mlflow--mlflow/.github/workflows/triage.md

Synthetic triage dataset

Each issue is separated by ---. Expected result is noted in the header.

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Issue 1 — UI bug, no screenshot (expect comment)

Title: Sidebar overlaps main content on narrow screens

Body: When I resize the browser window to less than 1000px, the left sidebar overlaps with the experiment table. The columns become unreadable and I can't click on any runs.

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Issue 2 — UI bug with screenshot (expect no comment)

Title: Run name truncated in experiment list

Body: The run name gets cut off when it's longer than 30 characters. See below:

MLflow 2.17.0, Python 3.11, macOS 14.

Steps:

1. Create a run with a long name
2. Open the experiment page
3. Observe the truncated name

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Issue 3 — Bug, no repro steps, no env info (expect comment)

Title: mlflow.log_metric silently drops NaN values

Body: I noticed that when I log NaN as a metric value, it just disappears. No error, no warning, nothing in the UI. This is really confusing because I thought my training was going fine but the metrics were just not being recorded.

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Issue 4 — Bug with repro steps and env info (expect no comment)

Title: mlflow server crashes on startup with SQLite backend

Body: Environment:

• OS: Ubuntu 22.04
• Python: 3.10.12
• MLflow: 2.18.0

Steps to reproduce:

1. Install mlflow 2.18.0
2. Run mlflow server --backend-store-uri sqlite:///mlflow.db
3. Server crashes with OperationalError: database is locked

Traceback:

Traceback (most recent call last):
  File "/home/user/.local/lib/python3.10/site-packages/mlflow/server/__init__.py", line 42, in _run_server
    store = _get_store(backend_store_uri)
  File "/home/user/.local/lib/python3.10/site-packages/mlflow/store/tracking/__init__.py", line 75, in _get_store
    return _tracking_store_registry.get_store(store_uri)
  File "/home/user/.local/lib/python3.10/site-packages/mlflow/store/tracking/sqlalchemy_store.py", line 112, in __init__
    self._setup_db()
sqlite3.OperationalError: database is locked

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Issue 5 — Feature request (expect no comment)

Title: Add support for grouping runs by tag in the UI

Body: It would be great to be able to group runs in the experiment view by a specific tag value. For example, I tag my runs with model_type=cnn or model_type=transformer and I'd love to see them grouped in the table.

This is similar to how you can group by "dataset" in Weights & Biases.

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Issue 6 — Bug with repro steps but no env info (expect comment)

Title: Autologging fails with PyTorch Lightning 2.5

Body: When I enable autologging with PyTorch Lightning 2.5, I get an AttributeError.

import mlflow

mlflow.pytorch.autolog()

trainer = pl.Trainer(max_epochs=5)
trainer.fit(model, dataloader)

AttributeError: module 'pytorch_lightning' has no attribute 'callbacks'

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Issue 7 — Bug with env info but no repro steps (expect comment)

Title: Model serving returns 500 on valid input

Body: I deployed a model using mlflow models serve and it returns 500 errors on inputs that used to work fine. This started happening after I upgraded MLflow.

Environment: Python 3.11, MLflow 2.18.0, macOS Sonoma.

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Issue 8 — UI bug, has env and repro but no screenshot (expect comment)

Title: Chart tooltip shows wrong metric value

Body: The tooltip on the metric chart shows a different value than what's actually plotted. The line is at ~0.95 but the tooltip says 0.42.

Environment: MLflow 2.17.0, Python 3.10, Chrome 130, Ubuntu 22.04

Steps:

1. Log 100 metric values for "accuracy"
2. Open the run page
3. Hover over the chart line near the end
4. Tooltip shows an incorrect value

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Issue 9 — Documentation issue (expect no comment)

Title: Typo in quickstart guide

Body: In the quickstart guide, the command mlflow server --port 500 should be mlflow server --port 5000. The wrong port causes a "permission denied" error on Linux.

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Issue 10 — Bug, detailed report with everything (expect no comment)

Title: mlflow.evaluate() fails with custom metrics on Spark DataFrame

Body: Environment:

• OS: CentOS 7
• Python: 3.10.8
• MLflow: 2.17.2
• PySpark: 3.5.0

Description: When passing a Spark DataFrame to mlflow.evaluate() with a custom metric function, the evaluation fails with a serialization error.

Steps to reproduce:

1. Create a Spark DataFrame with columns prediction and target
2. Define a custom metric: def rmse(eval_df, _): return np.sqrt(np.mean(...))
3. Call mlflow.evaluate(model, data=spark_df, extra_metrics=[rmse])

Error:

pickle.PicklingError: Could not serialize object

Expected behavior: The evaluation should work with Spark DataFrames just like it does with Pandas DataFrames.

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Issue 11 — Security Vulnerability (expect no comment - filtered by triage.py)

Title: Security Vulnerability: XSS in MLflow UI allows script injection

Body: I discovered a cross-site scripting vulnerability in the MLflow UI that allows an attacker to inject malicious JavaScript code through experiment names.

This is a serious security issue that needs immediate attention.

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Issue 12 — security vulnerability lowercase (expect no comment - filtered by triage.py)

Title: security vulnerability in authentication module

Body: Found a security issue in the authentication flow that allows bypassing login.