_METADATA_BY_EXPR_NAME.format( - self.experiment_name) + SELECT_MODEL_METADATA_BY_EXPR_NAME.format(self.experiment_name) ).fetchone() + a = pickle.loads(codecs.decode(final_result.model_file, "base64")) + pickled_model = codecs.encode(pickle.dumps(a), "base64").decode() if saved_result is None: - engine.execute(INSERT_MODEL_CORE.format( - final_result.model_name, final_result.model_file)) - elif saved_result[self.evaluation_criteria] > final_result[self.evaluation_criteria]: - engine.execute(UPDATE_MODEL_CORE.format( - final_result.model_file, final_result.model_name)) - engine.execute(UPDATE_MODEL_METADATA.format( - final_result.train_mae, - final_result.val_mae, - final_result.train_mse, - final_result.val_mse, - self.experiment_name) + engine.execute( + INSERT_MODEL_CORE.format(final_result.model_name, pickled_model) + ) + elif ( + saved_result[self.evaluation_criteria] + > final_result[self.evaluation_criteria] + ): + engine.execute( + UPDATE_MODEL_CORE.format(pickled_model, final_result.model_name) + ) + engine.execute( + UPDATE_MODEL_METADATA.format( + final_result.train_mae, + final_result.val_mae, + final_result.train_mse, + final_result.val_mse, + self.experiment_name, + ) ) - engine.execute( - DELETE_ALL_EXPERIMENTS_BY_EXPR_NAME.format( - self.experiment_name) - ) + engine.execute(DELETE_ALL_EXPERIMENTS_BY_EXPR_NAME.format(self.experiment_name)) def zip_model(model_path): @@ -285,8 +286,10 @@ def zip_model(model_path): with zipfile.ZipFile(model_buffer, "w") as zf: for root, dirs, files in os.walk(model_path): - def make_arcname(x): return os.path.join( - root.split(basedir)[-1], x) + + def make_arcname(x): + return os.path.join(root.split(basedir)[-1], x) + for dr in dirs: dir_path = os.path.join(root, dr) zf.write(filename=dir_path, arcname=make_arcname(dr)) @@ -329,16 +332,14 @@ def check_expr_over(experiment_id, experiment_name, experiment_path): minute = 60 while True: - time.sleep(1*minute) + time.sleep(1 * minute) expr_list = subprocess.getoutput("nnictl experiment list") - running_expr = [expr for expr in expr_list.split( - '\n') if experiment_id in expr] + running_expr = [expr for expr in expr_list.split("\n") if experiment_id in expr] print(running_expr) if running_expr and ("DONE" in running_expr[0]): - stop_expr = subprocess.getoutput("nnictl stop {}".format( - experiment_id)) + stop_expr = subprocess.getoutput("nnictl stop {}".format(experiment_id)) L.info(stop_expr) break @@ -347,17 +348,15 @@ def check_expr_over(experiment_id, experiment_name, experiment_path): break else: - model_path = os.path.join(experiment_path, - "temp", - "*_{}*".format(experiment_name)) + model_path = os.path.join( + experiment_path, "temp", "*_{}*".format(experiment_name) + ) exprs = glob.glob(model_path) if len(exprs) > 3: exprs.sort() [shutil.rmtree(_) for _ in exprs[3:]] - model_path = os.path.join(experiment_path, - "temp", - "*_{}*".format(experiment_name)) + model_path = os.path.join(experiment_path, "temp", "*_{}*".format(experiment_name)) exprs = glob.glob(model_path) if not exprs: return 0 @@ -373,19 +372,19 @@ def check_expr_over(experiment_id, experiment_name, experiment_path): saved_score = engine.execute(score_sql).fetchone() if not saved_score or (metrics[0] < saved_score[0]): - winner_model = os.path.join(os.path.join(experiment_path, - "temp", - experiment_name)) + winner_model = os.path.join( + os.path.join(experiment_path, "temp", experiment_name) + ) os.rename(exprs, winner_model) m_buffer = zip_model(winner_model) encode_model = codecs.encode(pickle.dumps(m_buffer), "base64").decode() sql_save_model = "INSERT INTO model_core VALUES ('%s', '%s')" - engine.execute(sql_save_model % (experiment_name, - encode_model)) - - sql_save_score = "INSERT INTO atmos_model_metadata VALUES ('%s', '%s', '%s', '%s')" - engine.execute(sql_save_score % (experiment_name, - experiment_id, - metrics[0], - metrics[1])) + engine.execute(sql_save_model % (experiment_name, encode_model)) + + sql_save_score = ( + "INSERT INTO atmos_model_metadata VALUES ('%s', '%s', '%s', '%s')" + ) + engine.execute( + sql_save_score % (experiment_name, experiment_id, metrics[0], metrics[1]) + ) L.info("saved model %s %s" % (experiment_id, experiment_name)) From edacff88d42ab702037016b50b68655efb8f482d Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Tue, 5 Oct 2021 10:52:08 +0900 Subject: [PATCH 42/47] Fix model update logic MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 모델 업데이트나 저장 과정을 수정하였습니다. --- .pre-commit-config.yaml | 7 +----- app/api/router/train.py | 55 ++++++++++++++++------------------------- app/query.py | 22 +++++++++++++++++ app/utils.py | 23 +++++++++++++++-- 4 files changed, 65 insertions(+), 42 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 00629bb..3cb791c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,11 +1,6 @@ repos: - - repo: https://github.com/PyCQA/isort - rev: 5.6.4 - hooks: - - id: isort - language_version: python3 - repo: https://github.com/psf/black rev: 20.8b1 hooks: - id: black - language_version: python3 \ No newline at end of file + language_version: python3 diff --git a/app/api/router/train.py b/app/api/router/train.py index 33ab418..d1a0e17 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -1,28 +1,24 @@ -# -*- coding: utf-8 -*- -import subprocess import multiprocessing -import re import os +import re +import subprocess from fastapi import APIRouter -from app.utils import NniWatcher, write_yml, get_free_port, base_dir, check_expr_over +from app.utils import NniWatcher, base_dir, check_expr_over, get_free_port, write_yml from logger import L - router = APIRouter( - prefix="/train", - tags=["train"], - responses={404: {"description": "Not Found"}} + prefix="/train", tags=["train"], responses={404: {"description": "Not Found"}} ) @router.put("/insurance") def train_insurance( - experiment_name: str = 'exp1', - experimenter: str = 'DongUk', - model_name: str = 'insurance_fee_model', - version: float = 0.1 + experiment_name: str = "exp1", + experimenter: str = "DongUk", + model_name: str = "insurance_fee_model", + version: float = 0.1, ): """ insurance와 관련된 학습을 실행하기 위한 API입니다. @@ -41,29 +37,21 @@ def train_insurance( """ PORT = get_free_port() L.info( - f"Train Args info\n\texperiment_name: {experiment_name}\n\texperimenter: {experimenter}\n\tmodel_name: {model_name}\n\tversion: {version}") - path = 'experiments/insurance/' + f"Train Args info\n\texperiment_name: {experiment_name}\n\texperimenter: {experimenter}\n\tmodel_name: {model_name}\n\tversion: {version}" + ) + path = "experiments/insurance/" try: - write_yml( - path, - experiment_name, - experimenter, - model_name, - version - ) + write_yml(path, experiment_name, experimenter, model_name, version) nni_create_result = subprocess.getoutput( - "nnictl create --port {} --config {}/{}.yml".format( - PORT, path, model_name) + "nnictl create --port {} --config {}/{}.yml".format(PORT, path, model_name) ) sucs_msg = "Successfully started experiment!" if sucs_msg in nni_create_result: p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") expr_id = p.findall(nni_create_result)[0] - nni_watcher = NniWatcher(expr_id, experiment_name) - m_process = multiprocessing.Process( - target=nni_watcher.excute - ) + nni_watcher = NniWatcher(expr_id, experiment_name, experimenter, version) + m_process = multiprocessing.Process(target=nni_watcher.excute) m_process.start() L.info(nni_create_result) @@ -71,7 +59,7 @@ def train_insurance( except Exception as e: L.error(e) - return {'error': str(e)} + return {"error": str(e)} @router.put("/atmos") @@ -90,20 +78,19 @@ def train_atmos(expr_name: str): """ nni_port = get_free_port() - expr_path = os.path.join(base_dir, 'experiments', expr_name) + expr_path = os.path.join(base_dir, "experiments", expr_name) try: nni_create_result = subprocess.getoutput( - "nnictl create --port {} --config {}/config.yml".format( - nni_port, expr_path)) + "nnictl create --port {} --config {}/config.yml".format(nni_port, expr_path) + ) sucs_msg = "Successfully started experiment!" if sucs_msg in nni_create_result: p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") expr_id = p.findall(nni_create_result)[0] m_process = multiprocessing.Process( - target=check_expr_over, - args=(expr_id, expr_name, expr_path) + target=check_expr_over, args=(expr_id, expr_name, expr_path) ) m_process.start() @@ -116,4 +103,4 @@ def train_atmos(expr_name: str): except Exception as e: L.error(e) - return {'error': str(e)} + return {"error": str(e)} diff --git a/app/query.py b/app/query.py index 134c99f..de2a52d 100644 --- a/app/query.py +++ b/app/query.py @@ -34,6 +34,28 @@ ) """ +INSERT_MODEL_METADATA = """ + INSERT INTO model_metadata ( + experiment_name, + model_core_name, + experimenter, + version, + train_mae, + val_mae, + train_mse, + val_mse + ) VALUES ( + '{}', + '{}', + '{}', + '{}', + '{}', + '{}', + '{}', + '{}' + ) +""" + UPDATE_MODEL_CORE = """ UPDATE model_core SET diff --git a/app/utils.py b/app/utils.py index 36acdd6..859a4ce 100644 --- a/app/utils.py +++ b/app/utils.py @@ -11,9 +11,11 @@ import time import zipfile + import tensorflow as tf import yaml + from app.database import engine from app.query import * from logger import L @@ -144,7 +146,7 @@ def write_yml(path, experiment_name, experimenter, model_name, version): "experimentName": f"{experiment_name}", "trialConcurrency": 1, "maxExecDuration": "1h", - "maxTrialNum": 1, + "maxTrialNum": 10, "trainingServicePlatform": "local", "searchSpacePath": "search_space.json", "useAnnotation": False, @@ -173,6 +175,8 @@ def __init__( self, experiment_id, experiment_name, + experimenter, + version, minute=1, is_kill=True, is_update=True, @@ -181,6 +185,8 @@ def __init__( ): self.experiment_id = experiment_id self.experiment_name = experiment_name + self.experimenter = experimenter + self.version = version self.is_kill = is_kill self.is_update = is_update self.top_cnt = top_cnt @@ -243,16 +249,29 @@ def model_final_update(self): a = pickle.loads(codecs.decode(final_result.model_file, "base64")) pickled_model = codecs.encode(pickle.dumps(a), "base64").decode() + if saved_result is None: engine.execute( INSERT_MODEL_CORE.format(final_result.model_name, pickled_model) ) + engine.execute( + INSERT_MODEL_METADATA.format( + self.experiment_name, + final_result.model_name, + self.experimenter, + self.version, + final_result.train_mae, + final_result.val_mae, + final_result.train_mse, + final_result.val_mse, + ) + ) elif ( saved_result[self.evaluation_criteria] > final_result[self.evaluation_criteria] ): engine.execute( - UPDATE_MODEL_CORE.format(pickled_model, final_result.model_name) + UPDATE_MODEL_CORE.format(pickled_model, saved_result.model_name) ) engine.execute( UPDATE_MODEL_METADATA.format( From e1707fc4f6555bd87d81ae78cb5cfc96b5fab57a Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Tue, 5 Oct 2021 10:54:09 +0900 Subject: [PATCH 43/47] Add black MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit pre-commit 을 black으로 수정했습니다. --- app/api/router/predict.py | 19 +++--- app/api/router/train.py | 1 + app/api/schemas.py | 3 +- app/database.py | 13 +++-- app/models.py | 15 +++-- app/utils.py | 1 - experiments/atmos_tmp_01/train.py | 19 +++--- experiments/expr_db.py | 2 +- experiments/insurance/trial.py | 96 +++++++++++++++---------------- logger.py | 7 ++- main.py | 9 ++- 11 files changed, 98 insertions(+), 87 deletions(-) diff --git a/app/api/router/predict.py b/app/api/router/predict.py index c294ba4..c73f3b1 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- from typing import List + import numpy as np from fastapi import APIRouter from starlette.concurrency import run_in_threadpool @@ -16,9 +17,7 @@ router = APIRouter( - prefix="/predict", - tags=["predict"], - responses={404: {"description": "Not Found"}} + prefix="/predict", tags=["predict"], responses={404: {"description": "Not Found"}} ) @@ -33,6 +32,7 @@ async def predict_insurance(info: ModelCorePrediction, model_name: str): Returns: insurance_fee(float): 보험료 예측값입니다. """ + def sync_call(info, model_name): """ none sync 함수를 sync로 만들어 주기 위한 함수이며 입출력은 부모 함수와 같습니다. @@ -45,15 +45,17 @@ def sync_call(info, model_name): pred = model.predict_target(test_set) return {"result": pred.tolist()[0]} + try: result = await run_in_threadpool(sync_call, info, model_name) L.info( - f"Predict Args info: {info}\n\tmodel_name: {model_name}\n\tPrediction Result: {result}") + f"Predict Args info: {info}\n\tmodel_name: {model_name}\n\tPrediction Result: {result}" + ) return result except Exception as e: