CLI (v2) sweep job YAML schema

APPLIES TO: Azure CLI ml extension v2 (current)

The source JSON schema can be found at https://azuremlschemas.azureedge.net/latest/sweepJob.schema.json.

Note

The YAML syntax detailed in this document is based on the JSON schema for the latest version of the ML CLI v2 extension. This syntax is guaranteed only to work with the latest version of the ML CLI v2 extension. You can find the schemas for older extension versions at https://azuremlschemasprod.azureedge.net/.

YAML syntax

Key	Type	Description	Allowed values	Default value
$schema	string	The YAML schema. If you use the Azure Machine Learning VS Code extension to author the YAML file, you can invoke schema and resource completions if you include $schema at the top of your file.
type	const	Required. The type of job.	sweep	sweep
name	string	Name of the job. Must be unique across all jobs in the workspace. If omitted, Azure Machine Learning autogenerates 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 autogenerates a human-readable adjective-noun identifier for the display name.
experiment_name	string	Organize the job under the experiment name. The run record of each job is organized under the corresponding experiment in the "Experiments" tab of the studio. If omitted, Azure Machine Learning defaults experiment_name 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.
sampling_algorithm	object	Required. The hyperparameter sampling algorithm to use over the search_space. One of RandomSamplingAlgorithm, GridSamplingAlgorithm,or BayesianSamplingAlgorithm.
search_space	object	Required. Dictionary of the hyperparameter search space. The hyperparameter name is the key, and the value is the parameter expression. Hyperparameters can be referenced in the trial.command with the ${{ search_space.<hyperparameter> }} expression.
search_space.<hyperparameter>	object	Visit Parameter expressions for the set of possible expressions to use.
objective.primary_metric	string	Required. The name of the primary metric reported by each trial job. The metric must be logged in the user's training script, using mlflow.log_metric() with the same corresponding metric name.
objective.goal	string	Required. The optimization goal of the objective.primary_metric.	maximize, minimize
early_termination	object	The early termination policy to use. A trial job is canceled when the criteria of the specified policy are met. If omitted, no early termination policy is applied. One of BanditPolicy, MedianStoppingPolicy,or TruncationSelectionPolicy.
limits	object	Limits for the sweep job. See Attributes of the limits key.
compute	string	Required. Name of the compute target on which to execute the job, with the azureml:<compute_name> syntax.
trial	object	Required. The job template for each trial. Each trial job is provided with a different combination of hyperparameter values that the system samples from the search_space. Visit Attributes of the trial key.
inputs	object	Dictionary of inputs to the job. The key is a name for the input within the context of the job and the value is the input value. Inputs can be referenced in the command using the ${{ inputs.<input_name> }} expression.
inputs.<input_name>	number, integer, boolean, string, or object	One of a literal value (of type number, integer, boolean, or string) or an object that contains a job input data specification.
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 can be referenced in the command using the ${{ outputs.<output_name> }} expression.
outputs.<output_name>	object	You can leave the object empty, and in that case, by default the output is of uri_folder type and Azure Machine Learning system-generates an output location for the output. All files to the output directory are written via read-write mount. To specify a different mode for the output, provide an object that contains the job output specification.
identity	object	The identity is used for data accessing. It can be User Identity Configuration, Managed Identity Configuration or None. For UserIdentityConfiguration, the identity of job submitter is used to access input data and write result to output folder. Otherwise, the managed identity of the compute target is used.

Sampling algorithms

RandomSamplingAlgorithm

Key	Type	Description	Allowed values	Default value
type	const	Required. The type of sampling algorithm.	random
seed	integer	A random seed to use to initialize the random number generation. If omitted, the default seed value is null.
rule	string	The type of random sampling to use. The default, random, uses simple uniform random sampling, while sobol uses the Sobol quasi-random sequence.	random, sobol	random

GridSamplingAlgorithm

Key	Type	Description	Allowed values
type	const	Required. The sampling algorithm type.	grid

BayesianSamplingAlgorithm

Key	Type	Description	Allowed values
type	const	Required. The sampling algorithm type.	bayesian

Early termination policies

BanditPolicy

Key	Type	Description	Allowed values	Default value
type	const	Required. The policy type.	bandit
slack_factor	number	The ratio used to calculate the allowed distance from the best performing trial. One of slack_factor or slack_amount is required.
slack_amount	number	The absolute distance allowed from the best performing trial. One of slack_factor or slack_amount is required.
evaluation_interval	integer	The frequency for applying the policy.		1
delay_evaluation	integer	The number of intervals for which to delay the first policy evaluation. If specified, the policy applies on every multiple of evaluation_interval that is greater than or equal to delay_evaluation.		0

