Question 84021 in Docker Question posted in
The official documentation can be found here.

RAPIDS.ai dependencies cuml and cudf not found no matter how I install – Docker

I have followed every version of the instructions on the AWS-EC2 setup for RAPIDS.ai: https://rapids.ai/cloud#AWS-EC2

I can confirm that I am using the exact instance type in the instructions, and following the steps exactly.

When I try to use the docker approach, the --gpus all command is not accepted.

When I try to use the conda approach, the install fails with the error:

PackageNotFoundError: Packages missing in current channels:
            
 - glibc

I have tried (many) different solutions provided to solve both of these problems, none of them seem to work. I really just need to test some python code with cuml and cudf imports in a notebook. Been at this for 7 hours (after giving up on my local and SageMaker).

Tags:

2

Answers


  1. Chosen as BEST ANSWER
    0

    Turns out, the frist AMI suggested in the documentation is not compatible. Use the Deep Learning NVIDIA one instead.


  2. 0

    You note that the --gpus all command is not accepted, which suggests that you do not have the NVIDIA Docker runtime installed.

    I followed the instructions you linked and I did run into an issue where the sudo yum install -y nvidia-docker2 command failed and I needed to disable an Amazon yum repo that was causing come conflicts as outlined in this issue.

    $ sudo yum-config-manager --disable amzn2-graphics

$ sudo yum install -y nvidia-docker2

$ sudo yum-config-manager --enable amzn2-graphics

    Once I’d done that and run sudo systemctl restart docker I was able to start the RAPIDS container.

    $ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786     rapidsai/rapidsai:cuda11.2-runtime-ubuntu18.04-py3.7
This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.download.nvidia.com/licenses/NVIDIA_Deep_Learning_Container_License.pdf

A JupyterLab server has been started!
To access it, visit http://localhost:8888 on your host machine.
Ensure the following arguments were added to "docker run" to expose the JupyterLab server to your host machine:
      -p 8888:8888 -p 8787:8787 -p 8786:8786
Make local folders visible by bind mounting to /rapids/notebooks/host
(rapids) root@be7253bb4fdb:/rapids/notebooks#
    Login or Signup to reply.
Please signup or login to give your own answer.

Back To Top
Search