def submit_for_inference(args: SubmitForInferenceConfig,
azure_config: AzureConfig) -> Optional[Path]:
"""
Create and submit an inference to AzureML, and optionally download the resulting segmentation.
:param azure_config: An object with all necessary information for accessing Azure.
:param args: configuration, see SubmitForInferenceConfig
:return: path to downloaded segmentation on local disc, or None if none.
"""
logging.info(f"Building Azure configuration from {args.settings}")
logging.info("Getting workspace")
workspace = azure_config.get_workspace()
logging.info("Identifying model")
model = Model(workspace=workspace, id=args.model_id)
model_id = model.id
logging.info(f"Identified model {model_id}")
source_directory = tempfile.TemporaryDirectory()
source_directory_name = source_directory.name
logging.info(
f"Building inference run submission in {source_directory_name}")
source_directory_path = Path(source_directory_name)
copy_image_file(args.image_file,
source_directory_path / DEFAULT_DATA_FOLDER)
# We copy over run_scoring.py, and score.py as well in case the model we're using
# does not have sufficiently recent versions of those files.
for base in ["run_scoring.py", "score.py"]:
shutil.copyfile(base, str(source_directory_path / base))
source_config = SourceConfig(
root_folder=source_directory_name,
entry_script=str(source_directory_path / "run_scoring.py"),
script_params={
"--data-folder": ".",
"--spawnprocess": "python",
"--model-id": model_id,
"score.py": ""
},
conda_dependencies_files=download_conda_dependency_files(
model, source_directory_path))
estimator = create_estimator_from_configs(workspace, azure_config,
source_config, [])
exp = Experiment(workspace=workspace, name=args.experiment_name)
run = exp.submit(estimator)
logging.info(f"Submitted run {run.id} in experiment {run.experiment.name}")
logging.info(f"Run URL: {run.get_portal_url()}")
if not args.keep_upload_folder:
source_directory.cleanup()
logging.info(f"Deleted submission directory {source_directory_name}")
if args.download_folder is None:
return None
logging.info("Awaiting run completion")
run.wait_for_completion()
logging.info(f"Run has completed with status {run.get_status()}")
download_path = choose_download_path(args.download_folder)
logging.info(f"Attempting to download segmentation to {download_path}")
run.download_file(DEFAULT_RESULT_IMAGE_NAME, str(download_path))
if download_path.exists():
logging.info(f"Downloaded segmentation to {download_path}")
else:
logging.warning("Segmentation NOT downloaded")
return download_path
def submit_for_inference(args: SubmitForInferenceConfig,
azure_config: AzureConfig) -> Optional[Path]:
"""
Create and submit an inference to AzureML, and optionally download the resulting segmentation.
:param azure_config: An object with all necessary information for accessing Azure.
:param args: configuration, see SubmitForInferenceConfig
:return: path to downloaded segmentation on local disc, or None if none.
"""
logging.info(f"Building Azure configuration from {args.settings}")
logging.info("Getting workspace")
workspace = azure_config.get_workspace()
logging.info("Identifying model")
model = Model(workspace=workspace, id=args.model_id)
model_id = model.id
logging.info(f"Identified model {model_id}")
source_directory = tempfile.TemporaryDirectory()
source_directory_path = Path(source_directory.name)
logging.info(
f"Building inference run submission in {source_directory_path}")
image_folder = source_directory_path / fixed_paths.DEFAULT_DATA_FOLDER
image = copy_image_file(args.image_file, image_folder)
model_sas_urls = model.get_sas_urls()
# Identifies all the files with basename "environment.yml" in the model and downloads them.
# These downloads should go into a temp folder that will most likely not be included in the model itself,
# because the AzureML run will later download the model into the same folder structure, and the file names might
# clash.
temp_folder = source_directory_path / "temp_for_scoring"
conda_files = download_files_from_model(model_sas_urls,
ENVIRONMENT_YAML_FILE_NAME,
dir_path=temp_folder)
if not conda_files:
raise ValueError(
"At least 1 Conda environment definition must exist in the model.")
# Copy the scoring script from the repository. This will start the model download from Azure, and invoke the
# scoring script.
entry_script = source_directory_path / Path(
fixed_paths.RUN_SCORING_SCRIPT).name
shutil.copyfile(
str(
fixed_paths.repository_root_directory(
fixed_paths.RUN_SCORING_SCRIPT)), str(entry_script))
source_config = SourceConfig(
root_folder=source_directory_path,
entry_script=entry_script,
script_params={
"--model-folder": ".",
"--model-id": model_id,
fixed_paths.SCORE_SCRIPT: "",
# The data folder must be relative to the root folder of the AzureML job. test_image_files
# is then just the file relative to the data_folder
"--data_folder": image.parent.name,
"--image_files": image.name
},
conda_dependencies_files=conda_files,
)
estimator = create_estimator_from_configs(azure_config, source_config, [])
exp = Experiment(workspace=workspace, name=args.experiment_name)
run = exp.submit(estimator)
logging.info(f"Submitted run {run.id} in experiment {run.experiment.name}")
logging.info(f"Run URL: {run.get_portal_url()}")
if not args.keep_upload_folder:
source_directory.cleanup()
logging.info(f"Deleted submission directory {source_directory_path}")
if args.download_folder is None:
return None
logging.info("Awaiting run completion")
run.wait_for_completion()
logging.info(f"Run has completed with status {run.get_status()}")
download_path = choose_download_path(args.download_folder)
logging.info(f"Attempting to download segmentation to {download_path}")
run.download_file(DEFAULT_RESULT_IMAGE_NAME, str(download_path))
if download_path.exists():
logging.info(f"Downloaded segmentation to {download_path}")
else:
logging.warning("Segmentation NOT downloaded")
return download_path