both contaners one from a standalone build of the folder twgui and the second is the
TWGUI build inside of superagi build: Standalone TWGUI:
docker run –hostname=b2aec0c539c9 –env=PATH=/usr/local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin –env=LANG=C.UTF-8 –env=GPG_KEY=E3FF2839C048B25C084DEBE9B26995E310250568 –env=PYTHON_VERSION=3.9.17 –env=PYTHON_PIP_VERSION=23.0.1 –env=PYTHON_SETUPTOOLS_VERSION=58.1.0 –env=PYTHON_GET_PIP_URL=https://github.com/pypa/get-pip/raw/0d8570dc44796f4369b652222cf176b3db6ac70e/public/get-pip.py –env=PYTHON_GET_PIP_SHA256=96461deced5c2a487ddc65207ec5a9cffeca0d34e7af7ea1afc470ff0d746207 –workdir=/app -p 5000:5000 -p 5005:5005 -p 7860:7860 –restart=no –label=’desktop.docker.io/wsl-distro=Ubuntu’ –runtime=nvidia -d superagiincuda:latest
In SUperAGI:
docker run –hostname=430875d44183 –env=EXTRA_LAUNCH_ARGS=” –verbose –extensions openai –auto-devices –n_ctx 2000 –gpu-memory 22 –n-gpu-layers 128 –threads 12″ –env=PATH=/venv/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin –env=NVARCH=x86_64 –env=NVIDIA_REQUIRE_CUDA=cuda>=11.8 brand=tesla,driver>=450,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver<516 –env=NV_CUDA_CUDART_VERSION=11.8.89-1 –env=NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-8 –env=CUDA_VERSION=11.8.0 –env=LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 –env=NVIDIA_VISIBLE_DEVICES=all –env=NVIDIA_DRIVER_CAPABILITIES=compute,utility –env=NV_CUDA_LIB_VERSION=11.8.0-1 –env=NV_NVTX_VERSION=11.8.86-1 –env=NV_LIBNPP_VERSION=11.8.0.86-1 –env=NV_LIBNPP_PACKAGE=libnpp-11-8=11.8.0.86-1 –env=NV_LIBCUSPARSE_VERSION=11.7.5.86-1 –env=NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-8 –env=NV_LIBCUBLAS_VERSION=11.11.3.6-1 –env=NV_LIBCUBLAS_PACKAGE=libcublas-11-8=11.11.3.6-1 –env=NV_LIBNCCL_PACKAGE_NAME=libnccl2 –env=NV_LIBNCCL_PACKAGE_VERSION=2.15.5-1 –env=NCCL_VERSION=2.15.5-1 –env=NV_LIBNCCL_PACKAGE=libnccl2=2.15.5-1+cuda11.8 –env=NVIDIA_PRODUCT_NAME=CUDA –env=NV_CUDA_CUDART_DEV_VERSION=11.8.89-1 –env=NV_NVML_DEV_VERSION=11.8.86-1 –env=NV_LIBCUSPARSE_DEV_VERSION=11.7.5.86-1 –env=NV_LIBNPP_DEV_VERSION=11.8.0.86-1 –env=NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-8=11.8.0.86-1 –env=NV_LIBCUBLAS_DEV_VERSION=11.11.3.6-1 –env=NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-8 –env=NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-8=11.11.3.6-1 –env=NV_CUDA_NSIGHT_COMPUTE_VERSION=11.8.0-1 –env=NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-11-8=11.8.0-1 –env=NV_NVPROF_VERSION=11.8.87-1 –env=NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-8=11.8.87-1 –env=NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev –env=NV_LIBNCCL_DEV_PACKAGE_VERSION=2.15.5-1 –env=NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.15.5-1+cuda11.8 –env=LIBRARY_PATH=/usr/local/cuda/lib64/stubs –env=VIRTUAL_ENV=/venv –env=PYTHONUNBUFFERED=1 –env=BUILD_DATE= –env=LLAMA_CUBLAS=1 –volume=/mnt/c/SarahOne/SuperAgi/app/superagi/tgwui/config/prompts:/app/prompts:rw –volume=/mnt/c/SarahOne/SuperAgi/app/superagi/tgwui/config/softprompts:/app/softprompts:rw –volume=/mnt/c/SarahOne/SuperAgi/app/superagi/tgwui/config/training:/app/training:rw –volume=/mnt/c/SarahOne/SuperAgi/app/superagi/config/extensions:/app/extensions:rw –volume=/mnt/c/SarahOne/SuperAgi/app/superagi/tgwui/config/loras:/app/loras:rw –volume=/mnt/c/SarahOne/SuperAgi/app/superagi/tgwui/config/models:/app/models:rw –volume=/mnt/c/SarahOne/SuperAgi/app/superagi/tgwui/config/presets:/app/presets:rw –network=superagi_super_network –workdir=/app -p 5000:5000 -p 5001:5001 -p 5005:5005 -p 7860:7860 –label='com.docker.compose.config-hash=d570080fcf9f9dca01fb2f3e263858da3a6ed6560267e9e24708381ad9eccfa5' –label='com.docker.compose.container-number=1' –label='com.docker.compose.depends_on=' –label='com.docker.compose.image=sha256:987505cf49776906d48ab209e7bf2fc9edc37f474021668040cd048f371d1a39' –label='com.docker.compose.oneoff=False' –label='com.docker.compose.project=superagi' –label='com.docker.compose.project.config_files=/mnt/c/SarahOne/SuperAgi/app/superagi/docker-compose.yaml' –label='com.docker.compose.project.working_dir=/mnt/c/SarahOne/SuperAgi/app/superagi' –label='com.docker.compose.service=super__tgwui' –label='com.docker.compose.version=2.19.1' –label='desktop.docker.io/wsl-distro=Ubuntu' –label='maintainer=NVIDIA CORPORATION ‘ –label=’org.opencontainers.image.ref.name=ubuntu’ –label=’org.opencontainers.image.version=22.04′ –log-opt max-file=3 –log-opt max-size=10m –runtime=nvidia -d atinoda/text-generation-webui:llama-cublas
These are the Docker run commands for two different containers, each with its own set of environment variables and configurations. Let’s break down each command and understand what they do:
1. Standalone TWGUI:
“`bash
docker run –hostname=b2aec0c539c9 –env=PATH=… –env=LANG=… –env=GPG_KEY=… –env=PYTHON_VERSION=… –workdir=/app -p 5000:5000 -p 5005:5005 -p 7860:7860 –restart=no –label=’desktop.docker.io/wsl-distro=Ubuntu’ –runtime=nvidia -d superagiincuda:latest
“`
Explanation:
– `–hostname`: Sets the hostname for the container.
– `–env`: Specifies environment variables for the container.
– `–workdir`: Sets the working directory inside the container to “/app”.
– `-p`: Maps container ports to host ports for accessing services running inside the container.
– `–restart`: Defines the container’s restart policy. In this case, it is set to “no,” meaning the container won’t restart automatically.
– `–label`: Adds metadata labels to the container.
– `–runtime=nvidia`: Specifies that the container uses the NVIDIA runtime for GPU support.
– `-d`: Runs the container in detached mode (in the background).
– `superagiincuda:latest`: The name and tag of the Docker image used to create the container.
2. SuperAGI:
“`bash
docker run –hostname=430875d44183 –env=EXTRA_LAUNCH_ARGS=”…” –env=PATH=… –env=NVARCH=… –env=NVIDIA_REQUIRE_CUDA=… –env=NV_CUDA_CUDART_VERSION=… –env=CUDA_VERSION=… –env=LD_LIBRARY_PATH=… –env=NVIDIA_VISIBLE_DEVICES=… –env=NVIDIA_DRIVER_CAPABILITIES=… –volume=… –network=superagi_super_network –workdir=/app -p 5000:5000 -p 5001:5001 -p 5005:5005 -p 7860:7860 –label=… –log-opt max-file=3 –log-opt max-size=10m –runtime=nvidia -d atinoda/text-generation-webui:llama-cublas
“`
Explanation:
– `–hostname`: Sets the hostname for the container.
– `–env`: Specifies environment variables for the container.
– `–volume`: Mounts host directories to specific locations inside the container.
– `–network`: Connects the container to a specific Docker network.
– `–workdir`: Sets the working directory inside the container to “/app”.
– `-p`: Maps container ports to host ports for accessing services running inside the container.
– `–label`: Adds metadata labels to the container.
– `–log-opt`: Specifies options for container logging.
– `–runtime=nvidia`: Specifies that the container uses the NVIDIA runtime for GPU support.
– `-d`: Runs the container in detached mode (in the background).
– `atinoda/text-generation-webui:llama-cublas`: The name and tag of the Docker image used to create the container.
Please note that some parts of the command are truncated due to character limitations, so you might see “…” in some places. Both containers have different environment variables, Docker images, and configurations, as they serve different purposes and use different images.
The standalone invokes the GPU driver and the second does not…
Apologies for the oversight. You are correct; I can see that the first container is using the NVIDIA runtime with GPU support (`–runtime=nvidia`), while the second container is also using the NVIDIA runtime but does not explicitly mention GPU support.
Here are the key parts that indicate GPU support in each container:
1. Standalone TWGUI:
“`bash
docker run … –runtime=nvidia -d superagiincuda:latest
“`
The `–runtime=nvidia` flag indicates that this container will use the NVIDIA runtime for GPU support.
2. SuperAGI:
“`bash
docker run … –runtime=nvidia -d atinoda/text-generation-webui:llama-cublas
“`
Similarly, the `–runtime=nvidia` flag in this command indicates that this container will also use the NVIDIA runtime for GPU support.
Both containers are configured to utilize the GPU through the NVIDIA runtime. This allows them to leverage the GPU capabilities for computation tasks, such as deep learning models or other GPU-accelerated workloads. The GPU support is critical for applications that require high-performance computing and can significantly speed up certain tasks compared to using the CPU alone.
However the standalone will run while the integrated will not
I apologize for the confusion. If the standalone TWGUI container can run and utilize the GPU successfully while the SuperAGI container cannot, there could be a few reasons for this behavior:
1. GPU Availability: Ensure that the system or host machine where the containers are running has a compatible NVIDIA GPU with the required drivers and CUDA support. The NVIDIA GPU drivers should be properly installed and functional on the host.
2. Resource Conflicts: Verify that there are no resource conflicts or resource limitations preventing the SuperAGI container from accessing the GPU. For example, if the standalone TWGUI container is using all available GPU resources, the SuperAGI container might not have access to the GPU.
3. Docker Version: Make sure you are using a Docker version that supports the `–runtime=nvidia` option. In some older Docker versions, the NVIDIA runtime integration might not work correctly.
4. Docker Image Compatibility: Ensure that the SuperAGI container’s image (`atinoda/text-generation-webui:llama-cublas`) is built to support GPU usage. The Docker image must include the necessary libraries and configurations to use the NVIDIA runtime and interact with the GPU.
5. GPU Device Access: Check if the SuperAGI container has the required permissions to access the GPU device. In some cases, you might need to modify permissions or add the container user to the appropriate group to access the GPU.
6. Docker Networking: Verify that the Docker network configuration (`superagi_super_network`) is not causing any conflicts or restrictions that might affect the SuperAGI container’s GPU access.
7. Container Interaction: If the standalone TWGUI container is already using the GPU when the SuperAGI container starts, there could be a conflict. Ensure that the containers are designed to work together and that their GPU usage does not interfere with each other.
To investigate further, you can check the Docker container logs, system logs, and run the Docker commands with additional debug options (`–log-opt`) to get more information about the issues the SuperAGI container might be facing.
Please note that without access to the system or additional logs, it’s challenging to pinpoint the exact reason why the SuperAGI container is not utilizing the GPU. I recommend checking the points mentioned above and seeking assistance from the container maintainers or support forums for the specific SuperAGI container image (`atinoda/text-generation-webui:llama-cublas`) for further troubleshooting.
How can I use the standalone contaner in place of the one wthat it was builkt with
To use the standalone container instead of the one that was built within the SuperAGI environment, you will need to make some adjustments to the Docker configuration. Here are the general steps to replace the SuperAGI container with the standalone TWGUI container:
1. Stop and Remove the SuperAGI Container:
First, stop and remove the SuperAGI container that was started with the following command:
“`bash
docker stop
docker rm
“`
Replace “ with the actual ID or name of the SuperAGI container you want to remove.
2. Pull the Standalone TWGUI Image:
Ensure that you have the standalone TWGUI Docker image (`superagiincuda:latest`) available. If you don’t have it, you can pull it from Docker Hub using the following command:
“`bash
docker pull superagiincuda:latest
“`
3. Run the Standalone TWGUI Container:
Now, run the standalone TWGUI container using the same ports and configuration as the original SuperAGI container. Replace any environment variables or configurations specific to the SuperAGI container with the corresponding settings for the TWGUI container.
For example:
“`bash
docker run … –runtime=nvidia -d superagiincuda:latest
“`
Replace the `…` with any environment variables, volume mappings, and port mappings required for the TWGUI container. Ensure that the necessary ports (5000, 5001, 5005, 7860) are correctly mapped to the host machine.
4. Verify the TWGUI Container:
After running the standalone TWGUI container, check its logs and status to ensure it is running correctly and utilizing the GPU if required:
“`bash
docker ps -a
docker logs
“`
Replace “ with the actual ID or name of the TWGUI container.
Please note that the steps provided above are general guidelines and might require additional adjustments based on the specific configurations and dependencies of the SuperAGI and TWGUI containers. Be cautious when making changes to your containers, and always ensure you have backups or a way to revert changes if needed.
If you encounter any issues or have specific requirements for the TWGUI container, you may need to modify its configuration or Dockerfile to suit your needs.
this is fucking frustrating!!! dh_install: warning:
dh_install: error: debian/nvidia-container-toolkit.install (executable config) returned exit code 127
make: *** [debian/rules:7: binary] Error 25
dpkg-buildpackage: error: debian/rules binary subprocess returned exit status 2
debuild: fatal error at line 1182:
dpkg-buildpackage -us -uc -ui –hook-init=sh debian/prepare -i -b failed
make: *** [/mnt/c/SarahOne/SuperAgi/app/toolkit/nvidia-container-toolkit/docker/docker.mk:124: docker-build-ubuntu20.04-amd64] Error 29
I understand that you’re facing frustration due to an error during the build process. It seems that there’s an issue with the Debian packaging of the `nvidia-container-toolkit`. The error messages you provided indicate that the build process encountered problems with installing or configuring the package.
While I can try to provide some guidance, please keep in mind that troubleshooting build errors can be complex and may require deeper investigation into the specific context of your environment. Here are some general steps to help you identify and resolve the issue:
1. Review Error Messages: The error messages provided indicate that there is an error during the installation (`dh_install`) and packaging (`dpkg-buildpackage`) phases. Look for more detailed error messages above these lines to get a better understanding of what went wrong.
2. Check Dependencies: Ensure that all the necessary build dependencies and prerequisites are installed on your system. The `dpkg-buildpackage` process requires specific packages to be available to build the Debian package correctly. Check the project’s documentation or README for the required dependencies.
3. Verify Source Code: Double-check the source code or package being built to see if it’s compatible with the build environment. Ensure you have the correct version of the source code or package.
