def create_run_config(azure_config: AzureConfig,
source_config: SourceConfig,
all_azure_dataset_ids: List[str],
all_dataset_mountpoints: List[str],
environment_name: str = "") -> ScriptRunConfig:
"""
Creates a configuration to run the InnerEye training script in AzureML.
:param azure_config: azure related configurations to use for model scale-out behaviour
:param source_config: configurations for model execution, such as name and execution mode
:param all_azure_dataset_ids: The name of all datasets on blob storage that will be used for this run.
:param all_dataset_mountpoints: When using the datasets in AzureML, these are the per-dataset mount points.
:param environment_name: If specified, try to retrieve the existing Python environment with this name. If that
is not found, create one from the Conda files provided in `source_config`. This parameter is meant to be used
when running inference for an existing model.
:return: The configured script run.
"""
dataset_consumptions = create_dataset_consumptions(
azure_config, all_azure_dataset_ids, all_dataset_mountpoints)
# AzureML seems to sometimes expect the entry script path in Linux format, hence convert to posix path
entry_script_relative_path = source_config.entry_script.relative_to(
source_config.root_folder).as_posix()
logging.info(
f"Entry script {entry_script_relative_path} ({source_config.entry_script} relative to "
f"source directory {source_config.root_folder})")
max_run_duration = None
if azure_config.max_run_duration:
max_run_duration = run_duration_string_to_seconds(
azure_config.max_run_duration)
workspace = azure_config.get_workspace()
run_config = RunConfiguration(
script=entry_script_relative_path,
arguments=source_config.script_params,
)
run_config.environment = get_or_create_python_environment(
azure_config, source_config, environment_name=environment_name)
run_config.target = azure_config.cluster
run_config.max_run_duration_seconds = max_run_duration
if azure_config.num_nodes > 1:
distributed_job_config = MpiConfiguration(
node_count=azure_config.num_nodes)
run_config.mpi = distributed_job_config
run_config.framework = "Python"
run_config.communicator = "IntelMpi"
run_config.node_count = distributed_job_config.node_count
if len(dataset_consumptions) > 0:
run_config.data = {
dataset.name: dataset
for dataset in dataset_consumptions
}
# Use blob storage for storing the source, rather than the FileShares section of the storage account.
run_config.source_directory_data_store = workspace.datastores.get(
WORKSPACE_DEFAULT_BLOB_STORE_NAME).name
script_run_config = ScriptRunConfig(
source_directory=str(source_config.root_folder),
run_config=run_config,
)
if azure_config.hyperdrive:
script_run_config = source_config.hyperdrive_config_func(
script_run_config) # type: ignore
return script_run_config
def create_run_config(azure_config: AzureConfig,
source_config: SourceConfig,
azure_dataset_id: str = "",
environment_name: str = "") -> ScriptRunConfig:
"""
Creates a configuration to run the InnerEye training script in AzureML.
:param azure_config: azure related configurations to use for model scale-out behaviour
:param source_config: configurations for model execution, such as name and execution mode
:param azure_dataset_id: The name of the dataset in blob storage to be used for this run. This can be an empty
string to not use any datasets.
:param environment_name: If specified, try to retrieve the existing Python environment with this name. If that
is not found, create one from the Conda files provided in `source_config`. This parameter is meant to be used
when running inference for an existing model.
:return: The configured script run.
"""
if azure_dataset_id:
azureml_dataset = get_or_create_dataset(azure_config, azure_dataset_id=azure_dataset_id)
if not azureml_dataset:
raise ValueError(f"AzureML dataset {azure_dataset_id} could not be found or created.")