MedianStoppingPolicy

Key	Type	Description	Allowed values	Default value
type	const	Required. The policy type.	median_stopping
evaluation_interval	integer	The frequency for applying the policy.		1
delay_evaluation	integer	The number of intervals for which to delay the first policy evaluation. If specified, the policy applies on every multiple of evaluation_interval that is greater than or equal to delay_evaluation.		0

TruncationSelectionPolicy

Key	Type	Description	Allowed values	Default value
type	const	Required. The policy type.	truncation_selection
truncation_percentage	integer	Required. The percentage of trial jobs to cancel at each evaluation interval.
evaluation_interval	integer	The frequency for applying the policy.		1
delay_evaluation	integer	The number of intervals for which to delay the first policy evaluation. If specified, the policy applies on every multiple of evaluation_interval that is greater than or equal to delay_evaluation.		0

Parameter expressions

Choice

Key	Type	Description	Allowed values
type	const	Required. The expression type.	choice
values	array	Required. The list of discrete values from which to choose.

Randint

Key	Type	Description	Allowed values
type	const	Required. The expression type.	randint
upper	integer	Required. The exclusive upper bound for the range of integers.

Qlognormal, qnormal

Key	Type	Description	Allowed values
type	const	Required. The expression type.	qlognormal, qnormal
mu	number	Required. The mean of the normal distribution.
sigma	number	Required. The standard deviation of the normal distribution.
q	integer	Required. The smoothing factor.

Qloguniform, quniform

Key	Type	Description	Allowed values
type	const	Required. The expression type.	qloguniform, quniform
min_value	number	Required. The minimum value in the range (inclusive).
max_value	number	Required. The maximum value in the range (inclusive).
q	integer	Required. The smoothing factor.

Lognormal, normal

Key	Type	Description	Allowed values
type	const	Required. The expression type.	lognormal, normal
mu	number	Required. The mean of the normal distribution.
sigma	number	Required. The standard deviation of the normal distribution.

Loguniform

Key	Type	Description	Allowed values
type	const	Required. The expression type.	loguniform
min_value	number	Required. The minimum value in the range is exp(min_value) (inclusive).
max_value	number	Required. The maximum value in the range is exp(max_value) (inclusive).

Uniform

Key	Type	Description	Allowed values
type	const	Required. The expression type.	uniform
min_value	number	Required. The minimum value in the range (inclusive).
max_value	number	Required. The maximum value in the range (inclusive).

Attributes of the limits key

Key	Type	Description	Default value
max_total_trials	integer	The maximum number of trial jobs.	1000
max_concurrent_trials	integer	The maximum number of trial jobs that can run concurrently.	Defaults to max_total_trials.
timeout	integer	The maximum time in seconds, that the entire sweep job is allowed to run. Once this limit is reached, the system cancels the sweep job, including all of its trials.	5184000
trial_timeout	integer	The maximum time in seconds each trial job is allowed to run. Once this limit is reached, the system cancels the trial.

Attributes of the trial key

Key	Type	Description	Default value
command	string	Required. The command to execute.
code	string	Local path to the source code directory to be uploaded and used for the job.
environment	string or object	Required. The environment to use for the job. This value can be either a reference to an existing versioned environment in the workspace or an inline environment specification. To reference an existing environment, use the azureml:<environment-name>:<environment-version> syntax. To define an environment inline, follow the Environment schema. Exclude the name and version properties because inline environments don't support them.
environment_variables	object	Dictionary of environment variable name-value pairs to set on the process where the command is executed.
distribution	object	The distribution configuration for distributed training scenarios. One of Mpi Configuration, PyTorch Configuration, or TensorFlow Configuration.
resources.instance_count	integer	The number of nodes to use for the job.	1

Distribution configurations

MpiConfiguration

Key	Type	Description	Allowed values
type	const	Required. Distribution type.	mpi
process_count_per_instance	integer	Required. The number of processes per node to launch for the job.

