Skema YAML pekerjaan klasifikasi multi-Label gambar ML otomatis CLI (v2)

Artikel
04/04/2023

BERLAKU UNTUK:Ekstensi ml Azure CLI v2 (saat ini)

Skema JSON sumber dapat ditemukan di https://azuremlsdk2.blob.core.windows.net/preview/0.0.1/autoMLImageClassificationMultilabelJob.schema.json.

Catatan

Sintaks YAML yang dirinci dalam dokumen ini didasarkan pada skema JSON untuk versi terbaru ekstensi CLI v2 ML. Sintaks ini dijamin hanya berfungsi dengan versi terbaru ekstensi CLI v2 ML. Anda dapat menemukan skema untuk versi ekstensi yang lebih lama di https://azuremlschemasprod.azureedge.net/.

Sintaks YAML

Untuk informasi tentang semua kunci dalam sintaks Yaml, lihat Sintaks yaml tugas klasifikasi gambar. Di sini kami hanya menjelaskan kunci yang memiliki nilai berbeda dibandingkan dengan apa yang ditentukan untuk tugas klasifikasi gambar.

Kunci	Jenis	Deskripsi	Nilai yang diizinkan	Nilai default
`task`	const	Wajib diisi. Jenis tugas AutoML.	`image_classification_multilabel`	`image_classification_multilabel`
`primary_metric`	string	Metrik yang akan dioptimalkan AutoML untuk pemilihan model.	`iou`	`iou`

Keterangan

Perintah az ml job dapat digunakan untuk mengelola pekerjaan Azure Machine Learning.

Contoh

Contoh tersedia di contoh repositori GitHub. Contoh yang relevan dengan pekerjaan klasifikasi multi-label gambar ditunjukkan di bawah ini.

YAML: Pekerjaan klasifikasi multi-label gambar AutoML

$schema: https://azuremlsdk2.blob.core.windows.net/preview/0.0.1/autoMLJob.schema.json
type: automl

experiment_name: dpv2-cli-automl-image-classification-multilabel-experiment
description: A multi-label Image classification job using fridge items dataset

compute: azureml:gpu-cluster

task: image_classification_multilabel
log_verbosity: debug
primary_metric: iou

target_column_name: label
training_data:
  # Update the path, if prepare_data.py is using data_path other than "./data"
  path: data/training-mltable-folder
  type: mltable
validation_data:
  # Update the path, if prepare_data.py is using data_path other than "./data"
  path: data/validation-mltable-folder
  type: mltable

limits:
  timeout_minutes: 60
  max_trials: 10
  max_concurrent_trials: 2

training_parameters:
  early_stopping: True
  evaluation_frequency: 1

sweep:
  sampling_algorithm: random
  early_termination:
    type: bandit
    evaluation_interval: 2
    slack_factor: 0.2
    delay_evaluation: 6

search_space:
  - model_name:
      type: choice
      values: [vitb16r224]
    learning_rate:
      type: uniform
      min_value: 0.005
      max_value: 0.05
    number_of_epochs:
      type: choice
      values: [15, 30]
    gradient_accumulation_step:
      type: choice
      values: [1, 2]

  - model_name:
      type: choice
      values: [seresnext]
    learning_rate:
      type: uniform
      min_value: 0.005
      max_value: 0.05
    validation_resize_size:
      type: choice
      values: [288, 320, 352]
    validation_crop_size:
      type: choice
      values: [224, 256]
    training_crop_size:
      type: choice
      values: [224, 256]

YAML: Pekerjaan alur klasifikasi multi-label gambar AutoML

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

description: Pipeline using AutoML Image Multilabel Classification task

display_name: pipeline-with-image-classification-multilabel
experiment_name: pipeline-with-automl

settings:
  default_compute: azureml:gpu-cluster

inputs:
  image_multilabel_classification_training_data:
    type: mltable
    # Update the path, if prepare_data.py is using data_path other than "./data"
    path: data/training-mltable-folder
  image_multilabel_classification_validation_data:
    type: mltable
    # Update the path, if prepare_data.py is using data_path other than "./data"
    path: data/validation-mltable-folder

jobs:
  image_multilabel_classification_node:
    type: automl
    task: image_classification_multilabel
    log_verbosity: info
    primary_metric: iou
    limits:
      timeout_minutes: 180
      max_trials: 10
      max_concurrent_trials: 2
    target_column_name: label
    training_data: ${{parent.inputs.image_multilabel_classification_training_data}}
    validation_data: ${{parent.inputs.image_multilabel_classification_validation_data}}
    training_parameters:
      early_stopping: True
      evaluation_frequency: 1
    sweep:
      sampling_algorithm: random
      early_termination:
        type: bandit
        evaluation_interval: 2
        slack_factor: 0.2
        delay_evaluation: 6
    search_space:
      - model_name:
          type: choice
          values: [vitb16r224]
        learning_rate:
          type: uniform
          min_value: 0.005
          max_value: 0.05
        number_of_epochs:
          type: choice
          values: [15, 30]
        gradient_accumulation_step:
          type: choice
          values: [1, 2]

      - model_name:
          type: choice
          values: [seresnext]
        learning_rate:
          type: uniform
          min_value: 0.005
          max_value: 0.05
        validation_resize_size:
          type: choice
          values: [288, 320, 352]
        validation_crop_size:
          type: choice
          values: [224, 256]
        training_crop_size:
          type: choice
          values: [224, 256]

    # currently need to specify outputs "mlflow_model" explicitly to reference it in following nodes
    outputs:
      best_model:
        type: mlflow_model
  register_model_node:
    type: command
    component: file:./components/component_register_model.yaml
    inputs:
      model_input_path: ${{parent.jobs.image_multilabel_classification_node.outputs.best_model}}
      model_base_name: fridge_items_multilabel_classification_model

Langkah berikutnya

Menginstal dan menggunakan CLI (v2)

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