def _run(uri, entry_point="main", version=None, parameters=None, experiment_id=None,
mode=None, cluster_spec=None, git_username=None, git_password=None, use_conda=True,
storage_dir=None, block=True, run_id=None):
"""
Helper that delegates to the project-running method corresponding to the passed-in mode.
Returns a ``SubmittedRun`` corresponding to the project run.
"""
if mode == "databricks":
mlflow.projects.databricks.before_run_validations(mlflow.get_tracking_uri(), cluster_spec)
exp_id = experiment_id or _get_experiment_id()
parameters = parameters or {}
work_dir = _fetch_project(uri=uri, force_tempdir=False, version=version,
git_username=git_username, git_password=git_password)
project = _project_spec.load_project(work_dir)
project.get_entry_point(entry_point)._validate_parameters(parameters)
if run_id:
active_run = tracking.MlflowClient().get_run(run_id)
else:
active_run = _create_run(uri, exp_id, work_dir, entry_point)
# Consolidate parameters for logging.
# `storage_dir` is `None` since we want to log actual path not downloaded local path
entry_point_obj = project.get_entry_point(entry_point)
final_params, extra_params = entry_point_obj.compute_parameters(parameters, storage_dir=None)
for key, value in (list(final_params.items()) + list(extra_params.items())):
tracking.MlflowClient().log_param(active_run.info.run_uuid, key, value)
# Add branch name tag if a branch is specified through -version
if _is_valid_branch_name(work_dir, version):
tracking.MlflowClient().set_tag(active_run.info.run_uuid, MLFLOW_GIT_BRANCH_NAME, version)
if mode == "databricks":
from mlflow.projects.databricks import run_databricks
return run_databricks(
remote_run=active_run,
uri=uri, entry_point=entry_point, work_dir=work_dir, parameters=parameters,
experiment_id=exp_id, cluster_spec=cluster_spec)
elif mode == "local" or mode is None:
# Synchronously create a conda environment (even though this may take some time) to avoid
# failures due to multiple concurrent attempts to create the same conda env.
conda_env_name = _get_or_create_conda_env(project.conda_env_path) if use_conda else None
# In blocking mode, run the entry point command in blocking fashion, sending status updates
# to the tracking server when finished. Note that the run state may not be persisted to the
# tracking server if interrupted
if block:
command = _get_entry_point_command(
project, entry_point, parameters, conda_env_name, storage_dir)
return _run_entry_point(command, work_dir, exp_id, run_id=active_run.info.run_uuid)
# Otherwise, invoke `mlflow run` in a subprocess
return _invoke_mlflow_run_subprocess(
work_dir=work_dir, entry_point=entry_point, parameters=parameters, experiment_id=exp_id,
use_conda=use_conda, storage_dir=storage_dir, run_id=active_run.info.run_uuid)
supported_modes = ["local", "databricks"]
raise ExecutionException("Got unsupported execution mode %s. Supported "
"values: %s" % (mode, supported_modes))
def _run(uri, entry_point="main", version=None, parameters=None, experiment_id=None,
mode=None, cluster_spec=None, git_username=None, git_password=None, use_conda=True,
use_temp_cwd=False, storage_dir=None, block=True):
exp_id = experiment_id or tracking._get_experiment_id()
if mode is None or mode == "local":
return _run_local(
uri=uri, entry_point=entry_point, version=version, parameters=parameters,
experiment_id=exp_id, use_conda=use_conda, use_temp_cwd=use_temp_cwd,
storage_dir=storage_dir, git_username=git_username, git_password=git_password,
block=block)
if mode == "databricks":
from mlflow.projects.databricks import run_databricks
return run_databricks(
uri=uri, entry_point=entry_point, version=version, parameters=parameters,
experiment_id=exp_id, cluster_spec=cluster_spec, git_username=git_username,
git_password=git_password)
supported_modes = ["local", "databricks"]
raise ExecutionException("Got unsupported execution mode %s. Supported "
"values: %s" % (mode, supported_modes))
def _run(uri, entry_point="main", version=None, parameters=None, experiment_id=None,
mode=None, cluster_spec=None, git_username=None, git_password=None, use_conda=True,
storage_dir=None, block=True, run_id=None):
"""
Helper that delegates to the project-running method corresponding to the passed-in mode.
