pose_detection_train.yaml

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# SPDX-License-Identifier: MIT

title: Pose Detection Train
version: 1.0.0
description: "Train a model with your custom data to detect human body poses in images and videos"
long_description: "Train a tailored model using your custom data, and deploy an endpoint, which detects human body poses and their position coordinates in images and videos."
author: "cnvrg"
author_email: "info@cnvrg.io"

tags:
  - training
tasks:
  - title: S3 Connector
    top: 300
    left: 0

    # Type must be according to the flow task conventions (data, exec, deploy)
    type: exec

    # The library data
    library: s3-connector
    library_version: 1.0.0

    # The flattened library parameters (in this case we have training library)
    command: python s3-connector.py

    requirements:
      cpu: 3.5
      memory: 8
      gpu: 0
      hpu: 0

    image: cnvrg:v5.0
    language: python3

    # The "arguments" property changes to "params", rest remains the same.
    # Params in the blueprint yaml can be used to override the arguments in the library.
    params:
      - key: endpoint
        type: 'categorical'
        values:
          - 'http://s3.amazonaws.com download'
      - key: bucketname
        type: 'categorical'
        values:
          - 'libhub-readme'
      - key: localdir
        type: 'categorical'
        values:
          - '/cnvrg'
      - key: prefix
        type: 'categorical'
        values:
          - 'pose_detection_data/'
  - title: Train Test Split
    top: 400
    left: 100

    # Type must be according to the flow task conventions (data, exec, deploy)
    type: exec

    # The library data
    library: pose-detection-recreate
    library_version: 1.0.0

    # The flattened library parameters (in this case we have training library)
    command: python3 recreate.py

    requirements:
      cpu: 3.5
      memory: 8
      gpu: 0
      hpu: 0

    image: cnvrg:v5.0
    language: python3

    # The "arguments" property changes to "params", rest remains the same.
    # Params in the blueprint yaml can be used to override the arguments in the library.
    params:
      - key: images
        type: 'categorical'
        values:
          - '/input/s3_connector/pose_detection_data'
  - title: Train
    top: 400
    left: 300

    # Type must be according to the flow task conventions (data, exec, deploy)
    type: exec

    # The library data
    library: pose-detection-train
    library_version: 1.0.0

    # The flattened library parameters (in this case we have training library)
    command: python3 pose.py

    requirements:
      cpu: 3.5
      memory: 8
      gpu: 0
      hpu: 0

    image: cnvrg:v5.0
    language: python3

    # The "arguments" property changes to "params", rest remains the same.
    # Params in the blueprint yaml can be used to override the arguments in the library.
    params:
      - key: train_dir
        type: 'categorical'
        values:
          - '/input/train_test_split/train/'
      - key: test_dir
        type: 'categorical'
        values:
          - '/input/train_test_split/test/'
  - title: Classify
    top: 400
    left: 500

    # Type must be according to the flow task conventions (data, exec, deploy)
    type: exec

    # The library data
    library: pose-detection-classification
    library_version: 1.0.0

    # The flattened library parameters (in this case we have training library)
    command: python3 pose_classify.py

    requirements:
      cpu: 3.5
      memory: 8
      gpu: 0
      hpu: 0

    image: cnvrg:v5.0
    language: python3

    # The "arguments" property changes to "params", rest remains the same.
    # Params in the blueprint yaml can be used to override the arguments in the library.
    params:
      - key: train_dir
        type: 'categorical'
        values:
          - '/input/train/train_data.csv'
      - key: test_dir
        type: 'categorical'
        values:
          - '/input/train/test_data.csv'
      - key: test_dir_img
        type: 'categorical'
        values:
          - '/input/train/poses_images_out_test/'
      - key: box_file
        type: 'categorical'
        values:
          - '/input/train/box_file.csv'
      - key: optimizer_1
        type: 'categorical'
        value:
          - 'adam'
      - key: loss_1
        type: 'categorical'
        values:
          - 'categorical_crossentropy'
      - key: epoch_1
        type: 'discrete'
        values:
          - '200'
      - key: patience_1
        type: 'discrete'
        values:
          - '20'
  - title: Deploy Endpoint
    top: 400
    left: 700

    # Type must be according to the flow task conventions (data, exec, deploy)
    type: deploy

    # The library data
    library: pose-detect-inference
    library_version: 1.0.0

    # The flattened library parameters (in this case we have inference library)
    kind: webservice

    requirements:
      cpu: 1
      memory: 4
      gpu: 0
      hpu: 0

    image: cnvrg:v5.0
    language: python3
    accept_files: false
    gunicorn_config:
      - key: workers
        value: '1'
    file_name: predict.py # the entrypoint file name
    function_name: predict # the entrypoint function
    prep_file: '' # preprocess file name
    prep_function: '' # preprocess function
    input_example:
      img: txt
    input_schema:
      img: file
    output_schema: {}
      
      
relations:
  - from: S3 Connector
    to: Train Test Split
  - from: Train Test Split
    to: Train
  - from: Train
    to: Classify
  - from: Classify
    to: Deploy Endpoint