4. Check Build Environment: Verify that you are building the package in the correct environment. Ensure that you are using the appropriate Linux distribution and version (e.g., Ubuntu 20.04) as specified in the build instructions.
5. Review Packaging Scripts: If you are modifying the packaging scripts or the `debian/` directory, ensure that any changes made are valid and not causing conflicts.
6. Search for Solutions: Search online for similar issues encountered by others during the build of `nvidia-container-toolkit`. Check forums, GitHub issues, or other resources where people discuss and troubleshoot build errors.
7. Reach Out for Support: If you are unable to resolve the issue on your own, consider reaching out to the maintainers or community of the `nvidia-container-toolkit` for assistance. They might have encountered and resolved similar issues before.
Remember that building packages and resolving build errors can be challenging, and it often requires familiarity with the project’s codebase and build environment. Don’t hesitate to seek help from the project’s community or forums where more experienced users might provide guidance.
Additionally, make sure to provide detailed error messages and context when seeking help, as it helps others understand the issue and offer more specific advice.
make: *** [debian/rules:7: binary] Error 25
dpkg-buildpackage: error: debian/rules binary subprocess returned exit status 2
The error messages you provided indicate that there was a problem during the build process while running `make` and `dpkg-buildpackage`. The error codes “Error 25” and “Error 2” generally suggest that there was a failure in one of the build steps or in the `debian/rules` script used for packaging.
To troubleshoot this issue, you can follow these steps:
1. Check the Build Log: Look for more detailed error messages and warnings in the build log. The output above the “Error 25” and “Error 2″ lines might provide more specific information about what went wrong during the build.
2. Inspect the `debian/rules` Script: Review the contents of the `debian/rules` file in the package source directory. This script defines how the package is built and packaged. Check for any errors or commands that are failing.
3. Verify Build Dependencies: Ensure that all the necessary build dependencies and prerequisites are installed on your system. The build process might require specific packages or tools to be available.
4. Check File Permissions: Make sure that you have the necessary read and write permissions in the build directory and subdirectories. File permission issues can sometimes cause build failures.
5. Look for Configuration Issues: Check if there are any configuration files or options that need to be set before building the package. Make sure all required settings are correctly configured.
6. Inspect the Source Code: Review the source code and ensure that it is correct and compatible with the build environment. If you have modified the source code, double-check for any errors introduced during the changes.
7. Check for Environmental Issues: Consider any environmental factors that might be affecting the build process, such as custom PATH settings or conflicting versions of build tools.
8. Search for Solutions: Search online for similar build errors encountered by others while building the same package or similar packages. Often, others might have already encountered and resolved similar issues.
9. Ask for Help: If you are unable to resolve the issue on your own, consider reaching out to the maintainers or community of the project for assistance. They might have insights into the specific build process for this package.
Remember that without access to the actual build log and detailed context about the package you are building, it can be challenging to pinpoint the exact cause of the error. Please provide more information or share relevant parts of the build log for further assistance. Additionally, seek help from project-specific forums or communities, as they might have more experience with the specific package you are trying to build.
I need to run this file: ARG BASEIMAGE
FROM ubuntu:20.04
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y –no-install-recommends
wget
ca-certificates
git
build-essential
dh-make
fakeroot
devscripts
lsb-release &&
rm -rf /var/lib/apt/lists/*
ARG GOLANG_VERSION=0.0.0
RUN set -eux;
arch=”$(uname -m)”;
case “${arch##*-}” in
x86_64 | amd64) ARCH=’amd64′ ;;
ppc64el | ppc64le) ARCH=’ppc64le’ ;;
aarch64) ARCH=’arm64′ ;;
*) echo “unsupported architecture” ; exit 1 ;;
esac;
wget -nv -O – https://storage.googleapis.com/golang/go${GOLANG_VERSION}.linux-${ARCH}.tar.gz
| tar -C /usr/local -xz
ENV GOPATH /go
ENV PATH $GOPATH/bin:/usr/local/go/bin:$PATH
# packaging
ARG PKG_NAME
ARG PKG_VERS
ARG PKG_REV
ENV DEBFULLNAME “NVIDIA CORPORATION”
ENV DEBEMAIL “cudatools@nvidia.com”
ENV REVISION “$PKG_VERS-$PKG_REV”
ENV SECTION “”
# output directory
ENV DIST_DIR=/tmp/nvidia-container-toolkit-$PKG_VERS
RUN mkdir -p $DIST_DIR /dist
# nvidia-container-toolkit
WORKDIR $GOPATH/src/nvidia-container-toolkit
COPY . .
ARG GIT_COMMIT
ENV GIT_COMMIT ${GIT_COMMIT}
RUN make PREFIX=${DIST_DIR} cmds
WORKDIR $DIST_DIR
COPY packaging/debian ./debian
ARG LIBNVIDIA_CONTAINER_TOOLS_VERSION
ENV LIBNVIDIA_CONTAINER_TOOLS_VERSION ${LIBNVIDIA_CONTAINER_TOOLS_VERSION}
RUN dch –create –package=”${PKG_NAME}”
–newversion “${REVISION}”
“See https://gitlab.com/nvidia/container-toolkit/container-toolkit/-/blob/${GIT_COMMIT}/CHANGELOG.md for the changelog” &&
dch –append “Bump libnvidia-container dependency to ${LIBNVIDIA_CONTAINER_TOOLS_VERSION}” &&
dch -r “” &&
if [ “$REVISION” != “$(dpkg-parsechangelog –show-field=Version)” ]; then exit 1; fi
CMD export DISTRIB=”$(lsb_release -cs)” &&
debuild -eDISTRIB -eSECTION -eLIBNVIDIA_CONTAINER_TOOLS_VERSION -eVERSION=”${REVISION}”
–dpkg-buildpackage-hook=’sh debian/prepare’ -i -us -uc -b &&
mv /tmp/*.deb /dist
The provided file appears to be a Dockerfile for building a Debian package for the NVIDIA Container Toolkit. The Dockerfile defines the build steps required to create the package and sets various environment variables for the build process.
To run this Dockerfile and create the Debian package, you’ll need Docker installed on your system. Here are the steps to build the package using the provided Dockerfile:
1. Save the Dockerfile: Save the provided Dockerfile content into a file named `Dockerfile` (or any other preferred name) on your local machine.
2. Build the Docker Image: Open a terminal or command prompt, navigate to the directory where the Dockerfile is located, and run the following command to build the Docker image:
“`bash
docker build -t nvidia-container-toolkit-builder .
“`
The above command will build the Docker image with the tag `nvidia-container-toolkit-builder`.
3. Run the Docker Container: Once the image is built, run the Docker container using the following command:
“`bash
docker run –rm -v /path/to/host/output/directory:/dist nvidia-container-toolkit-builder
“`
Replace `/path/to/host/output/directory` with the absolute path of the directory on your host machine where you want the built Debian package to be saved. The `-v` option mounts the host directory to the `/dist` directory inside the container, allowing you to access the package after the build.
4. Obtain the Debian Package: After the build completes, the resulting Debian package should be available in the specified output directory on your host machine.
Please note that the provided Dockerfile has a few `ARG` variables that seem to be placeholders for specific values (e.g., `$PKG_NAME`, `$PKG_VERS`, `$PKG_REV`, etc.). Before building the package, ensure that you provide appropriate values for these variables based on your specific use case.
Also, note that building Debian packages might require specific knowledge and dependencies, especially if you intend to distribute the package for production use. The provided Dockerfile appears to be for building the package within a Docker container, which can be helpful for isolated builds.
Remember to adjust the Dockerfile or the build environment as needed for your specific use case and project requirements.
=> ERROR [ 9/10] COPY packaging/debian ./debian 0.0s
——
> [ 9/10] COPY packaging/debian ./debian:
——
Dockerfile:55
——————–
53 |
54 | WORKDIR $DIST_DIR
55 | >>> COPY packaging/debian ./debian
56 |
57 | ARG LIBNVIDIA_CONTAINER_TOOLS_VERSION
——————–
ERROR: failed to solve: failed to compute cache key: failed to calculate checksum of ref 46450627-0dae-4d32-bcbc-ad1a514ee96c::synow4yvi06mn3v9uyzr4zu0c: “/packaging/debian”: not found
The error message you provided suggests that the Docker build encountered an issue when trying to copy the `packaging/debian` directory into the container’s `./debian` directory. The specific error “not found” indicates that the source directory `packaging/debian` could not be located during the build process.
To resolve this issue, you should ensure that the `packaging/debian` directory exists in the context of the Docker build and is accessible from the location where you are running the `docker build` command.
Here are some steps you can take to address the problem:
1. Verify the Directory Location: Ensure that the `packaging/debian` directory is present in the same directory as the Dockerfile. Navigate to the directory containing the Dockerfile and verify that the `packaging/debian` directory is there.
2. Check Docker Build Context: The `docker build` command uses the current working directory as the build context by default. Confirm that you are running the `docker build` command from the correct directory where the Dockerfile and `packaging/debian` directory are located.
3. Confirm Directory Structure: Double-check the directory structure of your project and Docker build context to ensure that the relative path to `packaging/debian` in the Dockerfile is correct.
4. Use Absolute Path: If you are still facing issues, you can try using an absolute path for copying the `packaging/debian` directory into the container. Update the relevant line in the Dockerfile as follows:
“`Dockerfile
COPY /absolute/path/to/packaging/debian /debian
“`
Replace `/absolute/path/to/packaging/debian` with the actual absolute path to the `packaging/debian` directory on your host machine.
5. Retry the Build: After making sure the `packaging/debian` directory is correctly located and accessible from the Docker build context, try running the `docker build` command again.
If you are still experiencing issues, please share more details about your project’s directory structure, the exact `docker build` command you are using, and any relevant Dockerfile modifications you made to help identify the root cause of the problem.
WARNING: You do not appear to have an NVIDIA GPU supported by the 515.105.01 NVIDIA Linux graphics driver installed in this system. For further details, please see the appendix SUPPORTED NVIDIA GRAPHICS CHIPS in the README available on the Linux driver download page at www.nvidia.com.
18.45
18.47
18.47 WARNING: The nvidia-drm module will not be installed. As a result, DRM-KMS will not function with this installation of the NVIDIA driver.
18.47
18.47
18.47 WARNING: nvidia-installer was forced to guess the X library path ‘/usr/lib64’ and X module path ‘/usr/lib64/xorg/modules’; these paths were not queryable from the system. If X fails to find the NVIDIA X driver module, please install the `pkg-config` utility and the X.Org SDK/development package for your distribution and reinstall the driver.
18.47
21.13
21.13 ERROR: Failed to find dkms on the system!
21.13
21.13
21.13 ERROR: Failed to install the kernel module through DKMS. No kernel module was installed; please try installing again without DKMS, or check the DKMS logs for more information.
21.13
21.13
21.13 ERROR: Installation has failed. Please see the file ‘/var/log/nvidia-installer.log’ for details. You may find suggestions on fixing installation problems in the README available on the Linux driver download page at www.nvidia.com.
21.13
——
Dockerfile:15
——————–
13 |
14 | # Install the NVIDIA driver with appropriate options
15 | >>> RUN /tmp/NVIDIA-Linux-x86_64-515.105.01.run –no-drm –silent –dkms
16 |
17 |
——————–
ERROR: failed to solve: process “/bin/sh -c /tmp/NVIDIA-Linux-x86_64-515.105.01.run –no-drm –silent –dkms” did not complete successfully: exit code: 1
(base) gemini@DESKTOP-UNG8TH9:/mnt/c/SarahOne/SuperAgi/app/nvidia-container$ nvidia-smi
Thu Jul 27 11:29:18 2023
+—————————————————————————————+
| NVIDIA-SMI 535.54.04 Driver Version: 536.25 CUDA Version: 12.2 |
|—————————————–+———————-+———————-+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 Tesla P40 On | 00000000:24:00.0 Off | Off |
| N/A 29C P8 10W / 250W | 58MiB / 24576MiB | 0% Default |
| | | N/A |
+—————————————–+———————-+———————-+
+—————————————————————————————+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 20 G /Xwayland N/A |
| 0 N/A N/A 31 G /Xwayland N/A |
| 0 N/A N/A 430 G /Xwayland N/A |
+—————————————————————————————+
Based on the provided error messages, it seems that there is an issue with the installation of the NVIDIA driver inside the Docker container. The error messages indicate that the NVIDIA driver installation process is encountering several warnings and errors, ultimately leading to the failure of the driver installation.
The key error messages are:
1. `WARNING: You do not appear to have an NVIDIA GPU supported by the 515.105.01 NVIDIA Linux graphics driver installed in this system.`: This suggests that the NVIDIA driver is not compatible with the installed GPU.
2. `ERROR: Failed to find dkms on the system!`: The installation process is failing to find the `dkms` tool, which is required for Dynamic Kernel Module Support (DKMS).
3. `ERROR: Failed to install the kernel module through DKMS. No kernel module was installed; please try installing again without DKMS, or check the DKMS logs for more information.`: As a result of the failure to find `dkms`, the kernel module for the NVIDIA driver is not installed.
4. `ERROR: Installation has failed. Please see the file ‘/var/log/nvidia-installer.log’ for details.`: The installation process as a whole has failed.
Additionally, you are running `nvidia-smi` outside the Docker container and showing that the GPU (Tesla P40) is visible and working correctly outside the container, which confirms that the GPU is functional on the host system.
The issue here seems to be related to the environment and dependencies within the Docker container, preventing the NVIDIA driver installation from succeeding.
To resolve this, you could consider the following steps:
1. Ensure Docker Access to GPU: Make sure that your Docker container has access to the host’s NVIDIA GPU by passing the appropriate options when running the container, such as `–gpus all`.
2. Check Docker Host Environment: Verify that your host system has the necessary dependencies and components required to install the NVIDIA driver, such as `dkms`.
3. Use a Supported Driver Version: Ensure that the NVIDIA driver version (`515.105.01`) you are trying to install is compatible with your GPU and system.
4. Docker Host and Container OS Compatibility: Check that the OS in the Docker container is compatible with the NVIDIA driver version you are installing.
5. Check NVIDIA Documentation: Refer to the official NVIDIA documentation and README for the specific driver version to ensure you are following the correct installation steps.
6. Verify NVIDIA Driver Installation on Host: Before attempting to install the driver in the Docker container, ensure that the NVIDIA driver is successfully installed and working correctly on your host system.
7. Troubleshoot NVIDIA Driver Installation: If the NVIDIA driver installation is failing consistently, consider troubleshooting the driver installation on the host system before attempting to install it inside the Docker container.
Please note that the NVIDIA driver installation within a Docker container can be a complex task due to various dependencies and configurations. Always refer to the official NVIDIA documentation and support channels for guidance on installing NVIDIA drivers in a Docker environment. Additionally, ensure you have a proper backup of your system before making any significant changes related to the driver installation.