Returns a ``SubmittedRun`` corresponding to the project run.
"""
exp_id = experiment_id or _get_experiment_id()
parameters = parameters or {}
work_dir = _fetch_project(uri=uri, force_tempdir=False, version=version,
git_username=git_username, git_password=git_password)
project = _project_spec.load_project(work_dir)
project.get_entry_point(entry_point)._validate_parameters(parameters)
if run_id:
active_run = tracking.get_service().get_run(run_id)
else:
active_run = _create_run(uri, exp_id, work_dir, entry_point, parameters)
if mode == "databricks":
from mlflow.projects.databricks import run_databricks
return run_databricks(
remote_run=active_run,
uri=uri, entry_point=entry_point, work_dir=work_dir, parameters=parameters,
experiment_id=exp_id, cluster_spec=cluster_spec)
elif mode == "local" or mode is None:
# Synchronously create a conda environment (even though this may take some time) to avoid
# failures due to multiple concurrent attempts to create the same conda env.
conda_env_name = _get_or_create_conda_env(project.conda_env_path) if use_conda else None
# In blocking mode, run the entry point command in blocking fashion, sending status updates
# to the tracking server when finished. Note that the run state may not be persisted to the
# tracking server if interrupted
if block:
command = _get_entry_point_command(
project, entry_point, parameters, conda_env_name, storage_dir)
return _run_entry_point(command, work_dir, exp_id, run_id=active_run.info.run_uuid)
# Otherwise, invoke `mlflow run` in a subprocess
return _invoke_mlflow_run_subprocess(
work_dir=work_dir, entry_point=entry_point, parameters=parameters, experiment_id=exp_id,
use_conda=use_conda, storage_dir=storage_dir, run_id=active_run.info.run_uuid)
supported_modes = ["local", "databricks"]
raise ExecutionException("Got unsupported execution mode %s. Supported "
"values: %s" % (mode, supported_modes))
def _run(uri, experiment_id, entry_point="main", version=None, parameters=None,
backend=None, backend_config=None, use_conda=True,
storage_dir=None, synchronous=True, run_id=None):
"""
Helper that delegates to the project-running method corresponding to the passed-in backend.
Returns a ``SubmittedRun`` corresponding to the project run.
"""
parameters = parameters or {}
work_dir = _fetch_project(uri=uri, force_tempdir=False, version=version)
project = _project_spec.load_project(work_dir)
_validate_execution_environment(project, backend)
project.get_entry_point(entry_point)._validate_parameters(parameters)
if run_id:
active_run = tracking.MlflowClient().get_run(run_id)
else:
active_run = _create_run(uri, experiment_id, work_dir, entry_point)
# Consolidate parameters for logging.
# `storage_dir` is `None` since we want to log actual path not downloaded local path
entry_point_obj = project.get_entry_point(entry_point)
final_params, extra_params = entry_point_obj.compute_parameters(parameters, storage_dir=None)
for key, value in (list(final_params.items()) + list(extra_params.items())):
tracking.MlflowClient().log_param(active_run.info.run_id, key, value)
repo_url = _get_git_repo_url(work_dir)
if repo_url is not None:
for tag in [MLFLOW_GIT_REPO_URL, LEGACY_MLFLOW_GIT_REPO_URL]:
tracking.MlflowClient().set_tag(active_run.info.run_id, tag, repo_url)
# Add branch name tag if a branch is specified through -version
if _is_valid_branch_name(work_dir, version):
for tag in [MLFLOW_GIT_BRANCH, LEGACY_MLFLOW_GIT_BRANCH_NAME]:
tracking.MlflowClient().set_tag(active_run.info.run_id, tag, version)
if backend == "databricks":
tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_BACKEND,
"databricks")
from mlflow.projects.databricks import run_databricks
return run_databricks(
remote_run=active_run,
uri=uri, entry_point=entry_point, work_dir=work_dir, parameters=parameters,
experiment_id=experiment_id, cluster_spec=backend_config)
elif backend == "local" or backend is None:
command = []
command_separator = " "
