mlflow.artifacts

APIs for interacting with artifacts in MLflow

mlflow.artifacts.download_artifacts(artifact_uri: Optional[str] = None, run_id: Optional[str] = None, artifact_path: Optional[str] = None, dst_path: Optional[str] = None) → str[source]

Download an artifact file or directory to a local directory.

Parameters

artifact_uri – URI pointing to the artifacts, such as "runs:/500cf58bee2b40a4a82861cc31a617b1/my_model.pkl", "models:/my_model/Production", or "s3://my_bucket/my/file.txt". Exactly one of artifact_uri or run_id must be specified.
run_id – ID of the MLflow Run containing the artifacts. Exactly one of run_id or artifact_uri must be specified.
artifact_path – (For use with run_id) If specified, a path relative to the MLflow Run’s root directory containing the artifacts to download.
dst_path – Path of the local filesystem destination directory to which to download the specified artifacts. If the directory does not exist, it is created. If unspecified, the artifacts are downloaded to a new uniquely-named directory on the local filesystem, unless the artifacts already exist on the local filesystem, in which case their local path is returned directly.

Returns

The location of the artifact file or directory on the local filesystem.

mlflow.artifacts.load_dict(artifact_uri: str) → dict[source]

Loads the artifact contents as a dictionary.

Parameters: artifact_uri – artifact location
Returns: dict

Example

import mlflow
with mlflow.start_run() as run:
    artifact_uri = run.info.artifact_uri
    mlflow.log_dict({"mlflow-version": "0.28", "n_cores": "10"}, "config.json")
    config_json = mlflow.artifacts.load_dict(artifact_uri + "/config.json")
    print(config_json)

Output

{'mlflow-version': '0.28', 'n_cores': '10'}

mlflow.artifacts.load_image(artifact_uri: str)[source]

Loads artifact contents as a PIL.Image.Image object

Parameters: artifact_uri – artifact location
Returns: PIL.Image

Example

import mlflow
from PIL import Image
with mlflow.start_run() as run:
    image = Image.new("RGB", (100, 100))
    artifact_uri = run.info.artifact_uri
    mlflow.log_image(image, "image.png")
    image  = mlflow.artifacts.load_image(artifact_uri + "/image.png")
    print(image)

Output

<PIL.PngImagePlugin.PngImageFile image mode=RGB size=100x100 at 0x11D2FA3D0>

mlflow.artifacts.load_text(artifact_uri: str) → str[source]

Loads the artifact contents as a string.

Parameters: artifact_uri – artifact location
Returns: str

Example

import mlflow
with mlflow.start_run() as run:
    artifact_uri = run.info.artifact_uri
    mlflow.log_text("This is a sentence", "file.txt")
    file_content = mlflow.artifacts.load_text(artifact_uri + "/file.txt")
    print(file_content)

Output

This is a sentence