ImageInstanceSegmentationJob Class

Configuration for AutoML Image Instance Segmentation job.

Constructor

ImageInstanceSegmentationJob(*, primary_metric: str | InstanceSegmentationPrimaryMetrics | None = None, **kwargs: Any)

Keyword-Only Parameters

Name	Description
primary_metric	Optional[str, InstanceSegmentationPrimaryMetrics] The primary metric to use for optimization. Default value: None

Examples

creating an automl image instance segmentation job


   from azure.ai.ml import automl, Input
   from azure.ai.ml.constants import AssetTypes
   from azure.ai.ml.automl import ObjectDetectionPrimaryMetrics

   image_instance_segmentation_job = automl.ImageInstanceSegmentationJob(
       experiment_name="my_experiment",
       compute="my_compute",
       training_data=Input(type=AssetTypes.MLTABLE, path="./training-mltable-folder"),
       validation_data=Input(type=AssetTypes.MLTABLE, path="./validation-mltable-folder"),
       tags={"my_custom_tag": "My custom value"},
       primary_metric=ObjectDetectionPrimaryMetrics.MEAN_AVERAGE_PRECISION,
   )

Methods

dump	Dumps the job content into a file in YAML format.
extend_search_space	Add search space for AutoML Image Object Detection and Image Instance Segmentation tasks.
set_data	Data settings for all AutoML Image jobs.
set_limits	Limit settings for all AutoML Image Jobs.
set_sweep	Sweep settings for all AutoML Image jobs.
set_training_parameters	Setting Image training parameters for for AutoML Image Object Detection and Image Instance Segmentation tasks.

dump

Dumps the job content into a file in YAML format.

dump(dest: str | PathLike | IO, **kwargs: Any) -> None

Parameters

Name	Description
dest Required	Union[<xref:PathLike>, str, IO[AnyStr]] 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.

Exceptions

Type	Description
FileExistsError	Raised if dest is a file path and the file already exists.
IOError	Raised if dest is an open file and the file is not writable.

extend_search_space

Add search space for AutoML Image Object Detection and Image Instance Segmentation tasks.

extend_search_space(value: SearchSpace | List[SearchSpace]) -> None

Parameters

Name	Description
value Required	Union[SearchSpace, List[SearchSpace]] Search through the parameter space

set_data

Data settings for all AutoML Image jobs.

set_data(*, training_data: Input, target_column_name: str, validation_data: Input | None = None, validation_data_size: float | None = None) -> None

Keyword-Only Parameters

Name	Description
training_data	Required. Training data.
target_column_name	Required. Target column name.
validation_data	Optional. Validation data. Default value: None
validation_data_size	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. Default value: None

Returns

Type	Description
	None

set_limits

Limit settings for all AutoML Image Jobs.

set_limits(*, max_concurrent_trials: int | None = None, max_trials: int | None = None, timeout_minutes: int | None = None) -> None

Keyword-Only Parameters

Name	Description
max_concurrent_trials	Maximum number of trials to run concurrently. Default value: None
max_trials	Maximum number of trials to run. Defaults to None. Default value: None
timeout_minutes	AutoML job timeout. Default value: None

Returns

Type	Description
	None

set_sweep

Sweep settings for all AutoML Image jobs.

set_sweep(*, sampling_algorithm: str | Random | Grid | Bayesian, early_termination: BanditPolicy | MedianStoppingPolicy | TruncationSelectionPolicy | None = None) -> None

Keyword-Only Parameters

Name	Description
sampling_algorithm	Required. Type of the hyperparameter sampling algorithms. Possible values include: "Grid", "Random", "Bayesian".
early_termination	Type of early termination policy. Default value: None

Returns

Type	Description
	None

set_training_parameters

Setting Image training parameters for for AutoML Image Object Detection and Image Instance Segmentation tasks.

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, box_detections_per_image: int | None = None, box_score_threshold: float | None = None, image_size: int | None = None, max_size: int | None = None, min_size: int | None = None, model_size: str | ModelSize | None = None, multi_scale: bool | None = None, nms_iou_threshold: float | None = None, tile_grid_size: str | None = None, tile_overlap_ratio: float | None = None, tile_predictions_nms_threshold: float | None = None, validation_iou_threshold: float | None = None, validation_metric_type: str | ValidationMetricType | None = None, log_training_metrics: str | LogTrainingMetrics | None = None, log_validation_loss: str | LogValidationLoss | None = None) -> None

