def test_names_only(self, str_node_inputs_list):
pipeline = Pipeline(str_node_inputs_list["nodes"])
description = pipeline.describe()
desc = description.split("\n")
test_desc = [
"#### Pipeline execution order ####",
"Inputs: input1, input2",
"",
"node1",
"node2",
"",
"Outputs: input4",
"##################################",
]
assert len(desc) == len(test_desc)
for res, example in zip(desc, test_desc):
assert res == example
def test_full(self, str_node_inputs_list):
pipeline = Pipeline(str_node_inputs_list["nodes"])
description = pipeline.describe(names_only=False)
desc = description.split("\n")
test_desc = [
"#### Pipeline execution order ####",
"Inputs: input1, input2",
"",
"node1: biconcat([input1,input2]) -> [input3]",
"node2: identity([input3]) -> [input4]",
"",
"Outputs: input4",
"##################################",
]
assert len(desc) == len(test_desc)
for res, example in zip(desc, test_desc):
assert res == example
def _start_mlflow_run(self, run_params: Dict[str, Any],
pipeline: Pipeline):
""" Log basic informations to MLFlow about pipeline if this pipeline is tagged with 'train' (creates a new MLFLow experiment and/or run named after training pipeline if it doesn't exists yet)
NOTE: If NNI is in dry run mode (mode used to generate NNI Classic NAS search space JSON file from a model which contains NNI NAS Mutables `LayerChoice` and/or `InputChoice`) we avoid creating any new MLFlow experiment/run nor logging anything else to mlflow during this dry run
"""
node_tags = functools.reduce(set.union,
[n.tags for n in pipeline.nodes])
if not deepcv.meta.nni_tools.is_nni_gen_search_space_mode() and (
'train' in run_params['tags'] or 'train' in node_tags):
if mlflow.active_run() is None:
# Create MLFlow run in an experiment named after pipeline involved in training and log various pipeline/datasets informations to mlflow. If we are running an NNI hp/nas search, mlflow experiment and run will be named after NNI experiment and trial ids for better consitency.
# TODO: find another way to name experiment as pipeline name is only available when running `kedro run --pipeline=<pipeline_name>` (e.g. special tag to node after which experiment is named)
if not deepcv.meta.nni_tools.is_nni_run_standalone(
): # 'STANDALONE' is NNI default experiment ID if python process haven't been started by NNI
nni_experiment = nni.get_experiment_id()
mlflow.set_experiment(nni_experiment)
mlflow.start_run(run_name=nni.get_trial_id())
# Flag indicating whether we are using NNI HP or Classic NAS API (Hyperparameter and/or Classic Neural Architecture search using NNI)
mlflow.set_tag('nni_standalone_mode', False)
mlflow.set_tag('nni_experiment_id', nni_experiment)
mlflow.set_tag('nni_trial_id', nni.get_trial_id())
mlflow.set_tag('nni_sequence_id', nni.get_sequence_id())
else:
pipeline_name = run_params['pipeline_name'].lower(
) if run_params['pipeline_name'] else 'default'
mlflow.set_experiment(
f'{self.project_ctx.project_name.lower()}_{pipeline_name}'
)
mlflow.start_run(
run_name=
f'{pipeline_name.lower()}_run_{run_params["run_id"]}')
mlflow.set_tag('nni_standalone_mode', True)
# Log basic informations about Kedro training pipeline to mlflow
mlflow.set_tags({
f'kedro_node_tag_{i}': tag
for i, tag in enumerate(node_tags)
})
mlflow.log_params({n: v for n, v in run_params.items() if v})
mlflow.log_param('pipeline.json', pipeline.to_json())
mlflow.log_param('pipeline.describe', pipeline.describe())
mlflow.log_param('pipeline.pipeline_datasets',
pipeline.data_sets())
""" The following code creates special mlflow tags about current repository infos, which is not done by mlflow when starting an MLFlow run from code instead of from `mlflow run` command
Code inspired from [`mlflow.projects._create_run`](https://www.mlflow.org/docs/latest/_modules/mlflow/projects.html) which doesn't seems to be called by `mlflow.start_run`
"""
tags = {
mlflow.utils.mlflow_tags.MLFLOW_SOURCE_NAME:
self.project_ctx.package_name,
mlflow.utils.mlflow_tags.MLFLOW_SOURCE_TYPE:
mlflow.entities.SourceType.to_string(
mlflow.entities.SourceType.PROJECT),
mlflow.utils.mlflow_tags.MLFLOW_PROJECT_ENTRY_POINT:
inspect.getsourcefile(type(self.project_ctx))
}
try:
repo = git.Repo(self.project_ctx.project_path,
search_parent_directories=True)
git_repo_url = repo.remote(
).url if 'origin' in repo.remotes else (
repo.remotes[0].url if len(repo.remotes) > 0 else '')
git_repo_url = re.sub(
r'git@([.\w]+):', r'https://\1/', git_repo_url).rstrip(
'.git') # Convert SSH git URL to http URL
mlflow.log_param(
'commit_url',
git_repo_url + f'/commit/{repo.head.commit.hexsha}/')
# We also set MLFLOW_SOURCE_NAME to repo URL so that MLFlow web UI is able to parse it and render commit and source hyperlinks (MLFLow only supports github URLs for now)
tags.update({
mlflow.utils.mlflow_tags.MLFLOW_SOURCE_NAME:
git_repo_url
if git_repo_url else self.project_ctx.project_name,
mlflow.utils.mlflow_tags.MLFLOW_GIT_BRANCH:
repo.active_branch.name,
mlflow.utils.mlflow_tags.MLFLOW_GIT_REPO_URL:
git_repo_url,
mlflow.utils.mlflow_tags.MLFLOW_GIT_COMMIT:
repo.head.commit.hexsha
})
# Change mlflow user to be git repository user instead of system user (if any git user is specified)
git_config_reader = repo.config_reader()
git_config_reader.read()
user = git_config_reader.get_value('user',
'name',
default=None)
email = git_config_reader.get_value('user',
'email',
default=None)
if user or email:
tags[mlflow.utils.mlflow_tags.MLFLOW_USER] = (
str(user) + (f' <{email}>' if email else '')
) if user else str(email)
except (ImportError, OSError, ValueError, IOError, KeyError,
git.GitError, configparser.Error) as e:
logging.warning(
f'Failed to import Git or to get repository informations. Error: {e}'
)
mlflow.set_tags(tags)
