I can't create a new file, the file system is reported as read-only

dusty-nv / jetson-containers

Machine Learning Containers for NVIDIA Jetson and JetPack-L4T

MIT License

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I can't create a new file, the file system is reported as read-only #15

Open land007 opened 4 years ago

land007 commented 4 years ago

Dockerfile FROM nvcr.io/nvidia/l4t-ml:r32.4.3-py3 Step 16/19 : RUN cd /usr/local/cuda/targets/aarch64-linux/lib && mkdir 123 ---> Running in d0f4ef0fa4c4 mkdir: cannot create directory '123': Read-only file system

dusty-nv commented 4 years ago

/usr/local/cuda is mounted from the host device in read-only mode, so you can't write to it from within the containers.

For more info, please see here: https://github.com/NVIDIA/nvidia-docker/wiki/NVIDIA-Container-Runtime-on-Jetson#mount-plugins

land007 commented 4 years ago

Currently I have the following version environment -library:

CUDA: 10.2.89
cuDNN: 8.0.0.145
TensorRT: 7.1.0.16 I wish to make a docker image of the following environment -library:
CUDA: 10.0
cuDNN: 7.5
TensorRT: 5.1.6 If docker depends on the environment of the host, it is impossible to complete such an image

dusty-nv commented 4 years ago

It is currently not supported to use a different version of CUDA/ect than what comes with JetPack-L4T.

However you could try making your own base image and install the CUDA packages inside your container. What you should do is move/rename /etc/nvidia-container-runtime/host-files-for-container.d/cuda.csv on your host device first (and cudnn.csv, tensorrt.csv), so those CUDA files don't get mounted into your container.

land007 commented 4 years ago

Thanks for your reply, it does not seem easy. The production of nvcr.io/nvidia/l4t-base does not seem to be in this project. I am not sure if the host cuda driver works for the low version cuda calls in Docker.

land007 commented 4 years ago

I finally understand what you mean. After repeated attempts, I achieved the downgrade of CUDA.link