[BUG] 简洁阐述问题 / 拉取docker镜像后，启动不起来，卡在模型加载地方不动

[zhangt-run]

No description provided.

[dosubot]

To address the issue where the process gets stuck at the model loading step after pulling the Docker image, consider the following steps:

1. Check System Resources: Ensure your system has adequate CPU, GPU, and RAM to load the model. Insufficient resources can cause the process to hang.

2. Verify NVIDIA CUDA Compatibility: The Docker image is based on nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04. Ensure your NVIDIA driver is compatible with CUDA 12.1.1. Incompatibility can lead to failures in CUDA-dependent operations, including model loading.

3. Use Correct Docker Runtime: Make sure to start the Docker container with the --gpus all flag to properly utilize NVIDIA GPUs.

For further assistance, you might find it helpful to review similar issues in the Langchain-Chatchat repository, such as problems with model loading at startup. These discussions could provide additional insights or solutions to your problem.

Details

Sources

• 启动失败，卡在模型加载前。
• [BUG] docker 启动 卡在 Loading checkpoint shards
• [BUG] linux 启动阶段加载模型卡住
• Dockerfile

About Dosu
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[zhangt-run]

上面关闭其他镜像解决了，但又出现新问题：

Windows使用docker部署，本机有GPU，但出现以下错误：
RuntimeError: No CUDA GPUs are available

请问怎么解决

[dosubot]

To resolve the RuntimeError: No CUDA GPUs are available error on Windows when deploying with Docker and your machine has a GPU, follow these steps:

1. Update NVIDIA Drivers: Ensure your NVIDIA drivers are up to date and compatible with CUDA 12.1.1. Download the latest drivers from NVIDIA's website.

2. Install NVIDIA Docker Toolkit: This is required for Docker to access the GPU. Ensure it's installed and up to date.

3. Configure WSL2: Docker on Windows uses WSL2 for Linux containers. Make sure WSL2 is correctly installed and configured to access the GPU.

4. Use the Correct Docker Command: Start your Docker container with the --gpus all flag, like so:

   docker run --gpus all -it --rm nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04
   

5. Check NVIDIA Container Toolkit: Ensure the NVIDIA Container Toolkit is correctly installed to allow Docker GPU access.

6. Match CUDA and Driver Versions: Verify that the CUDA version in your Docker image is compatible with your NVIDIA drivers. Adjust as necessary.

7. Adjust Docker Daemon Configuration: If needed, modify the Docker daemon's configuration to recognize NVIDIA GPUs by setting the default-runtime to nvidia.

8. Ensure Proper Permissions: Make sure Docker has the necessary permissions to access the GPU.

If these steps don't resolve the issue, consult Docker logs and the output of the nvidia-smi tool for further troubleshooting. Additionally, reviewing similar issues and their resolutions in the repository's issue tracker might provide further insights.

Details

Sources

• nvidia显卡驱动不兼容，导致0.2版容器运行报错
• SystemError: 8bit 模型需要 CUDA 支持，或者改用量化后模型！
• Dockerfile

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.