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
andrnnforw
implement cudnn RNNs (with @cangumeli). -
conv4
performance significantly improved using cudnnFind. -
batchnorm
implemented using CUDNN (@cangumeli). -
logp
performance significantly improved using cudnnSoftmaxForward. -
DBGFLAGS
andPROFILING
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
withColon
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).