Signed-off-by: Cedric Koffeto cedkoffeto@gmail.com

What changes are proposed in this pull request?

setInterval function added in metricsPlotPanel component which retrieves execution status and metrics history from API every 8 seconds to update the plot and automatically stops when all executions are complete. closes #2099

How is this patch tested?

• Unit tests
• Manually verified the proposed feature works correctly using this script:

import time
import numpy as np
import mlflow
import multiprocessing as mp


def log(run_id, slope, repeat):
    sleep = 10
    with mlflow.start_run(run_id=run_id):
        for epoch in range(1, repeat + 1):
            print(epoch)
            mlflow.log_metric(key="metric1", value=slope * epoch * np.log(epoch), step=epoch)
            mlflow.log_metric(key="metric2", value=slope * (1 / epoch) * np.log(epoch), step=epoch)
            time.sleep(sleep)


client = mlflow.tracking.MlflowClient()
run_uuids = [client.create_run("0").info.run_id for _ in range(2)]
runs_param = "[" + ",".join(map(lambda s: f"%22{s}%22", run_uuids)) + "]"

print(
    "URL:",
    r"http://localhost:3000/#/metric/metric1?runs=<<< runs_param >>>&experiment=0&plot_metric_keys=[%22metric1%22]&plot_layout={%22autosize%22:true,%22xaxis%22:{},%22yaxis%22:{}}&x_axis=step&y_axis_scale=linear&line_smoothness=1&show_point=true&deselected_curves=[]&last_linear_y_axis_range=[]".replace(
        "<<< runs_param >>>", runs_param
    ),
)

args_list = [(run_uuid, idx + 1, 5 + idx * 3) for idx, run_uuid in enumerate(run_uuids)]

with mp.Pool() as pool:
    pool.starmap(log, args_list)

It can be tested with a unit test to check if the plot is actually updated

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

Automatically update metric plots for in-progress runs

What component(s), interfaces, languages, and integrations does this PR affect?

Components

• area/artifacts: Artifact stores and artifact logging
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages
• area/examples: Example code
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/projects: MLproject format, project running backends
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

Interface

• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

Language

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes