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Keras-MXNet 2.2.4.2
Highlights
This release mainly includes a new feature, Amazon Elastic Inference Accelerator support, and several important bug fixes to make keras-mxnet compatible with MXNet 1.5.0.
New Feature
- Support for running Keras prediction with Apache MXNet backend utilizing Amazon Elastic Inference Accelerator. #236. For more details, please refer to documentation.
Bug fixes
- Fix SGD optimizer failure: #225
- Fix infer shape on
softmaxdue to 0-size tensor support in MXNet 1.5.0 #232 - Fix dataset test failure due to
numpyupdate #244
Documentation
- Update sparse documentation #202
Unsupported Functionalities
<< Same as previous release v2.2.4 >>
- Keras MXNet models does not support pickling #194
- 14 Keras operators are not supported with MXNet backend. Update operators, symbolic gradient, localconv1d, localconv2d, higher order functions, other operator like cumsum, cumprod, stack, ctc and more. See Operators missing with MXNet backend Github Issue for more details.
- Unsupported keras/examples list.
- Cross backend models are not supported. Training with TensorFlow backend and loading the Keras model with MXNet backend is not supported.
Known Issues
- MXNet 1.5.0 added dynamic shape support, but causing problem on Keras-MXNet RNN layer masking:
#229
<< Same as previous release v2.2.4 >>
- MXNet backend performance significantly drops with channels_last image_data_format. It is highly recommended to use channels_first image_data_format. See performance guide for more details.
MXNet backend does not support boolean. For example, in``topk operator with MXNet backend uses 0/1 instead of boolean. Issue - depthwise_conv2d supports depth_multiplier=1 only. Issue
Models with Custom Loss are not serializable. Issue
Installation
Install keras-mxnet
pip install keras-mxnet
Install MXNet - CPU
pip install mxnet-mkl --pre
Install MXNet - GPU
pip install mxnet-cu101mkl --pre
Contributions
Thanks to all the contributors for their contributions in this release:
@sandeep-krishnamurthy, @karan6181, @kalyc, @roywei
Other Resources and Getting Started
- Keras-MXNet installation guide - https://github.com/awslabs/keras-apache-mxnet/blob/master/docs/mxnet_backend/installation.md
- Using multi-gpu distributed training - https://github.com/awslabs/keras-apache-mxnet/blob/master/docs/mxnet_backend/multi_gpu_training.md
- Performance tuning guide for Keras with MXNet backend - https://github.com/awslabs/keras-apache-mxnet/blob/master/docs/mxnet_backend/performance_guide.md
- Export MXNet Model from Keras-MXNet - https://github.com/awslabs/keras-apache-mxnet/blob/master/docs/mxnet_backend/save_mxnet_model.md
- Checkpointing MXNet Model from Keras-MXNet - https://github.com/awslabs/keras-apache-mxnet/blob/master/docs/mxnet_backend/save_mxnet_model.md
- Using RNN in Keras-MXNet. Limitations and workarounds - https://github.com/awslabs/keras-apache-mxnet/blob/master/docs/mxnet_backend/using_rnn_with_mxnet_backend.md
- Use sparse data for training models in Keras-MXNet - https://github.com/awslabs/keras-apache-mxnet/blob/master/docs/mxnet_backend/using_sparse_data_with_mxnet_backend.md
- Keras-MXNet benchmark results -https://github.com/awslabs/keras-apache-mxnet/tree/master/benchmark