Command Class

Reference

Base class for command node, used for command component version consumption.

You should not instantiate this class directly. Instead, you should create it using the builder function: command().

Inheritance: azure.ai.ml.entities._builders.base_node.BaseNode

Command

azure.ai.ml.entities._job.pipeline._io.mixin.NodeWithGroupInputMixin

Command

Constructor

Command(*, component: str | CommandComponent, compute: str | None = None, inputs: Dict[str, Input | str | bool | int | float | Enum] | None = None, outputs: Dict[str, str | Output] | None = None, limits: CommandJobLimits | None = None, identity: Dict | ManagedIdentityConfiguration | AmlTokenConfiguration | UserIdentityConfiguration | None = None, distribution: Dict | MpiDistribution | TensorFlowDistribution | PyTorchDistribution | RayDistribution | DistributionConfiguration | None = None, environment: Environment | str | None = None, environment_variables: Dict | None = None, resources: JobResourceConfiguration | None = None, services: Dict[str, JobService | JupyterLabJobService | SshJobService | TensorBoardJobService | VsCodeJobService] | None = None, queue_settings: QueueSettings | None = None, **kwargs: Any)

Keyword-Only Parameters

Name	Description
component	Union[str, CommandComponent] The ID or instance of the command component or job to be run for the step.
compute	Optional[str] The compute target the job will run on.
inputs	Optional[dict[str, Union[ Input, str, bool, int, float, <xref:Enum>]]] A mapping of input names to input data sources used in the job.
outputs	Optional[dict[str, Union[str, Output]]] A mapping of output names to output data sources used in the job.
limits	CommandJobLimits The limits for the command component or job.
identity	Optional[Union[ dict[str, str], ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]] The identity that the command job will use while running on compute.
distribution	Optional[Union[dict, PyTorchDistribution, MpiDistribution, TensorFlowDistribution, RayDistribution]] The configuration for distributed jobs.
environment	Optional[Union[str, Environment]] The environment that the job will run in.
environment_variables	Optional[dict[str, str]] A dictionary of environment variable names and values. These environment variables are set on the process where the user script is being executed.
resources	Optional[JobResourceConfiguration] The compute resource configuration for the command.
services	Optional[dict[str, Union[JobService, JupyterLabJobService, SshJobService, TensorBoardJobService, VsCodeJobService]]] The interactive services for the node. This is an experimental parameter, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.
queue_settings	Optional[QueueSettings] Queue settings for the job.

Methods

clear
copy
dump	Dumps the job content into a file in YAML format.
fromkeys	Create a new dictionary with keys from iterable and values set to value.
get	Return the value for key if key is in the dictionary, else default.
items
keys
pop	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem	Remove and return a (key, value) pair as a 2-tuple. Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.
set_limits	Set limits for Command.
set_queue_settings	Set QueueSettings for the job.
set_resources	Set resources for Command.
setdefault	Insert key with a value of default if key is not in the dictionary. Return the value for key if key is in the dictionary, else default.
sweep	Turns the command into a sweep node with extra sweep run setting. The command component in the current command node will be used as its trial component. A command node can sweep multiple times, and the generated sweep node will share the same trial component. ]]]
update	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values

clear

clear() -> None.  Remove all items from D.

copy

copy() -> a shallow copy of D

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.

fromkeys

Create a new dictionary with keys from iterable and values set to value.

fromkeys(value=None, /)

Positional-Only Parameters

Name	Description
iterable Required
value	Default value: None

Parameters

Name	Description
type Required

get

Return the value for key if key is in the dictionary, else default.

get(key, default=None, /)

Positional-Only Parameters

Name	Description
key Required
default	Default value: None

items

items() -> a set-like object providing a view on D's items

keys

keys() -> a set-like object providing a view on D's keys

pop

If the key is not found, return the default if given; otherwise, raise a KeyError.

pop(k, [d]) -> v, remove specified key and return the corresponding value.

popitem

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

popitem()

set_limits

Set limits for Command.

set_limits(*, timeout: int, **kwargs: Any) -> None

Keyword-Only Parameters

Name	Description
timeout	int The timeout for the job in seconds.

