mlflow.config

mlflow.config.disable_system_metrics_logging()

Disable system metrics logging globally.

Calling this function will disable system metrics logging globally, but users can still opt in system metrics logging for individual runs by mlflow.start_run(log_system_metrics=True).

mlflow.config.enable_async_logging(enable=True)

Enable or disable async logging globally.

Parameters

enable – bool, if True, enable async logging. If False, disable async logging.

Example

import mlflow

mlflow.config.enable_async_logging(True)

with mlflow.start_run():
    mlflow.log_param("a", 1)  # This will be logged asynchronously

mlflow.config.enable_async_logging(False)
with mlflow.start_run():
    mlflow.log_param("a", 1)  # This will be logged synchronously

mlflow.config.enable_system_metrics_logging()

Enable system metrics logging globally.

Calling this function will enable system metrics logging globally, but users can still opt out system metrics logging for individual runs by mlflow.start_run(log_system_metrics=False).

mlflow.config.get_registry_uri() → str

Get the current registry URI. If none has been specified, defaults to the tracking URI.

Returns

The registry URI.

# Get the current model registry uri
mr_uri = mlflow.get_registry_uri()
print(f"Current model registry uri: {mr_uri}")

# Get the current tracking uri
tracking_uri = mlflow.get_tracking_uri()
print(f"Current tracking uri: {tracking_uri}")

# They should be the same
assert mr_uri == tracking_uri

Current model registry uri: file:///.../mlruns
Current tracking uri: file:///.../mlruns

mlflow.config.get_tracking_uri() → str

Get the current tracking URI. This may not correspond to the tracking URI of the currently active run, since the tracking URI can be updated via set_tracking_uri.

Returns

The tracking URI.

import mlflow

# Get the current tracking uri
tracking_uri = mlflow.get_tracking_uri()
print(f"Current tracking uri: {tracking_uri}")

Current tracking uri: file:///.../mlruns

mlflow.config.is_tracking_uri_set()

Returns True if the tracking URI has been set, False otherwise.

mlflow.config.set_registry_uri(uri: str) → None

Set the registry server URI. This method is especially useful if you have a registry server that’s different from the tracking server.

Parameters

uri – An empty string, or a local file path, prefixed with file:/. Data is stored locally at the provided file (or ./mlruns if empty). An HTTP URI like https://my-tracking-server:5000 or http://my-oss-uc-server:8080. A Databricks workspace, provided as the string “databricks” or, to use a Databricks CLI profile, “databricks://<profileName>”.

Example

import mflow

# Set model registry uri, fetch the set uri, and compare
# it with the tracking uri. They should be different
mlflow.set_registry_uri("sqlite:////tmp/registry.db")
mr_uri = mlflow.get_registry_uri()
print(f"Current registry uri: {mr_uri}")
tracking_uri = mlflow.get_tracking_uri()
print(f"Current tracking uri: {tracking_uri}")

# They should be different
assert tracking_uri != mr_uri

Output

Current registry uri: sqlite:////tmp/registry.db
Current tracking uri: file:///.../mlruns

mlflow.config.set_system_metrics_node_id(node_id)

Set the system metrics node id.

node_id is the identifier of the machine where the metrics are collected. This is useful in multi-node (distributed training) setup.

mlflow.config.set_system_metrics_samples_before_logging(samples)

Set the number of samples before logging system metrics.

Every time samples samples have been collected, the system metrics will be logged to mlflow. By default samples=1.

mlflow.config.set_system_metrics_sampling_interval(interval)

Set the system metrics sampling interval.

Every interval seconds, the system metrics will be collected. By default interval=10.

mlflow.config.set_tracking_uri(uri: Union[str, pathlib.Path]) → None

Set the tracking server URI. This does not affect the currently active run (if one exists), but takes effect for successive runs.

Parameters

uri –

• An empty string, or a local file path, prefixed with file:/. Data is stored locally at the provided file (or ./mlruns if empty).

• An HTTP URI like https://my-tracking-server:5000.

• A Databricks workspace, provided as the string “databricks” or, to use a Databricks CLI profile, “databricks://<profileName>”.

• A pathlib.Path instance

Example

import mlflow

mlflow.set_tracking_uri("file:///tmp/my_tracking")
tracking_uri = mlflow.get_tracking_uri()
print(f"Current tracking uri: {tracking_uri}")

Output

Current tracking uri: file:///tmp/my_tracking