Beranda>Terkait pemrograman>Kode sumber lainnya
Unduh

TensorFlow Roda yang dioptimalkan

Bangunan khusus untuk TensorFlow dengan optimasi platform, termasuk SSE, AVX dan FMA. Jika Anda melihat pesan seperti berikut dengan stock pip install tensorflow , Anda telah datang ke tempat yang tepat. 

 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

Roda ini dibangun untuk digunakan di Tinymind, platform pembelajaran mesin cloud. Jika Anda ingin menginstalnya di kotak Linux Anda sendiri (Ubuntu 16.04 LTS), Anda dapat melakukannya dengan: 

 # 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}

Daftar semua roda dapat ditemukan di halaman rilis.

Versi

Klik tautan di bawah ini untuk melompat ke versi rilis tertentu. Sekali lagi, mereka dibangun untuk Ubuntu 16.04 LTS kecuali dinyatakan lain.

Tf Membangun
1.1 CPU, GPU
1.2 CPU, GPU (Python 3.6 saja)
1.2.1 CPU, GPU
1.3 CPU, GPU dengan MPI
1.3.1 CPU, Debug CPU, GPU, GPU dengan MPI
1.4 CPU, Debug CPU, CPU MacOS, GPU (CUDA 8, CUDA 9 untuk menghitung 3.7, CUDA 9 untuk menghitung 3.7/6.0/7.0, CUDA 9 generik, CUDA 9 tanpa MKL)
1.4.1 CPU, GPU (CUDA 8, CUDA 9, CUDA 9.1)
1.5 CPU, GPU (CUDA 9, CUDA 9 tanpa MKL, CUDA 9.1, CUDA 9.1 tanpa MKL)
1.6 CPU, GPU (CUDA 9.1, CUDA 9.1 tanpa MKL)
1.7 CPU, GPU (CUDA 9, CUDA 9.1, Cudnn 7.1)

Harap dicatat bahwa mesin Anda perlu memiliki CPU Intel yang relatif baru (dan NVIDIA GPU jika Anda menggunakan versi GPU) untuk kompatibel dengan roda di bawah ini. Jika perangkat keras tidak terkini, roda tidak akan berfungsi.

Roda untuk TensorFlow 1.4.1 dan di atas berisi dukungan untuk GCP, S3 dan Hadoop. Bendera kompilasi meliputi: 

 --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

Roda yang kemungkinan besar akan Anda butuhkan tercantum di bawah ini. Butuh sesuatu atau roda tidak cocok untuk Anda? Mengajukan masalah. (Sayangnya, kami tidak akan dapat mengakomodasi permintaan roda windows, karena kami tidak memiliki mesin Windows sendiri.)

Versi Python Lengkungan 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-1.5.0-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 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

Membantu!

Bagian ini berisi tips untuk men -debug pengaturan Anda. Serius, cobalah Tinymind dan Anda tidak perlu membuang waktu untuk men -debug. Kami juga memiliki gambar Docker yang dapat Anda gunakan di mesin Anda sendiri. Jika bagian ini tidak menyelesaikan masalah Anda, pastikan untuk mengajukan masalah.

Cuda

Versi TensorFlow yang berbeda mendukung/membutuhkan versi CUDA yang berbeda:

Tf Cuda Cudnn Kemampuan menghitung
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 tidak berfungsi dengan CUDA 9, versi saat ini. Alih-alih sudo apt-get install cuda , Anda perlu melakukan sudo apt-get install cuda-8-0 . CUDA 8 Varian TensorFlow 1.4 GO dengan CUDNN 6.0, dan varian CUDA 9.x pergi dengan 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

Pastikan variabel lingkungan terkait CUDA diatur dengan benar: 

 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

Unduh cudnn yang benar dan instal sebagai berikut: 

 # 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/

Perpustakaan libcupti yang hilang? Instal dan tambahkan ke PATH Anda. 

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

Tensorrt

Roda tertentu mendukung Tensorrt. Untuk menginstal Tensorrt, pertama -tama unduh dari situs web Nvidia, lalu jalankan: 

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

MKL adalah Perpustakaan Kernel Intel Learning Learning, yang membuat pelatihan jaring saraf di CPU jauh lebih cepat. Jika Anda tidak memilikinya, instal seperti berikut: 

 # 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

Harap dicatat bahwa Ubuntu 16.04 LTS adalah lingkungan yang dimaksud. Jika Anda memiliki OS lama, Anda dapat mengalami masalah dengan versi GLIBC lama. Anda mungkin ingin memeriksa diskusi di sini untuk melihat apakah mereka akan membantu.

MPI

Menggunakan roda dengan dukungan MPI? Pastikan untuk menjalankan sudo apt-get install mpich .

Memperluas
Informasi Tambahan
Aplikasi Terkait
Direkomendasikan untuk Anda
Informasi Terkait Semua