зеркало из https://github.com/microsoft/hi-ml.git
Increase test coverage, add datasets to submission code (#22)
This commit is contained in:
Anton Schwaighofer
GitHub
Родитель
d52270a4d8
Коммит
be41c9be18
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@ -0,0 +1,7 @@
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<component name="CopyrightManager">
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<copyright>
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<option name="keyword" value="Copyright .* Microsoft Corporation" />
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<option name="notice" value="------------------------------------------------------------------------------------------ Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License (MIT). See LICENSE in the repo root for license information. ------------------------------------------------------------------------------------------" />
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<option name="myName" value="MIT license" />
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</copyright>
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</component>
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@ -0,0 +1,7 @@
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<component name="CopyrightManager">
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<settings>
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<module2copyright>
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<element module="All" copyright="MIT license" />
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</module2copyright>
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</settings>
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</component>
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@ -1,3 +1,6 @@
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[pytest]
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log_cli = True
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log_cli_level = DEBUG
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log_cli_level = DEBUG
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addopts=--strict-markers
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markers=
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fast: Tests that should run very fast, and can act as smoke tests to see if something goes terrribly wrong.
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@ -1,13 +1,9 @@
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import logging
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from typing import List
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from typing import Optional
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from typing import Union
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from pathlib import Path
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from typing import List, Optional, Union
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from azureml.core import Dataset
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from azureml.core import Datastore
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from azureml.core import Workspace
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from azureml.data import FileDataset
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from azureml.data import OutputFileDatasetConfig
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from azureml.core import Dataset, Datastore, Workspace
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from azureml.data import FileDataset, OutputFileDatasetConfig
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from azureml.data.dataset_consumption_config import DatasetConsumptionConfig
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@ -76,7 +72,7 @@ class DatasetConfig:
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version: Optional[int] = None,
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use_mounting: Optional[bool] = None,
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target_folder: str = "",
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local_folder: str = ""):
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local_folder: Optional[Path] = None):
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"""
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Creates a new configuration for using an AzureML dataset.
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:param name: The name of the dataset, as it was registered in the AzureML workspace. For output datasets,
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@ -15,21 +15,12 @@ import sys
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from contextlib import contextmanager
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Dict
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from typing import Generator
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from typing import List
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from typing import Optional
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from typing import Dict, Generator, List, Optional
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from azureml.core import Experiment
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from azureml.core import Run
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from azureml.core import RunConfiguration
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from azureml.core import ScriptRunConfig
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from azureml.core import Workspace
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from azureml.core import Experiment, Run, RunConfiguration, ScriptRunConfig, Workspace
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from health.azure.datasets import StrOrDatasetConfig
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from src.health.azure.himl_configs import SourceConfig
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from src.health.azure.himl_configs import WorkspaceConfig
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from src.health.azure.himl_configs import get_authentication
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from health.azure.datasets import StrOrDatasetConfig, _input_dataset_key, _output_dataset_key, _replace_string_datasets
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from src.health.azure.himl_configs import SourceConfig, WorkspaceConfig, get_authentication
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logger = logging.getLogger('health.azure')
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logger.setLevel(logging.DEBUG)
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@ -38,12 +29,13 @@ logger.setLevel(logging.DEBUG)
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RUN_CONTEXT = Run.get_context()
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WORKSPACE_CONFIG_JSON = "config.json"
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AZUREML_COMMANDLINE_FLAG = "--azureml"
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@dataclass
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class AzureRunInformation:
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input_datasets: List[Path]
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output_datasets: List[Path]
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input_datasets: List[Optional[Path]]
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output_datasets: List[Optional[Path]]
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run: Run
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is_running_in_azure: bool
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# In Azure, this would be the "outputs" folder. In local runs: "." or create a timestamped folder.
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@ -61,21 +53,19 @@ def is_running_in_azure(run: Run = RUN_CONTEXT) -> bool:
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return hasattr(run, 'experiment')
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def submit_to_azure_if_needed(
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workspace_config: Optional[WorkspaceConfig],
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workspace_config_path: Optional[Path],
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compute_cluster_name: str,
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snapshot_root_directory: Path,
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entry_script: Path,
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conda_environment_file: Path,
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script_params: List[str] = [],
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environment_variables: Dict[str, str] = {},
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ignored_folders: List[Path] = [],
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default_datastore: str = "",
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input_datasets: Optional[List[StrOrDatasetConfig]] = None,
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output_datasets: Optional[List[StrOrDatasetConfig]] = None,
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num_nodes: int = 1,
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) -> Run:
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def submit_to_azure_if_needed(entry_script: Path, # type: ignore
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compute_cluster_name: str,
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conda_environment_file: Path,
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workspace_config: Optional[WorkspaceConfig] = None,
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workspace_config_path: Optional[Path] = None,
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snapshot_root_directory: Optional[Path] = None,
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environment_variables: Optional[Dict[str, str]] = None,
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ignored_folders: Optional[List[Path]] = None,
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default_datastore: str = "",
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input_datasets: Optional[List[StrOrDatasetConfig]] = None,
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output_datasets: Optional[List[StrOrDatasetConfig]] = None,
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num_nodes: int = 1,
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) -> AzureRunInformation:
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"""
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Submit a folder to Azure, if needed and run it.
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@ -85,9 +75,33 @@ def submit_to_azure_if_needed(
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:param workspace_config_file: Optional path to workspace config file.
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:return: Run object for the submitted AzureML run.
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"""
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if all(["azureml" not in arg for arg in sys.argv]):
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logging.info("The flag azureml is not set, and so not submitting to AzureML")
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return
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cleaned_input_datasets = _replace_string_datasets(input_datasets or [],
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default_datastore_name=default_datastore)
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cleaned_output_datasets = _replace_string_datasets(output_datasets or [],
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default_datastore_name=default_datastore)
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in_azure = is_running_in_azure()
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if in_azure:
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returned_input_datasets = [RUN_CONTEXT.input_datasets[_input_dataset_key(index)]
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for index in range(len(cleaned_input_datasets))]
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returned_output_datasets = [RUN_CONTEXT.output_datasets[_output_dataset_key(index)]
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for index in range(len(cleaned_output_datasets))]
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return AzureRunInformation(
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input_datasets=returned_input_datasets,
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output_datasets=returned_output_datasets,
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run=RUN_CONTEXT,
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is_running_in_azure=True,
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output_folder=Path.cwd() / "outputs",
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log_folder=Path.cwd() / "logs"
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)
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if AZUREML_COMMANDLINE_FLAG not in sys.argv[1:]:
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return AzureRunInformation(
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input_datasets=[d.local_folder for d in cleaned_input_datasets],
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output_datasets=[d.local_folder for d in cleaned_output_datasets],
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run=RUN_CONTEXT,
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is_running_in_azure=False,
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output_folder=Path.cwd() / "outputs",
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log_folder=Path.cwd() / "logs"
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)
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if workspace_config_path and workspace_config_path.is_file():
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auth = get_authentication()
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workspace = Workspace.from_config(path=workspace_config_path, auth=auth)
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@ -101,12 +115,12 @@ def submit_to_azure_if_needed(
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snapshot_root_directory=snapshot_root_directory,
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conda_environment_file=conda_environment_file,
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entry_script=entry_script,
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script_params=script_params,
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script_params=[p for p in sys.argv[1:] if p != AZUREML_COMMANDLINE_FLAG],
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environment_variables=environment_variables)
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with append_to_amlignore(
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dirs_to_append=ignored_folders,
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snapshot_root_directory=snapshot_root_directory):
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dirs_to_append=ignored_folders or [],
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snapshot_root_directory=snapshot_root_directory or Path.cwd()):
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# TODO: InnerEye.azure.azure_runner.submit_to_azureml does work here with interupt handlers to kill interupted
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# jobs. We'll do that later if still required.
