Category : kubeflow

I am using ubuntu 20.04 while following book -> https://www.oreilly.com/library/view/kubeflow-for-machine/9781492050117/ on page 17, it says the following (only relevant parts) which I don’t understand…. You will want to store container images called a container registry. The container registry will be accessed by your Kubeflow cluster. I am going to use docker hub as container registry. ..

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I’m currently trying to deploy a pipeline on Kubeflow, but everytime I start it, it returns: This step is in Failed state with this message: OCI runtime create failed: container_linux.go:345: starting container process caused "exec: "python /usr/src/app/FeatureExtractor.py": stat python /usr/src/app/FeatureExtractor.py: no such file or directory": unknown This is my pipeline: it currently fails on all ..

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This is my cloudbuild.json { "steps": [ { "name": "gcr.io/cloud-builders/docker", "args": [ "build", "-t", "trainer_image", "." ], "dir": "./trainer_image/" }, { "name": "gcr.io/cloud-builders/docker", "args": [ "build", "-t", "base_image", "." ], "dir": "./base_image/" }, { "name": "gcr.io/dmgcp-pkg-internal-poc-oct-04/kfp-cli", "args": [ "dsl-compile –py covertype_training_pipeline.py –output covertype_training_pipeline.yaml" ], "env": [ "BASE_IMAGE=gcr.io/dmgcp-pkg-internal-poc-oct-04/base_image:test", "TRAINER_IMAGE=gcr.io/dmgcp-pkg-internal-poc-oct-04/trainer_image:test", "RUNTIME_VERSION=1.15", "PYTHON_VERSION=3.7", "COMPONENT_URL_SEARCH_PREFIX=https://raw.githubusercontent.com/kubeflow/pipelines/0.2.5/components/gcp/", "USE_KFP_SA=False" ], "dir": "./pipeline/" ..

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here is my cloudbuild.yaml file – name: ‘gcr.io/cloud-builders/docker’ args: [‘build’, ‘-t’, ‘gcr.io/$PROJECT_ID/$_TRAINER_IMAGE_NAME:$TAG_NAME’, ‘.’] dir: $_PIPELINE_FOLDER/trainer_image # Build the base image for lightweight components – name: ‘gcr.io/cloud-builders/docker’ args: [‘build’, ‘-t’, ‘gcr.io/$PROJECT_ID/$_BASE_IMAGE_NAME:$TAG_NAME’, ‘.’] dir: $_PIPELINE_FOLDER/base_image # Compile the pipeline – name: ‘gcr.io/$PROJECT_ID/kfp-cli’ args: – ‘-c’ – | dsl-compile –py $_PIPELINE_DSL –output $_PIPELINE_PACKAGE env: – ‘BASE_IMAGE=gcr.io/$PROJECT_ID/$_BASE_IMAGE_NAME:$TAG_NAME’ – ‘TRAINER_IMAGE=gcr.io/$PROJECT_ID/$_TRAINER_IMAGE_NAME:$TAG_NAME’ ..

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