Examples

Setting a timeout limit of 10 seconds on a Command.


   from azure.ai.ml import Input, Output, command

   command_node = command(
       environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:33",
       command='echo "hello world"',
       distribution={"type": "Pytorch", "process_count_per_instance": 2},
       inputs={
           "training_data": Input(type="uri_folder"),
           "max_epochs": 20,
           "learning_rate": 1.8,
           "learning_rate_schedule": "time-based",
       },
       outputs={"model_output": Output(type="uri_folder")},
   )

   command_node.set_limits(timeout=10)

set_queue_settings

Set QueueSettings for the job.

set_queue_settings(*, job_tier: str | None = None, priority: str | None = None) -> None

Keyword-Only Parameters

Name	Description
job_tier	Optional[str] The job tier. Accepted values are "Spot", "Basic", "Standard", or "Premium".
priority	Optional[str] The priority of the job on the compute. Defaults to "Medium".

Examples

Configuring queue settings on a Command.


   from azure.ai.ml import Input, Output, command

   command_node = command(
       environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:33",
       command='echo "hello world"',
       distribution={"type": "Pytorch", "process_count_per_instance": 2},
       inputs={
           "training_data": Input(type="uri_folder"),
           "max_epochs": 20,
           "learning_rate": 1.8,
           "learning_rate_schedule": "time-based",
       },
       outputs={"model_output": Output(type="uri_folder")},
   )

   command_node.set_queue_settings(job_tier="standard", priority="medium")

set_resources

Set resources for Command.

set_resources(*, instance_type: str | List[str] | None = None, instance_count: int | None = None, locations: List[str] | None = None, properties: Dict | None = None, docker_args: str | None = None, shm_size: str | None = None, **kwargs: Any) -> None

Keyword-Only Parameters

Name	Description
instance_type	Optional[Union[str, List[str]]] The type of compute instance to run the job on. If not specified, the job will run on the default compute target.
instance_count	Optional[int] The number of instances to run the job on. If not specified, the job will run on a single instance.
locations	Optional[List[str]] The list of locations where the job will run. If not specified, the job will run on the default compute target.
properties	Optional[dict] The properties of the job.
docker_args	Optional[str] The Docker arguments for the job.
shm_size	Optional[str] The size of the docker container's shared memory block. This should be in the format of (number)(unit) where the number has to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).

Examples

Setting resources on a Command.


   from azure.ai.ml import Input, Output, command

   command_node = command(
       environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:33",
       command='echo "hello world"',
       distribution={"type": "Pytorch", "process_count_per_instance": 2},
       inputs={
           "training_data": Input(type="uri_folder"),
           "max_epochs": 20,
           "learning_rate": 1.8,
           "learning_rate_schedule": "time-based",
       },
       outputs={"model_output": Output(type="uri_folder")},
   )

   command_node.set_resources(
       instance_count=1,
       instance_type="STANDARD_D2_v2",
       properties={"key": "new_val"},
       shm_size="3g",
   )

setdefault

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

setdefault(key, default=None, /)

Positional-Only Parameters

Name	Description
key Required
default	Default value: None

sweep

Turns the command into a sweep node with extra sweep run setting. The command component in the current command node will be used as its trial component. A command node can sweep multiple times, and the generated sweep node will share the same trial component.

]]]

sweep(*, primary_metric: str, goal: str, sampling_algorithm: str = 'random', compute: str | None = None, max_concurrent_trials: int | None = None, max_total_trials: int | None = None, timeout: int | None = None, trial_timeout: int | None = None, early_termination_policy: str | EarlyTerminationPolicy | None = None, search_space: Dict[str, Choice | LogNormal | LogUniform | Normal | QLogNormal | QLogUniform | QNormal | QUniform | Randint | Uniform] | None = None, identity: ManagedIdentityConfiguration | AmlTokenConfiguration | UserIdentityConfiguration | None = None, queue_settings: QueueSettings | None = None, job_tier: str | None = None, priority: str | None = None) -> Sweep

Keyword-Only Parameters

Name	Description
queue_settings	Optional[QueueSettings] The queue settings for the job.
job_tier	Optional[str] Experimental The job tier. Accepted values are "Spot", "Basic", "Standard", or "Premium".
priority	Optional[str] Experimental The compute priority. Accepted values are "low", "medium", and "high".
primary_metric Required
goal Required
sampling_algorithm	Default value: random
compute Required
max_concurrent_trials Required
max_total_trials Required
timeout Required
trial_timeout Required
early_termination_policy Required
search_space Required
identity Required

Returns

Type	Description
Sweep	A Sweep node with the component from current Command node as its trial component.

