无论我如何安装，RAPIDS.ai 依赖项 cuml 和 cudf 都找不到

发布于 2025-01-16 06:22:56 字数 514 浏览 6 评论 0原文

我已遵循 RAPIDS.ai 的 AWS-EC2 设置中的每个版本的说明： https://rapids .ai/cloud#AWS-EC2

我可以确认我使用的是说明中的确切实例类型，并严格按照步骤操作。

当我尝试使用 docker 方法时，不接受 --gpus all 命令。

当我尝试使用 conda 方法时，安装失败并出现错误：

PackageNotFoundError: Packages missing in current channels:
            
 - glibc

我已经尝试了（许多）提供的不同解决方案来解决这两个问题，但它们似乎都不起作用。我真的只需要在笔记本中使用 cuml 和 cudf 导入来测试一些 python 代码。已经这样做了 7 个小时（在放弃我的本地和 SageMaker 之后）。

原文

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).

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百变从容 2025-01-23 06:22:56

您注意到 --gpus all 命令未被接受，这表明您没有安装 NVIDIA Docker 运行时。

我按照您链接的说明操作，确实遇到了一个问题，sudo yum install -y nvidia-docker2 命令失败，我需要禁用导致冲突的 Amazon yum 存储库如本期所述。

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

$ sudo yum install -y nvidia-docker2

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

完成此操作并运行 sudo systemctl restart docker 后，我就能够启动 RAPIDS 容器。

$ 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#

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#

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