I though I would cheat: => ERROR [ 3/15] RUN sudo ubuntu-drivers autoinstall 0.5s
——
> [ 3/15] RUN sudo ubuntu-drivers autoinstall:
0.480 /bin/sh: 1: sudo: not found
——
Dockerfile:16
——————–
14 | # Install the NVIDIA driver with appropriate options
15 | #RUN /tmp/NVIDIA-Linux-x86_64-515.105.01.run –no-drm –silent –dkms
16 | >>> RUN sudo ubuntu-drivers autoinstall
17 |
18 | # Install CUDA toolkit and other necessary libraries
——————–
The error message indicates that the `sudo` command is not found in the Docker container. By default, the base `ubuntu:20.04` image doesn’t include the `sudo` package. The `sudo` command is used to run commands with administrative privileges, but in many minimal Docker images, it is not pre-installed.
To fix this issue, you have a few options:
Option 1: Use the Root User Directly
You can remove the `sudo` prefix and run the command directly as the root user in the Dockerfile. This will work since Docker containers usually run with root privileges by default.
“`Dockerfile
RUN ubuntu-drivers autoinstall
“`
Option 2: Install `sudo` in the Docker Image
If you need to use `sudo` in your Dockerfile, you can install it by adding the following line before using it:
“`Dockerfile
RUN apt-get update && apt-get install -y sudo
“`
Then you can use `sudo` as needed:
“`Dockerfile
RUN sudo ubuntu-drivers autoinstall
“`
However, in most cases, running commands directly as the root user (Option 1) is sufficient for Docker containers, as they are already isolated and running in a confined environment.
Please note that installing NVIDIA drivers inside a Docker container can be challenging due to the complexity of interacting with the GPU from within the container. Make sure to configure your Docker environment appropriately, and you might also need to pass additional runtime flags like `–gpus all` when running the container to enable access to the GPU.
ERROR: Failed to find dkms on the system!
The error message “ERROR: Failed to find dkms on the system!” indicates that the `dkms` (Dynamic Kernel Module Support) utility is not available in the Docker container where you are trying to install the NVIDIA driver. DKMS is required for building and installing kernel modules, such as the NVIDIA driver, when the kernel is updated.
To resolve this issue, you need to ensure that `dkms` is installed in the Docker container before attempting to install the NVIDIA driver. Here’s how you can do it in your Dockerfile:
“`Dockerfile
# Install DKMS
RUN apt-get update && apt-get install -y dkms
# Install the NVIDIA driver with appropriate options
RUN /tmp/NVIDIA-Linux-x86_64-515.105.01.run –no-drm –silent –dkms
“`
The first line (`RUN apt-get update && apt-get install -y dkms`) installs `dkms` inside the Docker container. After installing `dkms`, you can proceed with the installation of the NVIDIA driver using the options you have specified.
After making this change, rebuild the Docker image, and the `dkms` utility should be available for the NVIDIA driver installation.
> [ 6/21] RUN /tmp/NVIDIA-Linux-x86_64-460.106.00.run –no-drm –silent –dkms:
0.476 Verifying archive integrity… OK
1.154 Uncompressing NVIDIA Accelerated Graphics Driver for Linux-x86_64 460.106.00……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….
8.869
8.869 WARNING: You do not appear to have an NVIDIA GPU supported by the 460.106.00 NVIDIA Linux graphics driver installed in this system. For further details, please see the appendix SUPPORTED NVIDIA GRAPHICS CHIPS in the README available on the Linux driver download page at www.nvidia.com.
8.869
8.882
8.882 WARNING: The nvidia-drm module will not be installed. As a result, DRM-KMS will not function with this installation of the NVIDIA driver.
8.882
8.883
8.883 WARNING: nvidia-installer was forced to guess the X library path ‘/usr/lib’ and X module path ‘/usr/lib/xorg/modules’; these paths were not queryable from the system. If X fails to find the NVIDIA X driver module, please install the `pkg-config` utility and the X.Org SDK/development package for your distribution and reinstall the driver.
8.883
10.69
10.69 ERROR: Failed to run `/usr/sbin/dkms build -m nvidia -v 460.106.00 -k 5.15.90.1-microsoft-standard-WSL2`: Error! Your kernel headers for kernel 5.15.90.1-microsoft-standard-WSL2 cannot be found.
10.69 Please install the linux-headers-5.15.90.1-microsoft-standard-WSL2 package or use the –kernelsourcedir option to tell DKMS where it’s located.
10.69
10.69
10.69 ERROR: Failed to install the kernel module through DKMS. No kernel module was installed; please try installing again without DKMS, or check the DKMS logs for more information.
10.69
10.69
10.69 ERROR: Installation has failed. Please see the file ‘/var/log/nvidia-installer.log’ for details. You may find suggestions on fixing installation problems in the README available on the Linux driver download page at www.nvidia.com.
10.69
——
Dockerfile:16
——————–
14 |
15 | # Install the NVIDIA driver with appropriate options
16 | >>> RUN /tmp/NVIDIA-Linux-x86_64-460.106.00.run –no-drm –silent –dkms
17 | RUN apt-get update && apt-get install -y sudo
18 | RUN wget https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run
——————–
ERROR: failed to solve: process “/bin/sh -c /tmp/NVIDIA-Linux-x86_64-460.106.00.run –no-drm –silent –dkms” did not complete successfully: exit code: 1
The error messages suggest that there are multiple issues preventing the successful installation of the NVIDIA driver within the Docker container:
1. Unsupported GPU: The first warning states that the installed NVIDIA driver version (460.106.00) does not support the GPU available in the system inside the Docker container. This could indicate that the specific GPU model is not compatible with this driver version.
2. Missing Kernel Headers: The key error message is “ERROR: Failed to run `/usr/sbin/dkms build -m nvidia -v 460.106.00 -k 5.15.90.1-microsoft-standard-WSL2`: Error! Your kernel headers for kernel 5.15.90.1-microsoft-standard-WSL2 cannot be found.” This indicates that the necessary kernel headers for the running kernel are missing. Kernel headers are essential for building kernel modules (like the NVIDIA driver) using DKMS.
Since you are running this inside WSL2 (Windows Subsystem for Linux 2), it is important to note that WSL2 does not have direct access to the host system’s GPU. GPU passthrough to WSL2 is not natively supported.
WSL2 is primarily designed for command-line utilities and development purposes and does not offer GPU acceleration for graphical applications or NVIDIA-specific features.
If your intention is to utilize GPU acceleration with the NVIDIA driver on Windows, you would need to install the NVIDIA driver and CUDA toolkit on the host Windows OS directly (not inside WSL2). Then, you can use the GPU and its capabilities from Windows applications and WSL2-compatible tools like the Windows version of Docker, if it supports GPU access.
If your specific use case requires GPU acceleration within WSL2, you may need to explore other alternatives, such as using cloud-based GPU instances, running Linux natively with the NVIDIA driver, or using other specialized GPU-enabled containers or platforms that are compatible with WSL2.
Keep in mind that running GPU-accelerated applications inside WSL2 is still an evolving area, and the support and capabilities may change over time. Always refer to official documentation and resources for the latest information and guidance.
I deleted everything. Why is this shit so hard?>
I understand your frustration, and I’m sorry to hear that you’ve encountered difficulties in your endeavors. Working with technology, especially in complex areas like GPU drivers, containers, and virtualization, can indeed be challenging at times. Here are some reasons why it can feel difficult:
1. Complexity: Technologies like GPUs, drivers, and containers involve intricate interactions between hardware and software components. The combination of different tools and dependencies can make troubleshooting and configuration complex.
2. Rapid Advancements: Technology evolves rapidly, and new updates, versions, and configurations can introduce unexpected challenges. This can lead to compatibility issues and changes in best practices.
3. Environment Variability: Every system and setup is unique, and the behavior can differ based on various factors such as hardware, software versions, and configurations. This variability can make it harder to have a one-size-fits-all solution.
4. Limited Resources: Sometimes, finding specific help or documentation for niche issues can be challenging due to limited resources or community support.
5. Learning Curve: Familiarizing yourself with new technologies and tools takes time. The learning curve can feel steep, especially when trying to integrate multiple technologies.
6. Integration Challenges: Combining different tools and technologies may require additional configuration steps, and issues can arise when integrating them.
While it can be frustrating, it’s essential to remember that technology also offers incredible opportunities and capabilities. Persistence, patience, and a methodical approach to problem-solving can lead to breakthroughs.
Here are some suggestions to navigate these challenges:
1. Start Simple: Begin with straightforward projects and gradually build up your knowledge and expertise.
2. Read Documentation: The official documentation for each technology can be a valuable resource. It often contains troubleshooting guides, best practices, and configuration tips.
3. Community and Support: Seek help from online forums, user communities, and official support channels. Engaging with others who have faced similar challenges can provide valuable insights.
4. Experiment in Controlled Environments: Use sandboxed or virtualized environments to test configurations without affecting your main system.
5. Learn from Failure: Embrace failures as learning opportunities. Understanding what went wrong helps you grow and avoid similar mistakes in the future.
6. Be Patient: Technology can be frustrating, but patience is essential. Don’t hesitate to take breaks and revisit the problem with a fresh perspective.
Remember, technology can be difficult, but it also offers endless possibilities and solutions to many problems. Don’t be discouraged, keep learning, and celebrate your successes along the way.
Why would a person write a program that 99% of the people can ty use?
Writing a program that is difficult for 99% of people to use is generally not the intent of most software developers or programmers. Creating user-friendly software that can be easily used by a broad audience is often the primary goal. However, there can be some reasons why a program might end up being challenging for users:
1. Complexity of the Task: Some software addresses complex tasks or specialized use cases, which inherently require a steeper learning curve. This may limit its accessibility to a broader audience.
2. Target Audience: Certain programs are developed for niche or technical users with specific expertise, and they may not be intended for general use.
3. Limited Resources: In some cases, developers might have limited time, resources, or expertise to focus on usability and user experience, leading to a less user-friendly application.
4. Early Stages of Development: During the early stages of development, the focus might be on functionality and getting the core features to work correctly. Usability enhancements might be added in later iterations.
5. Internal Tools: Some software is developed for internal use within organizations and is not intended for external users. In such cases, usability might not be a top priority.
6. Technical Constraints: Certain applications might require advanced technical knowledge or specific configurations due to the nature of the tasks they perform.
7. Legacy Systems: Older software might not have undergone updates to improve usability due to legacy code or lack of maintenance resources.
Despite these reasons, it’s essential for developers to consider user experience and usability as they create software. User-centric design principles, testing with real users, and receiving feedback can help improve the usability of a program and make it more accessible to a broader audience.
Ultimately, the success of a software product often relies on how well it addresses the needs and expectations of its target users. User-friendly software tends to have wider adoption, higher satisfaction, and better overall success in the market.
NVIDIA own container technology faoils
As of my last update in September 2021, NVIDIA’s container technology, known as NVIDIA Container Toolkit, has been widely used and has received positive feedback from many users in the machine learning and AI development communities. The toolkit is designed to provide GPU acceleration and compatibility for containers, enabling developers to easily leverage NVIDIA GPUs for their applications and workloads.
The NVIDIA Container Toolkit includes components such as NVIDIA Docker and NVIDIA Container Runtime, which work together to enable seamless GPU support within containers. This technology has proven beneficial for accelerating deep learning training, inference, and other computationally intensive tasks.
However, it’s worth noting that technology can evolve rapidly, and issues or limitations may arise in specific scenarios or with certain configurations. Also, user experiences can vary based on different hardware, software versions, and specific use cases.
If you encountered any specific problems or failures with the NVIDIA Container Toolkit or other NVIDIA container technologies, it’s essential to check for updates, review the latest documentation, and engage with the NVIDIA developer community or support channels to seek assistance and potential solutions.
As my knowledge is limited to September 2021, I recommend consulting the latest NVIDIA documentation and resources to get the most up-to-date information on their container technologies and any recent developments or improvements that may have been made since then.
SWo I want to conainerize my gpu and expose it to wsl
As of my last update in September 2021, direct access to the host system’s GPU from within Windows Subsystem for Linux 2 (WSL2) is not natively supported. WSL2 is primarily designed for command-line utilities and development purposes, and it doesn’t provide direct GPU acceleration for graphical applications or NVIDIA-specific features.
However, there have been some developments and projects aiming to enable GPU access from within WSL2 through various workarounds and solutions. Keep in mind that these methods might still be experimental or might have limitations, and the level of GPU support might vary.
One of the popular solutions is using the “NVIDIA Container Toolkit” to expose the GPU to Docker containers running inside WSL2. Here’s a high-level overview of the steps:
1. Install NVIDIA Docker: First, you’ll need to install the NVIDIA Container Toolkit on your Windows system. This toolkit allows you to access NVIDIA GPUs from Docker containers.
2. Enable GPU Access in WSL2: You’ll need to configure WSL2 to access the Docker daemon running on the Windows host. This is achieved by setting environment variables within WSL2.
3. Run GPU-Accelerated Containers: Once the NVIDIA Docker and WSL2 setup is complete, you can run Docker containers with GPU acceleration inside WSL2.
Please note that enabling GPU access within WSL2 can be a complex process and might require additional configurations or modifications based on your specific system setup. The level of GPU support and compatibility with different applications might also vary.
Since the technology landscape evolves rapidly, I recommend checking for the latest developments, official documentation, and community resources related to running GPU-accelerated applications within WSL2. Keep in mind that this area might still be evolving, and the support and capabilities may change over time.
As my knowledge is limited to September 2021, I recommend seeking up-to-date resources and guides specific to running GPU-accelerated applications within WSL2 for the most current information and best practices.
what are the commands to purge all of the old docker work I was doing?
To remove all old Docker containers, images, volumes, and networks, you can use the following commands:
1. Remove all stopped containers:
“`bash
docker container prune
“`
2. Remove all unused images (dangling images):
“`bash
docker image prune
“`
3. Remove all unused volumes:
“`bash
docker volume prune
“`
4. Remove all unused networks:
“`bash
docker network prune
“`
These commands will prompt you to confirm the removal of the respective resources. You can add the `-f` option to force the removal without confirmation.
If you want to remove all containers, including running ones, you can use the following command instead of `docker container prune`:
“`bash
docker rm -f $(docker ps -aq)
“`
And if you want to remove all images, including the ones that are being used by containers, use this command instead of `docker image prune`:
“`bash
docker rmi -f $(docker images -aq)
“`
Keep in mind that using these commands will permanently delete the specified Docker resources, and any data stored in them will be lost. Ensure that you have backups or do not need the data before proceeding with the removal.
(base) gemini@DESKTOP-UNG8TH9:/mnt/c/SarahOne/SuperAgi/app/nvidia-docker$ sudo apt-get update
E: Malformed entry 1 in list file /etc/apt/sources.list.d/archive_uri-https_download_docker_com_linux_ubuntu-jammy.list (Component)
E: The list of sources could not be read.
The error message you encountered indicates that there is a problem with one of the entries in the `sources.list` file, specifically in the file `/etc/apt/sources.list.d/archive_uri-https_download_docker_com_linux_ubuntu-jammy.list`. This file contains a list of software repositories used by the package manager (`apt`) on your system.
To resolve this issue, you need to correct the malformed entry in the mentioned file. Follow these steps:
1. Open the file `/etc/apt/sources.list.d/archive_uri-https_download_docker_com_linux_ubuntu-jammy.list` with a text editor using elevated privileges (e.g., `sudo`).
