zh/integrations/mlflow/

[giscus[bot]]

zh/integrations/mlflow/

了解如何通过Ultralytics YOLO 设置和使用 MLflow 日志，以增强实验跟踪、模型重现性和性能改进。

https://docs.ultralytics.com/zh/integrations/mlflow/

[UltralyticsAssistant]

👋 Hello, thank you for starting this discussion and shining a spotlight on Ultralytics YOLO's integration with MLflow 🌟!

For users exploring this feature, we recommend checking out the documentation where you’ll find a detailed guide tailored for leveraging MLflow to enhance experiment tracking, model reproducibility, and performance insights.

If this is a 🐛 Bug Report, please include a minimum reproducible example to help us understand and resolve the issue effectively.

For general ❓ Questions or suggestions, feel free to provide more context or specific details—all input helps us improve 💡! If your query is training-related, ensuring you’re following our Tips for Best Training Results can be valuable.

If you're looking to engage with the wider Ultralytics community, here's how you can connect:

• Join discussions or get quick help on Discord 🎧.
• Dive into in-depth posts on Discourse.
• Share insights or seek advice on our Subreddit.

Upgrade

Ensure you're using the latest features by upgrading the ultralytics package. Please run the command below in a clean Python>=3.8 environment with PyTorch>=1.8:

pip install -U ultralytics

Environments

You can experiment with Ultralytics YOLO seamlessly in the following verified environments:

• Notebooks:
• Google Cloud: See the GCP Quickstart Guide.
• Amazon AWS: Refer to the AWS Quickstart Guide.
• Docker: Instructions available in our Docker Quickstart Guide .

Status

If the status badge above is green, all internal CI tests are passing ✅. This system ensures all YOLO Modes and Tasks function as intended across macOS, Windows, and Ubuntu environments.

This is an automated response 🤖, but no worries—an Ultralytics engineer will review and assist you further soon 🚀!