Develop

.gitignore

@@ -1 +1,6 @@
-.env
+.env
+*.pkl
+__pycache__
+tf_model/**/*
+log.txt
+experiments/**/temp/

.pre-commit-config.yaml

@@ -1,10 +1,6 @@
 repos:
-
-  - repo: https://github.com/PyCQA/flake8
-    rev: 3.9.2
+  - repo: https://github.com/psf/black
+    rev: 20.8b1
     hooks:
-      - id: flake8
-
-
-
-
+      - id: black
+        language_version: python3

README.md

@@ -1,2 +1,6 @@
 # MLOps
-👊 Build MLOps system step by step 👊
+👊 Build MLOps system step by step 👊
+
+## 문서
+
+- [API DOCS](./docs/api-list.md)

app/api/router/predict.py

@@ -0,0 +1,94 @@
+# -*- coding: utf-8 -*-
+from typing import List
+
+
+import numpy as np
+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 ScikitLearnModel, my_model
+from logger import L
+
+
+models.Base.metadata.create_all(bind=engine)
+
+
+router = APIRouter(
+    prefix="/predict", tags=["predict"], responses={404: {"description": "Not Found"}}
+)
+
+
+@router.put("/insurance")
+async def predict_insurance(info: ModelCorePrediction, model_name: str):
+    """
+    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()
+
+        info = info.dict()
+        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: {model_name}\n\tPrediction Result: {result}"
+        )
+        return result
+
+    except Exception as e:
+        L.error(e)
+        return {"result": "Can't predict", "error": str(e)}
+
+
+@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 {"result": "time series must have 72 values", "error": None}
+
+    def sync_pred_ts(time_series):
+        """
+        none sync 함수를  sync로 만들어 주기 위한 함수이며 입출력은 부모 함수와 같습니다.
+        """
+        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]}"
+        )
+
+        return {"result": result, "error": None}
+
+    try:
+        result = await run_in_threadpool(sync_pred_ts, time_series)
+        return result.tolist()
+
+    except Exception as e:
+        L.error(e)
+        return {"result": "Can't predict", "error": str(e)}

app/api/router/train.py

@@ -0,0 +1,109 @@
+import multiprocessing
+import os
+import re
+import subprocess
+
+
+from fastapi import APIRouter
+
+from app.utils import NniWatcher, ExperimentOwl, base_dir, get_free_port, write_yml
+from logger import L
+
+router = APIRouter(
+    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,
+):
+    """
+    insurance와 관련된 학습을 실행하기 위한 API입니다.
+
+    Args:
+        experiment_name (str): 실험이름. 기본 값: exp1
+        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
+
+    Returns:
+        msg: 실험 실행의 성공과 상관없이 포트번호를 포함한 NNI Dashboard의 주소를 반환합니다.
+
+    Note:
+        실험의 최종 결과를 반환하지 않습니다.
+    """
+    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/"
+    try:
+        write_yml(path, experiment_name, experimenter, model_name, version)
+        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]
+            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)
+            return {"msg": nni_create_result, "error": None}
+
+    except Exception as e:
+        L.error(e)
+        return {"msg": "Can't start experiment", "error": str(e)}
+
+
+@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.
+
+    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]
+            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.start()
+
+            L.info(nni_create_result)
+            return {"msg": nni_create_result, "error": None}
+
+        else:
+            L.error(nni_create_result)
+            return {"msg": nni_create_result, "error": None}
+
+    except Exception as e:
+        L.error(e)
+        return {"msg": "Can't start experiment", "error": str(e)}

app/api/schemas.py

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+from pydantic import BaseModel
+
+
+class ModelCoreBase(BaseModel):
+    model_name: str
+
+
+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
+    children: int
+    smoker: int
+    region: int
+
+
+class ModelCore(ModelCoreBase):
+    class Config:
+        orm_mode = True

app/database.py

@@ -1,31 +1,50 @@
 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
+load_dotenv(verbose=True)
 
 
 def connect(db):
+    """
+    database와의 연결을 위한 함수 입니다.
+
+    Args:
+        db(str): 사용할 데이터베이스의 이름을 전달받습니다.
 
-    load_dotenv(verbose=True)
+    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)
 
     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 = 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)
 
     return connection
 
 
 POSTGRES_DB = os.getenv("POSTGRES_DB")
-
 engine = connect(POSTGRES_DB)
 SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
 Base = declarative_base()

app/models.py

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+# -*- 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
+
+from app.database import Base
+
+KST = datetime.timezone(datetime.timedelta(hours=9))
+
+
+class ModelCore(Base):
+    __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"
+    )
+
+
+class ModelMetadata(Base):
+    __tablename__ = "model_metadata"
+
+    experiment_name = Column(String, primary_key=True)
+    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)
+    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())
+
+
+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)