# CNTK v2.4 Release Notes

## Highlights of this Release
- Move to CUDA9, cuDNN 7 and Visual Studio 2017.
- Removed Python 3.4 support.
- Support Volta GPU and FP16.
- Better ONNX support.
- CPU perf improvement.
- More OPs.

## OPs
- ``top_k`` operation: in the forward pass it computes the top (largest) k values and corresponding indices along the specified axis. In the backward pass the gradient is scattered to the top k elements (an element not in the top k gets a zero gradient).
- ``gather`` operation now supports an axis argument
- ``squeeze`` and ``expand_dims`` operations for easily removing and adding singleton axes
- ``zeros_like`` and ``ones_like`` operations. In many situations you can just rely on CNTK correctly broadcasting a simple 0 or 1 but sometimes you need the actual tensor.
- ``depth_to_space``: Rearranges elements in the input tensor from the depth dimension into spatial blocks. Typical use of this operation is for implementing sub-pixel convolution for some image super-resolution models.
- ``space_to_depth``: Rearranges elements in the input tensor from the spatial dimensions to the depth dimension. It is largely the inverse of DepthToSpace.

## ONNX
There have been several improvements to ONNX support in CNTK.

### Updates
- Updated ONNX ``Reshape`` op to handle ``InferredDimension``.
- Adding ``producer_name`` and ``producer_version`` fields to ONNX models.
- Handling the case when neither ``auto_pad`` nor ``pads`` atrribute is specified in ONNX ``Conv`` op.

### Bug fixes
- Fixed bug in ONNX ``Pooling`` op serialization
- Bug fix to create ONNX ``InputVariable`` with only one batch axis.
- Bug fixes and updates to implementation of ONNX ``Transpose`` op to match updated spec.
- Bug fixes and updates to implementation of ONNX ``Conv``, ``ConvTranspose``, and ``Pooling`` ops to match updated spec.

## Operators
### Group convolution
- Fixed bug in group convolution. Output of CNTK ``Convolution`` op will change for groups > 1. More optimized implementation of group convolution is expected in the next release.
- Better error reporting for group convolution in ``Convolution`` layer.

### Halide Binary Convolution
- The CNTK build can now use optional [Halide](http://halide-lang.org/) libraries to build ``Cntk.BinaryConvolution.so/dll`` library that can be used with the ``netopt`` module. The library contains optimized binary convolution operators that perform better than the python based binarized convolution operators. To enable Halide in the build, please download [Halide release](https://github.com/halide/Halide/releases) and set ``HALIDE_PATH`` environment varibale before starting a build. In Linux, you can use ``./configure --with-halide[=directory]`` to enable it. For more information on how to use this feature, please refer to [How_to_use_network_optimization](https://github.com/Microsoft/CNTK/blob/master/Manual/Manual_How_to_use_network_optimizations.ipynb).