pipeline.yml

$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
type: pipeline

experiment_name: "tensorflow_unet_pets"

# <jobs>
settings:
  default_datastore: azureml:workspaceblobstore
  continue_on_step_failure: true

jobs:
  prepare_data:
    type: command
    command: |
      tar xvfm ${{inputs.images_archive}} --no-same-owner -C ${{outputs.images_and_annotations}} &&
      tar xvfm ${{inputs.annotations_archive}} --no-same-owner -C ${{outputs.images_and_annotations}}

    inputs:
      images_archive:
        type: uri_file
        path: https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
      annotations_archive:
        type: uri_file
        path: https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz

    outputs:
      images_and_annotations:
        type: uri_folder
        mode: upload

    environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest

    compute: azureml:cpu-cluster

  train:
    type: command
    code: ./src/

    compute: azureml:gpu-cluster
    resources:
      instance_count: 1 # number of nodes
    distribution:
      # NOTE: using type:tensorflow will use all the right env variables (ex: TF_CONFIG)
      type: tensorflow
      worker_count: 1 # needs to match instance_count (!)

    environment: azureml://registries/azureml/environments/tensorflow-2.16-cuda11/labels/latest
    # uncomment below to use custom environment
    # environment:
    #   build: 
    #     path: ./environments/nvidia_tensorflow/

    # NOTE: set env var if needed
    environment_variables:
      # adjusts the level of info from NCCL tests
      NCCL_DEBUG: "INFO"
      NCCL_DEBUG_SUBSYS: "GRAPH,INIT,ENV"

      # relaxed Ordering can greatly help the performance of Infiniband networks in virtualized environments.
      NCCL_IB_PCI_RELAXED_ORDERING: "1"
      CUDA_DEVICE_ORDER: "PCI_BUS_ID"
      NCCL_SOCKET_IFNAME: "eth0"

    inputs:
      # data inputs
      images_and_annotations: ${{parent.jobs.prepare_data.outputs.images_and_annotations}}

      # oxford pets specifics
      images_type: "jpg"
      images_filename_pattern: "(.*)\\.jpg"
      masks_filename_pattern: "(.*)\\.png"
      num_classes: 3

      # data loading
      batch_size: 64
      num_workers: 5 # int or -1 (AUTOTUNE)
      prefetch_factor: 8 # int or -1 (AUTOTUNE)
      cache: "none" # "none" or "memory"

      # model
      model_arch: "unet"
      model_input_size: 160

      # training
      num_epochs: 7
      optimizer: "rmsprop"
      loss: "sparse_categorical_crossentropy"

      # distributed settings
      enable_profiling: False
      disable_cuda: False # to force disabling CUDA/GPU
      num_gpus: -1 # put n>=0 to artificially limit number of gpus
      distributed_strategy: "auto" # "auto" (recommended)
      distributed_backend: "nccl" # "auto", "ring" or "nccl" (recommended)

    outputs:
        checkpoints: # Path to export checkpoints
            type: uri_folder
        trained_model: # Path to the final model
            type: uri_folder

    command: >-
        python run.py 
        --train_images ${{inputs.images_and_annotations}}/images
        --train_masks ${{inputs.images_and_annotations}}/annotations/trimaps
        --test_images ${{inputs.images_and_annotations}}/images
        --test_masks ${{inputs.images_and_annotations}}/annotations/trimaps
        --images_filename_pattern "${{inputs.images_filename_pattern}}"
        --masks_filename_pattern "${{inputs.masks_filename_pattern}}"
        --batch_size ${{inputs.batch_size}}
        --num_workers ${{inputs.num_workers}}
        --prefetch_factor ${{inputs.prefetch_factor}}
        --cache ${{inputs.cache}}
        --model_arch ${{inputs.model_arch}}
        --num_classes ${{inputs.num_classes}}
        --model_input_size ${{inputs.model_input_size}}
        --num_epochs ${{inputs.num_epochs}}
        --optimizer ${{inputs.optimizer}}
        --loss ${{inputs.loss}}
        --num_gpus ${{inputs.num_gpus}}
        --model_output ${{outputs.trained_model}}
        --checkpoints ${{outputs.checkpoints}}
        --distributed_strategy ${{inputs.distributed_strategy}}
        --distributed_backend ${{inputs.distributed_backend}}
        --enable_profiling ${{inputs.enable_profiling}}

# </jobs>