MXNet FAQ¶
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.
Modeling¶
Scale¶
Speed¶
Deployment Environments¶
- 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?
Security¶
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.
Issue Tracker¶
We track bugs and new feature requests in the MXNet Github repo in the issues folder: mxnet/issues.
Roadmap¶
MXNet is evolving fast. To see what’s next and what we are working on internally, go to the MXNet Roadmap.
