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Rodas otimizadas do TensorFlow

Construções personalizadas para o TensorFlow com otimizações de plataforma, incluindo SSE, AVX e FMA. Se você está vendo mensagens como as seguintes com o estoque pip install tensorflow , você veio ao lugar certo. 

 The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.

or:
Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA

Essas rodas são construídas para uso no Tinymind, a plataforma de aprendizado de máquina em nuvem. Se você quiser instalá -los em sua própria caixa Linux (Ubuntu 16.04 LTS), você pode fazê -lo com: 

 # RELEASE is the git tag like tf1.1-cpu. WHEEL is the full wheel name.
pip --no-cache-dir install https://github.com/mind/wheels/releases/download/{RELEASE}/{WHEEL}

A lista de todas as rodas pode ser encontrada na página de lançamentos.

Versões

Clique nos links abaixo para saltar para versões específicas de liberação. Novamente, eles são construídos para o Ubuntu 16.04 LTS, a menos que indicado de outra forma.

Tf Construir
1.1 CPU, GPU
1.2 CPU, GPU (somente Python 3.6)
1.2.1 CPU, GPU
1.3 CPU, GPU com MPI
1.3.1 CPU, CPU Debug, GPU, GPU com MPI
1.4 CPU, CPU Debug, CPU MacOS, GPU (CUDA 8, CUDA 9 PARA COMPUTE 3.7, CUDA 9 PARA COMPUTE 3.7/6.0/7.0, CUDA 9 Genérico, CUDA 9 sem MKL)
1.4.1 CPU, GPU (CUDA 8, CUDA 9, CUDA 9.1)
1.5 CPU, GPU (CUDA 9, CUDA 9 sem MKL, CUDA 9.1, CUDA 9.1 sem MKL)
1.6 CPU, GPU (CUDA 9.1, CUDA 9.1 sem MKL)
1.7 CPU, GPU (CUDA 9, CUDA 9.1, CUDNN 7.1)

Observe que sua máquina precisa ter uma CPU Intel relativamente nova (e a NVIDIA GPU se você usar a versão da GPU) para ser compatível com as rodas abaixo. Se o hardware não estiver atualizado, as rodas não funcionarão.

As rodas para Tensorflow 1.4.1 e acima contêm suporte para GCP, S3 e Hadoop. Os sinalizadores de compilação incluem: 

 --config=opt --config=cuda --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0 --copt=-mavx --copt=-msse4.1 --copt=-msse4.2 --copt=-mavx2 --copt=-mfma --copt=-mfpmath=both

As rodas que você provavelmente precisará estão listadas abaixo. Precisa de algo ou uma roda não funciona para você? Arquivar um problema. (Infelizmente, não poderemos acomodar pedidos de rodas do Windows, pois não temos máquinas do Windows.)

