RunConfiguration Class
Represents configuration for experiment runs targeting different compute targets in Azure Machine Learning.
The RunConfiguration object encapsulates the information necessary to submit a training run in an experiment. Typically, you will not create a RunConfiguration object directly but get one from a method that returns it, such as the submit method of the Experiment class.
RunConfiguration is a base environment configuration that is also used in other types of configuration steps that depend on what kind of run you are triggering. For example, when setting up a PythonScriptStep, you can access the step's RunConfiguration object and configure Conda dependencies or access the environment properties for the run.
For examples of run configurations, see Select and use a compute target to train your model.
Initialize a RunConfiguration with the default settings.
- Inheritance
-
azureml._base_sdk_common.abstract_run_config_element._AbstractRunConfigElementRunConfiguration
Constructor
RunConfiguration(script=None, arguments=None, framework=None, communicator=None, conda_dependencies=None, _history_enabled=None, _path=None, _name=None, command=None)
Parameters
Name | Description |
---|---|
script
|
The relative path to the Python script file. The file path is relative to the source directory passed to submit. Default value: None
|
arguments
|
Command line arguments for the Python script file. Default value: None
|
framework
|
The targeted framework used in the run. Supported frameworks are Python, PySpark, TensorFlow, and PyTorch. Default value: None
|
communicator
|
The communicator used in the run. The supported communicators are None, ParameterServer, OpenMpi, and IntelMpi. Keep in mind that OpenMpi requires a custom image with OpenMpi installed. Use ParameterServer or OpenMpi for AmlCompute clusters. Use IntelMpi for distributed training jobs. Default value: None
|
conda_dependencies
|
When left at the default value of False, the system creates a Python environment,
which includes the packages specified in Default value: None
|
auto_prepare_environment
Required
|
DEPRECATED. This setting is no longer used. |
command
|
The command to be submitted for the run. The command property can also be used instead of script/arguments. Both command and script/argument properties cannot be used together to submit a run. To submit a script file using the command property - ['python', 'train.py', '–arg1', arg1_val] To run an actual command - ['ls'] Default value: None
|
_history_enabled
|
Default value: None
|
_path
|
Default value: None
|
_name
|
Default value: None
|
Remarks
We build machine learning systems typically to solve a specific problem. For example, we might be interested in finding the best model that ranks web pages that might be served as search results corresponding to a query. Our search for the best machine learning model may require us try out different algorithms, or consider different parameter settings, etc.
In the Azure Machine Learning SDK, we use the concept of an experiment to capture the notion that different training runs are related by the problem that they're trying to solve. An Experiment then acts as a logical container for these training runs, making it easier to track progress across training runs, compare two training runs directly, etc.
The RunConfiguration encapsulates execution environment settings necessary to submit a training run in an experiment. It captures both the shared structure of training runs that are designed to solve the same machine learning problem, as well as the differences in the configuration parameters (e.g., learning rate, loss function, etc.) that distinguish distinct training runs from each other.
In typical training scenarios, RunConfiguration is used by creating a ScriptRunConfig object that packages together a RunConfiguration object and an execution script for training.
The configuration of RunConfiguration includes:
Bundling the experiment source directory including the submitted script.
Setting the Command line arguments for the submitted script.
Configuring the path for the Python interpreter.
Obtain Conda configuration for to manage the application dependencies. The job submission process can use the configuration to provision a temp Conda environment and launch the application within. The temp environments are cached and reused in subsequent runs.
Optional usage of Docker and custom base images.
Optional choice of submitting the experiment to multiple types of Azure compute.
Optional choice of configuring how to materialize inputs and upload outputs.
Advanced runtime settings for common runtimes like spark and tensorflow.
The following example shows how to submit a training script on your local machine.
from azureml.core import ScriptRunConfig, RunConfiguration, Experiment
# create or load an experiment
experiment = Experiment(workspace, "MyExperiment")
# run a trial from the train.py code in your current directory
config = ScriptRunConfig(source_directory='.', script='train.py',
run_config=RunConfiguration())
run = experiment.submit(config)
The following example shows how to submit a training script on your cluster using the command property instead of script and arguments.
from azureml.core import ScriptRunConfig, Experiment
# create or load an experiment
experiment = Experiment(workspace, 'MyExperiment')
# create or retrieve a compute target
cluster = workspace.compute_targets['MyCluster']
# create or retrieve an environment
env = Environment.get(ws, name='MyEnvironment')
# configure and submit your training run
config = ScriptRunConfig(source_directory='.',
command=['python', 'train.py', '--arg1', arg1_val],
compute_target=cluster,
environment=env)
script_run = experiment.submit(config)
The following sample shows how to run a command on your cluster.
