wheels
Tensor
Construcciones personalizadas para TensorFlow con optimizaciones de plataforma, incluidas SSE, AVX y FMA. Si está viendo mensajes como los siguientes con el stock pip install tensorflow , ha venido al lugar correcto.
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
Estas ruedas están construidas para su uso en Tinymind, la plataforma de aprendizaje automático en la nube. Si desea instalarlos en su propia caja de Linux (Ubuntu 16.04 LTS), puede hacerlo con:
# 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}La lista de todas las ruedas se puede encontrar en la página de lanzamientos.
Haga clic en los enlaces a continuación para saltar a versiones de lanzamiento específicas. Nuevamente, están construidos para Ubuntu 16.04 LTS a menos que se indique lo contrario.
| TF | Construcciones |
|---|---|
| 1.1 | CPU, GPU |
| 1.2 | CPU, GPU (solo Python 3.6) |
| 1.2.1 | CPU, GPU |
| 1.3 | CPU, GPU con MPI |
| 1.3.1 | CPU, depuración de la CPU, GPU, GPU con MPI |
| 1.4 | CPU, depuración de la CPU, MacOS de CPU, GPU (CUDA 8, CUDA 9 para Compute 3.7, CUDA 9 para Compute 3.7/6.0/7.0, CUDA 9 Genérico, CUDA 9 sin MKL) |
| 1.4.1 | CPU, GPU (CUDA 8, CUDA 9, CUDA 9.1) |
| 1.5 | CPU, GPU (CUDA 9, CUDA 9 sin MKL, CUDA 9.1, CUDA 9.1 sin MKL) |
| 1.6 | CPU, GPU (CUDA 9.1, CUDA 9.1 sin MKL) |
| 1.7 | CPU, GPU (CUDA 9, CUDA 9.1, Cudnn 7.1) |
Tenga en cuenta que su máquina necesita tener una CPU Intel relativamente nueva (y NVIDIA GPU si usa la versión GPU) para ser compatible con las ruedas a continuación. Si el hardware no está actualizado, las ruedas no funcionarán.
Las ruedas para TensorFlow 1.4.1 y arriba contienen soporte para GCP, S3 y Hadoop. Las banderas de compilación incluyen:
--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
Las ruedas que probablemente necesite se enumeran a continuación. ¿Necesita algo o una rueda no funciona para usted? Presentar un problema. (Desafortunadamente, no podremos acomodar las solicitudes de ruedas de Windows, ya que no tenemos máquinas de Windows nosotros mismos).
| Versión | Pitón | Arco | Enlace |
|---|---|---|---|
| 1.1 | 2.7 | UPC | https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-cp27-cp27mu-linux_x86_64.whl |
| 1.1 | 3.5 | UPC | https://github.com/mind/wheels/releases/download/tf1.1-cpu/tensorflow-1.1.0-0-cp35-cp35m-linux_x86_64.whl |
| 1.1 | 3.6 | UPC | 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-1.1.0-0-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 | UPC | https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-0-cp27-cp27mu-linux_x86_64.whl |
| 1.2 | 3.5 | UPC | https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-0-cp35-cp35m-linux_x86_64.whl |
| 1.2 | 3.6 | UPC | https://github.com/mind/wheels/releases/download/tf1.2-cpu/tensorflow-1.2.0-0-cp36-cp36m-linux_x86_64.whl |
| 1.2 | 3.6 | GPU | https://github.com/mind/wheels/releases/download/tf1.2-gpu/tensorflow-1.2.0-0-cp36-cp36m-linux_x86_64.whl |
| 1.2.1 | 2.7 | UPC | 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 | UPC | 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 | UPC | 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 | UPC | https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp27-cp27mu-linux_x86_64.whl |
| 1.3 | 3.5 | UPC | https://github.com/mind/wheels/releases/download/tf1.3-cpu/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl |
| 1.3 | 3.6 | UPC | 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 | UPC | 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 | UPC | 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 | UPC | 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 | UPC | https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp27-cp27mu-linux_x86_64.whl |
| 1.4 | 3.5 | UPC | https://github.com/mind/wheels/releases/download/tf1.4-cpu/tensorflow-1.4.0-cp35-cp35m-linux_x86_64.whl |
| 1.4 | 3.6 | UPC | 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 | UPC | 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 | UPC | 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 | UPC | 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 | UPC | https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp27-cp27mu-linux_x86_64.whl |
| 1.5 | 3.5 | UPC | https://github.com/mind/wheels/releases/download/tf1.5-cpu/tensorflow-1.5.0-cp35-cp35m-linux_x86_64.whl |
| 1.5 | 3.6 | UPC | 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-1.5.0-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 | UPC | https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp27-cp27mu-linux_x86_64.whl |
| 1.6 | 3.5 | UPC | https://github.com/mind/wheels/releases/download/tf1.6-cpu/tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl |
| 1.6 | 3.6 | UPC | 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 |
Esta sección contiene consejos para depurar su configuración. Sin embargo, en serio, intente TinyMind y nunca necesitará perder el tiempo de depuración nuevamente. También tenemos imágenes Docker que puede usar en sus propias máquinas. Si esta sección no resuelve su problema, asegúrese de presentar un problema.
Las diferentes versiones de TensorFlow son compatibles con diferentes versiones CUDA:
| TF | Cuda | cudnn | Capacidad de calcular |
|---|---|---|---|
| 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 no funciona con CUDA 9, la versión actual. En lugar de sudo apt-get install cuda , debe hacer sudo apt-get install cuda-8-0 . Las variantes CUDA 8 de TensorFlow 1.4 van con Cudnn 6.0, y las variantes CUDA 9.X van con 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 cudaAsegúrese de que las variables de entorno relacionadas con CUDA se establezcan correctamente:
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
. ~ /.bashrcDescargue el Cudnn correcto e instálelo de la siguiente manera:
# 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 la biblioteca libcupti ? Instálelo y agréguelo a su PATH .
sudo apt-get install libcupti-dev
echo ' export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH ' >> ~ /.bashrcCiertas ruedas apoyan a Tensorrt. Para instalar Tensorrt, primero descargue desde el sitio web de Nvidia y luego ejecute:
sudo dpkg -i nv-tensorrt-repo-ubuntu1604-ga-cuda9.0-trt3.0.4-20180208_1-1_amd64.deb
sudo apt-get update
sudo apt-get install tensorrtMKL es la Biblioteca de núcleo de aprendizaje profundo de Intel, que hace que la capacitación de redes neuronales en CPU sea mucho más rápido. Si no lo tiene, instálelo como lo siguiente:
# If you don't have cmake
sudo apt install cmake
git clone https://github.com/01org/mkl-dnn.git
cd mkl-dnn/scripts && ./prepare_mkl.sh && cd ..
mkdir -p build && cd build && cmake .. && make
sudo make install
echo ' export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib ' >> ~ /.bashrcTenga en cuenta que Ubuntu 16.04 LTS es el entorno previsto. Si tiene un sistema operativo antiguo, puede tener problemas con las versiones antiguas de GLIBC. Es posible que desee ver las discusiones aquí para ver si ayudarían.
¿Usando una rueda con soporte MPI? Asegúrese de ejecutar sudo apt-get install mpich .