Examples

Creating a Sweep node from a Command job.


   from azure.ai.ml import command

   job = command(
       inputs=dict(kernel="linear", penalty=1.0),
       compute=cpu_cluster,
       environment=f"{job_env.name}:{job_env.version}",
       code="./scripts",
       command="python scripts/train.py --kernel $kernel --penalty $penalty",
       experiment_name="sklearn-iris-flowers",
   )

   # we can reuse an existing Command Job as a function that we can apply inputs to for the sweep configurations
   from azure.ai.ml.sweep import Uniform

   job_for_sweep = job(
       kernel=Uniform(min_value=0.0005, max_value=0.005),
       penalty=Uniform(min_value=0.9, max_value=0.99),
   )

   from azure.ai.ml.sweep import BanditPolicy

   sweep_job = job_for_sweep.sweep(
       sampling_algorithm="random",
       primary_metric="best_val_acc",
       goal="Maximize",
       max_total_trials=8,
       max_concurrent_trials=4,
       early_termination_policy=BanditPolicy(slack_factor=0.15, evaluation_interval=1, delay_evaluation=10),
   )

update

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

update([E], **F) -> None.  Update D from dict/iterable E and F.

values

values() -> an object providing a view on D's values

Attributes

base_path

The base path of the resource.

Returns

Type	Description
str	The base path of the resource.

code

The source code to run the job.

Returns

Type	Description
Optional[Union[str, PathLike]]

command

The command to be executed.

Returns

Type	Description
Optional[str]

component

The ID or instance of the command component or job to be run for the step.

Returns

Type	Description
Union[str, CommandComponent]	The ID or instance of the command component or job to be run for the step.

creation_context

The creation context of the resource.

Returns

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

distribution

The configuration for the distributed command component or job.

Returns

Type	Description
Union[PyTorchDistribution, MpiDistribution, TensorFlowDistribution, RayDistribution]	The configuration for distributed jobs.

id

The resource ID.

Returns

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

identity

The identity that the job will use while running on compute.

Returns

Type	Description
Optional[Union[<xref:azure.ai.ml.ManagedIdentityConfiguration>, <xref:azure.ai.ml.AmlTokenConfiguration>, <xref:azure.ai.ml.UserIdentityConfiguration>]]	The identity that the job will use while running on compute.

inputs

Get the inputs for the object.

Returns

Type	Description
Dict[str, Union[Input, str, bool, int, float]]	A dictionary containing the inputs for the object.

log_files

Job output files.

Returns

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

name

Get the name of the node.

Returns

Type	Description
str	The name of the node.

outputs

Get the outputs of the object.

Returns

Type	Description
Dict[str, Union[str, Output]]	A dictionary containing the outputs for the object.

parameters

MLFlow parameters to be logged during the job.

Returns

Type	Description
dict[str, str]	The MLFlow parameters to be logged during the job.

queue_settings

The queue settings for the command component or job.

Returns

Type	Description
QueueSettings	The queue settings for the command component or job.

resources

The compute resource configuration for the command component or job.

Returns

Type	Description
JobResourceConfiguration

services

The interactive services for the node.

This is an experimental parameter, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Returns

Type	Description
dict[str, Union[JobService, JupyterLabJobService, SshJobService, TensorBoardJobService, VsCodeJobService]]

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.

type

The type of the job.

Returns

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

Share via

Command Class

Constructor

Keyword-Only Parameters

Methods

clear

copy

dump

Parameters

Exceptions

fromkeys

Positional-Only Parameters

Parameters

get

Positional-Only Parameters

items

keys

pop

popitem

set_limits

Keyword-Only Parameters

Examples

set_queue_settings

Keyword-Only Parameters

Examples

set_resources

Keyword-Only Parameters

Examples

setdefault

Positional-Only Parameters

sweep

Keyword-Only Parameters

Returns

Examples

update

values

Attributes

base_path

Returns

code

Returns

command

Returns

component

Returns

creation_context

Returns

distribution

Returns

id

Returns

identity

Returns

inputs

Returns

log_files

Returns

name

Returns

outputs

Returns

parameters

Returns

queue_settings

Returns

resources

Returns

services

Returns

status

Returns

studio_url

Returns

type

Returns

Additional resources