L.error(e) - return {'error': str(e)} + return {"error": str(e)} @router.put("/atmos") @@ -68,7 +70,7 @@ async def predict_temperature(time_series: List[float]): List[float]: 입력받은 시간 이후 24시간 동안의 1시간 간격 온도 예측 시계열 입니다. """ if len(time_series) != 72: - L.error(f'input time_series: {time_series} is not valid') + L.error(f"input time_series: {time_series} is not valid") return "time series must have 72 values" def sync_pred_ts(time_series): @@ -78,8 +80,9 @@ def sync_pred_ts(time_series): time_series = np.array(time_series).reshape(1, -1, 1) result = my_model.predict_target(time_series) L.info( - f"Predict Args info: {time_series.flatten().tolist()}\n\tmodel_name: {my_model.model_name}\n\tPrediction Result: {result.tolist()[0]}") - + f"Predict Args info: {time_series.flatten().tolist()}\n\tmodel_name: {my_model.model_name}\n\tPrediction Result: {result.tolist()[0]}" + ) + return result try: diff --git a/app/api/router/train.py b/app/api/router/train.py index d1a0e17..1727003 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -3,6 +3,7 @@ import re import subprocess + from fastapi import APIRouter from app.utils import NniWatcher, base_dir, check_expr_over, get_free_port, write_yml diff --git a/app/api/schemas.py b/app/api/schemas.py index dffbee3..73118ea 100644 --- a/app/api/schemas.py +++ b/app/api/schemas.py @@ -8,7 +8,7 @@ class ModelCoreBase(BaseModel): class ModelCorePrediction(BaseModel): """ predict_insurance API의 입력 값 검증을 위한 pydantic 클래스입니다. - + Attributes: age(int) sex(int) @@ -17,6 +17,7 @@ class ModelCorePrediction(BaseModel): smoker(int) region(int) """ + age: int sex: int bmi: float diff --git a/app/database.py b/app/database.py index 60be38b..cc2bcf7 100644 --- a/app/database.py +++ b/app/database.py @@ -1,5 +1,6 @@ import os + from dotenv import load_dotenv from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker @@ -7,13 +8,14 @@ load_dotenv(verbose=True) + def connect(db): """ database와의 연결을 위한 함수 입니다. - + Args: db(str): 사용할 데이터베이스의 이름을 전달받습니다. - + Returns: created database engine: 데이터베이스에 연결된 객체를 반환합니다. @@ -32,9 +34,10 @@ def connect(db): POSTGRES_PORT = os.getenv("POSTGRES_PORT") POSTGRES_SERVER = os.getenv("POSTGRES_SERVER") - SQLALCHEMY_DATABASE_URL = \ - f'postgresql://{POSTGRES_USER}:{POSTGRES_PASSWORD}@' +\ - f'{POSTGRES_SERVER}:{POSTGRES_PORT}/{db}' + SQLALCHEMY_DATABASE_URL = ( + f"postgresql://{POSTGRES_USER}:{POSTGRES_PASSWORD}@" + + f"{POSTGRES_SERVER}:{POSTGRES_PORT}/{db}" + ) connection = create_engine(SQLALCHEMY_DATABASE_URL) diff --git a/app/models.py b/app/models.py index 5ba2f33..efd76e5 100644 --- a/app/models.py +++ b/app/models.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- import datetime + from sqlalchemy import Column, Integer, String, FLOAT, DateTime, ForeignKey, LargeBinary from sqlalchemy.sql.functions import now from sqlalchemy.orm import relationship @@ -11,21 +12,23 @@ class ModelCore(Base): - __tablename__ = 'model_core' + __tablename__ = "model_core" model_name = Column(String, primary_key=True) model_file = Column(LargeBinary, nullable=False) model_metadata_relation = relationship( - "ModelMetadata", backref="model_core.model_name") + "ModelMetadata", backref="model_core.model_name" + ) class ModelMetadata(Base): - __tablename__ = 'model_metadata' + __tablename__ = "model_metadata" experiment_name = Column(String, primary_key=True) - model_core_name = Column(String, ForeignKey( - 'model_core.model_name'), nullable=False) + model_core_name = Column( + String, ForeignKey("model_core.model_name"), nullable=False + ) experimenter = Column(String, nullable=False) version = Column(FLOAT) train_mae = Column(FLOAT, nullable=False) @@ -36,7 +39,7 @@ class ModelMetadata(Base): class TempModelData(Base): - __tablename__ = 'temp_model_data' + __tablename__ = "temp_model_data" id = Column(Integer, primary_key=True, autoincrement=True) model_name = Column(String, nullable=False) diff --git a/app/utils.py b/app/utils.py index 859a4ce..4ff3f93 100644 --- a/app/utils.py +++ b/app/utils.py @@ -15,7 +15,6 @@ import tensorflow as tf import yaml - from app.database import engine from app.query import * from logger import L diff --git a/experiments/atmos_tmp_01/train.py b/experiments/atmos_tmp_01/train.py index 1945af6..4a541c0 100644 --- a/experiments/atmos_tmp_01/train.py +++ b/experiments/atmos_tmp_01/train.py @@ -2,6 +2,7 @@ import sys import time from preprocessing import preprocess + sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import numpy as np @@ -15,9 +16,10 @@ from tensorflow.keras.layers import GRU from sklearn.metrics import mean_absolute_error, mean_squared_error + from expr_db import connect -physical_devices = tf.config.list_physical_devices('GPU') +physical_devices = tf.config.list_physical_devices("GPU") if physical_devices: tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) @@ -104,9 +106,9 @@ def main(params): if not os.path.exists(model_path): os.makedirs(model_path) - # 실험시작시간은 여러 모델간의 구분을 위해 임시로 넣었지만 + # 실험시작시간은 여러 모델간의 구분을 위해 임시로 넣었지만 # 여러 워커를 동시에 실행시킬 경우 겹칠 수 있음. 추후 변경 필요!! - filename = os.path.join(model_path, f'./{parent_dir}_{expr_time}') + filename = os.path.join(model_path, f"./{parent_dir}_{expr_time}") print(filename) checkpoint = ModelCheckpoint( filename, @@ -125,15 +127,16 @@ def main(params): callbacks=[early_stop, checkpoint], ) - y_true = y_test.reshape(y_test.shape[0], y_test.shape[1]) y_hat = model.predict(X_test) - + mae = mean_absolute_error(y_true, y_hat) mse = mean_squared_error(y_true, y_hat) - src_f = os.path.join(model_path, f'./{parent_dir}_{expr_time}') - dst_f = os.path.join(model_path, f'./{mae:.03f}_{mse:.03f}_{parent_dir}_{expr_time}') + src_f = os.path.join(model_path, f"./{parent_dir}_{expr_time}") + dst_f = os.path.join( + model_path, f"./{mae:.03f}_{mse:.03f}_{parent_dir}_{expr_time}" + ) os.rename(src_f, dst_f) nni.report_final_result(mae) @@ -141,4 +144,4 @@ def main(params): if __name__ == "__main__": params = nni.get_next_parameter() - main(params) \ No newline at end of file + main(params) diff --git a/experiments/expr_db.py b/experiments/expr_db.py index 9c5ad81..fc6ae08 100644 --- a/experiments/expr_db.py +++ b/experiments/expr_db.py @@ -3,7 +3,7 @@ import sqlalchemy -def connect(db='postgres'): +def connect(db="postgres"): """Returns