# MLFlow

RIME integrates with MLFlow Tracking. This integration unlocks the true power of 
AI Stress Testing when experimenting with many models. 

With the RIME SDK you are able to query for RIME metrics and include them in your 
MLFlow experiments. You can see in the below code snippet. To get a better understanding
of the integration, take a look at the 
<a href="https://www.loom.com/share/0b265c7e30604bd696063b215514d3e7" target="_blank">MFlow Demo Video.</a>
 

```python
import pandas as pd
import mlflow
from rime_sdk import RIMEClient, RIMEStressTestJob, RIMEProject, RIMEFirewall

# Set these before beginning!
BACKEND_URL = "rime-backend.<YOUR_ORG_NAME>.rime.dev"
API_KEY = "<YOUR_API_KEY>"

# Connect to your cluster
rime_client = RIMEClient(BACKEND_URL, API_KEY)

# Run the test!
stress_test = rime_client.start_stress_test(test_run_config=config)

# Query the results 
test_run_result = stress_test.get_test_run_result()
test_cases_result = stress_test.get_test_cases_result()

# Preparing the results to log
test_run_metrics = test_run_result.columns
test_cases_result.to_csv("test_cases_results.csv")

# Log experiment results to MFlow
with mlflow.start_run():
    for metric in test_run_metrics:
        mlflow.log_metric(column, test_run_result[column][0])
    mlflow.log_artifact("test_cases_results.csv")
mlflow.end_run()
```