# If a docker_env attribute is defined in MLproject then it takes precedence over conda yaml
# environments, so the project will be executed inside a docker container.
if project.docker_env:
tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_ENV,
"docker")
tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_BACKEND,
"local")
_validate_docker_env(project)
_validate_docker_installation()
image = _build_docker_image(work_dir=work_dir,
repository_uri=project.name,
base_image=project.docker_env.get('image'),
run_id=active_run.info.run_id)
command += _get_docker_command(image=image, active_run=active_run)
# Synchronously create a conda environment (even though this may take some time)
# to avoid failures due to multiple concurrent attempts to create the same conda env.
elif use_conda:
tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_ENV, "conda")
tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_BACKEND, "local")
command_separator = " && "
conda_env_name = _get_or_create_conda_env(project.conda_env_path)
command += _get_conda_command(conda_env_name)
# In synchronous mode, run the entry point command in a blocking fashion, sending status
# updates to the tracking server when finished. Note that the run state may not be
# persisted to the tracking server if interrupted
if synchronous:
command += _get_entry_point_command(project, entry_point, parameters, storage_dir)
command = command_separator.join(command)
return _run_entry_point(command, work_dir, experiment_id,
run_id=active_run.info.run_id)
# Otherwise, invoke `mlflow run` in a subprocess
return _invoke_mlflow_run_subprocess(
work_dir=work_dir, entry_point=entry_point, parameters=parameters,
experiment_id=experiment_id,
use_conda=use_conda, storage_dir=storage_dir, run_id=active_run.info.run_id)
elif backend == "kubernetes":
from mlflow.projects import kubernetes as kb
tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_ENV, "docker")
tracking.MlflowClient().set_tag(active_run.info.run_id, MLFLOW_PROJECT_BACKEND,
"kubernetes")
_validate_docker_env(project)
_validate_docker_installation()
kube_config = _parse_kubernetes_config(backend_config)
image = _build_docker_image(work_dir=work_dir,
repository_uri=kube_config["repository-uri"],
base_image=project.docker_env.get('image'),
run_id=active_run.info.run_id)
image_digest = kb.push_image_to_registry(image.tags[0])
submitted_run = kb.run_kubernetes_job(project.name,
active_run,
image.tags[0],
image_digest,
_get_entry_point_command(project, entry_point,
parameters, storage_dir),
_get_run_env_vars(
run_id=active_run.info.run_uuid,
experiment_id=active_run.info.experiment_id),
kube_config['kube-context'],
kube_config['kube-job-template'])
return submitted_run
supported_backends = ["local", "databricks", "kubernetes"]
raise ExecutionException("Got unsupported execution mode %s. Supported "
"values: %s" % (backend, supported_backends))
def _run(uri,
experiment_id,
entry_point="main",
version=None,
parameters=None,
docker_args=None,
backend_name=None,
backend_config=None,
use_conda=True,
storage_dir=None,
synchronous=True):
"""
Helper that delegates to the project-running method corresponding to the passed-in backend.
Returns a ``SubmittedRun`` corresponding to the project run.
