reference-automl-images-cli-classification.md

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CLI (v2) Automated ML Image Classification job YAML schema	Azure Machine Learning	Reference documentation for the CLI (v2) Automated ML Image Classification job YAML schema.	machine-learning	machine-learning	core	reference	cliv2	rasavage	rsavage2	10/11/2022	ssalgado

CLI (v2) Automated ML image classification job YAML schema

[!INCLUDE cli v2]

The source JSON schema can be found at https://azuremlsdk2.blob.core.windows.net/preview/0.0.1/autoMLImageClassificationJob.schema.json.

[!INCLUDE schema note]

YAML syntax

Key	Type	Description	Allowed values	Default value
$schema	string	The YAML schema. If the user uses the Azure Machine Learning VS Code extension to author the YAML file, including $schema at the top of the file enables the user to invoke schema and resource completions.
type	const	Required. The type of job.	automl	automl
task	const	Required. The type of AutoML task.	image_classification	image_classification
name	string	Name of the job. Must be unique across all jobs in the workspace. If omitted, Azure Machine Learning will autogenerate a GUID for the name.
display_name	string	Display name of the job in the studio UI. Can be non-unique within the workspace. If omitted, Azure Machine Learning will autogenerate a human-readable adjective-noun identifier for the display name.
experiment_name	string	Experiment name to organize the job under. Each job's run record will be organized under the corresponding experiment in the studio's "Experiments" tab. If omitted, Azure Machine Learning will default it to the name of the working directory where the job was created.
description	string	Description of the job.
tags	object	Dictionary of tags for the job.
compute	string	Name of the compute target to execute the job on. This compute can be either a reference to an existing compute in the workspace (using the azureml:<compute_name> syntax) or local to designate local execution. For more information on compute for AutoML image jobs, see Compute to run experiment section. Note: jobs in pipeline don't support local as compute. *		local
log_verbosity	number	Different levels of log verbosity.	not_set, debug, info, warning, error, critical	info
primary_metric	string	The metric that AutoML will optimize for model selection.	accuracy	accuracy
target_column_name	string	Required. The name of the column to target for predictions. It must always be specified. This parameter is applicable to training_data and validation_data.
training_data	object	Required. The data to be used within the job. It should contain both training feature columns and a target column. The parameter training_data must always be provided. For more information on keys and their descriptions, see Training or validation data section. For an example, see Consume data section.
validation_data	object	The validation data to be used within the job. It should contain both training features and label column (optionally a sample weights column). If validation_data is specified, then training_data and target_column_name parameters must be specified. For more information on keys and their descriptions, see Training or validation data section. For an example, see Consume data section
validation_data_size	float	What fraction of the data to hold out for validation when user validation data isn't specified.	A value in range (0.0, 1.0)
limits	object	Dictionary of limit configurations of the job. The key is name for the limit within the context of the job and the value is limit value. For more information, see Configure your experiment settings section.
training_parameters	object	Dictionary containing training parameters for the job. Provide an object that has keys as listed in following sections. - Model agnostic hyperparameters - Image classification (multi-class and multi-label) specific hyperparameters. For an example, see Supported model architectures section.
sweep	object	Dictionary containing sweep parameters for the job. It has two keys - sampling_algorithm (required) and early_termination. For more information and an example, see Sampling methods for the sweep, Early termination policies sections.
search_space	object	Dictionary of the hyperparameter search space. The key is the name of the hyperparameter and the value is the parameter expression. The user can find the possible hyperparameters from parameters specified for training_parameters key. For an example, see Sweeping hyperparameters for your model section.
search_space.<hyperparameter>	object	There are two types of hyperparameters: - Discrete Hyperparameters: Discrete hyperparameters are specified as a choice among discrete values. choice can be one or more comma-separated values, a range object, or any arbitrary list object. Advanced discrete hyperparameters can also be specified using a distribution - randint, qlognormal, qnormal, qloguniform, quniform. For more information, see this section. - Continuous hyperparameters: Continuous hyperparameters are specified as a distribution over a continuous range of values. Currently supported distributions are - lognormal, normal, loguniform, uniform. For more information, see this section. See Parameter expressions for the set of possible expressions to use.
outputs	object	Dictionary of output configurations of the job. The key is a name for the output within the context of the job and the value is the output configuration.
outputs.best_model	object	Dictionary of output configurations for best model. For more information, see Best model output configuration.

Training or validation data

Key	Type	Description	Allowed values	Default value
description	string	The detailed information that describes this input data.
path	string	Path can be a file path, folder path or pattern for paths. pattern specifies a search pattern to allow globbing(* and **) of files and folders containing data. Supported URI types are azureml, https, wasbs, abfss, and adl. For more information on how to use the azureml:// URI format, see Core yaml syntax. URI of the location of the artifact file. If this URI doesn't have a scheme (for example, http:, azureml: etc.), then it's considered a local reference and the file it points to is uploaded to the default workspace blob-storage as the entity is created.
mode	string	Dataset delivery mechanism.	direct	direct
type	const	In order to generate computer vision models, the user needs to bring labeled image data as input for model training in the form of an MLTable.	mltable	mltable

Best model output configuration

Key	Type	Description	Allowed values	Default value
type	string	Required. Type of best model. AutoML allows only mlflow models.	mlflow_model	mlflow_model
path	string	Required. URI of the location where the model-artifact file(s) are stored. If this URI doesn't have a scheme (for example, http:, azureml: etc.), then it's considered a local reference and the file it points to is uploaded to the default workspace blob-storage as the entity is created.
storage_uri	string	The HTTP URL of the Model. Use this URL with az storage copy -s THIS_URL -d DESTINATION_PATH --recursive to download the data.

Remarks

The az ml job command can be used for managing Azure Machine Learning jobs.

Examples

Examples are available in the examples GitHub repository. Examples relevant to image classification job are linked below.

YAML: AutoML image classification job

:::code language="yaml" source="~/azureml-examples-temp-fix/cli/jobs/automl-standalone-jobs/cli-automl-image-classification-multiclass-task-fridge-items/cli-automl-image-classification-multiclass-task-fridge-items.yml":::

YAML: AutoML image classification pipeline job

:::code language="yaml" source="~/azureml-examples-temp-fix/cli/jobs/pipelines/automl/image-multiclass-classification-fridge-items-pipeline/pipeline.yml":::

Next steps

• Install and use the CLI (v2)