def download_conda_dependency_files(model: Model, dir_path: Path) -> List[Path]:
"""
Identifies all the files with basename "environment.yml" in the model and downloads them
to tmp_environment_001.yml, tmp_environment_002.yml etc. Normally there will be one of these
if the model was build directly from a clone of the InnerEye-DeepLearning repo, or two if
it was built from the user's own directory which had InnerEye-Deeplearning as a submodule.
:param model: model to search in
:param dir_path: folder to write the tmp...yml files into
:return: a list of the tmp...yml files created
"""
url_dict = model.get_sas_urls()
downloaded: List[Path] = []
for path, url in url_dict.items():
if Path(path).name == ENVIRONMENT_YAML_FILE_NAME:
target_path = dir_path / f"tmp_environment_{len(downloaded) + 1:03d}.yml"
target_path.write_bytes(requests.get(url, allow_redirects=True).content)
# Remove additional information from the URL to make it more legible
index = url.find("?")
if index > 0:
url = url[:index]
logging.info(f"Downloaded {target_path} from {url}")
downloaded.append(target_path)
if not downloaded:
logging.warning(f"No {ENVIRONMENT_YAML_FILE_NAME} files found in the model!")
return downloaded
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 / DEFAULT_DATA_FOLDER
image = copy_image_file(args.image_file, image_folder, args.use_dicom)
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.")
# Retrieve the name of the Python environment that the training run used. This environment should have been
# registered. If no such environment exists, it will be re-create from the Conda files provided.
python_environment_name = model.tags.get(PYTHON_ENVIRONMENT_NAME, "")
# 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(RUN_SCORING_SCRIPT).name
shutil.copyfile(str(repository_root_directory(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,
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,
"--use_dicom",
str(args.use_dicom)
],
conda_dependencies_files=conda_files,
)
run_config = create_run_config(azure_config,
source_config,
environment_name=python_environment_name)
exp = Experiment(workspace=workspace, name=args.experiment_name)
run = exp.submit(run_config)
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