"""
tracking_store_uri = tracking.get_tracking_uri()
if backend_name:
backend = loader.load_backend(backend_name)
if backend:
submitted_run = backend.run(uri, entry_point, parameters, version,
backend_config, experiment_id,
tracking_store_uri)
tracking.MlflowClient().set_tag(submitted_run.run_id,
MLFLOW_PROJECT_BACKEND,
backend_name)
return submitted_run
work_dir = fetch_and_validate_project(uri, version, entry_point,
parameters)
project = load_project(work_dir)
_validate_execution_environment(project, backend_name)
existing_run_id = None
if backend_name == "local" and _MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG in backend_config:
existing_run_id = backend_config[_MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG]
active_run = get_or_create_run(existing_run_id, uri, experiment_id,
work_dir, version, entry_point, parameters)
if backend_name == "databricks":
tracking.MlflowClient().set_tag(active_run.info.run_id,
MLFLOW_PROJECT_BACKEND, "databricks")
from mlflow.projects.databricks import run_databricks
return run_databricks(remote_run=active_run,
uri=uri,
entry_point=entry_point,
work_dir=work_dir,
parameters=parameters,
experiment_id=experiment_id,
cluster_spec=backend_config)
elif backend_name == "local" or backend_name is None:
tracking.MlflowClient().set_tag(active_run.info.run_id,
MLFLOW_PROJECT_BACKEND, "local")
command_args = []
command_separator = " "
# If a docker_env attribute is defined in MLproject then it takes precedence over conda yaml
# environments, so the project will be executed inside a docker container.
if project.docker_env:
tracking.MlflowClient().set_tag(active_run.info.run_id,
MLFLOW_PROJECT_ENV, "docker")
_validate_docker_env(project)
_validate_docker_installation()
image = _build_docker_image(
work_dir=work_dir,
repository_uri=project.name,
base_image=project.docker_env.get('image'),
run_id=active_run.info.run_id)
command_args += _get_docker_command(
image=image,
active_run=active_run,
docker_args=docker_args,
volumes=project.docker_env.get("volumes"),
user_env_vars=project.docker_env.get("environment"))
# Synchronously create a conda environment (even though this may take some time)
# to avoid failures due to multiple concurrent attempts to create the same conda env.
elif use_conda:
tracking.MlflowClient().set_tag(active_run.info.run_id,
MLFLOW_PROJECT_ENV, "conda")
command_separator = " && "
conda_env_name = _get_or_create_conda_env(project.conda_env_path)
command_args += _get_conda_command(conda_env_name)
# In synchronous mode, run the entry point command in a blocking fashion, sending status
# updates to the tracking server when finished. Note that the run state may not be
# persisted to the tracking server if interrupted
if synchronous:
command_args += _get_entry_point_command(project, entry_point,
parameters, storage_dir)
command_str = command_separator.join(command_args)
return _run_entry_point(command_str,
work_dir,
experiment_id,
run_id=active_run.info.run_id)
# Otherwise, invoke `mlflow run` in a subprocess
return _invoke_mlflow_run_subprocess(work_dir=work_dir,
entry_point=entry_point,
parameters=parameters,
experiment_id=experiment_id,
use_conda=use_conda,
storage_dir=storage_dir,
run_id=active_run.info.run_id)
elif backend_name == "kubernetes":
from mlflow.projects import kubernetes as kb
tracking.MlflowClient().set_tag(active_run.info.run_id,
MLFLOW_PROJECT_ENV, "docker")
tracking.MlflowClient().set_tag(active_run.info.run_id,
MLFLOW_PROJECT_BACKEND, "kubernetes")
_validate_docker_env(project)
_validate_docker_installation()
kube_config = _parse_kubernetes_config(backend_config)
image = _build_docker_image(
work_dir=work_dir,
repository_uri=kube_config["repository-uri"],
base_image=project.docker_env.get('image'),
run_id=active_run.info.run_id)
image_digest = kb.push_image_to_registry(image.tags[0])
submitted_run = kb.run_kubernetes_job(
project.name, active_run, image.tags[0], image_digest,
_get_entry_point_command(project, entry_point, parameters,
storage_dir),
_get_run_env_vars(run_id=active_run.info.run_uuid,
experiment_id=active_run.info.experiment_id),
kube_config.get('kube-context', None),
kube_config['kube-job-template'])
return submitted_run
supported_backends = ["local", "databricks", "kubernetes"]
raise ExecutionException("Got unsupported execution mode %s. Supported "
"values: %s" % (backend_name, supported_backends))
def _run(
uri,
experiment_id,
entry_point,
version,
parameters,
docker_args,
backend_name,
backend_config,
use_conda,
storage_dir,
synchronous,
):
"""
Helper that delegates to the project-running method corresponding to the passed-in backend.
