Skema YAML pekerjaan klasifikasi multi-Label gambar ML otomatis CLI (v2)
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 yang berbeda dibandingkan dengan apa yang ditentukan untuk tugas klasifikasi gambar.
Tombol | Tipe | Deskripsi | Nilai yang diizinkan | Nilai default |
---|---|---|---|---|
task |
const | Harus 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 tugas 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