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Simplify MLflow auth to use native env var support

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Reverted to simpler approach - MLflow natively supports
MLFLOW_TRACKING_USERNAME and MLFLOW_TRACKING_PASSWORD environment
variables for HTTP Basic Auth.

Removed the manual URI construction since it's not needed.
The workflow already sets these env vars correctly.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Alexis Bruneteau 2025-10-01 19:57:15 +02:00
parent a4ddfb57be
commit 22db96b3eb
1 changed files with 6 additions and 16 deletions
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24
src/models/train.py
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@ -13,25 +13,15 @@ from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
import pandas as pd
# Configure MLflow with authentication
# Configure MLflow
# MLflow will automatically use MLFLOW_TRACKING_USERNAME and MLFLOW_TRACKING_PASSWORD env vars
tracking_uri = os.getenv("MLFLOW_TRACKING_URI", "https://mlflow.sortifal.dev")
username = os.getenv("MLFLOW_TRACKING_USERNAME")
password = os.getenv("MLFLOW_TRACKING_PASSWORD")
# Build authenticated URI if credentials are provided
if username and password:
# Extract protocol and host from URI
if tracking_uri.startswith("https://"):
auth_uri = f"https://{username}:{password}@{tracking_uri[8:]}"
elif tracking_uri.startswith("http://"):
auth_uri = f"http://{username}:{password}@{tracking_uri[7:]}"
else:
auth_uri = tracking_uri
mlflow.set_tracking_uri(auth_uri)
print(f"MLflow credentials configured for {tracking_uri}")
else:
mlflow.set_tracking_uri(tracking_uri)
print("MLflow configured without authentication")
if os.getenv("MLFLOW_TRACKING_USERNAME") and os.getenv("MLFLOW_TRACKING_PASSWORD"):
print(f"MLflow configured with authentication for {tracking_uri}")
else:
print(f"MLflow configured without authentication for {tracking_uri}")
# Try to set experiment, but handle auth errors gracefully
USE_MLFLOW = True