Returns a ``SubmittedRun`` corresponding to the project run.
"""
tracking_store_uri = tracking.get_tracking_uri()
backend_config[PROJECT_USE_CONDA] = use_conda
backend_config[PROJECT_SYNCHRONOUS] = synchronous
backend_config[PROJECT_DOCKER_ARGS] = docker_args
backend_config[PROJECT_STORAGE_DIR] = storage_dir
# TODO: remove this check once kubernetes execution has been refactored
if backend_name not in {"databricks", "kubernetes"}:
backend = loader.load_backend(backend_name)
if backend:
submitted_run = backend.run(
uri,
entry_point,
parameters,
version,
backend_config,
tracking_store_uri,
experiment_id,
)
tracking.MlflowClient().set_tag(submitted_run.run_id,
MLFLOW_PROJECT_BACKEND,
backend_name)
return submitted_run
work_dir = fetch_and_validate_project(uri, version, entry_point,
parameters)
project = load_project(work_dir)
_validate_execution_environment(project, backend_name)
active_run = get_or_create_run(None, uri, experiment_id, work_dir, version,
entry_point, parameters)
if backend_name == "databricks":
tracking.MlflowClient().set_tag(active_run.info.run_id,
MLFLOW_PROJECT_BACKEND, "databricks")
from mlflow.projects.databricks import run_databricks
return run_databricks(
remote_run=active_run,
uri=uri,
entry_point=entry_point,
work_dir=work_dir,
parameters=parameters,
experiment_id=experiment_id,
cluster_spec=backend_config,
)
elif backend_name == "kubernetes":
from mlflow.projects.docker import (
build_docker_image,
validate_docker_env,
validate_docker_installation,
)
from mlflow.projects import kubernetes as kb
tracking.MlflowClient().set_tag(active_run.info.run_id,
MLFLOW_PROJECT_ENV, "docker")
tracking.MlflowClient().set_tag(active_run.info.run_id,
MLFLOW_PROJECT_BACKEND, "kubernetes")
validate_docker_env(project)
validate_docker_installation()
kube_config = _parse_kubernetes_config(backend_config)
image = build_docker_image(
work_dir=work_dir,
repository_uri=kube_config["repository-uri"],
base_image=project.docker_env.get("image"),
run_id=active_run.info.run_id,
)
image_digest = kb.push_image_to_registry(image.tags[0])
submitted_run = kb.run_kubernetes_job(
project.name,
active_run,
image.tags[0],
image_digest,
get_entry_point_command(project, entry_point, parameters,
storage_dir),
get_run_env_vars(run_id=active_run.info.run_uuid,
experiment_id=active_run.info.experiment_id),
kube_config.get("kube-context", None),
kube_config["kube-job-template"],
)
return submitted_run
supported_backends = ["databricks", "kubernetes"] + list(
loader.MLFLOW_BACKENDS.keys())
raise ExecutionException("Got unsupported execution mode %s. Supported "
"values: %s" % (backend_name, supported_backends))
def _run(uri,
entry_point="main",
version=None,
parameters=None,
experiment_name=None,
experiment_id=None,
mode=None,
cluster_spec=None,
git_username=None,
git_password=None,
use_conda=True,
storage_dir=None,
block=True,
run_id=None):
"""
Helper that delegates to the project-running method corresponding to the passed-in mode.
Returns a ``SubmittedRun`` corresponding to the project run.