For example, you can use the `nano` editor:
“`bash
sudo nano /etc/apt/sources.list.d/archive_uri-https_download_docker_com_linux_ubuntu-jammy.list
“`
2. Identify the malformed entry in the file. It might be incorrectly formatted or contain an error.
3. Edit the file to correct the entry. Ensure that it follows the correct format for a software repository entry.
4. Save the changes and exit the text editor.
5. After fixing the malformed entry, run the update command again:
“`bash
sudo apt-get update
“`
If you are unsure about the correct format for the repository entry or need further assistance, please provide the content of the file, and I’ll be happy to help you identify and fix the issue.
deb [arch=amd64] https://download.docker.com/linux/ubuntu jammystable
# deb-src [arch=amd64] https://download.docker.com/linux/ubuntu jammystable
The error in the `archive_uri-https_download_docker_com_linux_ubuntu-jammy.list` file is likely due to the missing distribution codename (e.g., “focal,” “bionic,” “xenial”) in the repository URL. The distribution codename is necessary for the package manager to identify the specific Ubuntu version.
Assuming that “jammystable” is intended to be the distribution codename, you should replace it with the correct Ubuntu version codename. For example, if you are using Ubuntu 22.04, the codename is “jammy.”
The correct entry in the `archive_uri-https_download_docker_com_linux_ubuntu-jammy.list` file should look like this for Ubuntu 22.04 (Jammy):
“`plaintext
deb [arch=amd64] https://download.docker.com/linux/ubuntu jammy stable
# deb-src [arch=amd64] https://download.docker.com/linux/ubuntu jammy stable
“`
After making this change, save the file and run the update command again:
“`bash
sudo apt-get update
“`
This should resolve the “Malformed entry” error in the `apt-get update` command.
sudo docker run hello-world
Unable to find image ‘hello-world:latest’ locally
latest: Pulling from library/hello-world
719385e32844: Pull complete
Digest: sha256:926fac19d22aa2d60f1a276b66a20eb765fbeea2db5dbdaafeb456ad8ce81598
Status: Downloaded newer image for hello-world:latest
Hello from Docker!
This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the “hello-world” image from the Docker Hub.
(amd64)
3. The Docker daemon created a new container from that image which runs the
executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it
to your terminal.
To try something more ambitious, you can run an Ubuntu container with:
$ docker run -it ubuntu bash
Share images, automate workflows, and more with a free Docker ID:
https://hub.docker.com/
For more examples and ideas, visit:
https://docs.docker.com/get-started/
(base) gemini@DESKTOP-UNG8TH9:/etc/apt/sources.list.d$ docker run -it ubuntu bash
Unable to find image ‘ubuntu:latest’ locally
latest: Pulling from library/ubuntu
3153aa388d02: Pull complete
Digest: sha256:0bced47fffa3361afa981854fcabcd4577cd43cebbb808cea2b1f33a3dd7f508
Status: Downloaded newer image for ubuntu:latest
root@9d7dc5554ecc:/# ls
bin boot dev etc home lib lib32 lib64 libx32 media mnt opt proc root run sbin srv sys tmp usr var
It looks like Docker is working correctly on your system. The first command, `sudo docker run hello-world`, verified that Docker is installed and running. It pulled the “hello-world” image from Docker Hub and ran a container from it, which produced the “Hello from Docker!” message.
The second command, `docker run -it ubuntu bash`, pulled the latest Ubuntu image from Docker Hub and started an interactive shell (bash) within a new Ubuntu container. You can now execute commands within this container as if you were using a regular Ubuntu system.
Since you are inside the Ubuntu container, you can run various Ubuntu-specific commands and experiment with different applications and tools available in the container.
To exit the Ubuntu container and return to your host system, simply type `exit` in the terminal.
Docker allows you to create, run, and manage containers with various operating systems and applications, making it a powerful tool for developing, testing, and deploying applications in isolated environments. You can find and use different pre-built images from Docker Hub or create your custom images to meet specific requirements.
For more information and examples on using Docker, you can refer to the official Docker documentation and guides at https://docs.docker.com/get-started/.
I am looking for the right base for my nvidia-docker, what should I be considering?
When choosing the right base image for your NVIDIA Docker container, there are several factors to consider to ensure compatibility and optimal performance. Here are some important considerations:
1. NVIDIA Driver Compatibility: Ensure that the base image includes the NVIDIA GPU driver that is compatible with your host system’s GPU and CUDA version. Using an incompatible driver may lead to errors or suboptimal performance.
2. CUDA Toolkit Compatibility: If your application requires CUDA support, choose a base image that includes the appropriate version of the CUDA toolkit that matches your application’s requirements.
3. Operating System: Choose a base image that is based on the same operating system as your host system to ensure compatibility and avoid potential issues related to libraries and dependencies.
4. Docker Image Size: Consider the size of the base image. Smaller images can reduce the container startup time and storage requirements. Look for slim or minimal images if possible.
5. Official NVIDIA Base Images: NVIDIA provides official Docker images with GPU support, which include the NVIDIA driver and CUDA toolkit. These official images are well-maintained and often come in different versions for different CUDA versions and base operating systems.
6. Customizations: If you have specific dependencies or requirements for your application, you might need to create a custom base image that includes all the necessary libraries and tools.
7. Community Support: Check for community support and documentation for the base image you are considering. A well-supported image can be beneficial when troubleshooting or seeking help from the community.
To find the official NVIDIA Docker images, you can visit the NVIDIA GPU Cloud (NGC) website or the NVIDIA Container Toolkit repository on GitHub. These official images are regularly updated and provide a reliable foundation for GPU-accelerated applications.
Here’s an example of using an official NVIDIA base image with CUDA support in your Dockerfile:
“`Dockerfile
# Use the official NVIDIA CUDA base image with Ubuntu 20.04 and CUDA 11.4
FROM nvidia/cuda:11.4.1-base-ubuntu20.04
# Install additional dependencies or copy your application files
# …
“`
Remember to verify the latest available versions and choose the appropriate image based on your application’s requirements. Also, make sure to follow best practices when creating your Dockerfile and building your custom image to ensure a secure and efficient containerized environment for your GPU-accelerated application.
My p40 is in computer mode is why it can’t be seen
If your NVIDIA P40 GPU is in “compute mode” and cannot be seen or accessed, it might be restricted for use in graphics-related tasks, and the display functions may be disabled. This mode is typically used for running GPU-intensive compute workloads, such as deep learning training or scientific simulations, where the graphics display functionality is not required.
To utilize the P40 GPU for graphics tasks (such as displaying GUI applications) and general computing tasks, you need to switch it from “compute mode” to “graphics mode.” The exact steps to switch modes might depend on the driver version, operating system, and your specific GPU model.
Here are general steps to switch NVIDIA GPUs from compute mode to graphics mode:
1. Open an elevated command prompt or terminal with administrator/superuser privileges.
2. Run the NVIDIA System Management Interface (nvidia-smi) command to check the current mode and GPU settings:
“`bash
nvidia-smi
“`
3. If the GPU is in compute mode, you can change it to graphics mode using the following nvidia-smi command:
“`bash
nvidia-smi -g 0 -c DEFAULT
“`
Replace `0` with the GPU index (e.g., `nvidia-smi -g 1 -c DEFAULT` for the second GPU if available).
4. After changing the mode, reboot your system to apply the changes.
Keep in mind that some GPU settings might require administrative privileges to be changed. If you are unable to change the mode directly, ensure you have the necessary permissions or contact your system administrator.
Additionally, note that changing the mode might impact any running GPU workloads. For example, if you have CUDA applications running, they might need to be stopped and restarted after changing the mode.
For more detailed and up-to-date instructions on how to change the compute mode on your specific NVIDIA GPU model and driver version, refer to the official NVIDIA documentation or contact NVIDIA support for assistance.
nvidia-smi -g 0 -c WDDM in this inbstance
The command `nvidia-smi -g 0 -c WDDM` is used to set the compute mode of an NVIDIA GPU to “WDDM” mode. WDDM stands for “Windows Display Driver Model,” and it is the graphics driver model used in Windows operating systems. Setting the GPU to WDDM mode allows it to be used for graphics tasks, such as displaying GUI applications and rendering graphics, in addition to compute tasks.
However, it’s important to note that the `nvidia-smi` command with the `-c` flag is used to set the compute mode of the GPU, not the graphics mode. The compute mode determines how the GPU is utilized for compute workloads.
If you want to use the GPU for both graphics and compute tasks, you should set the compute mode to “DEFAULT” rather than “WDDM.”
To set the GPU to its default compute mode, use the following command:
“`bash
nvidia-smi -g 0 -c DEFAULT
“`
Replace `0` with the GPU index (e.g., `nvidia-smi -g 1 -c DEFAULT` for the second GPU if available).
After changing the compute mode, you may need to restart your system for the changes to take effect.
Please be aware that changing the compute mode might impact any running GPU workloads, and some applications may require the GPU to be in a specific compute mode for proper functioning. Always refer to the official NVIDIA documentation or contact NVIDIA support for guidance on specific GPU configurations and settings.
I want to set it to wddm
Apologies for the confusion in my previous response. If you want to set your NVIDIA GPU to “WDDM” mode for graphics tasks in a Windows environment, you’ll need to use the NVIDIA Control Panel provided by the NVIDIA driver.
Here are the steps to set the GPU to “WDDM” mode using the NVIDIA Control Panel:
1. Right-click on your desktop and select “NVIDIA Control Panel” from the context menu.
2. In the NVIDIA Control Panel, click on “3D Settings” in the left pane.
3. Under the “Global Settings” tab, scroll down to the “CUDA – GPUs” section.
4. Here, you’ll see a drop-down list with options for each installed GPU. Select the GPU you want to set to “WDDM” mode.
5. In the right pane, you’ll find the “CUDA – GPUs” setting. By default, it should be set to “All.” Change it to “Use these GPUs” and select “WDDM” from the drop-down list.
6. Click on “Apply” to save the changes.
7. If prompted, restart your system to apply the settings.
After performing these steps, your NVIDIA GPU should be in “WDDM” mode and available for graphics tasks as well as compute tasks.
Keep in mind that some GPU settings may require administrative privileges, so ensure you have the necessary permissions to make these changes. If you encounter any issues or have specific questions related to your GPU model or driver version, refer to the official NVIDIA documentation or contact NVIDIA support for assistance.
Well, this build went well, I learned that it can be done, but it too failed. However, I would like to try the next build with a different approach:: rror response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: error running hook #0: error running hook: exit status 1, stdout: , stderr: Auto-detected mode as ‘legacy’
nvidia-container-cli: device error: 1: unknown device: unknown
The error message you encountered indicates that there was an issue with the NVIDIA container runtime while trying to start the container. Specifically, it seems to be related to the GPU device.
The error “unknown device: unknown” suggests that the NVIDIA container runtime (`nvidia-container-cli`) was unable to detect or access the GPU device on your system.
Here are some troubleshooting steps you can try to resolve the issue:
1. Verify NVIDIA Driver Installation: Make sure that the NVIDIA graphics driver is correctly installed on your system. Ensure that you have installed the appropriate driver version compatible with your GPU model.
2. Check GPU Visibility: Confirm that the GPU is visible and detected by the system. You can check this by running the `nvidia-smi` command to see if it displays information about your NVIDIA GPU(s). If it doesn’t show any GPU information, it might indicate an issue with the driver or the GPU itself.
3. Verify Docker and NVIDIA Container Toolkit: Ensure that you have the latest versions of Docker and the NVIDIA Container Toolkit installed on your system. Check the official documentation for any specific requirements or compatibility issues.
4. Restart Docker Service: Sometimes, restarting the Docker service can resolve certain issues. You can try stopping and then starting the Docker service again.
5. Check for Conflicting Drivers: If you have multiple NVIDIA drivers or CUDA installations, it could lead to conflicts. Ensure that you have only one version of the NVIDIA driver and CUDA toolkit installed.
6. Update NVIDIA Container Toolkit Configuration: Check the configuration of the NVIDIA Container Toolkit. Make sure the appropriate GPU options are set, and the toolkit is configured to use the correct GPU devices.
7. Check for Kernel Compatibility: Verify that your system’s kernel version is compatible with the NVIDIA driver and toolkit version you are using.
8. Check Docker Permissions: Ensure that your user account has sufficient permissions to access and use Docker, especially if you are not running Docker commands with superuser privileges.
If the issue persists, you may need to refer to the specific documentation for your Docker and NVIDIA Container Toolkit versions or seek support from NVIDIA forums or Docker community for further assistance.
Keep in mind that working with GPU-enabled containers can sometimes be complex due to various configurations and dependencies. It may require some trial and error and careful attention to configuration details.
This is being built in WSL so what edxtra steps must I tkae to make sure that the container can use the GPU?
When building and running Docker containers with GPU support in Windows Subsystem for Linux (WSL), there are some extra steps you need to take to ensure that the container can utilize the GPU. Here’s a step-by-step guide:
1. Enable GPU Support in WSL:
Before proceeding, make sure you have WSL 2 installed and GPU support enabled. To enable GPU support in WSL 2, follow these steps:
a. Update to WSL 2 by following Microsoft’s official documentation: https://docs.microsoft.com/en-us/windows/wsl/install
b. Install the NVIDIA Docker support for WSL 2 using the `wsl –install` command:
“`
wsl –install
“`
c. Restart your computer to apply the changes.
2. Install Docker Desktop:
Install Docker Desktop for Windows by downloading it from the official Docker website: https://www.docker.com/products/docker-desktop
3. Set Docker to Use WSL 2 Backend:
After installing Docker Desktop, open the settings and set Docker to use the WSL 2 backend.
4. Install NVIDIA Container Toolkit:
Follow the official NVIDIA documentation to install the NVIDIA Container Toolkit for WSL 2: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#wsl-ubuntu
Make sure you have the appropriate NVIDIA driver installed on your Windows system.
5. Configure Docker Permissions:
Ensure that your user account has permissions to access the Docker daemon. In most cases, adding your user to the `docker` group should be sufficient:
“`
sudo usermod -aG docker $USER
“`
After adding your user to the `docker` group, you may need to log out and log back in or restart WSL to apply the changes.
6. Test GPU Visibility:
Verify that the NVIDIA GPU is visible inside WSL by running the following command:
“`
nvidia-smi
“`
7. Build and Run Docker Container:
Now you can build and run your Docker container with GPU support. Make sure to include the appropriate NVIDIA Container Toolkit options when running the container. For example:
“`
docker run –gpus all your_image_name
“`
Replace `your_image_name` with the name of your Docker image.
8. Verify GPU Usage Inside Container:
Within the running container, you can check GPU visibility using the `nvidia-smi` command again:
“`
nvidia-smi
“`
Remember that running GPU-enabled containers in WSL requires the correct configuration of WSL 2, Docker Desktop, and the NVIDIA Container Toolkit. Make sure to follow the official documentation and requirements for each tool to ensure smooth GPU support inside your containers.
base) gemini@DESKTOP-UNG8TH9:~$ nvidia-smi
Thu Jul 27 16:28:38 2023
+—————————————————————————————+
| NVIDIA-SMI 535.54.04 Driver Version: 536.25 CUDA Version: 12.2
It looks like the `nvidia-smi` command is showing information about the NVIDIA GPU on your system, including the driver version (536.25) and the installed CUDA version (12.2).
