This is a Dockerfile that can be used to create a Bright Deep Learning Docker image. Once instantiated using the nvidia-docker command, applications running within the container will have access the to GPU. This container is lightweight. While idle, it will use only 8MB of memory.
The Dockerfile
FROM centos:latest
ENV CUDA_VERSION 8.0.61
LABEL com.nvidia.cuda.version="${CUDA_VERSION}"
LABEL com.nvidia.volumes.needed="nvidia_driver"
ENV NVIDIA_CUDA_VERSION $CUDA_VERSION
ENV CUDA_PKG_VERSION 8-0-$CUDA_VERSION-1
# nvidia-container-runtime
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
COPY cm.repo /etc/yum.repos.d/cm.repo
COPY epel-testing.repo /etc/yum.repos.d/epel-testing.repo
COPY epel.repo /etc/yum.repos.d/epel.repo
COPY RPM-GPG-KEY-EPEL-7 /etc/pki/rpm-gpg/RPM-GPG-KEY-EPEL-7
COPY RPM-GPG-KEY-cm /etc/pki/rpm-gpg/RPM-GPG-KEY-cm
COPY http-parser-2.7.1-3.sdl7.x86_64.rpm /root/http-parser-2.7.1-3.sdl7.x86_64.rpm
RUN yum -y install /root/http-parser-2.7.1-3.sdl7.x86_64.rpm environment-modules cm-ml-distdeps
RUN echo 'export MODULEPATH=$MODULEPATH:/cm/shared/modulefiles' >> /root/.bashrc
The build directory
# ls -l -rw------- 1 root root 785 Sep 2 13:30 cm.repo -rw-r--r-- 1 root root 838 Sep 2 13:32 Dockerfile -rw-r--r-- 1 root root 951 Sep 2 13:31 epel.repo -rw-r--r-- 1 root root 1050 Sep 2 13:31 epel-testing.repo -rw-r--r-- 1 root root 30784 Sep 2 13:31 http-parser-2.7.1-3.sdl7.x86_64.rpm -rw-r--r-- 1 root root 1714 Sep 2 13:31 RPM-GPG-KEY-cm -rw-r--r-- 1 root root 1662 Sep 2 13:31 RPM-GPG-KEY-EPEL-7
Build the image
# docker build -t bright/dl/min/1 .
Push the image to the local Docker repository
# docker tag bright/dl/min/1 localhost:6000/bright/dl/min/1 # docker push localhost:6000/bright/dl/min/1
Run the container
Log into a node that has a GPU then load the CUDA and nvidia-docker modules. Instantiate the container using the ‘nvidia-docker’ command. The ‘-v /cm/shared:/cm/shared’ argument is required to mount the host:/cm/shared directory inside the container. The Bright Deep Learning stack is installed in /cm/shared.
# ssh rstober@gpu01 $ module load cuda80/toolkit nvidia-docker $ nvidia-docker run -v /cm/shared:/cm/shared -ti virgo-head:6000/bright/dl/min/1
The container starts. Load the CUDA and tensorflow modules (inside the container this time) so that you can access the GPU and then run tensorflow.
[root@7ca3a4e45601 /]# module load cuda80/toolkit tensorflow [root@7ca3a4e45601 /]# nvidia-smi Sat Sep 2 22:23:06 2017 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 375.66 Driver Version: 375.66 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Quadro K6000 Off | 0000:82:00.0 Off | 0 | | 26% 36C P8 20W / 225W | 0MiB / 11439MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+
Now that we know we can see the GPU, we’re ready to run Tensorflow.
[root@7ca3a4e45601 /]# python /cm/shared/apps/dl/tensorflow-scripts/comp-graph.py I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: name: Quadro K6000 major: 3 minor: 5 memoryClockRate (GHz) 0.9015 pciBusID 0000:82:00.0 Total memory: 11.17GiB Free memory: 11.10GiB I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro K6000, pci bus id: 0000:82:00.0) [3.0, 4.0] ('node3:', <tf.Tensor 'Add:0' shape=() dtype=float32>) ('sess.run(node3):', 7.0)