PyTorchConfiguration

Key	Type	Description	Allowed values	Default value
type	const	Required. Distribution type.	pytorch
process_count_per_instance	integer	The number of processes per node to launch for the job.		1

TensorFlowConfiguration

Key	Type	Description	Allowed values	Default value
type	const	Required. Distribution type.	tensorflow
worker_count	integer	The number of workers to launch for the job.		Defaults to resources.instance_count.
parameter_server_count	integer	The number of parameter servers to launch for the job.		0

Job inputs

Key	Type	Description	Allowed values	Default value
type	string	The type of job input. Specify uri_file for input data that points to a single file source, or uri_folder for input data that points to a folder source. For more information, visit Learn more about data access.	uri_file, uri_folder, mltable, mlflow_model	uri_folder
path	string	The path to the data to use as input. This value can be specified in a few ways: - A local path to the data source file or folder, for example, path: ./iris.csv. The data uploads during job submission. - A URI of a cloud path to the file or folder to use as the input. Supported URI types are azureml, https, wasbs, abfss, adl. For more information about use of the azureml:// URI format, visit Core yaml syntax. - An existing registered Azure Machine Learning data asset to use as the input. To reference a registered data asset, use the azureml:<data_name>:<data_version> syntax or azureml:<data_name>@latest (to reference the latest version of that data asset) - for example, path: azureml:cifar10-data:1 or path: azureml:cifar10-data@latest.
mode	string	Mode of how the data should be delivered to the compute target. For read-only mount (ro_mount), the data is consumed as a mount path. A folder is mounted as a folder and a file is mounted as a file. Azure Machine Learning resolves the input to the mount path. For download mode, the data is downloaded to the compute target. Azure Machine Learning resolves the input to the downloaded path. For just the URL of the storage location of the data artifact or artifacts, instead of mounting or downloading the data itself, use the direct mode. This passes in the URL of the storage location as the job input. In this case, you're fully responsible for handling credentials to access the storage.	ro_mount, download, direct	ro_mount

Job outputs

Key	Type	Description	Allowed values	Default value
type	string	The job output type. For the default uri_folder type, the output corresponds to a folder.	uri_file, uri_folder, mltable, mlflow_model	uri_folder
mode	string	Mode of the delivery of the output file or files to the destination storage. For the read-write mount mode (rw_mount), the output directory is a mounted directory. For the upload mode, all files written are uploaded at the end of the job.	rw_mount, upload	rw_mount

Identity configurations

UserIdentityConfiguration

Key	Type	Description	Allowed values
type	const	Required. Identity type.	user_identity

ManagedIdentityConfiguration

Key	Type	Description	Allowed values
type	const	Required. Identity type.	managed or managed_identity

Remarks

You can use the az ml job command to manage Azure Machine Learning jobs.

Examples

Visit the examples GitHub repository for examples. Several are shown here:

YAML: hello sweep

$schema: https://azuremlschemas.azureedge.net/latest/sweepJob.schema.json
type: sweep
trial:
  command: >-
    python hello-sweep.py
    --A ${{inputs.A}}
    --B ${{search_space.B}}
    --C ${{search_space.C}}
  code: src
  environment: azureml://registries/azureml/environments/sklearn-1.0/labels/latest
inputs:
  A: 0.5
sampling_algorithm: random
search_space:
  B:
    type: choice
    values: ["hello", "world", "hello_world"]
  C:
    type: uniform
    min_value: 0.1
    max_value: 1.0
objective:
  goal: minimize
  primary_metric: random_metric
limits:
  max_total_trials: 4
  max_concurrent_trials: 2
  timeout: 3600
display_name: hello-sweep-example
experiment_name: hello-sweep-example
description: Hello sweep job example.

YAML: basic Python model hyperparameter tuning

$schema: https://azuremlschemas.azureedge.net/latest/sweepJob.schema.json
type: sweep
trial:
  code: src
  command: >-
    python main.py 
    --iris-csv ${{inputs.iris_csv}}
    --C ${{search_space.C}}
    --kernel ${{search_space.kernel}}
    --coef0 ${{search_space.coef0}}
  environment: azureml://registries/azureml/environments/sklearn-1.0/labels/latest
inputs:
  iris_csv: 
    type: uri_file
    path: wasbs://datasets@azuremlexamples.blob.core.windows.net/iris.csv
compute: azureml:cpu-cluster
sampling_algorithm: random
search_space:
  C:
    type: uniform
    min_value: 0.5
    max_value: 0.9
  kernel:
    type: choice
    values: ["rbf", "linear", "poly"]
  coef0:
    type: uniform
    min_value: 0.1
    max_value: 1
objective:
  goal: minimize
  primary_metric: training_f1_score
limits:
  max_total_trials: 20
  max_concurrent_trials: 10
  timeout: 7200
display_name: sklearn-iris-sweep-example
experiment_name: sklearn-iris-sweep-example
description: Sweep hyperparemeters for training a scikit-learn SVM on the Iris dataset.

Next steps

• Install and use the CLI (v2)