a connection and a metadata object""" load_dotenv(verbose=True) diff --git a/experiments/insurance/trial.py b/experiments/insurance/trial.py index f6769b2..6c61747 100644 --- a/experiments/insurance/trial.py +++ b/experiments/insurance/trial.py @@ -1,25 +1,27 @@ -import os import codecs +import getopt +import os import pickle import sys -import getopt -sys.path.append(os.path.dirname( os.path.dirname( os.path.abspath(__file__) ) )) -from dotenv import load_dotenv -import pandas as pd -import numpy as np + +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + import nni -from sklearn.model_selection import train_test_split +import numpy as np +import pandas as pd +from dotenv import load_dotenv +from expr_db import connect +from query import * from sklearn.metrics import mean_absolute_error, mean_squared_error +from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import LabelEncoder -from sklearn.model_selection import KFold from xgboost.sklearn import XGBRegressor -from expr_db import connect -from query import * - load_dotenv(verbose=True) + def preprocess(x_train, x_valid, col_list): """ param: @@ -39,8 +41,7 @@ def preprocess(x_train, x_valid, col_list): encoder = LabelEncoder() for col in col_list: - tmp_x_train.loc[:, col] = encoder.fit_transform( - tmp_x_train.loc[:, col]) + tmp_x_train.loc[:, col] = encoder.fit_transform(tmp_x_train.loc[:, col]) tmp_x_valid.loc[:, col] = encoder.transform(tmp_x_valid.loc[:, col]) return tmp_x_train.values, tmp_x_valid.values @@ -56,18 +57,19 @@ def main(params, engine, experiment_info, connection): """ df = pd.read_sql(SELECT_ALL_INSURANCE, engine) - experimenter = experiment_info['experimenter'] - experiment_name = experiment_info['experiment_name'] - model_name = experiment_info['model_name'] - version = experiment_info['version'] + experimenter = experiment_info["experimenter"] + experiment_name = experiment_info["experiment_name"] + model_name = experiment_info["model_name"] + version = experiment_info["version"] - label_col = ['sex', 'smoker', 'region'] + label_col = ["sex", "smoker", "region"] y = df.charges.to_frame() x = df.iloc[:, :-1] x_train, x_valid, y_train, y_valid = train_test_split( - x, y, test_size=0.2, random_state=42) + x, y, test_size=0.2, random_state=42 + ) kf = KFold(n_splits=5, shuffle=True, random_state=42) @@ -85,8 +87,7 @@ def main(params, engine, experiment_info, connection): model = XGBRegressor(**params) # 모델 학습 및 Early Stopping 적용 - model.fit(x_tra, y_train, eval_set=[ - (x_val, y_valid)], early_stopping_rounds=10) + model.fit(x_tra, y_train, eval_set=[(x_val, y_valid)], early_stopping_rounds=10) y_train_pred = model.predict(x_tra) y_valid_pred = model.predict(x_val) @@ -109,27 +110,28 @@ def main(params, engine, experiment_info, connection): cv_mae_mean = np.mean(cv_mae) tr_mse_mean = np.mean(tr_mse) tr_mae_mean = np.mean(tr_mae) - pickled_model = codecs.encode( - pickle.dumps(fold_model), "base64").decode() - - connection.execute(INSERT_TEMP_MODEL.format( - model_name, - pickled_model, - experiment_name, - experimenter, - version, - tr_mae_mean, - cv_mae_mean, - tr_mse_mean, - cv_mse_mean - )) + pickled_model = codecs.encode(pickle.dumps(fold_model), "base64").decode() + + connection.execute( + INSERT_TEMP_MODEL.format( + model_name, + pickled_model, + experiment_name, + experimenter, + version, + tr_mae_mean, + cv_mae_mean, + tr_mse_mean, + cv_mse_mean, + ) + ) nni.report_final_result(cv_mae_mean) - print('Final result is %g', cv_mae_mean) - print('Send final result done.') + print("Final result is %g", cv_mae_mean) + print("Send final result done.") -if __name__ == '__main__': +if __name__ == "__main__": params = nni.get_next_parameter() engine = connect() argv = sys.argv @@ -139,21 +141,17 @@ def main(params, engine, experiment_info, connection): opts, etc_args = getopt.getopt( argv[1:], "e:n:m:v:", - [ - "experimenter=", - "experiment_name=", - "model_name=", - "version=" - ]) + ["experimenter=", "experiment_name=", "model_name=", "version="], + ) for opt, arg in opts: - if opt in ('-e', "--experimenter"): - experiment_info['experimenter'] = f"'{arg}'" + if opt in ("-e", "--experimenter"): + experiment_info["experimenter"] = f"'{arg}'" elif opt in ("-n", "--experiment_name"): - experiment_info['experiment_name'] = f"'{arg}'" + experiment_info["experiment_name"] = f"'{arg}'" elif opt in ("-m", "--model_name"): - experiment_info['model_name'] = f"'{arg}'" + experiment_info["model_name"] = f"'{arg}'" elif opt in ("-v", "--version"): - experiment_info['version'] = arg + experiment_info["version"] = arg except getopt.GetoptError: sys.exit(2) diff --git a/logger.py b/logger.py index 6b5880a..b3f056d 100644 --- a/logger.py +++ b/logger.py @@ -2,15 +2,16 @@ import logging.handlers from colorlog import ColoredFormatter -L = logging.getLogger('snowdeer_log') + +L = logging.getLogger("snowdeer_log") L.setLevel(logging.DEBUG) formatter = ColoredFormatter( fmt="%(log_color)s [%(levelname)s] %(reset)s %(asctime)s [%(filename)s:%(lineno)d - %(funcName)20s()]\n\t%(message)s", - datefmt='%y-%m-%d %H:%M:%S' + datefmt="%y-%m-%d %H:%M:%S", ) -fileHandler = logging.FileHandler('./log.txt') +fileHandler = logging.FileHandler("./log.txt") streamHandler = logging.StreamHandler() fileHandler.setFormatter(formatter) diff --git a/main.py b/main.py index 68d6f21..9e40cab 100644 --- a/main.py +++ b/main.py @@ -2,6 +2,7 @@ from fastapi.middleware.cors import CORSMiddleware import uvicorn + from app.api.router import predict, train app = FastAPI() @@ -26,8 +27,6 @@ def hello_world(): if __name__ == "__main__": - uvicorn.run("main:app", - host="0.0.0.0", - port=8000, - reload=True, - reload_dirs=['app/']) + uvicorn.run( + "main:app", host="0.0.0.0", port=8000, reload=True, reload_dirs=["app/"] + ) From dc240c9d3fa33d83e56506ed0cca91217384e397 Mon Sep 17 00:00:00 2001 From: hlchoi Date: Tue, 5 Oct 2021 20:46:52 +0900 Subject: [PATCH 44/47] =?UTF-8?q?modify=20atmos=20train=20api=201.=20?= =?UTF-8?q?=EB=8B=A8=EC=9D=BC=20=ED=95=A8=EC=88=98=EC=97=90=EC=84=9C=20?= =?UTF-8?q?=ED=81=B4=EB=9E=98=EC=8A=A4=EB=A1=9C=20=EB=8B=A4=EC=8B=9C=20?= =?UTF-8?q?=EB=A7=8C=EB=93=A4=EC=97=88=EC=8A=B5=EB=8B=88=EB=8B=A4.=202.=20?= =?UTF-8?q?=EC=8A=A4=EC=BD=94=EC=96=B4=EA=B0=80=20=ED=96=A5=EC=83=81?= =?UTF-8?q?