Versão Python Arco Link
1.1 2.7 CPU https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-cp27-cp27mu-linux_x86_64.whl
1.1 3.5 CPU https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-cp35-cp35m-linux_x86_64.whl
1.1 3.6 CPU https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-cp36-cp36m-linux_x86_64.whl
1.1 2.7 GPU https://github.com/mind/wheels/releases/download/tf1.1-gpu/tensorflow-1.1.0-cp27-cp27mu-linux_x86_64.whl
1.1 3.5 GPU https://github.com/mind/wheels/releases/download/tf1.1-gpu/tensorflow-1.1.0-cp35-cp35m-linux_x86_64.whl
1.1 3.6 GPU https://github.com/mind/wheels/releases/download/tf1.1-gpu/tensorflow-1.1.0-cp36-cp36m-linux_x86_64.whl
1.2 2.7 CPU https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-cp27-cp27mu-linux_x86_64.whl
1.2 3.5 CPU https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl
1.2 3.6 CPU https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
1.2 3.6 GPU https://github.com/mind/wheels/releases/download/tf1.2-gpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
1.2.1 2.7 CPU https://github.com/mind/wheels/releases/download/tf1.2.1-cpu/tensorflow-1.2.1-cp27-cp27mu-linux_x86_64.whl
1.2.1 3.5 CPU https://github.com/mind/wheels/releases/download/tf1.2.1-cpu/tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl
1.2.1 3.6 CPU https://github.com/mind/wheels/releases/download/tf1.2.1-cpu/tensorflow-1.2.1-cp36-cp36m-linux_x86_64.whl
1.2.1 2.7 GPU https://github.com/mind/wheels/releases/download/tf1.2.1-gpu/tensorflow-1.2.1-cp27-cp27mu-linux_x86_64.whl
1.2.1 3.5 GPU https://github.com/mind/wheels/releases/download/tf1.2.1-gpu/tensorflow-1.2.1-cp35-cp35m-linux_x86_64.whl
1.2.1 3.6 GPU https://github.com/mind/wheels/releases/download/tf1.2.1-gpu/tensorflow-1.2.1-cp36-cp36m-linux_x86_64.whl
1.3 2.7 CPU https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp27-cp27mu-linux_x86_64.whl
1.3 3.5 CPU https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl
1.3 3.6 CPU https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp36-cp36m-linux_x86_64.whl
1.3 2.7 GPU https://github.com/mind/wheels/releases/download/tf1.3-gpu/tensorflow-1.3.0-cp27-cp27mu-linux_x86_64.whl
1.3 3.5 GPU https://github.com/mind/wheels/releases/download/tf1.3-gpu/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl
1.3 3.6 GPU https://github.com/mind/wheels/releases/download/tf1.3-gpu/tensorflow-1.3.0-cp36-cp36m-linux_x86_64.whl
1.3.1 2.7 CPU https://github.com/mind/wheels/releases/download/tf1.3.1-cpu/tensorflow-1.3.1-cp27-cp27mu-linux_x86_64.whl
1.3.1 3.5 CPU https://github.com/mind/wheels/releases/download/tf1.3.1-cpu/tensorflow-1.3.1-cp35-cp35m-linux_x86_64.whl
1.3.1 3.6 CPU https://github.com/mind/wheels/releases/download/tf1.3.1-cpu/tensorflow-1.3.1-cp36-cp36m-linux_x86_64.whl
1.3.1 2.7 GPU https://github.com/mind/wheels/releases/download/tf1.3.1-gpu/tensorflow-1.3.1-cp27-cp27mu-linux_x86_64.whl
1.3.1 3.5 GPU https://github.com/mind/wheels/releases/download/tf1.3.1-gpu/tensorflow-1.3.1-cp35-cp35m-linux_x86_64.whl
1.3.1 3.6 GPU https://github.com/mind/wheels/releases/download/tf1.3.1-gpu/tensorflow-1.3.1-cp36-cp36m-linux_x86_64.whl
1.4 2.7 CPU https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp27-cp27mu-linux_x86_64.whl
1.4 3.5 CPU https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp35-cp35m-linux_x86_64.whl
1.4 3.6 CPU https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp36-cp36m-linux_x86_64.whl
1.4 2.7 GPU https://github.com/mind/wheels/releases/download/tf1.4-gpu/tensorflow-1.4.0-cp27-cp27mu-linux_x86_64.whl
1.4 3.5 GPU https://github.com/mind/wheels/releases/download/tf1.4-gpu/tensorflow-1.4.0-cp35-cp35m-linux_x86_64.whl
1.4 3.6 GPU https://github.com/mind/wheels/releases/download/tf1.4-gpu/tensorflow-1.4.0-cp36-cp36m-linux_x86_64.whl
1.4.1 2.7 CPU https://github.com/mind/wheels/releases/download/tf1.4.1-cpu/tensorflow-1.4.1-cp27-cp27mu-linux_x86_64.whl
1.4.1 3.5 CPU https://github.com/mind/wheels/releases/download/tf1.4.1-cpu/tensorflow-1.4.1-cp35-cp35m-linux_x86_64.whl
1.4.1 3.6 CPU https://github.com/mind/wheels/releases/download/tf1.4.1-cpu/tensorflow-1.4.1-cp36-cp36m-linux_x86_64.whl
1.4.1 2.7 GPU https://github.com/mind/wheels/releases/download/tf1.4.1-gpu/tensorflow-1.4.1-cp27-cp27mu-linux_x86_64.whl
1.4.1 3.5 GPU https://github.com/mind/wheels/releases/download/tf1.4.1-gpu/tensorflow-1.4.1-cp35-cp35m-linux_x86_64.whl
1.4.1 3.6 GPU https://github.com/mind/wheels/releases/download/tf1.4.1-gpu/tensorflow-1.4.1-cp36-cp36m-linux_x86_64.whl
1.5 2.7 CPU https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp27-cp27mu-linux_x86_64.whl
1.5 3.5 CPU https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl
1.5 3.6 CPU https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp36-cp36m-linux_x86_64.whl
1.5 2.7 GPU https://github.com/mind/wheels/releases/download/tf1.5-gpu/tensorflow-1.5.5-cp27-cp27mu-linux_x86_64.whl
1.5 3.5 GPU https://github.com/mind/wheels/releases/download/tf1.5-gpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl
1.5 3.6 GPU https://github.com/mind/wheels/releases/download/tf1.5-gpu/tensorflow-1.5.0-cp36-cp36m-linux_x86_64.whl
1.6 2.7 CPU https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp27-cp27mu-linux_x86_64.whl
1.6 3.5 CPU https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl
1.6 3.6 CPU https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp36-cp36m-linux_x86_64.whl
1.6 2.7 GPU https://github.com/mind/wheels/releases/download/tf1.6-gpu-cuda91/tensorflow-1.6.0-cp27-cp27mu-linux_x86_64.whl
1.6 3.5 GPU https://github.com/mind/wheels/releases/download/tf1.6-gpu-cuda91/tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl
1.6 3.6 GPU https://github.com/mind/wheels/releases/download/tf1.6-gpu-cuda91/tensorflow-1.6.0-cp36-cp36m-linux_x86_64.whl

