azure.ai.ml.automl.ImageClassificationJob.yml

### YamlMime:PythonClass
uid: azure.ai.ml.automl.ImageClassificationJob
name: ImageClassificationJob
fullName: azure.ai.ml.automl.ImageClassificationJob
module: azure.ai.ml.automl
inheritances:
- azure.ai.ml.entities._job.automl.image.automl_image_classification_base.AutoMLImageClassificationBase
summary: Configuration for AutoML multi-class Image Classification job.
constructor:
  syntax: 'ImageClassificationJob(*, primary_metric: str | ClassificationPrimaryMetrics
    | None = None, **kwargs: Any)'
  parameters:
  - name: primary_metric
    description: The primary metric to use for optimization.
    isRequired: true
    types:
    - <xref:typing.Optional>[<xref:str>, <xref:azure.ai.ml.automl.ClassificationMultilabelPrimaryMetrics>]
  - name: kwargs
    description: Job-specific arguments.
    isRequired: true
    types:
    - <xref:typing.Dict>[<xref:str>, <xref:typing.Any>]
  keywordOnlyParameters:
  - name: primary_metric
    isRequired: true
examples:
- "creating an automl image classification job<!--[!code-python[Main](les\\ml_samples_automl_image.py\
  \ )]-->\n\n<!-- literal_block {\"ids\": [], \"classes\": [], \"names\": [], \"dupnames\"\
  : [], \"backrefs\": [], \"source\": \"C:\\\\hostedtoolcache\\\\windows\\\\Python\\\
  \\3.11.9\\\\x64\\\\Lib\\\\site-packages\\\\py2docfx\\\\dist_temp\\\\8\\\\azure-ai-ml-1.16.1\\\
  \\samples\\\\ml_samples_automl_image.py\", \"xml:space\": \"preserve\", \"force\"\
  : false, \"language\": \"python\", \"highlight_args\": {\"linenostart\": 1}, \"\
  linenos\": false} -->\n\n````python\n\n   from azure.ai.ml import automl, Input\n\
  \   from azure.ai.ml.constants import AssetTypes\n   from azure.ai.ml.automl import\
  \ ClassificationMultilabelPrimaryMetrics\n\n   image_classification_job = automl.ImageClassificationJob(\n\
  \       experiment_name=\"my_experiment\",\n       compute=\"my_compute\",\n   \
  \    training_data=Input(type=AssetTypes.MLTABLE, path=\"./training-mltable-folder\"\
  ),\n       validation_data=Input(type=AssetTypes.MLTABLE, path=\"./validation-mltable-folder\"\
  ),\n       target_column_name=\"label\",\n       primary_metric=ClassificationMultilabelPrimaryMetrics.ACCURACY,\n\
  \       tags={\"my_custom_tag\": \"My custom value\"},\n   )\n\n   ````\n"
methods:
- uid: azure.ai.ml.automl.ImageClassificationJob.dump
  name: dump
  summary: Dumps the job content into a file in YAML format.
  signature: 'dump(dest: str | PathLike | IO, **kwargs: Any) -> None'
  parameters:
  - name: dest
    description: 'The local path or file stream to write the YAML content to.

      If dest is a file path, a new file will be created.

      If dest is an open file, the file will be written to directly.'
    isRequired: true
    types:
    - <xref:typing.Union>[<xref:PathLike>, <xref:str>, <xref:typing.IO>[<xref:typing.AnyStr>]]
  exceptions:
  - type: FileExistsError
    description: Raised if dest is a file path and the file already exists.
  - type: IOError
    description: Raised if dest is an open file and the file is not writable.
- uid: azure.ai.ml.automl.ImageClassificationJob.extend_search_space
  name: extend_search_space
  summary: Add Search space for AutoML Image Classification and Image Classification
    Multilabel tasks.
  signature: 'extend_search_space(value: SearchSpace | List[SearchSpace]) -> None'
  parameters:
  - name: value
    description: 'specify either an instance of ImageClassificationSearchSpace or
      list of

      ImageClassificationSearchSpace for searching through the parameter space'
    isRequired: true
    types:
    - <xref:typing.Union>[<xref:azure.ai.ml.automl.ImageClassificationSearchSpace>,
      <xref:typing.List>[<xref:azure.ai.ml.automl.ImageClassificationSearchSpace>]]
- uid: azure.ai.ml.automl.ImageClassificationJob.set_data
  name: set_data
  summary: Data settings for all AutoML Image jobs.
  signature: 'set_data(*, training_data: Input, target_column_name: str, validation_data:
    Input | None = None, validation_data_size: float | None = None) -> None'
  keywordOnlyParameters:
  - name: training_data
    description: Required. Training data.
  - name: target_column_name
    description: Required. Target column name.
  - name: validation_data
    description: Optional. Validation data.
  - name: validation_data_size
    description: 'Optional. The fraction of training dataset that needs to be set
      aside for

      validation purpose. Values should be in range (0.0 , 1.0).