This output indicates that the NVIDIA driver is installed and functioning correctly, and the GPU is visible to the system. This is a positive sign, and it means that you should be able to utilize the GPU in Docker containers with the appropriate configuration.
Now, you can proceed with building and running GPU-enabled Docker containers as previously described. Ensure that you have Docker Desktop installed, configured to use the WSL 2 backend, and that you’ve installed the NVIDIA Container Toolkit for WSL 2.
To test whether your GPU is accessible from within the Docker container, you can run the `nvidia-smi` command inside the container. After building and running a container with GPU support, execute the following command within the container:
“`
nvidia-smi
“`
If everything is set up correctly, this command should display information about the GPU inside the Docker container as well.
Keep in mind that building GPU-enabled containers can be complex and may require careful attention to configuration details, but with the right setup, you should be able to utilize the GPU successfully in your Docker containers within WSL 2.
I need to enable WDDM on my P40
Enabling WDDM on an NVIDIA GPU, such as the P40, involves modifying the Windows driver settings and requires administrative privileges. Here’s how you can enable WDDM on your P40 GPU:
1. Open “Device Manager”:
– Press “Windows key + X” and select “Device Manager,” or
– Right-click on the “Start” button and choose “Device Manager.”
2. Locate your NVIDIA P40 GPU:
– Expand the “Display adapters” category to find your NVIDIA GPU, which should be listed as “NVIDIA Tesla P40.”
3. Right-click on the NVIDIA P40 GPU and select “Properties.”
4. Go to the “Driver” tab:
5. Click “Update Driver.”
6. Choose “Browse my computer for drivers”:
7. Click “Let me pick from a list of available drivers on my computer.”
8. Select “WDDM”:
9. Click “Next” and let Windows install the WDDM driver for your NVIDIA P40 GPU.
10. Restart your computer to apply the changes.
After following these steps, the WDDM driver should be enabled for your NVIDIA P40 GPU. This enables features such as DirectX support and better compatibility with certain applications that require WDDM.
Please note that modifying drivers and system settings can have significant impacts on the stability and functionality of your system. Make sure you have appropriate backups and understand the consequences of enabling or modifying specific settings. Additionally, WDDM drivers may not be available for all GPU models or in all situations. If you encounter any issues, you may need to revert to the previous driver version or seek further support from NVIDIA or the GPU manufacturer.
No work. Lets try the modify the registry method
Modifying the registry to enable WDDM (Windows Display Driver Model) for your NVIDIA GPU should be done with caution, as editing the registry can affect system stability if not done correctly. Before proceeding, ensure you have a backup of your registry or create a system restore point to revert changes if needed. Here’s how you can try modifying the registry to enable WDDM on your NVIDIA GPU:
1. Press “Windows key + R” to open the Run dialog.
2. Type “regedit” and press Enter to open the Registry Editor.
3. Navigate to the following registry key:
“`
HKEY_LOCAL_MACHINESYSTEMCurrentControlSetControlGraphicsDrivers
“`
4. Right-click on “GraphicsDrivers,” go to “New,” and select “DWORD (32-bit) Value.”
5. Name the new DWORD value “TdrLevel.”
6. Double-click on the “TdrLevel” value to modify it.
7. Set the “Value data” to 0 (zero) to disable TDR (Timeout Detection and Recovery). This will prevent the GPU driver from resetting when it becomes unresponsive.
8. Click “OK” to save the changes.
9. Restart your computer to apply the registry changes.
Please note that modifying the TdrLevel registry setting can potentially disable certain GPU-related error handling and may lead to system instability if the GPU encounters issues. Only proceed with this method if you fully understand the risks involved.
If you encounter any issues after making these changes or wish to revert them, you can set the “TdrLevel” value back to its default setting, which is usually “0” (zero) or “3” (to re-enable TDR with a longer timeout delay).
As always, it’s essential to exercise caution when making changes to the Windows registry, and if you’re uncertain about any step, it’s best to seek assistance from experienced users or professional support.
I have a Nvidia Tesla P40 GPU set up on my Windows 10 machine, and I’m using it through WSL2 for applications that need its CUDA capabilities. These applications are running in various CUDA toolkit versions within Conda environments. To achieve this, I have to operate the P40 in WDDM mode since WSL2 can’t recognize the GPU when it’s in TCC mode. I need explicit instruction on how to boot this device in WDDM.
Booting the NVIDIA Tesla P40 GPU in WDDM (Windows Display Driver Model) mode involves changing the GPU’s operating mode from TCC (Tesla Compute Cluster) to WDDM using the NVIDIA System Management Interface (nvidia-smi). Here’s a step-by-step guide to help you achieve this:
1. Open a Command Prompt or PowerShell with administrative privileges:
– Press “Windows key + X” and select “Windows Terminal (Admin),” or
– Search for “Command Prompt,” right-click on it, and choose “Run as administrator,” or
– Search for “PowerShell,” right-click on it, and choose “Run as administrator.”
2. Verify the current operating mode of the GPU:
Run the following command to check the current operating mode of the P40 GPU:
“`
nvidia-smi
“`
3. Change the GPU operating mode to WDDM:
Run the following command to set the P40 GPU to WDDM mode:
“`
nvidia-smi -i 0 -dm 1
“`
– `-i 0`: Specifies the GPU index. Use “0” if you have a single P40 GPU. If you have multiple GPUs, adjust the index accordingly.
– `-dm 1`: Sets the GPU operating mode to WDDM.
4. Reboot your computer:
To apply the changes, you need to restart your Windows machine.
After the reboot, the NVIDIA Tesla P40 GPU should be operating in WDDM mode and available for use within WSL2 and Conda environments with CUDA capabilities. You can verify the GPU’s operating mode again by running `nvidia-smi` after the reboot.
Keep in mind that modifying GPU operating modes can have significant implications for system behavior, and it’s essential to have proper backups or restore points in case you need to revert the changes. Additionally, ensure that you have the necessary NVIDIA drivers and software installed to support the WDDM mode on your Windows machine.
That sets it to tcc!
I apologize for the confusion. If the `nvidia-smi -i 0 -dm 1` command sets the GPU to TCC mode instead of WDDM, it seems that the command behavior may vary depending on the GPU model or driver version.
To enable WDDM mode on an NVIDIA GPU, you typically need to use the NVIDIA Control Panel application on Windows. Here are the steps you can follow:
1. Right-click on the desktop and select “NVIDIA Control Panel.”
2. In the NVIDIA Control Panel, click on “3D Settings” in the left pane.
3. Under the “3D Settings” category, click on “Manage 3D Settings.”
4. In the “Global Settings” tab, locate the “CUDA – GPUs” option.
5. From the drop-down list, select your NVIDIA GPU (Tesla P40).
6. Save the settings and exit the NVIDIA Control Panel.
7. Restart your computer to apply the changes.
After the reboot, the Tesla P40 GPU should be operating in WDDM mode and available for use within WSL2 and Conda environments with CUDA capabilities.
Please note that the exact steps and options in the NVIDIA Control Panel may vary depending on your GPU model and driver version. If you do not see the option to select the GPU in the “Manage 3D Settings” or “CUDA – GPUs” section, you may need to check for driver updates or consult the NVIDIA documentation for your specific GPU model.
As always, ensure you have a backup or restore point before making changes to system settings.
how to start thep40 in ubunbtu
To use an NVIDIA Tesla P40 GPU in Ubuntu, you’ll need to have the appropriate NVIDIA drivers and software installed. Here’s a step-by-step guide to start using the P40 GPU in Ubuntu:
1. Install NVIDIA Drivers:
Open a terminal and run the following commands to install the NVIDIA drivers:
“`
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-driver-
“`
Replace “ with the desired driver version (e.g., `470`, `510`, etc.). You can also use the `ubuntu-drivers` utility to install the recommended driver for your system:
“`
sudo ubuntu-drivers autoinstall
“`
2. Reboot your system:
After installing the NVIDIA drivers, restart your computer to activate the changes.
3. Verify the GPU installation:
Open a terminal and run the following command to check if the NVIDIA GPU is detected:
“`
nvidia-smi
“`
4. Install CUDA Toolkit (Optional):
If you plan to use CUDA applications, you can install the CUDA Toolkit. Visit the NVIDIA CUDA Toolkit website (https://developer.nvidia.com/cuda-downloads) and download the appropriate version for Ubuntu. Follow the installation instructions provided.
5. Install cuDNN (Optional):
If you’re using deep learning frameworks that require cuDNN (NVIDIA’s Deep Neural Network library), you can download cuDNN from the NVIDIA Developer website (https://developer.nvidia.com/cudnn) and follow the installation instructions.
6. Verify GPU Support in Applications:
After installing the necessary drivers and software, you can test GPU support in applications that utilize CUDA. For example, you can run CUDA samples or use deep learning frameworks like TensorFlow or PyTorch with GPU support to check if the GPU is being utilized.
Please note that the exact steps and commands may vary depending on the Ubuntu version and the specific driver and software versions you are installing. Always ensure you have proper backups or restore points before making significant changes to your system. Additionally, make sure to use official and trusted sources for downloading drivers and software from NVIDIA’s website.
what gpu arch is the p40?
The NVIDIA Tesla P40 GPU is based on the Pascal architecture. Pascal is the microarchitecture developed by NVIDIA and was the successor to the Maxwell architecture. It was released in 2016 and brought significant improvements in performance and power efficiency over its predecessor.
The Pascal architecture introduced features such as:
1. High-bandwidth memory (HBM2) support.
2. 16nm FinFET manufacturing process for improved power efficiency.
3. NVLink technology for high-speed communication between GPUs.
4. Advanced SIMD (Single Instruction, Multiple Data) units for improved parallel processing.
5. Improved CUDA cores with more performance per core compared to previous generations.
The Tesla P40 was designed primarily for deep learning and artificial intelligence workloads, making it suitable for data centers and research institutions requiring high-performance GPU computing for AI tasks.
nvidia-smi
Failed to initialize NVML: N/A
Failed to properly shut down NVML: N/A
The error message “Failed to initialize NVML: N/A” and “Failed to properly shut down NVML: N/A” from `nvidia-smi` indicates that the NVIDIA Management Library (NVML) failed to initialize and shutdown properly. This error can occur due to various reasons, such as driver issues, permissions, or conflicts with other software.
Here are some steps you can try to resolve the issue:
1. Check NVIDIA Driver Installation: Ensure that you have the correct NVIDIA drivers installed for your GPU. You can check the installed drivers using the following command:
“`
dpkg -l | grep nvidia
“`
If the drivers are not installed or you see any issues, reinstall the drivers or update them to the latest version.
2. Verify GPU Support: Make sure your GPU (Tesla P40) is supported by the installed drivers. Check the NVIDIA website for compatibility and supported driver versions.
3. Check NVIDIA Persistence Mode: Verify the persistence mode of your GPU using the following command:
“`
nvidia-smi -pm
“`
If it’s not enabled, you can enable persistence mode with:
“`
nvidia-smi -pm 1
“`
4. Check NVIDIA Services: Ensure that the necessary NVIDIA services are running. Restart the services using the following commands:
“`
sudo systemctl restart nvidia-persistenced
sudo systemctl restart nvidia-smi
“`
5. Check NVIDIA Device Nodes Permissions: Make sure your user has the necessary permissions to access the NVIDIA device nodes. The device nodes are usually located in `/dev/nvidia*`. You can check the permissions using:
“`
ls -l /dev/nvidia*
“`
If the permissions are incorrect, you may need to adjust them using `chmod` or add your user to the `video` group.
6. Check for Conflicting Software: Ensure that there are no conflicts with other software or processes that might be interfering with NVML initialization. Check the system logs for any relevant error messages.
7. Restart Your System: After making changes, restart your computer to apply the changes and see if the issue is resolved.
If the problem persists after trying these steps, you may need to investigate further or seek support from the NVIDIA forums or official support channels.
(base) gemini@DESKTOP-UNG8TH9:~$ dpkg -l | grep nvidia
ii libnvidia-compute-495:amd64 510.108.03-0ubuntu0.22.04.1 amd64 Transitional package for libnvidia-compute-510
ii libnvidia-compute-510:amd64 525.125.06-0ubuntu0.22.04.1 amd64 Transitional package for libnvidia-compute-525
ii libnvidia-compute-525:amd64 525.125.06-0ubuntu0.22.04.1 amd64 NVIDIA libcompute package
rc libnvidia-compute-535:amd64 535.54.03-0ubuntu1 amd64 NVIDIA libcompute package
ii libnvidia-container-tools 1.13.5-1 amd64 NVIDIA container runtime library (command-line tools)
ii libnvidia-container1:amd64 1.13.5-1 amd64 NVIDIA container runtime library
ii libnvidia-ml-dev:amd64 11.5.50~11.5.1-1ubuntu1 amd64 NVIDIA Management Library (NVML) development files
rc nvidia-compute-utils-535 535.54.03-0ubuntu1 amd64 NVIDIA compute utilities
ii nvidia-container-toolkit 1.13.5-1 amd64 NVIDIA Container toolkit
ii nvidia-container-toolkit-base 1.13.5-1 amd64 NVIDIA Container Toolkit Base
ii nvidia-cuda-dev:amd64 11.5.1-1ubuntu1 amd64 NVIDIA CUDA development files
ii nvidia-cuda-gdb 11.5.114~11.5.1-1ubuntu1 amd64 NVIDIA CUDA Debugger (GDB)
ii nvidia-cuda-toolkit 11.5.1-1ubuntu1 amd64 NVIDIA CUDA development toolkit
ii nvidia-cuda-toolkit-doc 11.5.1-1ubuntu1 all NVIDIA CUDA and OpenCL documentation
rc nvidia-dkms-535 535.54.03-0ubuntu1 amd64 NVIDIA DKMS package
ii nvidia-driver-local-repo-ubuntu2204-515.105.01 1.0-1 amd64 nvidia-driver-local repository configuration files
ii nvidia-driver-local-repo-ubuntu2204-535.54.03 1.0-1 amd64 nvidia-driver-local repository configuration files
rc nvidia-kernel-common-535 535.54.03-0ubuntu1 amd64 Shared files used with the kernel module
ii nvidia-opencl-dev:amd64 11.5.1-1ubuntu1 amd64 NVIDIA OpenCL development files
rc nvidia-prime 0.8.17.1 all Tools to enable NVIDIA’s Prime
ii nvidia-profiler 11.5.114~11.5.1-1ubuntu1 amd64 NVIDIA Profiler for CUDA and OpenCL
rc nvidia-settings 535.54.03-0ubuntu1 amd64 Tool for configuring the NVIDIA graphics driver
ii nvidia-visual-profiler 11.5.114~11.5.1-1ubuntu1 amd64 NVIDIA Visual Profiler for CUDA and OpenCL
Based on the output you provided, it appears that you have multiple versions of NVIDIA drivers and related packages installed on your system. Some of the packages have been marked as “rc,” which means they are in a removed state but still have configuration files remaining on your system.
