Schema YAML del processo di comando dell'interfaccia della riga di comando (v2)

SI APPLICA A: estensione ML dell'interfaccia della riga di comando di Azure v2 (corrente)

Lo schema JSON di origine è disponibile in https://azuremlschemas.azureedge.net/latest/commandJob.schema.json.

Sintassi YAML

Configurazioni di distribuzione

MpiConfiguration

PyTorchConfiguration

TensorFlowConfiguration

Input del processo

Output del processo

Configurazioni di identità

UserIdentityConfiguration

ManagedIdentityConfiguration

Osservazioni:

Il az ml job comando può essere usato per la gestione dei processi di Azure Machine Learning.

Esempi

Gli esempi sono disponibili nel repository GitHub di esempi. Le sezioni seguenti illustrano alcuni esempi.

YAML: hello world

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: echo "hello world"
environment:
  image: library/python:latest

YAML: nome visualizzato, nome dell'esperimento, descrizione e tag

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: echo "hello world"
environment:
  image: library/python:latest
tags:
  hello: world
display_name: hello-world-example
experiment_name: hello-world-example
description: |
  # Azure Machine Learning "hello world" job

  This is a "hello world" job running in the cloud via Azure Machine Learning!

  ## Description

  Markdown is supported in the studio for job descriptions! You can edit the description there or via CLI.

YAML: variabili di ambiente

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: echo $hello_env_var
environment:
  image: library/python:latest
environment_variables:
  hello_env_var: "hello world"

YAML: codice sorgente

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: ls
code: src
environment:
  image: library/python:latest

YAML: input letterali

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  echo ${{inputs.hello_string}}
  echo ${{inputs.hello_number}}
environment:
  image: library/python:latest
inputs:
  hello_string: "hello world"
  hello_number: 42

YAML: scrivere in output predefiniti

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: echo "hello world" > ./outputs/helloworld.txt
environment:
  image: library/python:latest

YAML: scrivere nell'output dei dati denominati

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: echo "hello world" > ${{outputs.hello_output}}/helloworld.txt
outputs:
  hello_output:
environment:
  image: python

YAML: input del file URI dell'archivio dati

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  echo "--iris-csv: ${{inputs.iris_csv}}"
  python hello-iris.py --iris-csv ${{inputs.iris_csv}}
code: src
inputs:
  iris_csv:
    type: uri_file 
    path: azureml://datastores/workspaceblobstore/paths/example-data/iris.csv
environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest

YAML: input della cartella URI dell'archivio dati

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  ls ${{inputs.data_dir}}
  echo "--iris-csv: ${{inputs.data_dir}}/iris.csv"
  python hello-iris.py --iris-csv ${{inputs.data_dir}}/iris.csv
code: src
inputs:
  data_dir:
    type: uri_folder 
    path: azureml://datastores/workspaceblobstore/paths/example-data/
environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest

YAML: input del file URI

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  echo "--iris-csv: ${{inputs.iris_csv}}"
  python hello-iris.py --iris-csv ${{inputs.iris_csv}}
code: src
inputs:
  iris_csv:
    type: uri_file 
    path: https://azuremlexamples.blob.core.windows.net/datasets/iris.csv
environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest

YAML: input della cartella URI

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  ls ${{inputs.data_dir}}
  echo "--iris-csv: ${{inputs.data_dir}}/iris.csv"
  python hello-iris.py --iris-csv ${{inputs.data_dir}}/iris.csv
code: src
inputs:
  data_dir:
    type: uri_folder 
    path: wasbs://datasets@azuremlexamples.blob.core.windows.net/
environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest

YAML: Notebook tramite papermill

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: |
  pip install ipykernel papermill
  papermill hello-notebook.ipynb outputs/out.ipynb -k python
code: src
environment:
  image: library/python:3.11.6

YAML: training del modello Python di base

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
code: src
command: >-
  python main.py 
  --iris-csv ${{inputs.iris_csv}}
  --C ${{inputs.C}}
  --kernel ${{inputs.kernel}}
  --coef0 ${{inputs.coef0}}
inputs:
  iris_csv: 
    type: uri_file
    path: wasbs://datasets@azuremlexamples.blob.core.windows.net/iris.csv
  C: 0.8
  kernel: "rbf"
  coef0: 0.1
environment: azureml://registries/azureml/environments/sklearn-1.5/labels/latest
compute: azureml:cpu-cluster
display_name: sklearn-iris-example
experiment_name: sklearn-iris-example
description: Train a scikit-learn SVM on the Iris dataset.

YAML: training di base del modello R con il contesto di compilazione Docker locale

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
command: >
  Rscript train.R 
  --data_folder ${{inputs.iris}}
code: src
inputs:
  iris: 
    type: uri_file
    path: https://azuremlexamples.blob.core.windows.net/datasets/iris.csv
environment:
  build:
    path: docker-context
compute: azureml:cpu-cluster
display_name: r-iris-example
experiment_name: r-iris-example
description: Train an R model on the Iris dataset.

YAML: distributed PyTorch

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
code: src
command: >-
  python train.py
  --epochs ${{inputs.epochs}}
  --learning-rate ${{inputs.learning_rate}}
  --data-dir ${{inputs.cifar}}
inputs:
  epochs: 1
  learning_rate: 0.2
  cifar:
     type: uri_folder
     path: azureml:cifar-10-example@latest
environment: azureml:AzureML-acpt-pytorch-2.2-cuda12.1@latest
compute: azureml:gpu-cluster
distribution:
  type: pytorch
  process_count_per_instance: 1
resources:
  instance_count: 2
display_name: pytorch-cifar-distributed-example
experiment_name: pytorch-cifar-distributed-example
description: Train a basic convolutional neural network (CNN) with PyTorch on the CIFAR-10 dataset, distributed via PyTorch.

YAML: TensorFlow distribuito

$schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
code: src
command: >-
  python train.py
  --epochs ${{inputs.epochs}}
  --model-dir ${{inputs.model_dir}}
inputs:
  epochs: 1
  model_dir: outputs/keras-model
environment: azureml:AzureML-tensorflow-2.16-cuda12@latest
compute: azureml:gpu-cluster
resources:
  instance_count: 2
distribution:
  type: tensorflow
  worker_count: 2
display_name: tensorflow-mnist-distributed-example
experiment_name: tensorflow-mnist-distributed-example
description: Train a basic neural network with TensorFlow on the MNIST dataset, distributed via TensorFlow.

Passaggi successivi

• Installare e usare l'interfaccia della riga di comando (v2)