=EB=90=98=EB=A9=B4=20SQL=20=EC=BF=BC=EB=A6=AC=EB=A5=BC=20?= =?UTF-8?q?=EC=9D=B4=EC=9A=A9=ED=95=98=EC=97=AC=20=EB=AA=A8=EB=8D=B8?= =?UTF-8?q?=EA=B3=BC=20=EC=A0=90=EC=88=98=EB=A5=BC=20=EC=97=85=EB=8D=B0?= =?UTF-8?q?=EC=9D=B4=ED=8A=B8=20=ED=95=98=EB=8F=84=EB=A1=9D=20=EB=B3=80?= =?UTF-8?q?=EA=B2=BD=ED=95=98=EC=98=80=EC=8A=B5=EB=8B=88=EB=8B=A4.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/api/router/train.py | 8 +++- app/query.py | 22 ++++++++++ app/utils.py | 96 ++++++++++++++++++++++++++++++++++++++++- 3 files changed, 123 insertions(+), 3 deletions(-) diff --git a/app/api/router/train.py b/app/api/router/train.py index 1727003..bc2206f 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -6,7 +6,7 @@ from fastapi import APIRouter -from app.utils import NniWatcher, base_dir, check_expr_over, get_free_port, write_yml +from app.utils import NniWatcher, ExprimentOwl, base_dir, check_expr_over, get_free_port, write_yml from logger import L router = APIRouter( @@ -90,8 +90,12 @@ def train_atmos(expr_name: str): if sucs_msg in nni_create_result: p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") expr_id = p.findall(nni_create_result)[0] + check_expr = ExprimentOwl(expr_id, expr_name, expr_path) + check_expr.add("update_tfmodeldb") + check_expr.add("modelfile_cleaner") + m_process = multiprocessing.Process( - target=check_expr_over, args=(expr_id, expr_name, expr_path) + target=check_expr.execute ) m_process.start() diff --git a/app/query.py b/app/query.py index de2a52d..33f6e9b 100644 --- a/app/query.py +++ b/app/query.py @@ -78,3 +78,25 @@ DELETE FROM temp_model_data WHERE experiment_name = '{}' """ + +INSERT_OR_UPDATE_MODEL = """ +UPDATE model_core +SET model_name='{mn}', model_file='{mf}' +WHERE model_core.model_name='{mn}'; +INSERT INTO model_core (model_name, model_file) +SELECT '{mn}', '{mf}' +WHERE NOT EXISTS (SELECT 1 + FROM model_core as mc + WHERE mc.model_name = '{mn}'); +""" + +INSERT_OR_UPDATE_SCORE = """ +UPDATE atmos_model_metadata +SET mae='{score1}', mse='{score2}' +WHERE atmos_model_metadata.model_name='{mn}'; +INSERT INTO atmos_model_metadata (model_name, experiment_id, mae, mse) +SELECT '{mn}', '{expr_id}', '{score1}', '{score2}' +WHERE NOT EXISTS (SELECT 1 + FROM atmos_model_metadata as amm + WHERE amm.model_name = '{mn}'); +""" \ No newline at end of file diff --git a/app/utils.py b/app/utils.py index 4ff3f93..d3f4cb1 100644 --- a/app/utils.py +++ b/app/utils.py @@ -296,7 +296,7 @@ def zip_model(model_path): memory buffer: 모델을 압축한 메모리 버퍼를 반환합니다. Note: - 모델을 보조기억장치에 파일로 저장하지 않습니다. + 모델을 디스크에 파일로 저장하지 않습니다. """ model_buffer = io.BytesIO() @@ -406,3 +406,97 @@ def check_expr_over(experiment_id, experiment_name, experiment_path): sql_save_score % (experiment_name, experiment_id, metrics[0], metrics[1]) ) L.info("saved model %s %s" % (experiment_id, experiment_name)) + + +class ExprimentOwl: + + def __init__(self, experiment_id, experiment_name, experiment_path, mfile_manage = True, time = 5): + self.__minute = 60 + self.time = time * self.__minute + self.experiment_id = experiment_id + self.experiment_name = experiment_name + self.experiment_path = experiment_path + self.mfile_manage = mfile_manage + self.__func_list = [self.main] + + + def execute(self): + for func in self.__func_list: + func() + + + def add(self, func_name): + func = getattr(self, func_name) + self.__func_list.append(func) + + + def main(self): + while True: + time.sleep(self.__minute) + + expr_list = subprocess.getoutput("nnictl experiment list") + + running_expr = [expr for expr in expr_list.split("\n") if self.experiment_id in expr] + print(running_expr) + if running_expr and ("DONE" in running_expr[0]): + stop_expr = subprocess.getoutput("nnictl stop {}".format(self.experiment_id)) + L.info(stop_expr) + break + + elif self.experiment_id not in expr_list: + L.info(expr_list) + break + + else: + if self.mfile_manage: + model_path = os.path.join( + self.experiment_path, "temp", "*_{}*".format(self.experiment_name) + ) + exprs = glob.glob(model_path) + if len(exprs) > 3: + exprs.sort() + [shutil.rmtree(_) for _ in exprs[3:]] + + + def update_tfmodeldb(self): + model_path = os.path.join(self.experiment_path, "temp", "*_{}*".format(self.experiment_name)) + exprs = glob.glob(model_path) + if not exprs: + return 0 + + exprs.sort() + exprs = exprs[0] + metrics = os.path.basename(exprs).split("_")[:2] + metrics = [float(metric) for metric in metrics] + + score_sql = """SELECT mae + FROM atmos_model_metadata + WHERE model_name = '{}' + ORDER BY mae;""".format(self.experiment_name) + saved_score = engine.execute(score_sql).fetchone() + + if not saved_score or (metrics[0] < saved_score[0]): + winner_model = os.path.join( + os.path.join(self.experiment_path, "temp", self.experiment_name) + ) + os.rename(exprs, winner_model) + m_buffer = zip_model(winner_model) + encode_model = codecs.encode(pickle.dumps(m_buffer), "base64").decode() + + engine.execute(INSERT_OR_UPDATE_MODEL.format(mn=self.experiment_name, + mf=encode_model)) + + engine.execute( + INSERT_OR_UPDATE_SCORE.format(mn = self.experiment_name, + expr_id = self.experiment_id, + score1 = metrics[0], + score2 = metrics[1]) + ) + L.info("saved model %s %s" % (self.experiment_id, self.experiment_name)) + + def modelfile_cleaner(self): + model_path = os.path.join( + self.experiment_path, "temp", "*_{}*".format(self.experiment_name) + ) + exprs = glob.glob(model_path) + [shutil.rmtree(_) for _ in exprs] From cea6b63f60695a27e7b8569336a96f33fcbc4620 Mon Sep 17 00:00:00 2001 From: hlchoi Date: Wed, 6 Oct 2021 11:39:30 +0900 Subject: [PATCH 45/47] =?UTF-8?q?add=20docstring=20and=20delete=20useless?= =?UTF-8?q?=20function=20=EC=83=88=EB=A1=9C=EB=A7=8C=EB=93=A0=20=EC=8B=A4?= =?UTF-8?q?=ED=97=98=EA=B4=80=EB=A6=AC=20=ED=81=B4=EB=9E=98=EC=8A=A4?= =?UTF-8?q?=EC=97=90=20docstring=EC=9D=84=20=EC=B6=94=EA=B0=80=ED=95=98?= =?UTF-8?q?=EA=B3=A0=20=EA=B8=B0=EC=A1=B4=EC=9D=98=20=EC=8B=A4=ED=97=98?= =?UTF-8?q?=EA=B4=80=EB=A6=AC=20=ED=95=A8=EC=88=98=EB=A5=BC=20=EC=82=AD?= =?UTF-8?q?=EC=A0=9C=ED=95=98=EC=98=80=EC=8A=B5=EB=8B=88=EB=8B=A4.