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GPU Acceleration

GPU acceleration with CUDA

Most up-to-date docs and recommendations: https://www.tensorflow.org/install/gpu

CUDA 11.0.3, cuDNN 8.0.5 - Tested working

  1. Install CUDA 11.0.3

  2. Install cuDNN 8.0.5 for CUDA 11.0. This requires signing up for the nvidia developer program. To install, copy the bin/, lib/, include/ dirs into your CUDA install dirs.

    Platform CUDA path
    Linux /usr/local/cuda-X.Y
    Windows C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v<XX.Y>

  3. Set the environment variables CUDA_PATH and CUDA_PATH_V11_0 if needed. These are usually set by the CUDA installer.

CUDA 11.1, cuDNN 8.0.5 - not tested

CUDA 11.2.1, cuDNN 8.1 - may work, requires hack

Getting this to run at all requires you to make a link for cusolver10. Even with this fix I ran into out-of-memory issues, so your mileage may vary. See https://github.com/tensorflow/tensorflow/issues/44777.

Linux:

cd $LD_LIBRARY_PATH
sudo ln libcusolver.so.11 libcusolver.so.10

Windows:

cd "%CUDA_PATH_V11_2%\bin"
mklink /h cusolver64_10.dll cusolver64_11.dll

Enable XLA for Neural Networks

For neural network training, there are flags defined to enable XLA and GPU options. Note that XLA and especially GPU acceleration may be slower than the defaults if you are not configuring a larger batch size. The default settings produce matrices that are too small to effectively take advantage of these features.

> load -t GME
> pred
> lstm -h  # display the help menu which lists all args
> lstm --xla_gpu --force_allow_gpu_growth true  # enable xla with gpu acceleration and flexible memory usage

Globally enable XLA

XLA is a domain specific compiler for linear algebra that can accelerate TensorFlow models if you can get it working. https://www.tensorflow.org/xla

Env var name Value
XLA_FLAGS --xla_gpu_cuda_data_dir=CUDA_PATH_V11_0
TF_XLA_FLAGS --tf_xla_cpu_global_jit --tf_xla_enable_xla_devices
To enable profiling (useful for debugging), add --xla_hlo_profile to XLA_FLAGS

Globally enable GPU + XLA

By default, TensorFlow will allocate 100% of your available GPU memory before it starts doing work. Using TF_FORCE_GPU_ALLOW_GROWTH below will override this behavior so that it only allocates memory as needed.

Env var name Value
TF_XLA_FLAGS --tf_xla_auto_jit=2 --tf_xla_enable_xla_devices
TF_FORCE_GPU_ALLOW_GROWTH true