named_input = azureml_dataset.as_named_input(INPUT_DATA_KEY)
dataset_consumption = named_input.as_mount() if azure_config.use_dataset_mount else named_input.as_download()
else:
dataset_consumption = None
# AzureML seems to sometimes expect the entry script path in Linux format, hence convert to posix path
entry_script_relative_path = source_config.entry_script.relative_to(source_config.root_folder).as_posix()
logging.info(f"Entry script {entry_script_relative_path} ({source_config.entry_script} relative to "
f"source directory {source_config.root_folder})")
max_run_duration = None
if azure_config.max_run_duration:
max_run_duration = run_duration_string_to_seconds(azure_config.max_run_duration)
workspace = azure_config.get_workspace()
run_config = RunConfiguration(
script=entry_script_relative_path,
arguments=source_config.script_params,
)
run_config.environment = get_or_create_python_environment(azure_config, source_config,
environment_name=environment_name)
run_config.target = azure_config.cluster
run_config.max_run_duration_seconds = max_run_duration
if azure_config.num_nodes > 1:
distributed_job_config = MpiConfiguration(node_count=azure_config.num_nodes)
run_config.mpi = distributed_job_config
run_config.framework = "Python"
run_config.communicator = "IntelMpi"
run_config.node_count = distributed_job_config.node_count
if dataset_consumption:
run_config.data = {dataset_consumption.name: dataset_consumption}
# Use blob storage for storing the source, rather than the FileShares section of the storage account.
run_config.source_directory_data_store = workspace.datastores.get(WORKSPACE_DEFAULT_BLOB_STORE_NAME).name
script_run_config = ScriptRunConfig(
source_directory=str(source_config.root_folder),
run_config=run_config,
)
if azure_config.hyperdrive:
script_run_config = source_config.hyperdrive_config_func(script_run_config) # type: ignore
return script_run_config
def create_estimator_from_configs(
azure_config: AzureConfig, source_config: SourceConfig,
estimator_inputs: List[DatasetConsumptionConfig]) -> PyTorch:
"""
Create an return a PyTorch estimator from the provided configuration information.
:param azure_config: Azure configuration, used to store various values for the job to be submitted
:param source_config: source configutation, for other needed values
:param estimator_inputs: value for the "inputs" field of the estimator.
:return:
"""
# AzureML seems to sometimes expect the entry script path in Linux format, hence convert to posix path
entry_script_relative_path = Path(source_config.entry_script).relative_to(
source_config.root_folder).as_posix()
logging.info(
f"Entry script {entry_script_relative_path} ({source_config.entry_script} relative to "
f"source directory {source_config.root_folder})")
environment_variables = {
"AZUREML_OUTPUT_UPLOAD_TIMEOUT_SEC":
str(source_config.upload_timeout_seconds),
"MKL_SERVICE_FORCE_INTEL":
"1",
**(source_config.environment_variables or {})
}
# Merge the project-specific dependencies with the packages that InnerEye itself needs. This should not be
# necessary if the innereye package is installed. It is necessary when working with an outer project and
# InnerEye as a git submodule and submitting jobs from the local machine.
# In case of version conflicts, the package version in the outer project is given priority.
conda_dependencies = merge_conda_dependencies(
source_config.conda_dependencies_files) # type: ignore
if azure_config.pip_extra_index_url:
# When an extra-index-url is supplied, swap the order in which packages are searched for.
# This is necessary if we need to consume packages from extra-index that clash with names of packages on
# pypi
conda_dependencies.set_pip_option(
f"--index-url {azure_config.pip_extra_index_url}")
conda_dependencies.set_pip_option(
"--extra-index-url https://pypi.org/simple")
# create Estimator environment
framework_version = pytorch_version_from_conda_dependencies(
conda_dependencies)
logging.info(f"PyTorch framework version: {framework_version}")
max_run_duration = None
if azure_config.max_run_duration:
max_run_duration = run_duration_string_to_seconds(
azure_config.max_run_duration)
workspace = azure_config.get_workspace()
estimator = PyTorch(
source_directory=source_config.root_folder,
entry_script=entry_script_relative_path,
script_params=source_config.script_params,
compute_target=azure_config.cluster,
# Use blob storage for storing the source, rather than the FileShares section of the storage account.
source_directory_data_store=workspace.datastores.get(
WORKSPACE_DEFAULT_BLOB_STORE_NAME),
inputs=estimator_inputs,
environment_variables=environment_variables,
shm_size=azure_config.docker_shm_size,
use_docker=True,
use_gpu=True,
framework_version=framework_version,
max_run_duration_seconds=max_run_duration)
estimator.run_config.environment.python.conda_dependencies = conda_dependencies
# We'd like to log the estimator config, but conversion to string fails when the Estimator has some inputs.
# logging.info(azure_util.estimator_to_string(estimator))
if azure_config.hyperdrive:
estimator = source_config.hyperdrive_config_func(
estimator) # type: ignore
return estimator