I developed a machine learning model and integrated it with Flask app.When I try to run the docker image for the app, it says I do not have a GPU access. How should I write a Dockerfile such that I can use "cuda gpu" inside the container ? Below is the current state of Dockerfile. ..
I have a Dockerfile which installs PyTorch library from the source code. Here is the snippet from Dockerfile which performs the installation from source code of pytorch RUN cd /tmp/ && git clone https://github.com/pytorch/pytorch.git && cd pytorch && git submodule sync && git submodule update –init –recursive && sudo TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0" python3 ..
I am currently trying to run an old pytorch code that only support pytorch version 1.4 and cuda version 10.1 My goto solution is to use pytorch/pytorch:1.4-cuda10.1-cudnn7-devel docker image that has the right requirements for my project. But when I run a python interpreter and start using the GPU with pytorch, the process hangs for ..
The NVIDIA NGC container catalog has a broad range of GPU-optimised containers for common activities such as deep learning. How does one find what is inside the Docker images? For example, I require an image with Pytorch 1.4 and Python 3.6 or 3.7, but the Pytorch tags go from pytorch:17.10 to pytorch:21.06-py3 (where xx.xx is ..
I have a python machine learning script for wich I need special hardware (a 8-GPU machine or Tensor Processing units). Therefore I run the code on a cloud server. Using Containers as known in docker looks like a very convenient way to execute the deep learning pytorch code. But there will likely be errors. How ..
I’m using torchserve to serve my pytorch model. I’m using this walkthrough. https://cloud.google.com/ai-platform/training/docs/getting-started-pytorch I add a –requirements-file=requirements.txt file to the RUN torch-model-archiver command. And my requirements file consists of – ”’ google-cloud-storage opencv-python ”’ I add RUN pip3 install opencv-python to the Dockerfile, aswell as RUN pip3 google-cloud-storage. At the top of the docker file ..
I am trying to use the base images provided by NVIDIA that let us use their GPUs via Docker containers. Because I am using docker, there is no need for me to have CUDA Toolkit or CuDNN on my system. All I need to have is the right driver – which I have. I can ..
So my workflow is gonna be a bit wonky, but I’m not permitted to use Docker, so I have Singularity instead. I’m running some code that is giving me this error: RuntimeError: nvrtc: error: failed to open libnvrtc-builtins.so.11.1. Make sure that libnvrtc-builtins.so.11.1 is installed correctly. nvrtc compilation failed: If more details are needed, I can ..
python setup.py develop yields outputs that can be checked here, left output is the result of running the command within the Dockerfile with RUN python setup.py develop, while the right side is the output of CMD [ "python", "setup.py", "develop"] or ENTRYPOINT [ "python", "/app/src/setup.py", "develop"] or connecting to an interactive shell and executing the ..
EDIT 2: Rewrote my whole question because of my discovery in EDIT 1. Minimal working Docker image: FROM pytorch/pytorch:1.8.0-cuda11.1-cudnn8-devel as base RUN python -c "import torch; print(torch.cuda.is_available())" RUN nvcc –version RUN cat /proc/driver/nvidia/version Step 3/4 : RUN nvcc –version —> Running in b8810e20ccc6 nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2020 NVIDIA Corporation Built ..