# Knet v0.9.0 supports windows, beats new benchmarks

**URL:** <https://discourse.julialang.org/t/knet-v0-9-0-supports-windows-beats-new-benchmarks/8024>\
**Category:** Machine Learning\
**Created:** [December 27, 2017, 5:11pm UTC](https://discourse.julialang.org/t/knet-v0-9-0-supports-windows-beats-new-benchmarks/8024 "2017-12-27T17:11:58Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![denizyuret](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/denizyuret/32/568_2.png) [@denizyuret](https://discourse.julialang.org/u/denizyuret)\
**Post date:** [December 27, 2017, 5:11pm UTC](https://discourse.julialang.org/t/knet-v0-9-0-supports-windows-beats-new-benchmarks/8024/1 "2017-12-27T17:11:58Z")

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Knet v0.9.0 released with significant performance improvements, new benchmarks and windows support.

## Compatibility

- Windows GPU support implemented. (tested with VS-2015, Cuda 9.1)
- MacOS GPU support improved: nvml only used when available.
- CUDA up to v"9.1" and cuDNN up to v"7.0.5" are tested.
- Pre-0.6 Julia versions no longer supported.

## General

- `rnninit` and `rnnforw` implement cudnn RNNs (with @cangumeli).
- `conv4` performance significantly improved using cudnnFind.
- `batchnorm` implemented using CUDNN (@cangumeli).
- `logp` performance significantly improved using cudnnSoftmaxForward.
- `DBGFLAGS` and `PROFILING` constants defined in Knet.jl.
- `optimizers` creates optimization structs for the whole model.
- `dropout` now detects training mode automatically.
- `nll` returns negative log likelihood given score matrix and answer index vector.
- `accuracy` returns ratio of correct answers given score matrix and answer index vector.
- `minibatch(x,y,b)` returns a batch iterator.
- `knetgc` is now exported to cudaFree garbage collected pointers.
- `randn!`, `mean(a,dims)`, `reshape` with `Colon` is now supported by KnetArray (@CarloLucibello).
- Using CUDAapi and CUDAdrv in build.jl if installed.
- Got rid of the Combinatorics dependency in test.
- `curandInit` called at initialization to prevent memory fill before first dropout.
- `deconv4` bug fixed (@ilkerkesen).

## Documentation and Examples

- New benchmarking notebooks under examples/DeepLearningFrameworks (with @kirnap, @ilkarman).
- Knet/data now has download utilities: cifar.jl, fashion-mnist.jl, gutenberg.jl, housing.jl, imagenet.jl, imdb.jl, mikolovptb.jl, mnist.jl, treebank.jl, wikiner.jl
- All examples updated to use the new RNNs and replaced/supported with IJulia notebooks.
- New variational-autoencoder example (@CarloLucibello).
- DyNet benchmark examples added (@ilkerkesen).
- Deep Convolutional Generative Adversarial Networks example added (@ilkerkesen).