tensorboard_component.yaml

#!/usr/bin/env/python3
# Copyright (c) Facebook, Inc. and its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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name: Create Tensorboard visualization
description: |
  Pre-creates Tensorboard visualization for a given Log dir URI.
  This way the Tensorboard can be viewed before the training completes.
  The output Log dir URI should be passed to a trainer component that will write Tensorboard logs to that directory.
inputs:
- {name: Log dir URI, description: 'Tensorboard log path'}
- {name: Image, default: '', description: 'Tensorboard docker image'}
- {name: Pod Template Spec, default: 'null', description: 'Pod template specification'}
outputs:
- {name: Log dir URI, description: 'Tensorboard log output'}
- {name: MLPipeline UI Metadata, description: 'MLPipeline UI Metadata output'}
implementation:
  container:
    image: public.ecr.aws/pytorch-samples/alpine:latest
    command:
    - sh
    - -ex
    - -c
    - |
      log_dir="$0"
      output_log_dir_path="$1"
      output_metadata_path="$2"
      pod_template_spec="$3"
      image="$4"

      mkdir -p "$(dirname "$output_log_dir_path")"
      mkdir -p "$(dirname "$output_metadata_path")"
      echo "$log_dir" > "$output_log_dir_path"

      echo '
          {
            "outputs" : [{
              "type": "tensorboard",
              "source": "'"$log_dir"'",
              "image": "'"$image"'",
              "pod_template_spec": '"$pod_template_spec"'
            }]
          }
      ' >"$output_metadata_path"
    - {inputValue: Log dir URI}
    - {outputPath: Log dir URI}
    - {outputPath: MLPipeline UI Metadata}
    - {inputValue: Pod Template Spec}
    - {inputValue: Image}