Ajuda!

Esta seção contém dicas para depurar sua configuração. Sério, experimente o Tinymind fora e você nunca precisará perder tempo depurando tempo novamente. Também temos imagens do Docker que você pode usar em suas próprias máquinas. Se esta seção não resolver seu problema, certifique -se de apresentar um problema.

CUDA

Diferentes versões do tensorflow suportam/requerem diferentes versões CUDA:

Tf CUDA cudnn Capacidade de computação
1.1, 1.2 8.0 5.1 3.7 (K80)
1.2.1-1.3.1 8.0 6.0 3.7
1.4 8.0/9.0 6.0/7.0 3.7, 6.0 (P100), 7.0 (V100)
1.4.1 8.0/9.0/9.1 6.0/7.0 3.7, 6.0, 7.0
1.5 9.0/9.1 7.0 3.7, 6.0, 7.0
1.6 9.1 7.0 3.7, 6.0, 7.0
1.7 9.0/9.1 7.0/7.1 3.7, 6.0, 7.0

Tensorflow <1.4 não funciona com o CUDA 9, a versão atual. Em vez de sudo apt-get install cuda , você precisa fazer sudo apt-get install cuda-8-0 . CUDA 8 variantes do tensorflow 1.4 GO com CUDNN 6.0 e as variantes CUDA 9.X vão com cudnn 7.x. 

 # Install CUDA 8
curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda-8-0

# Install CUDA 9
curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda

Verifique se as variáveis ​​de ambiente relacionadas ao CUDA estão definidas corretamente: 

 echo ' export CUDA_HOME=/usr/local/cuda ' >> ~ /.bashrc
echo ' export PATH=$PATH:$CUDA_HOME/bin ' >> ~ /.bashrc
echo ' export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64 ' >> ~ /.bashrc
. ~ /.bashrc

Faça o download do CUDNN correto e instale -o da seguinte forma: 

 # The cuDNN tar file.
tar xzvf cudnn-9.0-linux-x64-v7.0.tgz
sudo cp cuda/lib64/ * /usr/local/cuda/lib64/
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/

Falta a biblioteca libcupti ? Instale -o e adicione -o ao seu PATH . 

sudo apt-get install libcupti-dev
echo ' export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH ' >> ~ /.bashrc

Tensorrt

Certas rodas suportam Tensorrt. Para instalar o Tensorrt, primeiro faça o download no site da NVIDIA e depois execute: 

sudo dpkg -i nv-tensorrt-repo-ubuntu1604-ga-cuda9.0-trt3.0.4-20180208_1-1_amd64.deb
sudo apt-get update
sudo apt-get install tensorrt

Mkl

A MKL é a Biblioteca Deep Learning Kernel da Intel, que torna as redes neurais de treinamento na CPU muito mais rápido. Se você não tiver, instale -o como o seguinte: 

 # If you don't have cmake
sudo apt install cmake

git clone https://github.com/01org/mkl-dnn.git
cd mkl-dnn/scripts && ./prepare_mkl.sh && cd ..
mkdir -p build && cd build && cmake .. && make
sudo make install

echo ' export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib ' >> ~ /.bashrc

Glibc 2.23

Observe que o Ubuntu 16.04 LTS é o ambiente pretendido. Se você tiver um sistema operacional antigo, pode ter problemas com as versões antigas do Glibc. Você pode verificar as discussões aqui para ver se elas ajudariam.

MPI

Usando uma roda com suporte MPI? Certifique-se de executar sudo apt-get install mpich .

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