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test tag and push
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Michael Duong
parent
eb41a8d1a0
commit
cc2079403a
2 changed files with 100 additions and 92 deletions
172
.github/workflows/MLPipelinePostMerge.yml
vendored
172
.github/workflows/MLPipelinePostMerge.yml
vendored
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@ -1,58 +1,95 @@
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name: Register the model for the given pipeline branch
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# on:
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# push:
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# branches:
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# - "model-**"
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on:
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pull_request:
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types:
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- closed
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push:
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branches:
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- "master"
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- "model-**"
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# on:
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# pull_request:
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# types:
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# - closed
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# branches:
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# - "master"
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permissions: write-all
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jobs:
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Promote-Model-To-Dev:
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if: github.event.pull_request.merged == true
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runs-on: ubuntu-latest
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# Promote-Model-To-Dev:
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# if: github.event.pull_request.merged == true
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# runs-on: ubuntu-latest
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# steps:
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# - uses: actions/checkout@v3
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# - name: Install packages to retrieve artifacts
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# run: |
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# pip install --upgrade pip
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# pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
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# - name: Retrieve artifacts (dvc.lock)
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# env:
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# AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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# AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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# run: |
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# cd modules/ml-pipeline/src/pipeline/src
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# dvc pull -r experiments
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# - name: Push artifacts to Dev
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# env:
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# AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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# AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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# run: |
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# cd modules/ml-pipeline/src/pipeline/src
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# dvc push -r dev
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Register-New-Model-Dev:
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# if: github.event.pull_request.merged == true
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- name: Install packages to retrieve artifacts
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- uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Install packages to register model
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env:
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AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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run: |
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pip install --upgrade pip
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pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
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- name: Retrieve artifacts (dvc.lock)
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- name: Register Model
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env:
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AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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run: |
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cd modules/ml-pipeline/src/pipeline/src
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dvc pull -r experiments
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- name: Push artifacts to Dev
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env:
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AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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run: |
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cd modules/ml-pipeline/src/pipeline/src
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dvc push -r dev
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REGISTER_MODEL_NAME=$(echo ${{ github.event.pull_request.head.ref }} | awk -F"-" '{print $1}')
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# REGISTER_MODEL_NAME=$(echo ${{github.ref_name}} | awk -F"-" '{print $1}')
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git config user.name "Github-Bot"
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git config user.email "Github-Bot@no-reply.com"
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# Register-New-Model-Dev:
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# if: github.event.pull_request.merged == true
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git tag model@v0.0.1
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git push origin model@0.0.1
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# gto register test --repo https://github.com/Hestia-Homes/ML/
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# echo "chicken" >> test.md
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# gto register ${REGISTER_MODEL_NAME}
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# gto assign regression --stage dev
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# gto show
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# Register-Prediction-Image-Dev:
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# needs: Promote-Model-To-Dev
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# # needs: [Promote-Model-To-Dev, Register-New-Model-Dev] WILL ADD BACK ONCE REGISTER WORKS
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# runs-on: ubuntu-latest
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# steps:
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# - uses: actions/checkout@v2
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# with:
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# fetch-depth: 0
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# - name: Install packages to register model
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# steps:
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# - uses: actions/checkout@v3
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# - name: Install packages to retrieve artifacts
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# env:
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# AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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# AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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@ -60,65 +97,30 @@ jobs:
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# pip install --upgrade pip
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# pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
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# - name: Register Model
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# - name: Retrieve artifacts (dvc.lock)
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# env:
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# AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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# AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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# run: |
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# cd modules/ml-pipeline/src/pipeline/src
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# dvc pull -r dev
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# - name: Build Prediction docker image (TODO - NEED LAMBDA IMAGE, need to add version from gto registry)
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# run: |
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# cd modules/ml-pipeline/src/pipeline/
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# REGISTER_MODEL_NAME=$(echo ${{ github.event.pull_request.head.ref }} | awk -F"-" '{print $1}')
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# # REGISTER_MODEL_NAME=$(echo ${{github.ref_name}} | awk -F"-" '{print $1}')
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# docker build . --file Prediction.Dockerfile --tag ${REGISTER_MODEL_NAME}
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# git config user.name "Github-Bot"
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# git config user.email "Github-Bot@no-reply.com"
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# - name: ECR Login - Dev
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# env:
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# AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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# AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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# run: |
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# echo "LOGIN TO ECR"
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# # gto register test --repo https://github.com/Hestia-Homes/ML/
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# # echo "chicken" >> test.md
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# git checkout master
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# gto register ${REGISTER_MODEL_NAME}
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# gto assign regression --stage dev
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# gto show
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Register-Prediction-Image-Dev:
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needs: Promote-Model-To-Dev
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# needs: [Promote-Model-To-Dev, Register-New-Model-Dev] WILL ADD BACK ONCE REGISTER WORKS
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- name: Install packages to retrieve artifacts
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env:
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AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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run: |
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pip install --upgrade pip
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pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
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- name: Retrieve artifacts (dvc.lock)
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env:
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AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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run: |
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cd modules/ml-pipeline/src/pipeline/src
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dvc pull -r dev
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- name: Build Prediction docker image (TODO - NEED LAMBDA IMAGE, need to add version from gto registry)
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run: |
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cd modules/ml-pipeline/src/pipeline/
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REGISTER_MODEL_NAME=$(echo ${{ github.event.pull_request.head.ref }} | awk -F"-" '{print $1}')
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docker build . --file Prediction.Dockerfile --tag ${REGISTER_MODEL_NAME}
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- name: ECR Login - Dev
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env:
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AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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run: |
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echo "LOGIN TO ECR"
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- name: Push Prediction image to ECR - Dev
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env:
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AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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run: |
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echo "PUSH TO ECR"
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# - name: Push Prediction image to ECR - Dev
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# env:
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# AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
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# AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
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# run: |
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# echo "PUSH TO ECR"
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@ -50,7 +50,7 @@ def prepare_data(
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new_feature_funcs: dict,
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output_train_filepath: str = "train.parquet",
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output_test_filepath: str = "test.parquet",
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) -> Tuple[pd.DataFrame, pd.DataFrame]:
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) -> Tuple[pd.DataFrame, Union[pd.DataFrame, None]]:
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"""
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Given a client and location, load data into the pipeline
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:param dataclient: DataClient, Determines how to get data from the given provider (cloud or local)
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logger.info("--- Splitting data ---")
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logger.info("----------------------")
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train, test = train_test_split(
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data, train_size=train_proportion, test_size=(1 - train_proportion)
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)
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if train_proportion == 1:
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train = data
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test = None
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else:
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train, test = train_test_split(
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data, train_size=train_proportion, test_size=(1 - train_proportion)
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)
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logger.info("-----------------------")
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logger.info("--- Outputting data ---")
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datahandler.save_data(
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dataclient=output_dataclient, obj=train, location=output_train_filepath
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)
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datahandler.save_data(
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dataclient=output_dataclient, obj=test, location=output_test_filepath
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)
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if test:
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datahandler.save_data(
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dataclient=output_dataclient, obj=test, location=output_test_filepath
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)
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return train, test
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