11name : model-deploy-on-release
22on :
33 release :
4- types : [published]
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 : ${{ secrets.IBM_CREDENTIALS_PASS }}
18- run : |
19- # Install requirements
20- pip install -r requirements.txt
21-
22- # Pull data & run-cache from S3 and reproduce pipeline
23- dvc pull --run-cache
24- dvc repro
25-
26- # Decrypt credentials file
27- gpg --quiet --batch --yes --decrypt --passphrase="$CRED_SECRET" --output credentials.yaml credentials.yaml.gpg
28-
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
4+ types :
5+ - ' published'
6+
7+ jobs :
8+ run :
9+ runs-on : [ubuntu-latest]
10+ container : docker://dvcorg/cml-py3:latest
11+
12+ steps :
13+ - uses : actions/checkout@v2
14+ - name : ' Deploy/Update on new release'
15+
16+ shell : bash
17+
18+ env :
19+ repo_token : ${{ secrets.GITHUB_TOKEN }}
20+ AWS_ACCESS_KEY_ID : ${{ secrets.AWS_ACCESS_KEY_ID }}
21+ AWS_SECRET_ACCESS_KEY : ${{ secrets.AWS_SECRET_ACCESS_KEY }}
22+ CRED_SECRET : ${{ secrets.IBM_CREDENTIALS_PASS }}
23+
24+ run : |
25+ # Install requirements
26+ pip install -r requirements.txt
27+
28+ # Pull data & run-cache from S3 and reproduce pipeline
29+ dvc pull --run-cache
30+ dvc repro
31+
32+ # Decrypt credentials file
33+ gpg --quiet --batch --yes --decrypt --passphrase="$CRED_SECRET" --output credentials.yaml credentials.yaml.gpg
34+
35+ # Check if there is a deployment already, if positive update it, otherwise deploys it for the first time
36+ ./src/scripts/Scripts/git_release_pipeline.sh
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