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# coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: ml_samples_command_configurations.py
DESCRIPTION:
These samples demonstrate different ways to configure Command jobs and components.
USAGE:
python ml_samples_command_configurations.py
"""
import os
class CommandConfigurationOptions(object):
def ml_command_config(self):
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
subscription_id = os.environ["AZURE_SUBSCRIPTION_ID"]
resource_group = os.environ["RESOURCE_GROUP_NAME"]
workspace_name = "test-ws1"
credential = DefaultAzureCredential()
ml_client = MLClient(credential, subscription_id, resource_group, workspace_name=workspace_name)
from azure.ai.ml import Input, Output
from azure.ai.ml.entities import CommandJob, CommandJobLimits
# [START command_job_definition]
command_job = CommandJob(
code="./src",
command="python train.py --ss {search_space.ss}",
inputs={"input1": Input(path="trial.csv")},
outputs={"default": Output(path="./foo")},
compute="trial",
environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:33",
limits=CommandJobLimits(timeout=120),
)
# [END command_job_definition]
# [START command_function]
from azure.ai.ml import Input, Output, command
train_func = 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")},
)
# [END command_function]
# [START command_component_definition]
from azure.ai.ml.entities import CommandComponent
component = CommandComponent(
name="sample_command_component_basic",
display_name="CommandComponentBasic",
description="This is the basic command component",
tags={"tag": "tagvalue", "owner": "sdkteam"},
version="1",
outputs={"component_out_path": {"type": "uri_folder"}},
command="echo Hello World",
code="./src",
environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:33",
)
# [END command_component_definition]
# [START command_job_resource_configuration]
from azure.ai.ml import MpiDistribution
from azure.ai.ml.entities import JobResourceConfiguration
trial = CommandJob(
environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:33",
command="echo hello world",
distribution=MpiDistribution(),
environment_variables={"ENV1": "VAR1"},
resources=JobResourceConfiguration(instance_count=2, instance_type="STANDARD_BLA"),
code="./",
)
# [END command_job_resource_configuration]
# [START command_set_resources]
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",
)
# [END command_set_resources]
# [START command_set_limits]
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)
# [END command_set_limits]
# [START command_set_queue_settings]
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")
# [END command_set_queue_settings]
if __name__ == "__main__":
sample = CommandConfigurationOptions()
sample.ml_command_config()
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