      Applied only when validation dataset is not provided.'
  return:
    description: None
- uid: azure.ai.ml.automl.ImageClassificationJob.set_limits
  name: set_limits
  summary: Limit settings for all AutoML Image Jobs.
  signature: 'set_limits(*, max_concurrent_trials: int | None = None, max_trials:
    int | None = None, timeout_minutes: int | None = None) -> None'
  keywordOnlyParameters:
  - name: max_concurrent_trials
    description: Maximum number of trials to run concurrently.
  - name: max_trials
    description: Maximum number of trials to run. Defaults to None.
  - name: timeout_minutes
    description: AutoML job timeout.
  return:
    description: None
- uid: azure.ai.ml.automl.ImageClassificationJob.set_sweep
  name: set_sweep
  summary: Sweep settings for all AutoML Image jobs.
  signature: 'set_sweep(*, sampling_algorithm: str | Random | Grid | Bayesian, early_termination:
    BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy | None = None)
    -> None'
  keywordOnlyParameters:
  - name: sampling_algorithm
    description: 'Required. Type of the hyperparameter sampling

      algorithms. Possible values include: "Grid", "Random", "Bayesian".'
  - name: early_termination
    description: Type of early termination policy.
  return:
    description: None
- uid: azure.ai.ml.automl.ImageClassificationJob.set_training_parameters
  name: set_training_parameters
  summary: Setting Image training parameters for AutoML Image Classification and Image
    Classification Multilabel tasks.
  signature: 'set_training_parameters(*, advanced_settings: str | None = None, ams_gradient:
    bool | None = None, beta1: float | None = None, beta2: float | None = None, checkpoint_frequency:
    int | None = None, checkpoint_run_id: str | None = None, distributed: bool | None
    = None, early_stopping: bool | None = None, early_stopping_delay: int | None =
    None, early_stopping_patience: int | None = None, enable_onnx_normalization: bool
    | None = None, evaluation_frequency: int | None = None, gradient_accumulation_step:
    int | None = None, layers_to_freeze: int | None = None, learning_rate: float |
    None = None, learning_rate_scheduler: str | LearningRateScheduler | None = None,
    model_name: str | None = None, momentum: float | None = None, nesterov: bool |
    None = None, number_of_epochs: int | None = None, number_of_workers: int | None
    = None, optimizer: str | StochasticOptimizer | None = None, random_seed: int |
    None = None, step_lr_gamma: float | None = None, step_lr_step_size: int | None
    = None, training_batch_size: int | None = None, validation_batch_size: int | None
    = None, warmup_cosine_lr_cycles: float | None = None, warmup_cosine_lr_warmup_epochs:
    int | None = None, weight_decay: float | None = None, training_crop_size: int
    | None = None, validation_crop_size: int | None = None, validation_resize_size:
    int | None = None, weighted_loss: int | None = None) -> None'
  keywordOnlyParameters:
  - name: advanced_settings
    description: Settings for advanced scenarios.
    types:
    - <xref:str>
  - name: ams_gradient
    description: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
    types:
    - <xref:bool>
  - name: beta1
    description: 'Value of ''beta1'' when optimizer is ''adam'' or ''adamw''. Must
      be a float in the

      range [0, 1].'
    types:
    - <xref:float>
  - name: beta2
    description: 'Value of ''beta2'' when optimizer is ''adam'' or ''adamw''. Must
      be a float in the

      range [0, 1].'
    types:
    - <xref:float>
  - name: checkpoint_frequency
    description: 'Frequency to store model checkpoints. Must be a positive

      integer.'
    types:
    - <xref:int>
  - name: checkpoint_run_id
    description: 'The id of a previous run that has a pretrained checkpoint for

      incremental training.'
    types:
    - <xref:str>
  - name: distributed
    description: Whether to use distributed training.
    types:
    - <xref:bool>
  - name: early_stopping
    description: Enable early stopping logic during training.
    types:
    - <xref:bool>
  - name: early_stopping_delay
    description: 'Minimum number of epochs or validation evaluations to wait

      before primary metric improvement

      is tracked for early stopping. Must be a positive integer.'
    types:
    - <xref:int>
  - name: early_stopping_patience
    description: 'Minimum number of epochs or validation evaluations with no

      primary metric improvement before

      the run is stopped. Must be a positive integer.'
    types:
    - <xref:int>
  - name: enable_onnx_normalization
    description: Enable normalization when exporting ONNX model.
    types:
    - <xref:bool>
  - name: evaluation_frequency
    description: 'Frequency to evaluate validation dataset to get metric scores.

      Must be a positive integer.'
    types:
    - <xref:int>
  - name: gradient_accumulation_step
    description: 'Gradient accumulation means running a configured number of

      "GradAccumulationStep" steps without

      updating the model weights while accumulating the gradients of those steps,
      and then using

      the accumulated gradients to compute the weight updates. Must be a positive
      integer.'
    types:
    - <xref:int>
  - name: layers_to_freeze
    description: 'Number of layers to freeze for the model. Must be a positive

      integer.

      For instance, passing 2 as value for ''seresnext'' means

      freezing layer0 and layer1. For a full list of models supported and details
      on layer freeze,

      please

      see: [https://docs.microsoft.com/en-us/azure/machine-learning/reference-automl-images-hyperparameters#model-agnostic-hyperparameters](https://docs.microsoft.com/en-us/azure/machine-learning/reference-automl-images-hyperparameters#model-agnostic-hyperparameters).   #
      pylint: disable=line-too-long'
  - name: learning_rate
    description: Initial learning rate. Must be a float in the range [0, 1].
    types:
    - <xref:float>
  - name: learning_rate_scheduler
    description: 'Type of learning rate scheduler. Must be ''warmup_cosine'' or

      ''step''. Possible values include: "None", "WarmupCosine", "Step".'
  - name: model_name
    description: 'Name of the model to use for training.

      For more information on the available models please visit the official documentation:

      [https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models).'
  - name: momentum
    description: 'Value of momentum when optimizer is ''sgd''. Must be a float in
      the range [0,

      1].'
    types:
    - <xref:float>
  - name: nesterov
    description: Enable nesterov when optimizer is 'sgd'.
    types:
    - <xref:bool>
  - name: number_of_epochs
    description: Number of training epochs. Must be a positive integer.
    types:
    - <xref:int>
  - name: number_of_workers
    description: Number of data loader workers. Must be a non-negative integer.
    types:
    - <xref:int>
  - name: optimizer
    description: 'Type of optimizer. Possible values include: "None", "Sgd", "Adam",
      "Adamw".'
  - name: random_seed
    description: Random seed to be used when using deterministic training.
    types:
    - <xref:int>
  - name: step_lr_gamma
    description: 'Value of gamma when learning rate scheduler is ''step''. Must be
      a float

      in the range [0, 1].'
    types:
    - <xref:float>
  - name: step_lr_step_size
    description: 'Value of step size when learning rate scheduler is ''step''. Must
      be

      a positive integer.'
    types:
    - <xref:int>
  - name: training_batch_size
    description: Training batch size. Must be a positive integer.
    types:
    - <xref:int>
  - name: validation_batch_size
    description: Validation batch size. Must be a positive integer.
    types:
    - <xref:int>
  - name: warmup_cosine_lr_cycles
    description: 'Value of cosine cycle when learning rate scheduler is

      ''warmup_cosine''. Must be a float in the range [0, 1].'
    types:
    - <xref:float>
  - name: warmup_cosine_lr_warmup_epochs
    description: 'Value of warmup epochs when learning rate scheduler is

      ''warmup_cosine''. Must be a positive integer.'
    types:
    - <xref:int>
  - name: weight_decay
    description: 'Value of weight decay when optimizer is ''sgd'', ''adam'', or ''adamw''.
      Must

      be a float in the range[0, 1].'
    types:
    - <xref:float>
  - name: training_crop_size
    description: 'Image crop size that is input to the neural network for the

      training dataset. Must be a positive integer.'
    types:
    - <xref:int>
  - name: validation_crop_size
    description: 'Image crop size that is input to the neural network for the

      validation dataset. Must be a positive integer.'
    types:
    - <xref:int>
  - name: validation_resize_size
    description: 'Image size to which to resize before cropping for validation

      dataset. Must be a positive integer.'
    types:
    - <xref:int>
  - name: weighted_loss
    description: 'Weighted loss. The accepted values are 0 for no weighted loss.

