(You may want to see the already built releases before building yourself)
This project aims to build the Tensorflow from the source using a Dockerfile. The idea is to simplify the build so that one can choose the the Cuda/CuDNN version which better fits ones environment.
As Tensorflow pre-built binaries require specific Cuda/CuDnn versions up to the patch version, e.g., #15656 you will struggle if you have Cuda 9.1/2, as pre-built binaries won't work as they require cuda 9.0.
The default configuration of the Dockerfile will build:
- Python 3.5
- Tensorflow 1.7
- Cuda 9.1.85
- CuDNN 7.1.3
As it downloads the packages straight from Nvidia you can build the one
you prefer with Cuda, the package comes straight from here.
The following versions are then available, just replace the CUDA_VERSION
variable by one of the following:
- 8.0.44
- 8.0.61
- 9.0.176
- 9.1.85
- 9.2.88
- 9.2.148
- 10.0.130
- 10.1.105
- 10.1.168
- 10.1.243
Likewise, CuDNN version can be one of the following. Just replace the CUDNN_PKG_VERSION
variable to the one you prefer. The packages come directly from here.
- 7.0.1.13-1+cuda8.0
- 7.0.2.38-1+cuda8.0
- 7.0.3.11-1+cuda8.0
- 7.0.3.11-1+cuda9.0
- 7.0.4.31-1+cuda8.0
- 7.0.4.31-1+cuda9.0
- 7.0.5.15-1+cuda8.0
- 7.0.5.15-1+cuda9.0
- 7.0.5.15-1+cuda9.1
- 7.1.1.5-1+cuda8.0
- 7.1.1.5-1+cuda9.0
- 7.1.1.5-1+cuda9.1
- 7.1.2.21-1+cuda8.0
- 7.1.2.21-1+cuda9.0
- 7.1.2.21-1+cuda9.1
- 7.1.3.16-1+cuda8.0
- 7.1.3.16-1+cuda9.0
- 7.1.3.16-1+cuda9.1
- 7.1.4.18-1+cuda8.0
- 7.1.4.18-1+cuda9.0
- 7.1.4.18-1+cuda9.2
- 7.2.1.38-1+cuda8.0
- 7.2.1.38-1+cuda9.0
- 7.2.1.38-1+cuda9.2
- 7.3.0.29-1+cuda9.0
- 7.3.0.29-1+cuda10.0
- 7.3.1.20-1+cuda9.0
- 7.3.1.20-1+cuda9.2
- 7.3.1.20-1+cuda10.0
- 7.4.1.5-1+cuda9.0
- 7.4.1.5-1+cuda9.2
- 7.4.1.5-1+cuda10.0
- 7.4.2.24-1+cuda9.0
- 7.4.2.24-1+cuda9.2
- 7.4.2.24-1+cuda10.0
- 7.5.0.56-1+cuda9.0
- 7.5.0.56-1+cuda9.2
- 7.5.0.56-1+cuda10.0
- 7.5.0.56-1+cuda10.1
- 7.5.1.10-1+cuda9.0
- 7.5.1.10-1+cuda9.2
- 7.5.1.10-1+cuda10.0
- 7.5.1.10-1+cuda10.1
- 7.6.0.64-1+cuda9.0
- 7.6.0.64-1+cuda9.2
- 7.6.0.64-1+cuda10.0
- 7.6.0.64-1+cuda10.1
- 7.6.1.34-1+cuda9.0
- 7.6.1.34-1+cuda9.2
- 7.6.1.34-1+cuda10.0
- 7.6.1.34-1+cuda10.1
- 7.6.2.24-1+cuda9.0
- 7.6.2.24-1+cuda9.2
- 7.6.2.24-1+cuda10.0
- 7.6.2.24-1+cuda10.1
- 7.6.3.30-1+cuda9.0
- 7.6.3.30-1+cuda9.2
- 7.6.3.30-1+cuda10.0
- 7.6.3.30-1+cuda10.1
- 7.6.4.38-1+cuda9.0
- 7.6.4.38-1+cuda9.2
- 7.6.4.38-1+cuda10.0
- 7.6.4.38-1+cuda10.1
One thing to note is that some of the packages will respect the Cuda version you are looking for, e.g.,
apt-get install ...
cuda-command-line-tools-9-1 \
cuda-cublas-dev-9-1 \
cuda-cudart-dev-9-1 \
cuda-cufft-dev-9-1 \
cuda-curand-dev-9-1 \
cuda-cusolver-dev-9-1 \
cuda-cusparse-dev-9-1 \
have the 9-1 in the package names, if you are building a Cuda 10.1 then
you should replace the 9-1 everywhere by 10-1.
Just run (sudo) docker build -t build-tensorflow . in the same directory
as the one where the Dockerfile is stored. The tensorflow well will be
stored on the /tmp/pip directory of the container.
There are several ways to copy data from container to the host machine. Here is a suggestion:
sudo docker cp CONTAINER_ID:/tmp/pip/tensorflow-1.7.1-cp35-cp35m-linux_x86_64.whl .
To get the CONTAINER_ID just run docker ps -alq or docker ps then see the CONTAINER_ID
respective to the build-tensorflow image, or the one you -t image tag you gave in the
building command.