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@ -115,6 +129,16 @@ def submit_to_azure_if_needed(
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run_config = RunConfiguration(
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script=entry_script_relative_path,
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arguments=source_config.script_params)
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inputs = {}
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for index, d in enumerate(cleaned_input_datasets):
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consumption = d.to_input_dataset(workspace=workspace, dataset_index=index)
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inputs[consumption.name] = consumption
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outputs = {}
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for index, d in enumerate(cleaned_output_datasets):
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out = d.to_output_dataset(workspace=workspace, dataset_index=index)
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outputs[out.name] = out
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run_config.data = inputs
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run_config.output_data = outputs
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script_run_config = ScriptRunConfig(
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source_directory=str(source_config.snapshot_root_directory),
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run_config=run_config,
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@ -134,7 +158,7 @@ def submit_to_azure_if_needed(
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logging.info("Experiment URL: {}".format(experiment.get_portal_url()))
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logging.info("Run URL: {}".format(run.get_portal_url()))
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logging.info("==============================================================================\n")
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return run
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exit(0)
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@contextmanager
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@ -2,7 +2,8 @@ from pathlib import Path
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from torchvision.datasets import MNIST
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from health.azure.aml import submit_to_azure_if_needed, DatasetConfig
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from health.azure.datasets import DatasetConfig
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from health.azure.himl import submit_to_azure_if_needed
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def main() -> None:
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@ -3,20 +3,28 @@
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# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
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# ------------------------------------------------------------------------------------------
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import logging
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from pathlib import Path
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from unittest import mock
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import pytest
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from health.azure.himl import AzureRunInformation
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from health.azure.himl import submit_to_azure_if_needed
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logger = logging.getLogger('test.health.azure')
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logger.setLevel(logging.DEBUG)
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@pytest.mark.fast
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def test_submit_to_azure_if_needed() -> None:
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"""
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Test that submit_to_azure_if_needed can be called.
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Test that submit_to_azure_if_needed can be called, and returns immediately.
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"""
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with pytest.raises(Exception) as ex:
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submit_to_azure_if_needed(
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workspace_config=None,
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workspace_config_path=None)
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with mock.patch("sys.argv", [""]):
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result = submit_to_azure_if_needed(entry_script=Path(__file__),
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compute_cluster_name="foo",
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conda_environment_file=Path("env.yml"),
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)
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assert isinstance(result, AzureRunInformation)
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assert not result.is_running_in_azure
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@ -1,14 +1,14 @@
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from unittest import mock
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import pytest
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from azureml.data import OutputFileDatasetConfig
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from azureml.core import Dataset
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from azureml.data import FileDataset, OutputFileDatasetConfig
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from azureml.data.azure_storage_datastore import AzureBlobDatastore
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from azureml.data.dataset_consumption_config import DatasetConsumptionConfig
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from health.azure.datasets import DatasetConfig
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from health.azure.datasets import get_datastore
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from testhiml.health.azure.utils import DEFAULT_DATASTORE
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from testhiml.health.azure.utils import DEFAULT_WORKSPACE
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from health.azure.datasets import (DatasetConfig, _input_dataset_key, _output_dataset_key, _replace_string_datasets,
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get_datastore, get_or_create_dataset)
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from testhiml.health.azure.utils import DEFAULT_DATASTORE, default_aml_workspace
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def test_datasetconfig_init() -> None:
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@ -17,40 +17,34 @@ def test_datasetconfig_init() -> None:
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assert "name of the dataset must be a non-empty string" in str(ex)
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def test_get_datastore_fails() -> None:
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"""
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Retrieving a datastore that does not exist should fail
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"""
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does_not_exist = "does_not_exist"
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with pytest.raises(ValueError) as ex:
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get_datastore(workspace=DEFAULT_WORKSPACE, datastore_name=does_not_exist)
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assert f"Datastore {does_not_exist} was not found" in str(ex)
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def test_get_datastore_without_name() -> None:
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"""
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Trying to get a datastore without name should only work if there is a single datastore
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"""
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assert len(DEFAULT_WORKSPACE.datastores) > 1
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with pytest.raises(ValueError) as ex:
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get_datastore(workspace=DEFAULT_WORKSPACE, datastore_name="")
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assert "No datastore name provided" in str(ex)
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def test_get_datastore() -> None:
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"""
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Tests getting a datastore by name.
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Test retrieving a datastore from the AML workspace.
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"""
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# Retrieving a datastore that does not exist should fail
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does_not_exist = "does_not_exist"
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workspace = default_aml_workspace()
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with pytest.raises(ValueError) as ex:
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get_datastore(workspace=workspace, datastore_name=does_not_exist)
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assert f"Datastore {does_not_exist} was not found" in str(ex)
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# Trying to get a datastore without name should only work if there is a single datastore
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assert len(workspace.datastores) > 1
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with pytest.raises(ValueError) as ex:
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get_datastore(workspace=workspace, datastore_name="")
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assert "No datastore name provided" in str(ex)
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# Retrieve a datastore by name
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name = DEFAULT_DATASTORE
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datastore = get_datastore(workspace=DEFAULT_WORKSPACE, datastore_name=name)
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datastore = get_datastore(workspace=workspace, datastore_name=name)
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assert isinstance(datastore, AzureBlobDatastore)
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assert datastore.name == name
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assert len(DEFAULT_WORKSPACE.datastores) > 1
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assert len(workspace.datastores) > 1
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# Now mock the datastores property of the workspace, to pretend there is only a single datastore.
