1.2.1. mbl.analysis.run_searcher#

Classes

RunSearcher(tracking_uri, **kwargs)

param tracking_uri

Address of local or remote tracking server. If not provided, defaults
class mbl.analysis.run_searcher.RunSearcher(tracking_uri, **kwargs)[source]#

Bases: mlflow.tracking.client.MlflowClient

Parameters

  • tracking_uri (str) – Address of local or remote tracking server. If not provided, defaults to the service set by mlflow.tracking.set_tracking_uri. See Where Runs Get Recorded for more info.

  • registry_uri – Address of local or remote model registry server. If not provided, defaults to the service set by mlflow.tracking.set_registry_uri. If no such service was set, defaults to the tracking uri of the client.

__init__(tracking_uri, **kwargs)[source]#
Parameters

  • tracking_uri (str) –

    Address of local or remote tracking server. If not provided, defaults to the service set by mlflow.tracking.set_tracking_uri. See Where Runs Get Recorded for more info.

  • registry_uri – Address of local or remote model registry server. If not provided, defaults to the service set by mlflow.tracking.set_registry_uri. If no such service was set, defaults to the tracking uri of the client.

static filter(n, h, penalty=0.0, s_target=0, seed=None, chi=None, offset=None, overall_const=None, relative_offset=None, method=None)[source]#
Parameters

  • n (int) –

  • h (float) –

  • penalty (float) –

  • s_target (int) –

  • seed (Optional[int]) –

  • chi (Optional[int]) –

  • offset (Optional[float]) –

  • overall_const (Optional[float]) –

  • relative_offset (Optional[float]) –

  • method (Optional[str]) –

Returns:

Examples: Query string takes the form,

query = (

“params.model = ‘CNN’ ” “and params.layers = ‘10’ ” “and metrics.`prediction accuracy` >= 0.945”

)

query(experiment_id, filter_string=None, **kwargs)[source]#
Parameters

  • experiment_id (int) –

  • filter_string (Optional[str]) –

list_artifacts(experiment_id, filter_string, path=None, **kwargs)[source]#

List the artifacts for a run.

Parameters

  • run_id – The run to list artifacts from.

  • path (Optional[str]) – The run’s relative artifact path to list from. By default it is set to None or the root artifact path.

  • experiment_id (int) –

  • filter_string (str) –

Returns

List of mlflow.entities.FileInfo

Return type

List[mlflow.entities.file_info.FileInfo]

Example#
from mlflow import MlflowClient

 def print_artifact_info(artifact):
    print("artifact: {}".format(artifact.path))
    print("is_dir: {}".format(artifact.is_dir))
    print("size: {}".format(artifact.file_size))

features = "rooms zipcode, median_price, school_rating, transport"
labels = "price"

# Create a run under the default experiment (whose id is '0').
client = MlflowClient()
experiment_id = "0"
run = client.create_run(experiment_id)

# Create some artifacts and log under the above run
for file, content in [("features", features), ("labels", labels)]:
    with open("{}.txt".format(file), 'w') as f:
        f.write(content)
    client.log_artifact(run.info.run_id, "{}.txt".format(file))

# Fetch the logged artifacts
artifacts = client.list_artifacts(run.info.run_id)
for artifact in artifacts:
    print_artifact_info(artifact)
client.set_terminated(run.info.run_id)
Output#
artifact: features.txt
is_dir: False
size: 53
artifact: labels.txt
is_dir: False
size: 5
download_artifacts(experiment_id, filter_string, path=None, dst_path=None, **kwargs)[source]#

Warning

mlflow.tracking.client.MlflowClient.download_artifacts is deprecated since 2.0. This method will be removed in a future release. Use mlflow.artifacts.download_artifacts instead.

Download an artifact file or directory from a run to a local directory if applicable, and return a local path for it.

Parameters

  • run_id – The run to download artifacts from.

  • path (Optional[str]) – Relative source path to the desired artifact.

  • dst_path (Optional[str]) – Absolute path of the local filesystem destination directory to which to download the specified artifacts. This directory must already exist. If unspecified, the artifacts will either be downloaded to a new uniquely-named directory on the local filesystem or will be returned directly in the case of the LocalArtifactRepository.

  • experiment_id (int) –

  • filter_string (str) –

Returns

Local path of desired artifact.

Example#
import os
import mlflow
from mlflow import MlflowClient

features = "rooms, zipcode, median_price, school_rating, transport"
with open("features.txt", 'w') as f:
    f.write(features)

# Log artifacts
with mlflow.start_run() as run:
    mlflow.log_artifact("features.txt", artifact_path="features")

# Download artifacts
client = MlflowClient()
local_dir = "/tmp/artifact_downloads"
if not os.path.exists(local_dir):
    os.mkdir(local_dir)
local_path = client.download_artifacts(run.info.run_id, "features", local_dir)
print("Artifacts downloaded in: {}".format(local_path))
print("Artifacts: {}".format(os.listdir(local_path)))
Output#
Artifacts downloaded in: /tmp/artifact_downloads/features
Artifacts: ['features.txt']