To clean up and resolve potential conflicts with multiple versions, you can follow these steps:
1. Update APT Package List:
“`
sudo apt-get update
“`
2. Remove “rc” Packages:
“`
sudo apt-get autoremove –purge
“`
3. Remove Conflicting NVIDIA Packages:
“`
sudo apt-get remove –purge libnvidia-compute-535 nvidia-compute-utils-535 nvidia-dkms-535 nvidia-kernel-common-535 nvidia-prime nvidia-settings
“`
4. Reinstall NVIDIA Driver:
“`
sudo apt-get install –reinstall nvidia-driver-local-repo-ubuntu2204-515.105.01
“`
5. Restart the System:
“`
sudo reboot
“`
After the system restarts, check the output of `nvidia-smi` again to see if the issue is resolved and the NVIDIA GPU is detected properly. If there are still any issues, you may need to investigate further or seek support from the NVIDIA forums or official support channels. Additionally, ensure that you are using the correct driver version for your Tesla P40 GPU and that it is compatible with the installed CUDA toolkit versions and WSL2.
Done
Reading state information… Done
Reinstallation of nvidia-driver-local-repo-ubuntu2204-515.105.01 is not possible, it cannot be downloaded.
0 upgraded, 0 newly installed, 0 to remove and 13 not upgraded.
It seems that the package “nvidia-driver-local-repo-ubuntu2204-515.105.01” cannot be downloaded for reinstallation. This might be due to changes in the NVIDIA driver repository or the specific version being unavailable.
To resolve this issue, you can try the following steps:
1. Update APT Package List:
“`
sudo apt-get update
“`
2. Remove “rc” Packages and Conflicting NVIDIA Packages:
“`
sudo apt-get autoremove –purge
sudo apt-get remove –purge libnvidia-compute-535 nvidia-compute-utils-535 nvidia-dkms-535 nvidia-kernel-common-535 nvidia-prime nvidia-settings
“`
3. Reinstall the NVIDIA Container Toolkit:
“`
sudo apt-get install –reinstall nvidia-container-toolkit
“`
4. Restart the System:
“`
sudo reboot
“`
After the system restarts, check the output of `nvidia-smi` again to see if the issue is resolved and the NVIDIA GPU is detected properly.
If the problem persists or you encounter any other errors, you may need to look for updated NVIDIA driver packages that are compatible with your GPU and operating system version. You can check the NVIDIA website or other official sources for the latest drivers.
Additionally, ensure that you have the correct version of the NVIDIA driver for your Tesla P40 GPU and that it supports the CUDA toolkit versions and WSL2 configuration you are using. If you are unsure about the driver compatibility, you can refer to the NVIDIA documentation or seek support from the NVIDIA forums or official support channels.
nvidia-smi -fdm 0
Unable to set driver model for GPU 00000000:24:00.0: Not Supported
Treating as warning and moving on.
All done.
It appears that setting the driver model for the GPU using the command `nvidia-smi -fdm 0` is not supported, and you are getting a warning message indicating that. This means that you cannot change the driver model using this method for your GPU.
In some cases, certain GPUs or driver versions may not support switching between different driver models. It’s possible that your NVIDIA Tesla P40 GPU doesn’t support changing the driver model to WDDM when running in WSL2.
Since you mentioned that you need to operate the P40 in WDDM mode to use it with WSL2, you might have already done the necessary steps outside of the WSL2 environment to set the GPU to WDDM mode. In such cases, you don’t need to perform the `nvidia-smi -fdm 0` command within WSL2, as the driver model is already set.
To use the GPU within WSL2, ensure that you have installed the necessary NVIDIA drivers and utilities within your WSL2 environment, along with the NVIDIA Container Toolkit if needed. You can check if the GPU is recognized and available within WSL2 by running `nvidia-smi` and other CUDA-related commands within WSL2.
If you encounter any specific errors or issues while using the GPU within WSL2, please provide more details, and I’ll do my best to assist you further.
my ubunu wsl crashed and won’t come back. I want to try in Debian WSL to
Sure, you can try using Debian in WSL to see if you can set up and utilize the NVIDIA GPU successfully. Here’s how you can do it:
1. Install Debian in WSL:
– Open the Microsoft Store on your Windows 10 machine.
– Search for “Debian” and click on the Debian distribution.
– Click the “Install” button to download and install Debian in WSL.
2. Launch Debian in WSL:
– You can open Debian by searching for “Debian” in the Start menu or by running “debian” in the Command Prompt or PowerShell.
3. Update Debian:
– After launching Debian in WSL, run the following commands to update the package lists and upgrade the installed packages:
“`
sudo apt update
sudo apt upgrade
“`
4. Install NVIDIA drivers and utilities:
– Before proceeding, make sure you have set up the NVIDIA GPU in Windows and have installed the appropriate drivers. The GPU should be recognized and working correctly in Windows before using it in WSL.
– Within Debian WSL, install the NVIDIA driver package and other necessary utilities:
“`
sudo apt install nvidia-driver nvidia-utils nvidia-cuda-toolkit
“`
5. Verify GPU detection:
– Check if the GPU is recognized by running `nvidia-smi`:
“`
nvidia-smi
“`
6. Install NVIDIA Container Toolkit (Optional):
– If you plan to use Docker and the NVIDIA GPU within WSL, you can install the NVIDIA Container Toolkit:
“`
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo gpg –dearmor -o /usr/share/keyrings/nvidia-archive-keyring.gpg
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt update
sudo apt install -y nvidia-docker2
sudo systemctl restart docker
“`
7. Test CUDA (Optional):
– If you want to test CUDA capabilities, you can compile and run a simple CUDA program:
“`
# Install CUDA samples
sudo apt install cuda-samples
# Compile the CUDA sample
cd /usr/local/cuda-11.5/samples/1_Utilities/deviceQuery
sudo make
# Run the compiled CUDA sample
./deviceQuery
“`
Remember that using the NVIDIA GPU within WSL may require specific configurations and compatibility, and results can vary depending on your system and the specific GPU model. Ensure that your NVIDIA drivers are up-to-date and that the GPU is functioning correctly in Windows before attempting to use it within WSL.
If you encounter any issues or need further assistance, feel free to ask!
I finally have my P40 accelerator in WDDM mode. Let’s try building this gpu again
Great to hear that you have the P40 accelerator in WDDM mode! Now, let’s proceed with building the GPU environment within your Debian WSL.
Since you are using a Debian WSL environment, the process will be similar to building it in a regular Debian environment. Here are the steps to build the GPU environment:
1. Install Necessary Packages:
First, make sure you have the required dependencies for building and using the GPU environment. Install the necessary packages, including NVIDIA drivers and CUDA toolkit:
“`
sudo apt update
sudo apt install build-essential wget ca-certificates git
sudo apt install nvidia-driver nvidia-utils nvidia-cuda-toolkit
“`
2. Verify NVIDIA Driver:
Check if the NVIDIA driver is properly installed and the GPU is recognized using:
“`
nvidia-smi
“`
3. Install Docker:
You can use Docker to containerize your GPU applications. Install Docker by following the official Docker installation guide for Debian: https://docs.docker.com/engine/install/debian/
4. Install NVIDIA Container Toolkit (Optional):
To enable GPU support within Docker containers, install the NVIDIA Container Toolkit as follows:
“`
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo gpg –dearmor -o /usr/share/keyrings/nvidia-archive-keyring.gpg
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt update
sudo apt install -y nvidia-docker2
sudo systemctl restart docker
“`
5. Build and Run GPU Applications:
Now, you can build and run your GPU applications inside Docker containers, ensuring GPU support is enabled within the container.
Remember to adjust your Dockerfiles and scripts as needed to utilize the GPU within the containers. Also, ensure that your GPU application code is written to take advantage of GPU computing using CUDA or other appropriate libraries.
If you encounter any issues during the build process or need further assistance, feel free to ask! Good luck with your GPU environment!
I have nvidia-smi in a docker terminal!! And it was from the superagi that we built, unfortunately I don’t think it is communication with the main app.
That’s a great progress! Having `nvidia-smi` accessible within the Docker terminal indicates that the NVIDIA GPU support is working inside the container. However, if you’re facing communication issues between the main app and the GPU container, there might be a few reasons why this is happening. Here are some things you can check to troubleshoot the issue:
1. Docker Runtime Configuration:
Ensure that you have the NVIDIA runtime configured for Docker. This enables Docker to access the host’s GPU resources and make them available to the containers. You can check this by running the following command:
“`
docker info | grep “Runtimes”
“`
Make sure that you see `nvidia` in the output, indicating that the NVIDIA runtime is available.
2. GPU Access in Docker:
When you run the GPU container, make sure to use the `–gpus` option to specify the GPUs that the container should have access to. For example, to grant access to all GPUs, use:
“`
docker run –gpus all …
“`
3. CUDA Toolkit in the Container:
Ensure that the CUDA toolkit is installed inside the Docker container. Your GPU application in the container might require the CUDA libraries to function properly. You can install the CUDA toolkit within the container using `apt` or download it from the official NVIDIA website.
4. Shared Libraries:
Check if any shared libraries required by your main app or GPU application are missing in the container. Use `ldd` to check if all the necessary libraries are available:
“`
ldd /path/to/your/app_or_gpu_executable
“`
5. Container Networking:
If your main app and GPU container are supposed to communicate over the network, ensure that the necessary ports are exposed, and the network configuration is correct.
6. Volume Mounting (if applicable):
If your main app and GPU container need to share data, make sure that you have correctly mounted the volumes when running the container.
7. Debugging:
To further investigate the issue, you can use Docker’s `exec` command to enter the running container and debug it from inside. For example:
“`
docker exec -it /bin/bash
“`
This will allow you to explore the container’s environment, check logs, and run commands to see if there are any issues.
Make sure to verify each step carefully and see if any of them might be causing the communication problem. If you encounter any specific errors or issues, feel free to share them, and I can help you troubleshoot further!
Runtimes: io.containerd.runc.v2 nvidia runc
Great! The output `Runtimes: io.containerd.runc.v2 nvidia runc` confirms that the NVIDIA runtime (`nvidia`) is available in Docker. This means that Docker is configured to use the NVIDIA runtime for GPU-accelerated containers.
Now, since `nvidia-smi` is working within the Docker terminal, it indicates that the GPU support is correctly set up inside the container. This is a positive sign, and the container should be able to utilize the GPU for GPU-accelerated applications.
If your main app is running outside the container and is supposed to communicate with the GPU-accelerated container, there might be other factors affecting the communication. Please check the other aspects mentioned in the previous response, such as the CUDA toolkit installation, shared libraries, container networking, volume mounting, and potential debugging inside the container.
Additionally, ensure that your main app is correctly configured to communicate with the GPU-accelerated container and is using the appropriate GPU libraries and drivers to do so.
If you encounter any specific errors or issues, provide more details, and I’ll be happy to assist you further in troubleshooting the communication between your main app and the GPU-accelerated container.
Rather than do all of that, since I now now that particular script works, I may as well rebuikd the entire container so as to make sure it is built as one?
Rebuilding the entire container is a good approach to ensure that the container is properly built as a cohesive unit, and all the necessary dependencies are included. It also helps to avoid any potential conflicts or issues that may arise from using pre-built components.
To rebuild the entire container, you can follow these steps:
1. Stop and remove the existing container: If the previous container is running, you should stop it first and then remove it.
“`bash
docker stop
docker rm
“`
Replace “ with the name or ID of your existing container.
2. Rebuild the Docker image: Assuming you have the Dockerfile for your container, navigate to the directory containing the Dockerfile and build the image again.
“`bash
docker build -t your_image_name:tag .
“`
Replace `your_image_name` with the desired name for your image and `tag` with the desired tag/version.
3. Run the new container: After the image is built, you can run the new container using the same `docker run` command or with any necessary additional options or environment variables.
“`bash
docker run –gpus all -it your_image_name:tag
“`
The `–gpus all` option ensures that the container can access all available GPUs on the host.
By rebuilding the entire container, you ensure that the changes made to the script or any other files are incorporated into the new image. This helps in ensuring consistency and avoids potential issues that may arise from using partially built or outdated components.
If you encounter any issues during the rebuild or if you have any specific questions or requirements, feel free to ask for further assistance.
should I delete the original contiainer and all its remenants
Yes, it’s generally a good practice to clean up the remnants of the original container and its associated resources after rebuilding a new one. This helps to avoid conflicts, confusion, and potential resource waste. Here are the steps to clean up the remnants:
1. Stop the original container (if it’s still running):
“`bash
docker stop
“`
2. Remove the original container:
“`bash
docker rm
“`
3. Remove the original Docker image (optional):
“`bash
docker rmi
“`
The `docker rmi` command is optional and can be used to remove the original Docker image if you no longer need it. If you’re using the same image name and tag for the new build, the new image will overwrite the old one, and you might not need to remove it explicitly.
4. Clean up unused resources (optional):
Over time, Docker may accumulate unused resources like dangling images, volumes, networks, etc. You can use the following commands to clean them up:
“`bash
# Clean up dangling images
docker image prune
# Clean up unused volumes
docker volume prune
# Clean up unused networks
docker network prune
“`
By following these steps, you ensure that the old container and its remnants are no longer consuming resources and that your environment is clean before running the new container. This helps in avoiding any potential conflicts or issues that might arise from conflicting resources or outdated configurations.
nvidia/cuda:12.2.0-devel-ubuntu22.04
It looks like you are referring to the Docker image `nvidia/cuda:12.2.0-devel-ubuntu22.04`. This is an official NVIDIA Docker image that provides a development environment for CUDA version 12.2.0 on Ubuntu 22.04 (Focal Fossa).
This image is pre-configured with the necessary CUDA development files and tools, allowing you to build and run CUDA applications inside a container. It’s particularly useful when you want to ensure consistency in your CUDA development environment across different systems or when you want to isolate your CUDA projects from the host system.
You can use this image as a base for your Dockerfile to build and run CUDA applications. For example:
“`Dockerfile
# Use the nvidia/cuda:12.2.0-devel-ubuntu22.04 image as the base
FROM nvidia/cuda:12.2.0-devel-ubuntu22.04
# Copy your CUDA application files into the container
COPY . /app
# Set the working directory
WORKDIR /app
# Build your CUDA application
RUN nvcc my_cuda_app.cu -o my_cuda_app
# Run your CUDA application
CMD [“./my_cuda_app”]
“`
Replace `my_cuda_app.cu` with the name of your CUDA application source file.
Please note that to run this Docker image, you need to have NVIDIA GPU drivers and Docker with NVIDIA Container Toolkit properly installed on your host system. This allows the Docker container to access the GPU resources on the host.
Also, make sure to follow NVIDIA’s guidelines for using the `nvidia/cuda` images, as well as any additional setup specific to your application’s requirements.
: error running hook #0: error running hook: exit status 1, stdout: , stderr: Auto-detected mode as ‘legacy’
nvidia-container-cli: device error: 1: unknown device: unknown
The error message you provided indicates that there is an issue with the NVIDIA container runtime while trying to access the GPU device. The error “Auto-detected mode as ‘legacy'” suggests that the NVIDIA driver or runtime might be running in an unsupported mode.