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# With that in place, we can get the datastore without the name
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faked_stores = {name: datastore}
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with mock.patch("azureml.core.Workspace.datastores", faked_stores):
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single_store = get_datastore(workspace=DEFAULT_WORKSPACE, datastore_name="")
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single_store = get_datastore(workspace=workspace, datastore_name="")
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assert isinstance(single_store, AzureBlobDatastore)
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assert single_store.name == name
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@ -59,20 +53,22 @@ def test_dataset_input() -> None:
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"""
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Test turning a dataset setup object to an actual AML input dataset.
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"""
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workspace = default_aml_workspace()
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# This dataset must exist in the workspace already, or at least in blob storage.
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dataset_config = DatasetConfig(name="hello_world", datastore=DEFAULT_DATASTORE)
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aml_dataset = dataset_config.to_input_dataset(workspace=DEFAULT_WORKSPACE, dataset_index=1)
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aml_dataset = dataset_config.to_input_dataset(workspace=workspace, dataset_index=1)
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assert isinstance(aml_dataset, DatasetConsumptionConfig)
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assert aml_dataset.path_on_compute is None
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assert aml_dataset.mode == "download"
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# Downloading or mounting to a given path
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target_folder = "/tmp/foo"
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dataset_config = DatasetConfig(name="hello_world", datastore=DEFAULT_DATASTORE, target_folder=target_folder)
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aml_dataset = dataset_config.to_input_dataset(workspace=DEFAULT_WORKSPACE, dataset_index=1)
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aml_dataset = dataset_config.to_input_dataset(workspace=workspace, dataset_index=1)
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assert isinstance(aml_dataset, DatasetConsumptionConfig)
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assert aml_dataset.path_on_compute == target_folder
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# Use mounting instead of downloading
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dataset_config = DatasetConfig(name="hello_world", datastore=DEFAULT_DATASTORE, use_mounting=True)
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aml_dataset = dataset_config.to_input_dataset(workspace=DEFAULT_WORKSPACE, dataset_index=1)
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aml_dataset = dataset_config.to_input_dataset(workspace=workspace, dataset_index=1)
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assert isinstance(aml_dataset, DatasetConsumptionConfig)
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assert aml_dataset.mode == "mount"
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@ -82,8 +78,9 @@ def test_dataset_output() -> None:
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Test turning a dataset setup object to an actual AML output dataset.
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"""
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name = "new_dataset"
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workspace = default_aml_workspace()
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dataset_config = DatasetConfig(name=name, datastore=DEFAULT_DATASTORE)
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aml_dataset = dataset_config.to_output_dataset(workspace=DEFAULT_WORKSPACE, dataset_index=1)
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aml_dataset = dataset_config.to_output_dataset(workspace=workspace, dataset_index=1)
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assert isinstance(aml_dataset, OutputFileDatasetConfig)
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assert isinstance(aml_dataset.destination, tuple)
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assert aml_dataset.destination[0].name == DEFAULT_DATASTORE
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@ -91,10 +88,70 @@ def test_dataset_output() -> None:
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assert aml_dataset.mode == "mount"
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# Use downloading instead of mounting
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dataset_config = DatasetConfig(name="hello_world", datastore=DEFAULT_DATASTORE, use_mounting=False)
|
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aml_dataset = dataset_config.to_input_dataset(workspace=DEFAULT_WORKSPACE, dataset_index=1)
|
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assert aml_dataset.mode == "download"
|
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aml_dataset = dataset_config.to_output_dataset(workspace=workspace, dataset_index=1)
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assert isinstance(aml_dataset, OutputFileDatasetConfig)
|
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assert aml_dataset.mode == "upload"
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# Mounting at a fixed folder is not possible
|
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with pytest.raises(ValueError) as ex:
|
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dataset_config = DatasetConfig(name=name, datastore=DEFAULT_DATASTORE, target_folder="something")
|
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dataset_config.to_output_dataset(workspace=DEFAULT_WORKSPACE, dataset_index=1)
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dataset_config.to_output_dataset(workspace=workspace, dataset_index=1)
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assert "Output datasets can't have a target_folder set" in str(ex)
|
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|
||||
|
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def test_datasets_from_string() -> None:
|
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"""
|
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Test the conversion of datasets that are only specified as strings.
