Performance monitoring in local mode

Performances of a compute plan can be retrieved - with the get_performances(CP_KEY) function of the SDK - on the Substra GUI when using the deployed mode. However in the local mode, there is no GUI. This page explains how to use MLFlow to perform live monitoring of the compute plan performances in local mode.

Performance monitoring using MLFlow

During a compute plan in local mode, the performances of your testtuples are saved in a performance.json file as soon as the task is done. This json file is stored in your .../local_worker/live_performances/compute_plan_key folder.

The python script below reads the json file and plot the metrics results in live into a mlflow server, creating a plot for each metric and testtuple in your compute plan.

To run it, update CP_KEY on the script below, run the python script, and launch the mlflow ui command in a dedicated terminal. Your metric results appears and is updated in live at the given url in your terminal.

This script will automatically end if the performance.json file as not been updated in the last minute. For some compute plan, this parameter should be changed regarding the necessary time to perform each round.
import pandas as pd
import json
from pathlib import Path
from mlflow import log_metric
import time
import os

TIMEOUT = 60  # Number of seconds to stop the script after the last update of the json file
CP_KEY = "..."  # Compute plan key
POLLING_FREQUENCY = 10  # Try to read the updates in the file every 10 seconds

path_to_json = Path("local-worker") / "live_performances" / CP_KEY / "performances.json"

# Wait for the file to be found
start = time.time()
while not path_to_json.exists():
    if time.time() - start >= TIMEOUT:
        raise TimeoutError("The performance file does not exist, maybe no test task has been executed yet.")

logged_rows = []
last_update = time.time()

while (time.time() - last_update) <= TIMEOUT:

    if last_update == os.path.getmtime(str(path_to_json)):

    last_update = os.path.getmtime(str(path_to_json))

    time.sleep(1)  # Waiting for the json to be fully written
    dict_perf = json.load(

    df = pd.DataFrame(dict_perf)

    for _, row in df.iterrows():
        if row["testtuple_key"] in logged_rows:


        step = int(row["round_idx"]) or int(row["testtuple_rank"])

        log_metric(f"{row['metric_name']}_{row['worker']}", row["performance"], step)