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/api/router/train.py | 4 +- app/utils.py | 149 +++++++++++++++++++++------------------- 2 files changed, 82 insertions(+), 71 deletions(-) diff --git a/app/api/router/train.py b/app/api/router/train.py index bc2206f..cd6fe2a 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -6,7 +6,7 @@ from fastapi import APIRouter -from app.utils import NniWatcher, ExprimentOwl, base_dir, check_expr_over, get_free_port, write_yml +from app.utils import NniWatcher, ExperimentOwl, base_dir, get_free_port, write_yml from logger import L router = APIRouter( @@ -90,7 +90,7 @@ def train_atmos(expr_name: str): if sucs_msg in nni_create_result: p = re.compile(r"The experiment id is ([a-zA-Z0-9]+)\n") expr_id = p.findall(nni_create_result)[0] - check_expr = ExprimentOwl(expr_id, expr_name, expr_path) + check_expr = ExperimentOwl(expr_id, expr_name, expr_path) check_expr.add("update_tfmodeldb") check_expr.add("modelfile_cleaner") diff --git a/app/utils.py b/app/utils.py index d3f4cb1..038f7f2 100644 --- a/app/utils.py +++ b/app/utils.py @@ -170,6 +170,26 @@ def write_yml(path, experiment_name, experimenter, model_name, version): class NniWatcher: + """ + experiment_id를 입력받아 해당 id를 가진 nni 실험을 모니터링하고 모델 파일을 관리해주는 클래스입니다. + 생성되는 scikit learn 모델을 DB의 임시 테이블에 저장하여 주기적으로 업데이트 합니다. + 이후 실험의 모든 프로세스가 종료되면 가장 성능이 좋은 모델과 점수를 업데이트 합니다. + + Attributes: + experiment_id(str): nni experiment를 실행할 때 생성되는 id + experiment_name(str): 실험의 이름 + experimenter(str): 실험자의 이름 + version(str): 실험의 버전 + minute(int): 감시 주기 + is_kill(bool, default=True) + is_update(bool, default=True) + top_cnt(int, default=3) + evaluation_criteria(str, default="val_mae") + + Examples: + >>> watcher = NniWatcher(expr_id, experiment_name, experimenter, version) + >>> watcher.execute() + """ def __init__( self, experiment_id, @@ -195,10 +215,16 @@ def __init__( self._running_experiment = None def excute(self): + """ + 모든 함수를 실행합니다. + """ self.watch_process() self.model_final_update() def get_running_experiment(self): + """ + 실행중인 실험의 목록을 가져와 저장합니다. + """ self._experiment_list = subprocess.getoutput("nnictl experiment list") self._running_experiment = [ expr @@ -208,6 +234,10 @@ def get_running_experiment(self): L.info(self._running_experiment) def watch_process(self): + """ + 사용자가 지정한 시간을 주기로 실험 프로세스가 진행 중인지 감시하고 "DONE"상태로 변경되면 실험을 종료합니다. + 모델의 score를 DB에 주기적으로 업데이트 해줍니다. + """ if self.is_kill: while True: self.get_running_experiment() @@ -229,6 +259,9 @@ def watch_process(self): time.sleep(self._wait_minute) def model_keep_update(self): + """ + scikit learn 모델의 성능을 DB에 업데이트 합니다. + """ engine.execute( UPDATE_TEMP_MODEL_DATA.format( self.experiment_name, self.evaluation_criteria, self.top_cnt @@ -236,6 +269,9 @@ def model_keep_update(self): ) def model_final_update(self): + """ + 실험 종료시 실행되는 함수로 모델의 최종 점수와 모델 파일을 DB에 업데이트 해줍니다. + """ final_result = engine.execute( SELECT_TEMP_MODEL_BY_EXPR_NAME.format( self.experiment_name, self.evaluation_criteria @@ -335,81 +371,38 @@ def get_free_port(): return free_port -def check_expr_over(experiment_id, experiment_name, experiment_path): - """ - train API에서 사용되기 위하여 만들어 졌습니다. experiment_id를 입력받아 해당 id를 가진 nni 실험을 모니터링 합니다. 현재 추상화되어있지 않아 코드 재사용성이 부족하며 개선이 필요합니다. - * 파일로 생성되는 모델이 너무 많아지지 않도록 유지합니다.(3개 이상 모델이 생성되면 성능순으로 3위 미만은 삭제) - * nnictl experiment list(shell command)를 주기적으로 호출하여 실험이 현제 진행중인지 파악합니다. - * 실험의 상태가 DONE으로 변경되면 최고점수 모델을 데이터베이스에 저장하고 nnictl stop experiment_id를 실행하여 실험을 종료한 후 프로세스가 종료됩니다. - - Args: - experiment_id(str) - experiment_name(str) - experiment_path(str) +class ExperimentOwl: """ - minute = 60 + experiment_id를 입력받아 해당 id를 가진 nni 실험을 모니터링하고 모델 파일을 관리해주는 클래스입니다. + 필요한 기능을 instance.add("method name") 메서드로 추가하여 사용할 수 있습니다. - while True: - time.sleep(1 * minute) + 현재 보유한 기능 + 1. (기본)nnictl experiment list(shell command)를 주기적으로 호출하여 실험이 현재 진행중인지 파악합니다. + 실험의 상태가 DONE으로 변경되면 최고점수 모델을 데이터베이스에 저장하고 nnictl stop experiment_id를 실행하여 실험을 종료한 후 프로세스가 종료됩니다. - expr_list = subprocess.getoutput("nnictl experiment list") + 2. 파일로 생성되는 모델이 너무 많아지지 않도록 유지합니다.(3개 이상 모델이 생성되면 성능순으로 3위 미만은 삭제) instance 생성 시 + mfile_manage = False로 기능을 사용하지 않을 수 있습니다.(default True) - running_expr = [expr for expr in expr_list.split("\n") if experiment_id in expr] - print(running_expr) - if running_expr and ("DONE" in running_expr[0]): - stop_expr = subprocess.getoutput("nnictl stop {}".format(experiment_id)) - L.info(stop_expr) - break + 3. (method) update_tfmodelbd + 텐서플로우를 이용한 실험 시 생성되는 모델을 실험이 종료되면 DB에 저장하거나 점수가 향상되었을 시 업데이트 해줍니다. - elif experiment_id not in expr_list: - L.info(expr_list) - break + 4. (method) modelfile_cleaner + 모든 실험이 종료되고 모델이 저장되면 temp 폴더에 있는 모델파일들을 모두 지워줍니다. - else: - model_path = os.path.join( - experiment_path, "temp", "*_{}*".format(experiment_name) - ) - exprs = glob.glob(model_path) - if len(exprs) > 3: - exprs.sort() - [shutil.rmtree(_) for _ in exprs[3:]] - - model_path = os.path.join(experiment_path, "temp", "*_{}*".format(experiment_name)) - exprs = glob.glob(model_path) - if not exprs: - return 0 - - exprs.sort() - exprs = exprs[0] - metrics = os.path.basename(exprs).split("_")[:2] - metrics = [float(metric) for metric in metrics] - - score_sql = """SELECT mae - FROM atmos_model_metadata - ORDER BY mae;""" - saved_score = engine.execute(score_sql).fetchone() - - if not saved_score or (metrics[0] < saved_score[0]): - winner_model = os.path.join( - os.path.join(experiment_path, "temp", experiment_name) - ) - os.rename(exprs, winner_model) - m_buffer = zip_model(winner_model) - encode_model = codecs.encode(pickle.dumps(m_buffer), "base64").decode() - sql_save_model = "INSERT INTO model_core VALUES ('%s', '%s')" - engine.execute(sql_save_model % (experiment_name, encode_model)) - - sql_save_score = ( - "INSERT INTO atmos_model_metadata VALUES ('%s', '%s', '%s', '%s')" - ) - engine.execute( - sql_save_score % (experiment_name, experiment_id, metrics[0], metrics[1]) - ) - L.info("saved model %s %s" % (experiment_id, experiment_name)) + Attributes: + experiment_id(str): nni experiment를 실행할 때 생성되는 id + experiment_name(str): 실험의 이름 + experiment_path(str): 실험코드가 있는 경로 + mfile_manage(bool, default=True): 주기적으로 파일 삭제 여부 + time(int or float, default=5): 감시주기(분) + Examples: + >>> owl = ExperimentOwl(id, name, path) + >>> owl.add("update_tfmodeldb") + >>> owl.add("modelfile_cleaner") + >>> owl.execute() + """ -class ExprimentOwl: - def __init__(self, experiment_id, experiment_name, experiment_path, mfile_manage = True, time = 5): self.__minute = 60 self.time = time * self.__minute @@ -421,6 +414,9 @@ def __init__(self, experiment_id, experiment_name, experiment_path, mfile_manage def execute(self): + """ + instance.add("method name")으로 저장된 메서드들을 순서대로 모두 실행시킵니다. + """ for func in self.__func_list: func() @@ -431,6 +427,11 @@ def add(self, func_name): def main(self): + """ + ExperimentOwl클래스로 인스턴스를 생성 후 실행시 기본적으로 실행되는 기능입니다. + 사용자가 지정한 시간을 주기로 실험 프로세스가 진행 중인지 감시하고 "DONE"상태로 변경되면 실험을 종료합니다. + 인스턴스 생성 시 mfile_manage옵션이 True이면 모델 파일이 너무 많아지지 않게 점수 순서로 3위 이하는 삭제합니다.(default True) + """ while True: time.sleep(self.__minute) @@ -459,6 +460,9 @@ def main(self): def update_tfmodeldb(self): + """ + 실험이 종료되면 모델을 DB에 저장하거나 이미 같은 이름의 모델이 존재할 시 점수를 비교하여 업데이트 합니다. + """ model_path = os.path.join(self.experiment_path, "temp", "*_{}*".format(self.experiment_name)) exprs = glob.glob(model_path) if not exprs: @@ -479,13 +483,15 @@ def update_tfmodeldb(self): winner_model = os.path.join( os.path.join(self.experiment_path, "temp", self.experiment_name) ) + if os.path.exists: + shutil.rmtree(winner_model) os.rename(exprs, winner_model) + m_buffer = zip_model(winner_model) encode_model = codecs.encode(pickle.dumps(m_buffer), "base64").decode() engine.execute(INSERT_OR_UPDATE_MODEL.format(mn=self.experiment_name, mf=encode_model)) - engine.execute( INSERT_OR_UPDATE_SCORE.format(mn = self.experiment_name, expr_id = self.experiment_id, @@ -494,9 +500,14 @@ def update_tfmodeldb(self): ) L.info("saved model %s %s" % (self.experiment_id, self.experiment_name)) + def modelfile_cleaner(self): + """ + temp 폴더에 있는 모든 모델파일을 삭제합니다. + 가장 마지막에 실행하여 저장되고 남은 모델파일들을 삭제하는 용도로 사용할 수 있습니다. + """ model_path = os.path.join( - self.experiment_path, "temp", "*_{}*".format(self.experiment_name) + self.experiment_path, "temp", "*" ) exprs = glob.glob(model_path) [shutil.rmtree(_) for _ in exprs] From dd2266aa5d5a68598037d1fca55db9e8034bde61 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Wed, 6 Oct 2021 13:37:59 +0900 Subject: [PATCH 46/47] Modify docstring and return figure MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit docstring 내용 일부 추가하였고 return 하는 형태가 제각각이어서 통일하기위해 수정하였습니다. --- app/api/router/predict.py | 8 ++--- app/api/router/train.py | 14 ++++---- app/utils.py | 67 +++++++++++++++++++++------------------ docs/api-list.md | 19 ++++------- 4 files changed, 53 insertions(+), 55 deletions(-) diff --git a/app/api/router/predict.py b/app/api/router/predict.py index c73f3b1..4c76874 100644 --- a/app/api/router/predict.py +++ b/app/api/router/predict.py @@ -55,7 +55,7 @@ def sync_call(info, model_name): except Exception as e: L.error(e) - return {"error": str(e)} + return {"result": "Can't predict", "error": str(e)} @router.put("/atmos") @@ -71,7 +71,7 @@ async def predict_temperature(time_series: List[float]): """ if len(time_series) != 72: L.error(f"input time_series: {time_series} is not valid") - return "time series must have 72 values" + return {"result": "time series must have 72 values", "error": None} def sync_pred_ts(time_series): """ @@ -83,7 +83,7 @@ def sync_pred_ts(time_series): f"Predict Args info: {time_series.flatten().tolist()}\n\tmodel_name: {my_model.model_name}\n\tPrediction Result: {result.tolist()[0]}" ) - return result + return {"result": result, "error": None} try: result = await run_in_threadpool(sync_pred_ts, time_series) @@ -91,4 +91,4 @@ def sync_pred_ts(time_series): except Exception as e: L.error(e) - return {"error": str(e)} + return {"result": "Can't predict", "error": str(e)} diff --git a/app/api/router/train.py b/app/api/router/train.py index cd6fe2a..7798b73 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -56,11 +56,11 @@ def train_insurance( m_process.start() L.info(nni_create_result) - return nni_create_result + return {"msg": nni_create_result, "error": None} except Exception as e: L.error(e) - return {"error": str(e)} + return {"msg": "Can't start experiment", "error": str(e)} @router.put("/atmos") @@ -93,10 +93,8 @@ def train_atmos(expr_name: str): check_expr = ExperimentOwl(expr_id, expr_name, expr_path) check_expr.add("update_tfmodeldb") check_expr.add("modelfile_cleaner") - - m_process = multiprocessing.Process( - target=check_expr.execute - ) + + m_process = multiprocessing.Process(target=check_expr.execute) m_process.start() L.info(nni_create_result) @@ -104,8 +102,8 @@ def train_atmos(expr_name: str): else: L.error(nni_create_result) - return {"error": nni_create_result} + return {"msg": nni_create_result, "error": None} except Exception as e: L.error(e) - return {"error": str(e)} + return {"msg": "Can't start experiment", "error": str(e)} diff --git a/app/utils.py b/app/utils.py index 038f7f2..57fa070 100644 --- a/app/utils.py +++ b/app/utils.py @@ -174,22 +174,22 @@ class NniWatcher: experiment_id를 입력받아 해당 id를 가진 nni 실험을 모니터링하고 모델 파일을 관리해주는 클래스입니다. 생성되는 scikit learn 모델을 DB의 임시 테이블에 저장하여 주기적으로 업데이트 합니다. 이후 실험의 모든 프로세스가 종료되면 가장 성능이 좋은 모델과 점수를 업데이트 합니다. - + Attributes: experiment_id(str): nni experiment를 실행할 때 생성되는 id experiment_name(str): 실험의 이름 experimenter(str): 실험자의 이름 version(str): 실험의 버전 minute(int): 감시 주기 - is_kill(bool, default=True) - is_update(bool, default=True) - top_cnt(int, default=3) - evaluation_criteria(str, default="val_mae") + is_kill(bool, default=True): 실험 감시하며 실험이 끝나면 종료할지 결정하는 변수 + top_cnt(int, default=3): 임시로 최대 몇개의 실험을 저장할지 결정하는 변수 + evaluation_criteria(str, default="val_mae"): 어떤 평가기준으로 모델을 업데이트 할지 결정하는 변수 Examples: >>> watcher = NniWatcher(expr_id, experiment_name, experimenter, version) >>> watcher.execute() """ + def __init__( self, experiment_id, @@ -198,7 +198,6 @@ def __init__( version, minute=1, is_kill=True, - is_update=True, top_cnt=3, evaluation_criteria="val_mae", ): @@ -207,7 +206,6 @@ def __init__( self.experimenter = experimenter self.version = version self.is_kill = is_kill - self.is_update = is_update self.top_cnt = top_cnt self.evaluation_criteria = evaluation_criteria self._wait_minute = minute * 20 @@ -254,8 +252,7 @@ def watch_process(self): break else: - if self.is_update: - self.model_keep_update() + self.model_keep_update() time.sleep(self._wait_minute) def model_keep_update(self): @@ -380,7 +377,7 @@ class ExperimentOwl: 1. (기본)nnictl experiment list(shell command)를 주기적으로 호출하여 실험이 현재 진행중인지 파악합니다. 