@@ -2,29 +2,31 @@ name: model-deploy-on-release
22on :
33 release :
44 types : [published]
5- runs-on : [ubuntu-latest]
6- container : docker://dvcorg/cml-py3:latest
7- steps :
8- - uses : actions/checkout@v2
9- - name : ' Train and Evaluate model'
10- shell : bash
11- env :
12- repo_token : ${{ secrets.GITHUB_TOKEN }}
13- AWS_ACCESS_KEY_ID : ${{ secrets.AWS_ACCESS_KEY_ID }}
14- AWS_SECRET_ACCESS_KEY : ${{ secrets.AWS_SECRET_ACCESS_KEY }}
15- CRED_SECRET : ${{ secrets.IBM_CREDENTIALS_PASS }}
16- run : |
17- # Install requirements
18- pip install -r requirements.txt
5+ jobs :
6+ run :
7+ runs-on : [ubuntu-latest]
8+ container : docker://dvcorg/cml-py3:latest
9+ steps :
10+ - uses : actions/checkout@v2
11+ - name : ' Train and Evaluate model'
12+ shell : bash
13+ env :
14+ repo_token : ${{ secrets.GITHUB_TOKEN }}
15+ AWS_ACCESS_KEY_ID : ${{ secrets.AWS_ACCESS_KEY_ID }}
16+ AWS_SECRET_ACCESS_KEY : ${{ secrets.AWS_SECRET_ACCESS_KEY }}
17+ CRED_SECRET : ${{ secrets.IBM_CREDENTIALS_PASS }}
18+ run : |
19+ # Install requirements
20+ pip install -r requirements.txt
1921
20- # Pull data & run-cache from S3 and reproduce pipeline
21- dvc pull --run-cache
22- dvc repro
22+ # Pull data & run-cache from S3 and reproduce pipeline
23+ dvc pull --run-cache
24+ dvc repro
2325
24- # Decrypt credentials file
25- gpg --quiet --batch --yes --decrypt --passphrase="$CRED_SECRET" --output credentials.yaml credentials.yaml.gpg
26+ # Decrypt credentials file
27+ gpg --quiet --batch --yes --decrypt --passphrase="$CRED_SECRET" --output credentials.yaml credentials.yaml.gpg
2628
27- # Check if there is a deployment already, if positive update it, otherwise deploys it for the first time
28- ./src/scripts/Scripts/git_release_pipeline.sh
29+ # Check if there is a deployment already, if positive update it, otherwise deploys it for the first time
30+ ./src/scripts/Scripts/git_release_pipeline.sh
2931
30-
32+
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