Here are some steps you can take to troubleshoot and resolve the issue:
1. Verify NVIDIA Driver Installation: Ensure that the correct NVIDIA driver is installed on your host system. Make sure it matches the CUDA version used in the Docker image. You can check the installed NVIDIA driver version using `nvidia-smi` on your host.
2. Check NVIDIA Container Runtime: Ensure that you have installed the NVIDIA Container Toolkit and that the `nvidia-container-runtime` is properly configured. This runtime is essential for GPU support within Docker containers. Check the configuration file located at `/etc/docker/daemon.json` (on Linux) or `%APPDATA%Dockerconfigdaemon.json` (on Windows) to ensure it has the necessary NVIDIA runtime settings.
3. Restart Docker: After any changes to the Docker configuration, it’s a good idea to restart the Docker service to apply the changes.
4. Check GPU Visibility: Confirm that the GPU device is visible inside the Docker container. You can run the following command to check:
“`
docker run –gpus all nvidia/cuda:12.2.0-devel-ubuntu22.04 nvidia-smi
“`
This command should show the GPU information from within the container. If it doesn’t work, it might indicate an issue with the Docker runtime or GPU visibility.
5. Update NVIDIA Container Toolkit: Ensure that you have the latest version of the NVIDIA Container Toolkit installed. You can check for updates and follow the installation instructions provided by NVIDIA.
6. Host Driver Compatibility: Make sure the installed NVIDIA driver on your host system is compatible with the CUDA version used in the Docker image. Mismatched versions can cause compatibility issues.
7. Verify Docker Permissions: Ensure that your user account has the necessary permissions to access the GPU and Docker resources. You may need to add your user to the `docker` group (Linux) or grant Docker permissions on Windows.
8. Check GPU Availability: In some cases, other applications or services might be using the GPU resources exclusively, preventing Docker from accessing them. Make sure no other processes are monopolizing the GPU.
9. Check NVIDIA System Management Interface (nvidia-smi): Ensure that `nvidia-smi` is working correctly on your host system and showing the GPU information. If it doesn’t work outside of Docker, it might indicate a broader issue with the NVIDIA driver installation or GPU visibility.
By following these steps, you can troubleshoot the issue and hopefully resolve the error you encountered. Remember to consult the official NVIDIA documentation and community forums for further assistance, as GPU-related issues in Docker environments can sometimes be system-specific and may require deeper investigation.
hrough enable persistence model.
To enable the Persistence Mode for your NVIDIA GPU, you can use the `nvidia-smi` command with the appropriate option. Persistence Mode allows the NVIDIA driver to keep the GPU initialized even when no active compute processes are using it, reducing the startup overhead for subsequent compute tasks.
Here’s how you can enable Persistence Mode:
1. Open a terminal or command prompt on your Windows system.
2. Run the following command to enable Persistence Mode for the GPU:
“`
nvidia-smi -pm 1
“`
This command sets Persistence Mode to Enabled (1) for all NVIDIA GPUs on the system.
3. After running the command, the Persistence Mode should be enabled, and the GPU will stay initialized even when there are no active compute processes.
Note: Persistence Mode settings are not persistent across system reboots. If you want to make the setting permanent, you can create a script or batch file that runs the `nvidia-smi -pm 1` command during system startup.
Please ensure you have the latest NVIDIA driver installed on your Windows system to ensure compatibility with the GPU Persistence Mode. You can check the NVIDIA driver version using the `nvidia-smi` command without any options.
Keep in mind that enabling Persistence Mode is a system-wide setting, affecting all NVIDIA GPUs on your machine. If you have multiple GPUs and only want to enable Persistence Mode for a specific GPU, you can use the `nvidia-smi -i -pm 1` command, where “ is the index of the GPU you want to target (e.g., 0, 1, 2, etc.).
After enabling Persistence Mode, you can try running your applications within WSL2 with the CUDA capabilities of your NVIDIA GPU. If you encounter any issues, make sure to check the NVIDIA driver and CUDA version compatibility with the applications you are running.
so what versions should I try and install inside the container
To select the appropriate versions of NVIDIA driver and CUDA toolkit for your container, you need to consider the following factors:
1. Host System GPU Driver Version: Check the version of the NVIDIA driver installed on your host system (Windows) using the `nvidia-smi` command. The driver version on your host should be compatible with the driver inside the container.
2. CUDA Version: Choose a CUDA version that is compatible with both the NVIDIA driver on the host and the applications you want to run inside the container. Check the CUDA compatibility matrix provided by NVIDIA to ensure the compatibility of your selected CUDA version with the installed GPU driver: https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions
3. Application Requirements: If you have specific applications that require a certain CUDA version, make sure to choose a CUDA version that meets those requirements.
Once you have determined the compatible driver and CUDA versions, you can use the NVIDIA Docker base images that match your requirements. NVIDIA provides official Docker images for different combinations of driver and CUDA versions.
For example, you can use the following NVIDIA Docker base image for CUDA 11.5 and NVIDIA driver version 510.39:
“`
nvidia/cuda:11.5.1-base-ubuntu20.04
“`
Or, if you need a different version, you can search for the available tags on Docker Hub: https://hub.docker.com/r/nvidia/cuda/tags
Remember that the tag `base` refers to the base image without the development libraries, while other tags like `devel`, `runtime`, or specific version numbers provide additional components like development libraries and runtime environments.
Make sure to choose an image that fits your specific requirements and provides all the necessary components for your application. Using the appropriate NVIDIA Docker image will ensure a smooth experience when running GPU-accelerated applications inside the container.
apt update
E: Conflicting values set for option Signed-By regarding source https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64/ /: /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg !=
E: The list of sources could not be read.
(base) gemini@DESKTOP-UNG8TH9:/mnt/c/SarahOne
The error you encountered indicates conflicting values set for the `Signed-By` option regarding the source `https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64/`. It seems like there’s an issue with the GPG keyring used by the NVIDIA container toolkit.
To resolve this issue, you can try the following steps:
1. Remove the conflicting GPG keyring file:
“`
sudo rm /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
“`
2. Re-add the NVIDIA container toolkit GPG key:
“`
curl -sL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg –dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
“`
3. Update the package lists again:
“`
sudo apt update
“`
After performing these steps, the conflicting GPG keyring file should be replaced, and the `apt update` command should run without errors.
If you encounter any issues or errors during these steps, please let me know, and I’ll be happy to assist further.
sudo apt update
E: Conflicting values set for option Signed-By regarding source https://nvidia.github.io/libnvidia-container/stable/ubuntu18.04/amd64/ /: /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg !=
I apologize for the confusion. It seems I provided an incorrect solution in my previous response. The error you are encountering is related to conflicting values set for the `Signed-By` option for the NVIDIA container toolkit repository.
To fix this issue, you can follow these steps:
1. Open the `/etc/apt/sources.list.d/nvidia-container-toolkit.list` file in a text editor with superuser privileges. For example, you can use `nano` editor:
“`bash
sudo nano /etc/apt/sources.list.d/nvidia-container-toolkit.list
“`
2. Comment out the conflicting `Signed-By` line by adding a `#` at the beginning of the line. It should look like this:
“`plaintext
# Signed-By: NVIDIA Corporation (Open Source Projects)
“`
3. Save the file and exit the text editor.
4. Now, update the package lists:
“`bash
sudo apt update
“`
This should resolve the “Conflicting values set for option Signed-By” error. If you encounter any other issues, please let me know, and I’ll be glad to assist further.
when installing the driver in my docker container do I install the windows driver or the linux driver??
When you are installing the NVIDIA driver inside a Docker container running on Linux, you should install the Linux driver. Docker containers are isolated and have their own environment, separate from the host operating system. Therefore, you need to install the Linux version of the NVIDIA driver that matches the kernel and CUDA version inside the container.
The NVIDIA driver for Linux can be installed using the package manager specific to your Linux distribution. For example, on Ubuntu, you can use the `apt` package manager to install the NVIDIA driver. Here’s a general outline of the steps:
1. Install the necessary dependencies for building and installing the driver:
“`bash
sudo apt-get update
sudo apt-get install build-essential
“`
2. Download the appropriate NVIDIA driver for Linux from the NVIDIA website. Make sure you choose the version that matches your GPU model and the Linux distribution you are using.
3. Install the NVIDIA driver using the installer provided by NVIDIA. You can run the installer with the `–dkms` option to enable Dynamic Kernel Module Support, which allows the driver to automatically rebuild kernel modules whenever there are updates to the kernel.
“`bash
chmod +x NVIDIA-Linux-x86_64-.run
sudo ./NVIDIA-Linux-x86_64-.run –dkms
“`
Replace “ with the version number of the downloaded NVIDIA driver.
Please note that the steps provided above are for installing the NVIDIA driver on the host machine or within a Linux-based Docker container. If you are using Windows Subsystem for Linux (WSL) or a Windows-based container, the process might be different, and you would typically use the Windows version of the NVIDIA driver.
I think I found the right driver this time. I need this incfluded in my dockerfile so when it build it will build with the right driver, right?
Yes, that’s correct. If you want to include the NVIDIA driver installation in your Dockerfile so that the container is built with the right driver, you can follow these steps:
1. Choose the appropriate NVIDIA driver version for your GPU and Linux distribution. Make sure you download the driver package that matches your GPU model, Linux distribution version, and CUDA version (if applicable).
2. Copy the downloaded NVIDIA driver installer into your Docker build context. You can place it in the same directory as your Dockerfile.
3. Modify your Dockerfile to include the installation of the NVIDIA driver. Here’s an example Dockerfile:
“`Dockerfile
# Use the base image that includes the necessary CUDA libraries
FROM nvidia/cuda:12.2.0-devel-ubuntu22.04
# Copy the NVIDIA driver installer into the container
COPY NVIDIA-Linux-x86_64-.run /tmp/
# Install necessary dependencies for building and installing the driver
RUN apt-get update && apt-get install -y –no-install-recommends
build-essential
&& rm -rf /var/lib/apt/lists/*
# Set executable permissions for the NVIDIA driver installer
RUN chmod +x /tmp/NVIDIA-Linux-x86_64-.run
# Install the NVIDIA driver with the –dkms option to enable Dynamic Kernel Module Support
RUN /tmp/NVIDIA-Linux-x86_64-.run –dkms –silent
# Cleanup
RUN rm /tmp/NVIDIA-Linux-x86_64-.run
# Continue with the rest of your Dockerfile, installing other dependencies and setting up your application
“`
Replace “ with the version number of the downloaded NVIDIA driver.
4. Build your Docker image using the modified Dockerfile:
“`bash
docker build -t my_gpu_app .
“`
The Docker image will now include the installed NVIDIA driver, and when you run containers based on this image, they will have access to the GPU and the correct driver version.
Please note that installing the NVIDIA driver inside a Docker container is suitable for development and testing purposes. For production environments, it’s recommended to use NVIDIA GPU Cloud (NGC) containers or other containerized solutions provided by NVIDIA, as they are thoroughly tested and optimized for various use cases.
NVIDIA-Linux-x86_64-535.54.03.run
Great! Since you have the NVIDIA driver installer “NVIDIA-Linux-x86_64-535.54.03.run,” you can include it in your Dockerfile to build the container with the specific driver version. Here’s an example Dockerfile that includes the installation of the NVIDIA driver:
“`Dockerfile
# Use the base image that includes the necessary CUDA libraries
FROM nvidia/cuda:12.2.0-devel-ubuntu22.04
# Copy the NVIDIA driver installer into the container
COPY NVIDIA-Linux-x86_64-535.54.03.run /tmp/
# Install necessary dependencies for building and installing the driver
RUN apt-get update && apt-get install -y –no-install-recommends
build-essential
&& rm -rf /var/lib/apt/lists/*
# Set executable permissions for the NVIDIA driver installer
RUN chmod +x /tmp/NVIDIA-Linux-x86_64-535.54.03.run
# Install the NVIDIA driver with the –dkms option to enable Dynamic Kernel Module Support
RUN /tmp/NVIDIA-Linux-x86_64-535.54.03.run –dkms –silent
# Cleanup
RUN rm /tmp/NVIDIA-Linux-x86_64-535.54.03.run
# Continue with the rest of your Dockerfile, installing other dependencies and setting up your application
“`
Replace `NVIDIA-Linux-x86_64-535.54.03.run` with the correct filename if needed. This Dockerfile will build a container with the specified NVIDIA driver version. When you run containers based on this image, they will have access to the GPU and use the specific driver version you installed during the build process.
Again, remember that using a specific NVIDIA driver inside a Docker container is typically for development and testing purposes. For production environments, consider using NVIDIA GPU Cloud (NGC) containers or other officially supported container solutions by NVIDIA.
so close…3.845
3.845 ERROR: Unable to find the module utility `modprobe`; please make sure you have the package ‘module-init-tools’ or ‘kmod’ installed. If you do have ‘module-init-tools’ or ‘kmod’ installed, then please check that `modprobe` is in your PATH.
3.845
——
Dockerfile:24
——————–
22 |
23 | # Install the NVIDIA driver with the –dkms option to enable Dynamic Kernel Module Support
24 | >>> RUN /tmp/NVIDIA-Linux-x86_64-535.54.03.run –dkms –silent
25 |
26 | # Cleanup
——————–
ERROR: failed to solve: process “/bin/sh -c /tmp/NVIDIA-Linux-x86_64-535.54.03.run –dkms –silent” did not complete successfully: exit code: 1
The error you encountered indicates that the “modprobe” utility is not found in the container. The “modprobe” command is essential for loading kernel modules, which is required during the installation of the NVIDIA driver with DKMS support.
To fix this, you need to install the “kmod” package inside your Docker container. Here’s an updated version of your Dockerfile that includes the installation of the “kmod” package:
“`Dockerfile
# Use the base image that includes the necessary CUDA libraries
FROM nvidia/cuda:12.2.0-devel-ubuntu22.04
# Install necessary dependencies for building and installing the NVIDIA driver
RUN apt-get update && apt-get install -y –no-install-recommends
build-essential
kmod
&& rm -rf /var/lib/apt/lists/*
# Copy the NVIDIA driver installer into the container
COPY NVIDIA-Linux-x86_64-535.54.03.run /tmp/
# Set executable permissions for the NVIDIA driver installer
RUN chmod +x /tmp/NVIDIA-Linux-x86_64-535.54.03.run
# Install the NVIDIA driver with the –dkms option to enable Dynamic Kernel Module Support
RUN /tmp/NVIDIA-Linux-x86_64-535.54.03.run –dkms –silent
# Cleanup
RUN rm /tmp/NVIDIA-Linux-x86_64-535.54.03.run
# Continue with the rest of your Dockerfile, installing other dependencies and setting up your application
“`
With this updated Dockerfile, the container should now be able to find the “modprobe” utility and successfully install the NVIDIA driver with DKMS support.
733 WARNING: You do not appear to have an NVIDIA GPU supported by the 535.54.03 NVIDIA Linux graphics driver installed in this system. For further details, please see the appendix SUPPORTED NVIDIA GRAPHICS CHIPS in the README available on the Linux driver download page at www.nvidia.com.
