ADO artifact of Recommenders in the three environments

reco_utils/__init__.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT License.
 
 __title__ = "Microsoft Recommenders"
-__version__ = "2019.09"
+__version__ = "2019.9"
 __author__ = "RecoDev Team at Microsoft"
 __license__ = "MIT"
 __copyright__ = "Copyright 2018-present Microsoft Corporation"

scripts/generate_conda_file.py

@@ -45,7 +45,7 @@
     "mock": "mock==2.0.0",
     "numpy": "numpy>=1.13.3",
     "pandas": "pandas>=0.23.4,<1.0.0",
-    "pip": "pip>=19.0.3",
+    "pip": "pip>=19.2",
     "pytest": "pytest>=3.6.4",
     "pytorch": "pytorch-cpu>=1.0.0",
     "seaborn": "seaborn>=0.8.1",
@@ -84,7 +84,7 @@
     "nbconvert": "nbconvert==5.5.0",
     "pydocumentdb": "pydocumentdb>=2.3.3",
     "pymanopt": "pymanopt==0.2.3",
-    "xlearn": "xlearn==0.40a1"
+    "xlearn": "xlearn==0.40a1",
 }
 
 PIP_GPU = {"nvidia-ml-py3": "nvidia-ml-py3>=7.352.0"}

setup.py

@@ -1,19 +1,26 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
-from setuptools import setup
-from os import chdir, path
+import time
+from setuptools import setup, find_packages
+from os import chdir, path, environ
 
 chdir(path.abspath(path.dirname(__file__)))
-VERSION = __import__("reco_utils.__init__").VERSION
+version = __import__("reco_utils.__init__").VERSION
 
 # Get the long description from the README file
 with open(path.join("reco_utils", "README.md"), encoding="utf-8") as f:
     LONG_DESCRIPTION = f.read()
 
+HASH = environ.get("HASH", None)
+if HASH is not None:
+    version += ".post" + str(int(time.time()))
+
+name = environ.get("NAME", "reco_utils")
+
 setup(
-    name="reco_utils",
-    version=VERSION,
+    name=name,
+    version=version,
     description="Recommender System Utilities",
     long_description=LONG_DESCRIPTION,
     long_description_content_type="text/markdown",
@@ -30,6 +37,8 @@
         "Programming Language :: Python :: 3.6",
     ],
     keywords="recommendations recommenders recommender system engine machine learning python spark gpu",
-    packages=["reco_utils"],
+    package_dir={"reco_utils": "reco_utils"},
+    packages=find_packages(where=".", exclude=["tests", "scripts"]),
     python_requires=">=3.6, <4",
 )
+

tests/ci/azure_artifact_feed.yaml

@@ -0,0 +1,99 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+# To install recommenders artifact (with manual login to Azure):
+# $ pip install artifacts-keyring --pre
+# Choose the version to install based on your environment:
+# $ pip install reco-utils --index-url=https://pkgs.dev.azure.com/best-practices/recommenders/_packaging/recommenders/pypi/simple/
+# $ pip install reco-utils-gpu --index-url=https://pkgs.dev.azure.com/best-practices/recommenders/_packaging/recommenders/pypi/simple/
+# $ pip install reco-utils-pyspark --index-url=https://pkgs.dev.azure.com/best-practices/recommenders/_packaging/recommenders/pypi/simple/
+
+# To install recommenders artifact programmatically with Personal Access Token (PAT):
+# First generate the PAT:
+# 1. Go to the Personal Access Tokens screen in ADO => User (icon in top right)
+# 2. Generate a new token, with at least Packages/Read permissions selected
+# 3. Add the PAT in the index-url=https://{feed_name}:{PAT}@pkgs.dev.azure.com/{org_name}/{project_name}/_packaging/{feed_name}/pypi/simple/
+# Install dependencies and Recommenders library
+# $ pip install artifacts-keyring --pre
+# Choose the version to install based on your environment:
+# $ pip install reco-utils --index-url=https://recommenders:{PAT}@pkgs.dev.azure.com/best-practices/recommenders/_packaging/recommenders/pypi/simple/
+# $ pip install reco-utils-gpu --index-url=https://recommenders:{PAT}@pkgs.dev.azure.com/best-practices/recommenders/_packaging/recommenders/pypi/simple/
+# $ pip install reco-utils-pyspark --index-url=https://recommenders:{PAT}@pkgs.dev.azure.com/best-practices/recommenders/_packaging/recommenders/pypi/simple/
+
+# NOTE: 
+# This yaml file is used to generate ADO artifacts for Recommenders library.
+# We use the same yaml file to generate the library in different environments: CPU, GPU and PySpark
+# When setting the pipeline, we need to define the variables env_name, env_flag and library_name
+# To add the variables, go to the pipeline, press edit and then variables.
+# An example of the variables we use in the GPU environment:
+# env_name=artifact_reco_gpu
+# env_flag=--gpu
+# library_name=reco_utils_gpu
+
+# The global variables are defined in ADO/Pipelines/Library
+# https://dev.azure.com/best-practices/recommenders/_library?itemType=VariableGroups
+variables:
+- group: LinuxAgentPool
+
+# The pipeline will be triggered when we merge to master
+pr: none
+trigger:
+- master
+
+jobs:
+- job: Artifact
+  displayName: 'Create Recommenders artifact'
+  timeoutInMinutes: 30 # how long to run the job before automatically cancelling
+  pool:
+    name: $(Agent_Pool)
+
+  steps:
+  - bash: |
+      echo "##vso[task.prependpath]/data/anaconda/bin"
+      conda env list
+    displayName: 'Add Conda to PATH'
+
+  - script: |
+      conda env remove -n $(env_name) -y   
+    workingDirectory: tests
+    displayName: 'Conda remove'
+    continueOnError: true
+    condition: always() # this step will always run, even if the pipeline is canceled
+
+  - script: |
+      python ./scripts/generate_conda_file.py --name $(env_name) $(env_flag)
+      conda env create --quiet -f $(env_name).yaml 2> log
+    displayName: 'Setup Conda Env'
+
+  - script: |
+      . /anaconda/etc/profile.d/conda.sh && \
+      conda activate $(env_name) && \
+      pip install wheel twine keyring artifacts-keyring && \
+      pip list
+    displayName: 'Install dependencies'
+
+  - script: |
+      . /anaconda/etc/profile.d/conda.sh && \
+      conda activate $(env_name) && \
+      rm -rf dist && \
+      HASH=True NAME=$(library_name) python setup.py sdist bdist_wheel
+    displayName: 'Build wheel'
+
+  - task: TwineAuthenticate@1
+    inputs:
+      artifactFeed: recommenders/recommenders
+    displayName: 'Twine Authenticate'
+
+  # Artifact package: https://dev.azure.com/best-practices/recommenders/_packaging?_a=feed&feed=recommenders
+  - script: |
+      . /anaconda/etc/profile.d/conda.sh && \
+      conda activate $(env_name) && \
+      python -m twine upload -r recommenders --config-file $(PYPIRC_PATH) dist/*.whl --verbose
+    displayName: 'Upload wheel'
+
+  - script: |
+      conda env remove -n $(env_name) -y   
+    workingDirectory: tests
+    displayName: 'Conda remove'
+    continueOnError: true
+    condition: always() # this step will always run, even if the pipeline is canceled