def from_artifact_uri(artifact_uri, store):
"""
Given an artifact URI for an Experiment Run (e.g., /local/file/path or s3://my/bucket),
returns an ArtifactReposistory instance capable of logging and downloading artifacts
on behalf of this URI.
:param store: An instance of AbstractStore which the artifacts are registered in.
"""
if artifact_uri.startswith("s3:/"):
# Import these locally to avoid creating a circular import loop
from mlflow.store.s3_artifact_repo import S3ArtifactRepository
return S3ArtifactRepository(artifact_uri)
elif artifact_uri.startswith("gs:/"):
from mlflow.store.gcs_artifact_repo import GCSArtifactRepository
return GCSArtifactRepository(artifact_uri)
elif artifact_uri.startswith("wasbs:/"):
from mlflow.store.azure_blob_artifact_repo import AzureBlobArtifactRepository
return AzureBlobArtifactRepository(artifact_uri)
elif artifact_uri.startswith("sftp:/"):
from mlflow.store.sftp_artifact_repo import SFTPArtifactRepository
return SFTPArtifactRepository(artifact_uri)
elif artifact_uri.startswith("dbfs:/"):
from mlflow.store.dbfs_artifact_repo import DbfsArtifactRepository
if not isinstance(store, DatabricksStore):
raise MlflowException(
'`store` must be an instance of DatabricksStore.')
return DbfsArtifactRepository(artifact_uri,
store.http_request_kwargs)
else:
from mlflow.store.local_artifact_repo import LocalArtifactRepository
return LocalArtifactRepository(artifact_uri)
def dbfs_artifact_repo_factory(artifact_uri):
"""
Returns an ArtifactRepository subclass for storing artifacts on DBFS.
This factory method is used with URIs of the form ``dbfs:/<path>``. DBFS-backed artifact
storage can only be used together with the RestStore.
:param artifact_uri: DBFS root artifact URI (string).
:return: Subclass of ArtifactRepository capable of storing artifacts on DBFS.
"""
cleaned_artifact_uri = artifact_uri.rstrip('/')
if mlflow.utils.databricks_utils.is_dbfs_fuse_available() \
and os.environ.get(USE_FUSE_ENV_VAR, "").lower() != "false":
# If the DBFS FUSE mount is available, write artifacts directly to /dbfs/... using
# local filesystem APIs
file_uri = "file:///dbfs/{}".format(
strip_prefix(cleaned_artifact_uri, "dbfs:/"))
return LocalArtifactRepository(file_uri)
return DbfsRestArtifactRepository(cleaned_artifact_uri)
def from_artifact_uri(artifact_uri):
"""
Given an artifact URI for an Experiment Run (e.g., /local/file/path or s3://my/bucket),
returns an ArtifactReposistory instance capable of logging and downloading artifacts
on behalf of this URI.
"""
if artifact_uri.startswith("s3:/"):
# Import these locally to avoid creating a circular import loop
from mlflow.store.s3_artifact_repo import S3ArtifactRepository
return S3ArtifactRepository(artifact_uri)
elif artifact_uri.startswith("gs:/"):
from mlflow.store.gcs_artifact_repo import GCSArtifactRepository
return GCSArtifactRepository(artifact_uri)
elif artifact_uri.startswith("wasbs:/"):
from mlflow.store.azure_blob_artifact_repo import AzureBlobArtifactRepository
return AzureBlobArtifactRepository(artifact_uri)
else:
from mlflow.store.local_artifact_repo import LocalArtifactRepository
return LocalArtifactRepository(artifact_uri)
def local_artifact_repo(local_artifact_root):
from mlflow.utils.file_utils import path_to_local_file_uri
return LocalArtifactRepository(artifact_uri=path_to_local_file_uri(local_artifact_root))
def local_artifact_repo(local_artifact_root):
return LocalArtifactRepository(artifact_uri=local_artifact_root)