from azureml.core import ScriptRunConfig, Experiment
# create or load an experiment
experiment = Experiment(workspace, 'MyExperiment')
# create or retrieve a compute target
cluster = workspace.compute_targets['MyCluster']
# create or retrieve an environment
env = Environment.get(ws, name='MyEnvironment')
# configure and submit your training run
config = ScriptRunConfig(source_directory='.',
command=['ls', '-l'],
compute_target=cluster,
environment=env)
script_run = experiment.submit(config)
Variables
Name | Description |
---|---|
environment
|
The environment definition. This field configures the Python environment. It can be configured to use an existing Python environment or configure to setup a temp environment for the experiment. The definition is also responsible for setting the required application dependencies. |
max_run_duration_seconds
|
The maximum time allowed for the run. The system will attempt to automatically cancel the run if it took longer than this value. |
node_count
|
The number of nodes to use for the job. |
priority
|
The priority of the job for scheduling policy. |
history
|
The configuration section used to disable and enable experiment history logging features. |
spark
|
When the platform is set to PySpark, the Spark configuration section is used to set the default SparkConf for the submitted job. |
hdi
|
The HDI configuration section takes effect only when the target is set to an Azure HDI compute. The HDI Configuration is used to set the YARN deployment mode. The default deployment mode is cluster. |
docker
|
The Docker configuration section is used to set variables for the Docker environment. |
tensorflow
|
The configuration section used to configure distributed TensorFlow parameters.
This parameter takes effect only when the |
mpi
|
The configuration section used to configure distributed MPI job parameters.
This parameter takes effect only when the |
pytorch
|
The configuration section used to configure distributed PyTorch job parameters.
This parameter takes effect only when the |
paralleltask
|
The configuration section used to configure distributed paralleltask job parameters.
This parameter takes effect only when the |
data_references
|
All the data sources are available to the run during execution based on each configuration. For each item of the dictionary, the key is a name given to the data source and the value is a DataReferenceConfiguration. |
data
|
All the data to make available to the run during execution. |
datacaches
|
<xref:buildin.list>[DatacacheConfiguration]
All the data to make datacache available to the run during execution. |
output_data
|
All the outputs that should be uploaded and tracked for this run. |
source_directory_data_store
|
The backing datastore for the project share. |
amlcompute
|
The details of the compute target to be created during experiment. The configuration only takes effect when the compute target is AmlCompute. |
kubernetescompute
|
The details of the compute target to be used during the experiment. The configuration only takes effect when the compute target is KubernetesCompute. |
services
|
Endpoints to interactive with the compute resource. Allowed endpoints are Jupyter, JupyterLab, VS Code, Tensorboard, SSH, and Custom ports. |
Methods
delete |
Delete a run configuration file. Raises a UserErrorException if the configuration file is not found. |
load |
Load a previously saved run configuration file from an on-disk file. If If |
save |
Save the RunConfiguration to a file on disk. A UserErrorException is raised when:
If If This method is useful when editing the configuration manually or when sharing the configuration with the CLI. |
delete
Delete a run configuration file.
Raises a UserErrorException if the configuration file is not found.
static delete(path, name)
Parameters
Name | Description |
---|---|
path
Required
|
A user selected root directory for run configurations. Typically this is the Git Repository or the Python project root directory. The configuration is deleted from a sub directory named .azureml. |
name
Required
|
The configuration file name. |
Exceptions
Type | Description |
---|---|
UserErrorException
|
load
Load a previously saved run configuration file from an on-disk file.
If path
points to a file, the RunConfiguration is loaded from that file.
If path
points to a directory, which should be a project directory, then the RunConfiguration is loaded
from <path>/.azureml/<name> or <path>/aml_config/<name>.
static load(path, name=None)
Parameters
Name | Description |
---|---|
path
Required
|
A user selected root directory for run configurations. Typically this is the Git Repository or the Python project root directory. For backward compatibility, the configuration will also be loaded from .azureml or aml_config sub directory. If the file is not in those directories, the file is loaded from the specified path. |
name
|
The configuration file name. Default value: None
|
Returns
Type | Description |
---|---|
The run configuration object. |
Exceptions
Type | Description |
---|---|
UserErrorException
|
save
Save the RunConfiguration to a file on disk.
A UserErrorException is raised when:
The RunConfiguration can't be saved with the name specified.
No
name
parameter was specified.The
path
parameter is invalid.
If path
is of the format <dir_path>/<file_name>, where <dir_path> is a valid directory, then the
RunConfiguration is saved at <dir_path>/<file_name>.
If path
points to a directory, which should be a project directory, then the RunConfiguration is saved
at <path>/.azureml/<name> or <path>/aml_config/<name>.
This method is useful when editing the configuration manually or when sharing the configuration with the CLI.
save(path=None, name=None, separate_environment_yaml=False)
Parameters
Name | Description |
---|---|
separate_environment_yaml
|
Indicates whether to save the Conda environment configuration. If True, the Conda environment configuration is saved to a YAML file named 'environment.yml'. Default value: False
|
path
|
A user selected root directory for run configurations. Typically this is the Git Repository or the Python project root directory. The configuration is saved to a sub directory named .azureml. Default value: None
|
name
|
[Required] The configuration file name. Default value: None
|
Returns
Type | Description |
---|---|
Exceptions
Type | Description |
---|---|
UserErrorException
|
Attributes
auto_prepare_environment
Get the auto_prepare_environment
parameter. This is a deprecated and unused setting.
environment_variables
target
Get compute target where the job is scheduled for execution.
The default target is "local" referring to the local machine. Available cloud compute targets can be found using the function compute_targets.
Returns
Type | Description |
---|---|
The target name |
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