tensorflow cannot access GPU in Docker RuntimeError: cuda runtime error (100) : no CUDA-capable device is detected at /pytorch/aten/src/THC/THCGeneral.cpp:50 pytorch cannot access GPU in Docker The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. I tensorflow/core/common_runtime/gpu/gpu_:81] No GPU devices available on machine. docker: Error response from daemon: Container command 'nvidia-smi' not found or does not exist.Įrror: Docker does not find Nvidia drivers I tensorflow/stream_executor/cuda/cuda_:150] kernel reported version is: 352.93 When you attempt to run your container that needs the GPU in Docker, you might receive any of the following errors. It is called the NVIDIA Container Toolkit! Nvidia Container Toolkit ( Citation) Potential Errors in Docker Luckily, you have found the solution explained here. Certain things like the CPU drivers are pre-configured for you, but the GPU is not configured when you run a docker container. To add another layer of difficulty, when Docker starts a container - it starts from almost scratch. The configuration steps change based on your machine's operating system and the kind of NVIDIA GPU that your machine has. In this post, we walk through the steps required to access your machine's GPU within a Docker container.Ĭonfiguring the GPU on your machine can be immensely difficult.

Nvidia docker for mac