Keyword-Only Parameters

Name	Description
advanced_settings	str Settings for advanced scenarios. Default value: None
ams_gradient	bool Enable AMSGrad when optimizer is 'adam' or 'adamw'. Default value: None
beta1	float Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. Default value: None
beta2	float Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. Default value: None
checkpoint_frequency	int Frequency to store model checkpoints. Must be a positive integer. Default value: None
checkpoint_run_id	str The id of a previous run that has a pretrained checkpoint for incremental training. Default value: None
distributed	bool Whether to use distributed training. Default value: None
early_stopping	bool Enable early stopping logic during training. Default value: None
early_stopping_delay	int Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. Default value: None
early_stopping_patience	int Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. Default value: None
enable_onnx_normalization	bool Enable normalization when exporting ONNX model. Default value: None
evaluation_frequency	int Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. Default value: None
gradient_accumulation_step	int 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. Default value: None
layers_to_freeze	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://free.blessedness.top/azure/machine-learning/reference-automl-images-hyperparameters#model-agnostic-hyperparameters. # pylint: disable=line-too-long Default value: None
learning_rate	float Initial learning rate. Must be a float in the range [0, 1]. Default value: None
learning_rate_scheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. Possible values include: "None", "WarmupCosine", "Step". Default value: None
model_name	Name of the model to use for training. For more information on the available models please visit the official documentation: https://free.blessedness.top/azure/machine-learning/how-to-auto-train-image-models. Default value: None
momentum	float Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. Default value: None
nesterov	bool Enable nesterov when optimizer is 'sgd'. Default value: None
number_of_epochs	int Number of training epochs. Must be a positive integer. Default value: None
number_of_workers	int Number of data loader workers. Must be a non-negative integer. Default value: None
optimizer	Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw". Default value: None
random_seed	int Random seed to be used when using deterministic training. Default value: None
step_lr_gamma	float Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. Default value: None
step_lr_step_size	int Value of step size when learning rate scheduler is 'step'. Must be a positive integer. Default value: None
training_batch_size	int Training batch size. Must be a positive integer. Default value: None
validation_batch_size	int Validation batch size. Must be a positive integer. Default value: None
warmup_cosine_lr_cycles	float Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. Default value: None
warmup_cosine_lr_warmup_epochs	int Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. Default value: None
weight_decay	float Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. Default value: None
box_detections_per_image	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. Default value: None
box_score_threshold	float During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. Default value: None
image_size	Image size for training and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. Default value: None
max_size	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. Default value: None
min_size	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. Default value: None
model_size	Model size. Must be 'small', 'medium', 'large', or 'extra_large'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. Default value: None
multi_scale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. Default value: None
nms_iou_threshold	float IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. Default value: None
tile_grid_size	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Default value: None
tile_overlap_ratio	float Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Default value: None
tile_predictions_nms_threshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. NMS: Non-maximum suppression. Default value: None
validation_iou_threshold	float IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. Default value: None
validation_metric_type	str or ValidationMetricType Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. Default value: None
log_training_metrics	str or <xref:azure.mgmt.machinelearningservices.models.LogTrainingMetrics> indicates whether or not to log training metrics. Must be 'Enable' or 'Disable' Default value: None
log_validation_loss	str or <xref:azure.mgmt.machinelearningservices.models.LogValidationLoss> indicates whether or not to log validation loss. Must be 'Enable' or 'Disable' Default value: None

Attributes

base_path

The base path of the resource.

Returns

Type	Description
str	The base path of the resource.

creation_context

The creation context of the resource.

Returns

Type	Description
Optional[SystemData]	The creation metadata for the resource.

id

The resource ID.

Returns

Type	Description
Optional[str]	The global ID of the resource, an Azure Resource Manager (ARM) ID.

inputs

limits

Returns the limit settings for all AutoML Image jobs.

Returns

Type	Description
ImageLimitSettings	The limit settings.

log_files

Job output files.

Returns

Type	Description
Optional[Dict[str, str]]	The dictionary of log names and URLs.

log_verbosity

Returns the verbosity of the logger.

Returns

Type	Description
<xref:azure.ai.ml._restclient.v2023_04_01_preview.models.LogVerbosity>	The log verbosity.

outputs

primary_metric

search_space

status

The status of the job.

Common values returned include "Running", "Completed", and "Failed". All possible values are:

NotStarted - This is a temporary state that client-side Run objects are in before cloud submission.
Starting - The Run has started being processed in the cloud. The caller has a run ID at this point.
Provisioning - On-demand compute is being created for a given job submission.
Preparing - The run environment is being prepared and is in one of two stages:
- Docker image build
- conda environment setup
Queued - The job is queued on the compute target. For example, in BatchAI, the job is in a queued state

while waiting for all the requested nodes to be ready.
Running - The job has started to run on the compute target.
Finalizing - User code execution has completed, and the run is in post-processing stages.
CancelRequested - Cancellation has been requested for the job.
Completed - The run has completed successfully. This includes both the user code execution and run

post-processing stages.
Failed - The run failed. Usually the Error property on a run will provide details as to why.
Canceled - Follows a cancellation request and indicates that the run is now successfully cancelled.
NotResponding - For runs that have Heartbeats enabled, no heartbeat has been recently sent.

Returns

Type	Description
Optional[str]	Status of the job.

studio_url

Azure ML studio endpoint.

Returns

Type	Description
Optional[str]	The URL to the job details page.

sweep

Returns the sweep settings for all AutoML Image jobs.

Returns

Type	Description
ImageSweepSettings	The sweep settings.

task_type

Get task type.

Returns

Type	Description
str	The type of task to run. Possible values include: "classification", "regression", "forecasting".

test_data

Get test data.

Returns

Type	Description
Input	Test data input

training_data

Get training data.

Returns

Type	Description
Input	Training data input

training_parameters

type

The type of the job.

Returns

Type	Description
Optional[str]	The type of the job.

validation_data

Get validation data.

Returns

Type	Description
Input	Validation data input

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ImageInstanceSegmentationJob Class

Constructor

Keyword-Only Parameters

Examples

Methods

dump

Parameters

Exceptions

extend_search_space

Parameters

set_data

Keyword-Only Parameters

Returns

set_limits

Keyword-Only Parameters

Returns

set_sweep

Keyword-Only Parameters

Returns

set_training_parameters

Keyword-Only Parameters

Attributes

base_path

Returns

creation_context

Returns

id

Returns

inputs

limits

Returns

log_files

Returns

log_verbosity

Returns

outputs

primary_metric

search_space

status

Returns

studio_url

Returns

sweep

Returns

task_type

Returns

test_data

Returns

training_data

Returns

training_parameters

type

Returns

validation_data

Returns

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