실험의 상태가 DONE으로 변경되면 최고점수 모델을 데이터베이스에 저장하고 nnictl stop experiment_id를 실행하여 실험을 종료한 후 프로세스가 종료됩니다. - 2. 파일로 생성되는 모델이 너무 많아지지 않도록 유지합니다.(3개 이상 모델이 생성되면 성능순으로 3위 미만은 삭제) instance 생성 시 + 2. 파일로 생성되는 모델이 너무 많아지지 않도록 유지합니다.(3개 이상 모델이 생성되면 성능순으로 3위 미만은 삭제) instance 생성 시 mfile_manage = False로 기능을 사용하지 않을 수 있습니다.(default True) 3. (method) update_tfmodelbd @@ -403,7 +400,9 @@ class ExperimentOwl: >>> owl.execute() """ - def __init__(self, experiment_id, experiment_name, experiment_path, mfile_manage = True, time = 5): + def __init__( + self, experiment_id, experiment_name, experiment_path, mfile_manage=True, time=5 + ): self.__minute = 60 self.time = time * self.__minute self.experiment_id = experiment_id @@ -412,7 +411,6 @@ def __init__(self, experiment_id, experiment_name, experiment_path, mfile_manage self.mfile_manage = mfile_manage self.__func_list = [self.main] - def execute(self): """ instance.add("method name")으로 저장된 메서드들을 순서대로 모두 실행시킵니다. @@ -420,12 +418,10 @@ def execute(self): for func in self.__func_list: func() - def add(self, func_name): func = getattr(self, func_name) self.__func_list.append(func) - def main(self): """ ExperimentOwl클래스로 인스턴스를 생성 후 실행시 기본적으로 실행되는 기능입니다. @@ -437,10 +433,14 @@ def main(self): expr_list = subprocess.getoutput("nnictl experiment list") - running_expr = [expr for expr in expr_list.split("\n") if self.experiment_id in expr] + running_expr = [ + expr for expr in expr_list.split("\n") if self.experiment_id in expr + ] print(running_expr) if running_expr and ("DONE" in running_expr[0]): - stop_expr = subprocess.getoutput("nnictl stop {}".format(self.experiment_id)) + stop_expr = subprocess.getoutput( + "nnictl stop {}".format(self.experiment_id) + ) L.info(stop_expr) break @@ -451,19 +451,22 @@ def main(self): else: if self.mfile_manage: model_path = os.path.join( - self.experiment_path, "temp", "*_{}*".format(self.experiment_name) + self.experiment_path, + "temp", + "*_{}*".format(self.experiment_name), ) exprs = glob.glob(model_path) if len(exprs) > 3: exprs.sort() [shutil.rmtree(_) for _ in exprs[3:]] - def update_tfmodeldb(self): """ 실험이 종료되면 모델을 DB에 저장하거나 이미 같은 이름의 모델이 존재할 시 점수를 비교하여 업데이트 합니다. """ - model_path = os.path.join(self.experiment_path, "temp", "*_{}*".format(self.experiment_name)) + model_path = os.path.join( + self.experiment_path, "temp", "*_{}*".format(self.experiment_name) + ) exprs = glob.glob(model_path) if not exprs: return 0 @@ -476,7 +479,9 @@ def update_tfmodeldb(self): score_sql = """SELECT mae FROM atmos_model_metadata WHERE model_name = '{}' - ORDER BY mae;""".format(self.experiment_name) + ORDER BY mae;""".format( + self.experiment_name + ) saved_score = engine.execute(score_sql).fetchone() if not saved_score or (metrics[0] < saved_score[0]): @@ -486,28 +491,28 @@ def update_tfmodeldb(self): if os.path.exists: shutil.rmtree(winner_model) os.rename(exprs, winner_model) - + m_buffer = zip_model(winner_model) encode_model = codecs.encode(pickle.dumps(m_buffer), "base64").decode() - engine.execute(INSERT_OR_UPDATE_MODEL.format(mn=self.experiment_name, - mf=encode_model)) engine.execute( - INSERT_OR_UPDATE_SCORE.format(mn = self.experiment_name, - expr_id = self.experiment_id, - score1 = metrics[0], - score2 = metrics[1]) + INSERT_OR_UPDATE_MODEL.format(mn=self.experiment_name, mf=encode_model) + ) + engine.execute( + INSERT_OR_UPDATE_SCORE.format( + mn=self.experiment_name, + expr_id=self.experiment_id, + score1=metrics[0], + score2=metrics[1], + ) ) L.info("saved model %s %s" % (self.experiment_id, self.experiment_name)) - def modelfile_cleaner(self): """ temp 폴더에 있는 모든 모델파일을 삭제합니다. 가장 마지막에 실행하여 저장되고 남은 모델파일들을 삭제하는 용도로 사용할 수 있습니다. """ - model_path = os.path.join( - self.experiment_path, "temp", "*" - ) + model_path = os.path.join(self.experiment_path, "temp", "*") exprs = glob.glob(model_path) [shutil.rmtree(_) for _ in exprs] diff --git a/docs/api-list.md b/docs/api-list.md index 87cf666..bd022a6 100644 --- a/docs/api-list.md +++ b/docs/api-list.md @@ -23,8 +23,6 @@ PUT {{API_URL}}/train/insurance | 파라미터 | 파라미터 유형 | 데이터 타입 | 필수 여부 | 설명 | | --------------- | ------------- | ----------- | --------- | ----------------------- | -| `PORT` | `body` | `int` | `(default) 8080` | NNi 포트번호 | -| `experiment_sec` | `body` | `int` | `(default) 20` | 학습시간(초) | | `experiment_name` | `body` | `str` | `(default) exp1` | 학습이름 | | `experimenter` | `body` | `str` | `(default) DongUk` | 연구자 이름 | | `model_name` | `body` | `str` | `(default) insurance_fee_model` | 학습 모델 이름 | @@ -37,15 +35,13 @@ PUT {{API_URL}}/train/insurance | 키 | 데이터 타입 | 설명 | | -------------- | ----------- | ------------- | -| `msg` | `string` | NNi실험을 확인할 수 있는 링크주소 | +| `msg` | `string` | NNI Dashboard 정보 | | `error` | `string` | 에러내용 | ```jsonc { - "result": 'Check out {{API_URL}}:{PORT}', -} -{ + "msg": Info Message, "error": "Error info" } ``` @@ -66,10 +62,13 @@ PUT {{API_URL}}/train/insurance | 키 | 데이터 타입 | 설명 | | -------------- | ----------- | ------------- | +| `msg` | `string` | NNI Dashboard 정보 | | `error` | `string` | 에러내용 | + ```jsonc { + "msg": Info Message, "error": "Error info" } ``` @@ -106,8 +105,6 @@ PUT {{API_URL}}/predict/insurance ```jsonc { "result": 3213.123, -} -{ "error": "Error info" } ``` @@ -132,15 +129,13 @@ PUT {{API_URL}}/predict/atmos | 키 | 데이터 타입 | 설명 | | -------------- | ----------- | ------------- | -| x | `List[float]` | 예측된 향후 24일간 온도값 | +| `result` | `List[float]` | 예측된 향후 24일간 온도값 | | `error` | `string` | 에러내용 | ```jsonc { - [32.32, 33.32, 34.11...] -} -{ + "result": [32.32, 33.32, 34.11...], "error": "Error info" } ``` From c794eff8ee5fe16e8768984d3ee96de1f2fb34d3 Mon Sep 17 00:00:00 2001 From: ehddnr301 Date: Wed, 6 Oct 2021 14:03:35 +0900 Subject: [PATCH 47/47] Fix forgot return part MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit return 수정에서 빠진부분이 있어서 추가하였습니다. --- app/api/router/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/app/api/router/train.py b/app/api/router/train.py index 7798b73..5fda7b8 100644 --- a/app/api/router/train.py +++ b/app/api/router/train.py @@ -98,7 +98,7 @@ def train_atmos(expr_name: str): m_process.start() L.info(nni_create_result) - return nni_create_result + return {"msg": nni_create_result, "error": None} else: L.error(nni_create_result)