This section addresses common questions about how to use MXNet. These include performance issues, e.g., how to train with multiple GPUs. They also include workflow questions, e.g., how to visualize a neural network computation graph. These answers are fairly focused. For more didactic, self-contained introductions to neural networks and full working examples, visit the tutorials section.
- Can I run MXNet on smart or mobile devices?
- How to use data from S3 for training?
- How to run MXNet on AWS?
- How to do distributed training using MXNet on AWS?
- How do I run MXNet on a Raspberry Pi for computer vision?
- How do I run Keras 2 with MXNet backend?
- How to convert MXNet models to Apple CoreML format?
Extend and Contribute to MXNet¶
- How do I join the MXNet development discussion?
- How do I contribute a patch to MXNet?
- How do I implement operators in MXNet backend?
- How do I create new operators in MXNet?
- How do I implement sparse operators in MXNet backend?
- How do I contribute an example or tutorial?
- How do I set MXNet’s environmental variables?
Questions about Using MXNet¶
If you need help with using MXNet, have questions about applying it to a particular kind of problem, or have a discussion topic, please use our forum.
We track bugs and new feature requests in the MXNet Github repo in the issues folder: mxnet/issues.