Mlops-Sagemaker-integration
MLFlow provides explicit AWS SageMaker support in its operationalization code.
create --prefix ./env python=3.8 -y && conda activate ./env
Run 2-3 times with different alpha and li ratio
python main.py alpha l1_ratio
- Install AWS CLI
- Open Cmd : aws configure
- Paste Public key , private key and Region
- Run below command
- AmazonEC2ContainerRegistryFullAccess
- AmazonS3FullAccess
- EC2InstanceProfileForImageBuilderECRContainerBuilds
- AWSAppRunnerServicePolicyForECRAccess
Create Image and Upload on docker
mlflow sagemaker build-and-push-container
mlflow server
--backend-store-uri mysql://admin:pass@endpoint/DB-name
--default-artifact-root ./artifacts
--host 127.0.0.1
-p 5000
Send Ml-runs on Aws-S3 Bucket
python utils/sagemaker_integration.py
To build and Push Container
mlflow sagemaker build-and-push-container
Check Errors If occurred try:
docker.errors.DockerException: Install pypiwin32 package to enable npipe:// support
Solution : Instead of pip install pypiwin32 Try : conda install -c anaconda pywin32
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