      1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights.
      Must be

      0 or 1 or 2.'
    types:
    - <xref:int>
attributes:
- uid: azure.ai.ml.automl.ImageClassificationJob.base_path
  name: base_path
  summary: The base path of the resource.
  return:
    description: The base path of the resource.
    types:
    - <xref:str>
- uid: azure.ai.ml.automl.ImageClassificationJob.creation_context
  name: creation_context
  summary: The creation context of the resource.
  return:
    description: The creation metadata for the resource.
    types:
    - <xref:typing.Optional>[<xref:azure.ai.ml.entities.SystemData>]
- uid: azure.ai.ml.automl.ImageClassificationJob.id
  name: id
  summary: The resource ID.
  return:
    description: The global ID of the resource, an Azure Resource Manager (ARM) ID.
    types:
    - <xref:typing.Optional>[<xref:str>]
- uid: azure.ai.ml.automl.ImageClassificationJob.inputs
  name: inputs
- uid: azure.ai.ml.automl.ImageClassificationJob.limits
  name: limits
  summary: Returns the limit settings for all AutoML Image jobs.
  return:
    description: The limit settings.
    types:
    - <xref:azure.ai.ml.automl.ImageLimitSettings>
- uid: azure.ai.ml.automl.ImageClassificationJob.log_files
  name: log_files
  summary: Job output files.
  return:
    description: The dictionary of log names and URLs.
    types:
    - <xref:typing.Optional>[<xref:typing.Dict>[<xref:str>, <xref:str>]]
- uid: azure.ai.ml.automl.ImageClassificationJob.log_verbosity
  name: log_verbosity
  summary: Returns the verbosity of the logger.
  return:
    description: The log verbosity.
    types:
    - <xref:azure.ai.ml._restclient.v2023_04_01_preview.models.LogVerbosity>
- uid: azure.ai.ml.automl.ImageClassificationJob.outputs
  name: outputs
- uid: azure.ai.ml.automl.ImageClassificationJob.primary_metric
  name: primary_metric
- uid: azure.ai.ml.automl.ImageClassificationJob.search_space
  name: search_space
  summary: 'List[~azure.ai.ml.automl.ImageClassificationSearchSpace]

    :return: Search space for AutoML Image Classification and Image Classification
    Multilabel tasks.'
- uid: azure.ai.ml.automl.ImageClassificationJob.status
  name: status
  summary: "The status of the job.\n\nCommon values returned include \"Running\",\
    \ \"Completed\", and \"Failed\". All possible values are:\n\n   * NotStarted -\
    \ This is a temporary state that client-side Run objects are in before cloud submission.\
    \ \n\n   * Starting - The Run has started being processed in the cloud. The caller\
    \ has a run ID at this point. \n\n   * Provisioning - On-demand compute is being\
    \ created for a given job submission. \n\n   * Preparing - The run environment\
    \ is being prepared and is in one of two stages:\n\n        * Docker image build\
    \ \n\n        * conda environment setup \n\n   * Queued - The job is queued on\
    \ the compute target. For example, in BatchAI, the job is in a queued state\n\n\
    \        while waiting for all the requested nodes to be ready.\n\n   * Running\
    \ - The job has started to run on the compute target. \n\n   * Finalizing - User\
    \ code execution has completed, and the run is in post-processing stages. \n\n\
    \   * CancelRequested - Cancellation has been requested for the job. \n\n   *\
    \ Completed - The run has completed successfully. This includes both the user\
    \ code execution and run\n\n        post-processing stages.\n\n   * Failed - The\
    \ run failed. Usually the Error property on a run will provide details as to why.\
    \ \n\n   * Canceled - Follows a cancellation request and indicates that the run\
    \ is now successfully cancelled. \n\n   * NotResponding - For runs that have Heartbeats\
    \ enabled, no heartbeat has been recently sent."
  return:
    description: Status of the job.
    types:
    - <xref:typing.Optional>[<xref:str>]
- uid: azure.ai.ml.automl.ImageClassificationJob.studio_url
  name: studio_url
  summary: Azure ML studio endpoint.
  return:
    description: The URL to the job details page.
    types:
    - <xref:typing.Optional>[<xref:str>]
- uid: azure.ai.ml.automl.ImageClassificationJob.sweep
  name: sweep
  summary: Returns the sweep settings for all AutoML Image jobs.
  return:
    description: The sweep settings.
    types:
    - <xref:azure.ai.ml.automl.ImageSweepSettings>
- uid: azure.ai.ml.automl.ImageClassificationJob.task_type
  name: task_type
  summary: Get task type.
  return:
    description: 'The type of task to run. Possible values include: "classification",
      "regression", "forecasting".'
    types:
    - <xref:str>
- uid: azure.ai.ml.automl.ImageClassificationJob.test_data
  name: test_data
  summary: Get test data.
  return:
    description: Test data input
    types:
    - <xref:azure.ai.ml.Input>
- uid: azure.ai.ml.automl.ImageClassificationJob.training_data
  name: training_data
  summary: Get training data.
  return:
    description: Training data input
    types:
    - <xref:azure.ai.ml.Input>
- uid: azure.ai.ml.automl.ImageClassificationJob.training_parameters
  name: training_parameters
  summary: '~azure.ai.ml.automl.ImageModelSettingsClassification

    :return: Training parameters for AutoML Image Classification and Image Classification
    Multilabel tasks.'
- uid: azure.ai.ml.automl.ImageClassificationJob.type
  name: type
  summary: The type of the job.
  return:
    description: The type of the job.
    types:
    - <xref:typing.Optional>[<xref:str>]
- uid: azure.ai.ml.automl.ImageClassificationJob.validation_data
  name: validation_data
  summary: Get validation data.
  return:
    description: Validation data input
    types:
    - <xref:azure.ai.ml.Input>