ML/.github/workflows/MLPipelinePostMerge.yml
2023-09-12 23:08:38 +01:00

124 lines
4 KiB
YAML

name: Register the model for the given pipeline branch
# on:
# push:
# branches:
# - "model-**"
on:
pull_request:
types:
- closed
branches:
- "master"
permissions: write-all
jobs:
Promote-Model-To-Dev:
if: github.event.pull_request.merged == true
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Install packages to retrieve artifacts
run: |
pip install --upgrade pip
pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
- name: Retrieve artifacts (dvc.lock)
env:
AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
run: |
cd modules/ml-pipeline/src/pipeline/src
dvc pull -r experiments
- name: Push artifacts to Dev
env:
AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
run: |
cd modules/ml-pipeline/src/pipeline/src
dvc push -r dev
# Register-New-Model-Dev:
# if: github.event.pull_request.merged == true
# runs-on: ubuntu-latest
# steps:
# - uses: actions/checkout@v2
# with:
# fetch-depth: 0
# - name: Install packages to register model
# env:
# AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
# AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
# run: |
# pip install --upgrade pip
# pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
# - name: Register Model
# env:
# AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
# AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
# run: |
# REGISTER_MODEL_NAME=$(echo ${{ github.event.pull_request.head.ref }} | awk -F"-" '{print $1}')
# # REGISTER_MODEL_NAME=$(echo ${{github.ref_name}} | awk -F"-" '{print $1}')
# git config user.name "Github-Bot"
# git config user.email "Github-Bot@no-reply.com"
# # gto register test --repo https://github.com/Hestia-Homes/ML/
# # echo "chicken" >> test.md
# git checkout master
# gto register ${REGISTER_MODEL_NAME}
# gto assign regression --stage dev
# gto show
Register-Prediction-Image-Dev:
needs: Promote-Model-To-Dev
# needs: [Promote-Model-To-Dev, Register-New-Model-Dev] WILL ADD BACK ONCE REGISTER WORKS
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Install packages to retrieve artifacts
env:
AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
run: |
pip install --upgrade pip
pip install -r modules/ml-pipeline/src/pipeline/src/requirements/version_control/requirements.txt
- name: Retrieve artifacts (dvc.lock)
env:
AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
run: |
cd modules/ml-pipeline/src/pipeline/src
dvc pull -r dev
- name: Build Prediction docker image (TODO - NEED LAMBDA IMAGE, need to add version from gto registry)
run: |
cd modules/ml-pipeline/src/pipeline/
REGISTER_MODEL_NAME=$(echo ${{ github.event.pull_request.head.ref }} | awk -F"-" '{print $1}')
docker build . --file Prediction.Dockerfile --tag ${REGISTER_MODEL_NAME}
- name: ECR Login - Dev
env:
AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
run: |
echo "LOGIN TO ECR"
- name: Push Prediction image to ECR - Dev
env:
AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
run: |
echo "PUSH TO ECR"