"""
if mode == "databricks":
mlflow.projects.databricks.before_run_validations(
mlflow.get_tracking_uri(), cluster_spec)
if experiment_name:
exp_id = tracking.MlflowClient().get_experiment_by_name(
experiment_name)
else:
exp_id = experiment_id or _get_experiment_id()
parameters = parameters or {}
work_dir = _fetch_project(uri=uri,
force_tempdir=False,
version=version,
git_username=git_username,
git_password=git_password)
project = _project_spec.load_project(work_dir)
_validate_execution_environment(project, mode)
project.get_entry_point(entry_point)._validate_parameters(parameters)
if run_id:
active_run = tracking.MlflowClient().get_run(run_id)
else:
active_run = _create_run(uri, exp_id, work_dir, entry_point)
# Consolidate parameters for logging.
# `storage_dir` is `None` since we want to log actual path not downloaded local path
entry_point_obj = project.get_entry_point(entry_point)
final_params, extra_params = entry_point_obj.compute_parameters(
parameters, storage_dir=None)
for key, value in (list(final_params.items()) +
list(extra_params.items())):
tracking.MlflowClient().log_param(active_run.info.run_uuid, key, value)
repo_url = _get_git_repo_url(work_dir)
if repo_url is not None:
for tag in [MLFLOW_GIT_REPO_URL, LEGACY_MLFLOW_GIT_REPO_URL]:
tracking.MlflowClient().set_tag(active_run.info.run_uuid, tag,
repo_url)
# Add branch name tag if a branch is specified through -version
if _is_valid_branch_name(work_dir, version):
for tag in [MLFLOW_GIT_BRANCH, LEGACY_MLFLOW_GIT_BRANCH_NAME]:
tracking.MlflowClient().set_tag(active_run.info.run_uuid, tag,
version)
if mode == "databricks":
from mlflow.projects.databricks import run_databricks
return run_databricks(remote_run=active_run,
uri=uri,
entry_point=entry_point,
work_dir=work_dir,
parameters=parameters,
experiment_id=exp_id,
cluster_spec=cluster_spec)
elif mode == "local" or mode is None:
command = []
command_separator = " "
# If a docker_env attribute is defined in MLProject then it takes precedence over conda yaml
# environments, so the project will be executed inside a docker container.
if project.docker_env:
tracking.MlflowClient().set_tag(active_run.info.run_uuid,
MLFLOW_PROJECT_ENV, "docker")
_validate_docker_env(project.docker_env)
_validate_docker_installation()
image = _build_docker_image(work_dir=work_dir,
project=project,
active_run=active_run)
command += _get_docker_command(image=image, active_run=active_run)
# Synchronously create a conda environment (even though this may take some time)
# to avoid failures due to multiple concurrent attempts to create the same conda env.
elif use_conda:
tracking.MlflowClient().set_tag(active_run.info.run_uuid,
MLFLOW_PROJECT_ENV, "conda")
command_separator = " && "
conda_env_name = _get_or_create_conda_env(project.conda_env_path)
command += _get_conda_command(conda_env_name)
# In blocking mode, run the entry point command in blocking fashion, sending status updates
# to the tracking server when finished. Note that the run state may not be persisted to the
# tracking server if interrupted
if block:
command += _get_entry_point_command(project, entry_point,
parameters, storage_dir)
command = command_separator.join(command)
return _run_entry_point(command,
work_dir,
exp_id,
run_id=active_run.info.run_uuid)
# Otherwise, invoke `mlflow run` in a subprocess
return _invoke_mlflow_run_subprocess(work_dir=work_dir,
entry_point=entry_point,
parameters=parameters,
experiment_id=exp_id,
use_conda=use_conda,
storage_dir=storage_dir,
run_id=active_run.info.run_uuid)
supported_modes = ["local", "databricks"]
raise ExecutionException("Got unsupported execution mode %s. Supported "
"values: %s" % (mode, supported_modes))