|
||||
"""
|
||||
dataset1 = "foo"
|
||||
dataset2 = "bar"
|
||||
store = "store"
|
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default_store = "default"
|
||||
original = [dataset1, DatasetConfig(name=dataset2, datastore=store)]
|
||||
replaced = _replace_string_datasets(original, default_datastore_name=default_store)
|
||||
assert len(replaced) == len(original)
|
||||
for d in replaced:
|
||||
assert isinstance(d, DatasetConfig)
|
||||
assert replaced[0].name == dataset1
|
||||
assert replaced[0].datastore == default_store
|
||||
assert replaced[1] == original[1]
|
||||
|
||||
|
||||
def test_get_dataset() -> None:
|
||||
"""
|
||||
Test if a dataset that does not yet exist can be created from a folder in blob storage
|
||||
"""
|
||||
# A folder with a single tiny file
|
||||
tiny_dataset = "himl-tiny_dataset"
|
||||
workspace = default_aml_workspace()
|
||||
# When creating a dataset, we need a non-empty name
|
||||
with pytest.raises(ValueError) as ex:
|
||||
get_or_create_dataset(workspace=workspace,
|
||||
datastore_name=DEFAULT_DATASTORE,
|
||||
dataset_name="")
|
||||
assert "No dataset name" in str(ex)
|
||||
# Check first that there is no dataset yet of that name. If there is, delete that dataset (it would come
|
||||
# from previous runs of this test)
|
||||
try:
|
||||
existing_dataset = Dataset.get_by_name(workspace, name=tiny_dataset)
|
||||
existing_dataset.unregister_all_versions()
|
||||
except Exception as ex:
|
||||
assert "Cannot find dataset registered" in str(ex)
|
||||
dataset = get_or_create_dataset(workspace=workspace,
|
||||
datastore_name=DEFAULT_DATASTORE,
|
||||
dataset_name=tiny_dataset)
|
||||
assert isinstance(dataset, FileDataset)
|
||||
# We should now be able to get that dataset without special means
|
||||
dataset2 = Dataset.get_by_name(workspace, name=tiny_dataset)
|
||||
# Delete the dataset again
|
||||
dataset2.unregister_all_versions()
|
||||
|
||||
|
||||
def test_dataset_keys() -> None:
|
||||
"""
|
||||
Check that dataset keys are non-empty strings, and that inputs and outputs have different keys.
|
||||
"""
|
||||
in1 = _input_dataset_key(1)
|
||||
out1 = _output_dataset_key(1)
|
||||
assert in1
|
||||
assert out1
|
||||
assert in1 != out1
|
||||
|
|
|
|||
|
|
@ -3,9 +3,7 @@ from pathlib import Path
|
|||
from azureml.core import Workspace
|
||||
|
||||
from health.azure.himl import WORKSPACE_CONFIG_JSON
|
||||
from health.azure.himl_configs import SUBSCRIPTION_ID
|
||||
from health.azure.himl_configs import get_secret_from_environment
|
||||
from health.azure.himl_configs import get_authentication
|
||||
from health.azure.himl_configs import SUBSCRIPTION_ID, get_authentication, get_secret_from_environment
|
||||
|
||||
|
||||
def repository_root() -> Path:
|
||||
|
|
@ -15,7 +13,7 @@ def repository_root() -> Path:
|
|||
return Path(__file__).parent.parent.parent.parent
|
||||
|
||||
|
||||
def aml_workspace() -> Workspace:
|
||||
def default_aml_workspace() -> Workspace:
|
||||
"""
|
||||
Gets the default AzureML workspace that is used for testing.
|
||||
"""
|
||||
|
|
@ -31,5 +29,4 @@ def aml_workspace() -> Workspace:
|
|||
resource_group="InnerEye-DeepLearning")
|
||||
|
||||
|
||||
DEFAULT_WORKSPACE = aml_workspace()
|
||||
DEFAULT_DATASTORE = "innereyedatasets"
|
||||
DEFAULT_DATASTORE = "innereyedatasets"
|
||||
|
|
|
|||
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