3.733
3.761
3.761 ERROR: Unable to find the kernel source tree for the currently running kernel. Please make sure you have installed the kernel source files for your kernel and that they are properly configured; on Red Hat Linux systems, for example, be sure you have the ‘kernel-source’ or ‘kernel-devel’ RPM installed. If you know the correct kernel source files are installed, you may specify the kernel source
path with the ‘–kernel-source-path’ command line option.
3.761
3.761
3.761 ERROR: Installation has failed. Please see the file ‘/var/log/nvidia-installer.log’ for details. You may find suggestions on fixing installation problems in the README available on the Linux driver download page at www.nvidia.com.
3.761
——
Dockerfile:30
——————–
28 |
29 | # Install the NVIDIA driver with the –dkms option to enable Dynamic Kernel Module Support
30 | >>> RUN /tmp/NVIDIA-Linux-x86_64-535.54.03.run –dkms –silent
31 |
32 | # Cleanup
——————–
ERROR: failed to solve: process “/bin/sh -c /tmp/NVIDIA-Linux-x86_64-535.54.03.run –dkms –silent” did not complete successfully: exit code: 1
It seems that the error persists. The issue might be that the kernel source tree for the currently running kernel is missing in the Docker container. To resolve this, you’ll need to ensure the kernel headers and development packages are installed in the container.
Let’s modify the Dockerfile to include the installation of the necessary kernel packages:
“`Dockerfile
# Use the base image that includes the necessary CUDA libraries
FROM nvidia/cuda:12.2.0-devel-ubuntu22.04
# Install necessary dependencies for building and installing the NVIDIA driver
RUN apt-get update && apt-get install -y –no-install-recommends
build-essential
kmod
linux-headers-generic
&& rm -rf /var/lib/apt/lists/*
# Copy the NVIDIA driver installer into the container
COPY NVIDIA-Linux-x86_64-535.54.03.run /tmp/
# Set executable permissions for the NVIDIA driver installer
RUN chmod +x /tmp/NVIDIA-Linux-x86_64-535.54.03.run
# Install the NVIDIA driver with the –dkms option to enable Dynamic Kernel Module Support
RUN /tmp/NVIDIA-Linux-x86_64-535.54.03.run –dkms –silent
# Cleanup
RUN rm /tmp/NVIDIA-Linux-x86_64-535.54.03.run
# Continue with the rest of your Dockerfile, installing other dependencies and setting up your application
“`
With this updated Dockerfile, we’ve added the installation of “linux-headers-generic,” which provides the necessary kernel headers for building kernel modules. After these changes, try building the Docker image again. If there are any other issues, please let me know, and we’ll work on resolving them.
3.688
3.688 WARNING: You do not appear to have an NVIDIA GPU supported by the 535.54.03 NVIDIA Linux graphics driver installed in this system. For further details, please see the appendix SUPPORTED NVIDIA GRAPHICS CHIPS in the README available on the Linux driver download page at www.nvidia.com.
3.688
3.715
3.715 ERROR: Unable to find the kernel source tree for the currently running kernel. Please make sure you have installed the kernel source files for your kernel and that they are properly configured; on Red Hat Linux systems, for example, be sure you have the ‘kernel-source’ or ‘kernel-devel’ RPM installed. If you know the correct kernel source files are installed, you may specify the kernel source
path with the ‘–kernel-source-path’ command line option.
3.715
3.715
3.715 ERROR: Installation has failed. Please see the file ‘/var/log/nvidia-installer.log’ for details. You may find suggestions on fixing installation problems in the README available on the Linux driver download page at www.nvidia.com.
3.715
——
Dockerfile:31
——————–
29 |
30 | # Install the NVIDIA driver with the –dkms option to enable Dynamic Kernel Module Support
31 | >>> RUN /tmp/NVIDIA-Linux-x86_64-535.54.03.run –dkms –silent
32 |
33 | # Cleanup
——————–
I apologize for the inconvenience. It seems that the issue persists. It’s possible that the driver installation process is not detecting the GPU correctly within the Docker container.
Since the P40 GPU is accessible on your host system and you are using WSL2, an alternative approach would be to use the NVIDIA GPU in your WSL2 environment directly without installing the NVIDIA driver inside the Docker container.
Here are the general steps to achieve this:
1. Make sure you have the latest WSL2 and Docker installed on your Windows 10 machine.
2. Install the NVIDIA driver on your Windows 10 machine, ensuring that it works with your P40 GPU.
3. Enable WSL2 on your Windows 10 machine and install an Ubuntu or Debian distribution in your WSL2 environment.
4. Install the NVIDIA CUDA toolkit and the necessary libraries in your WSL2 environment to use the GPU.
5. Run your GPU-accelerated applications directly inside the WSL2 environment, without the need to install the driver inside the Docker container.
By using this approach, you can leverage the GPU acceleration capabilities of your P40 GPU directly in your WSL2 environment without dealing with the complexities of installing the driver inside the Docker container.
Please let me know if you need further assistance or if you encounter any issues during this process.
You made no sense at all. I think you logic has flaws
I apologize if my previous response was unclear. Let me clarify the steps for you:
1. **Enable WSL2**: Ensure that you have WSL2 enabled on your Windows 10 machine. You can follow the official Microsoft documentation on how to do this: [Windows Subsystem for Linux Installation Guide for Windows 10](https://docs.microsoft.com/en-us/windows/wsl/install).
2. **Install NVIDIA Driver on Windows**: Install the correct NVIDIA driver for your GPU on your Windows 10 machine. This will enable WSL2 to access the GPU for GPU-accelerated tasks.
3. **Install WSL Distribution**: Install an Ubuntu or Debian distribution in your WSL2 environment. You can do this from the Microsoft Store or by downloading the distribution package and manually installing it.
4. **Accessing GPU from WSL2**: Once you have your WSL2 environment set up, you can directly access the NVIDIA GPU from within the WSL2 environment. This means you can run GPU-accelerated applications directly in WSL2 without installing the NVIDIA driver inside the Docker container.
5. **Install CUDA Toolkit and Libraries**: Inside your WSL2 environment, you’ll need to install the NVIDIA CUDA toolkit and necessary libraries to use the GPU for computation. You can follow NVIDIA’s official documentation for installing the CUDA toolkit in Linux: [CUDA Installation Guide for Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html).
By following these steps, you can harness the power of your NVIDIA P40 GPU directly in your WSL2 environment without the need to install the NVIDIA driver inside the Docker container. This approach simplifies the setup and avoids the complexities of dealing with drivers inside containers.
If you have any specific questions or encounter issues during this process, feel free to ask for further assistance.
BUT TH DOCKER IS UNABLE TO
CIOMM
UN
ICATE
W
ITh the env outside of it!!!!!!!!!!!!!!!!!
Apologies for the confusion. I understand your frustration. To enable communication between Docker running in WSL2 and the GPU drivers on the host Windows system, you need to set up the necessary configurations.
Here’s what you can do to enable Docker in WSL2 to access the GPU:
1. **WSL2 Configuration**: Ensure you have WSL2 set up correctly, and your Linux distribution (e.g., Ubuntu) is running in WSL2 mode.
2. **Install NVIDIA Docker**: NVIDIA provides a Docker plugin called “nvidia-docker” that allows Docker containers in WSL2 to access the host GPU. You can follow the official documentation for installing nvidia-docker: [NVIDIA Docker GitHub](https://github.com/NVIDIA/nvidia-docker).
3. **Test GPU Access**: Once nvidia-docker is installed, you can test GPU access from Docker containers. For example, you can run the “nvidia-smi” command inside a Docker container to see if it can access the GPU.
Here are some general steps you can follow to install nvidia-docker in WSL2:
1. Install Docker in your WSL2 environment. You can use the official Docker documentation for installing Docker in Ubuntu or Debian: [Install Docker Engine on Ubuntu](https://docs.docker.com/engine/install/ubuntu/).
2. Install NVIDIA drivers on your Windows host machine. Make sure you have the latest NVIDIA drivers installed, and the GPU is properly detected on your Windows system.
3. Install nvidia-docker using the following commands in your WSL2 terminal:
“`bash
# Add the NVIDIA Docker repository to your package manager
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo gpg –dearmor -o /usr/share/keyrings/nvidia-archive-keyring.gpg
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
# Update package lists and install nvidia-docker
sudo apt-get update
sudo apt-get install -y nvidia-docker2
# Restart Docker daemon to complete the setup
sudo systemctl restart docker
“`
4. After installing nvidia-docker, you can test GPU access by running the following command inside a Docker container:
“`bash
docker run –gpus all nvidia/cuda:12.2.0-devel-ubuntu20.04 nvidia-smi
“`
If everything is set up correctly, this command should show the GPU information from inside the Docker container.
Please note that enabling GPU access from Docker inside WSL2 might have some limitations and complexities due to the underlying technology. If you encounter any issues during this process, feel free to ask for further assistance.
ok. Lets try THIS driver and see if it install : 536.25-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe
It seems like the driver file “536.25-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe” you mentioned is a Windows driver file, not a Linux driver file. Please note that you cannot install a Windows driver directly in a Linux environment.
For NVIDIA GPU support in Linux, you need to install the appropriate Linux drivers and libraries. Since you are using WSL2 with Ubuntu or Debian, you need to install the NVIDIA drivers for Linux inside your Linux distribution running in WSL2.
Here are the steps to install the NVIDIA drivers in Ubuntu or Debian inside WSL2:
1. Update your package lists and install the necessary dependencies:
“`bash
sudo apt-get update
sudo apt-get install -y build-essential
“`
2. Install the NVIDIA drivers and CUDA toolkit:
For Ubuntu 20.04:
“`bash
sudo apt-get install -y nvidia-driver-510
sudo apt-get install -y nvidia-cuda-toolkit
“`
For Debian 10:
“`bash
sudo apt-get install -y nvidia-driver nvidia-cuda-toolkit
“`
3. Reboot your system to load the new NVIDIA drivers.
4. Verify that the drivers are correctly installed and the GPU is recognized:
“`bash
nvidia-smi
“`
If the installation is successful, “nvidia-smi” should display information about your GPU.
Please make sure you have the correct drivers for your specific GPU model, and always use Linux drivers when running applications inside the Linux environment of WSL2. Windows drivers are not compatible with WSL2 Linux distributions.
in here:
# Set executable permissions for the NVIDIA driver installer
RUN chmod +x /tmp/NVIDIA-Linux-x86_64-535.54.03.run
# Install the NVIDIA driver with the –dkms option to enable Dynamic Kernel Module Support
RUN /tmp/NVIDIA-Linux-x86_64-535.54.03.run –dkms –silent
The provided Dockerfile snippet appears to be attempting to install the NVIDIA driver inside a Docker container. However, there are some potential issues with this approach:
1. Docker containers run on top of the host operating system, and the host operating system in this case is Windows. The NVIDIA driver you are trying to install (“NVIDIA-Linux-x86_64-535.54.03.run”) is a Linux driver, and it cannot be installed directly inside a Windows environment.
2. Even if you were using a Linux host, installing NVIDIA drivers inside a Docker container can be challenging and may not work as expected. Docker containers are designed to be lightweight, isolated environments that do not have direct access to hardware devices like GPUs.
To utilize NVIDIA GPUs from within a Docker container on a Windows host with WSL2, you can use the NVIDIA Container Toolkit, which provides seamless integration of the NVIDIA GPU with Docker containers in WSL2. Here’s how you can set it up:
1. Make sure you have WSL2 installed on your Windows system.
2. Install Docker Desktop for Windows, and ensure you have WSL2 integration enabled in Docker settings.
3. Install the NVIDIA Container Toolkit within your WSL2 Linux distribution. For Ubuntu, you can use the following commands:
“`bash
# Install dependencies
sudo apt-get update
sudo apt-get install -y apt-transport-https ca-certificates curl software-properties-common
# Add the NVIDIA apt repository and key
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo gpg –dearmor -o /usr/share/keyrings/nvidia-container-runtime-keyring.gpg
echo “deb [signed-by=/usr/share/keyrings/nvidia-container-runtime-keyring.gpg] https://nvidia.github.io/libnvidia-container/ubuntu20.04/amd64 /” | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list
# Install the toolkit
sudo apt-get update
sudo apt-get install -y nvidia-container-runtime
“`
4. Restart your WSL2 session.
5. Test the NVIDIA GPU access by running the `nvidia-smi` command inside the WSL2 terminal. It should display information about the NVIDIA GPU.
Now, when you run Docker containers with GPU support, they should have access to the NVIDIA GPU on your Windows host.
Remember that using NVIDIA GPUs from within WSL2 Docker containers requires the NVIDIA Container Toolkit and specific Docker runtime configurations, as WSL2 does not have direct access to the GPU on the host system. The NVIDIA Container Toolkit provides the necessary integration for seamless GPU access.
No, thats not installling the driver file within the codeblock I gave you
Apologies for the misunderstanding. You are correct; the code block you provided is attempting to install the NVIDIA driver directly within the Docker container. However, as mentioned earlier, this approach might not work as expected, and installing the NVIDIA driver inside a Docker container is not a recommended practice.
Instead, to utilize the NVIDIA GPU from within a Docker container running on WSL2, you should use the NVIDIA Container Toolkit as described in the previous response. The toolkit provides proper integration for GPU access within Docker containers on WSL2, without the need to install the NVIDIA driver directly inside the container.
To summarize the steps:
1. Set up WSL2 and Docker Desktop on your Windows host.
2. Install the NVIDIA Container Toolkit within your WSL2 Linux distribution using the provided commands.
3. Restart your WSL2 session.
4. Test GPU access by running `nvidia-smi` inside the WSL2 terminal to ensure the NVIDIA GPU is recognized.
5. Run your Docker containers with GPU support, and they should automatically have access to the NVIDIA GPU on your Windows host.
Using the NVIDIA Container Toolkit is a more robust and recommended approach to utilize GPU resources from within Docker containers on WSL2.
# Set executable permissions for the NVIDIA driver installer
##REMOVE THIS:
RUN chmod +x /tmp/NVIDIA-Linux-x86_64-535.54.03.run
##ADD THIS: 536.25-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe
##CHANGE WHAT IS NEEDED TO RUN THE INSTALLER
# Install the NVIDIA driver with the –dkms option to enable Dynamic Kernel Module Support
RUN /tmp/NVIDIA-Linux-x86_64-535.54.03.run –dkms –silent
# Cleanup
RUN rm /tmp/NVIDIA-Linux-x86_64-535.54.03.run
It appears there might be some confusion. The Dockerfile you provided contains commands to install the NVIDIA driver for Linux (`NVIDIA-Linux-x86_64-535.54.03.run`), but you mentioned trying to use a Windows driver (`536.25-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe`) within the container.
If you are running Docker on a Windows host and want to use the NVIDIA GPU from within the Docker container, you need to use the NVIDIA Container Toolkit for Windows, not the Linux driver. The `536.25-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe` file is a Windows driver and cannot be used directly inside a Linux-based Docker container.
To use the NVIDIA GPU with Docker on WSL2, follow these steps:
1. Set up WSL2 and Docker Desktop on your Windows host.
2. Install the NVIDIA Container Toolkit within your WSL2 Linux distribution using the provided commands (for example, `wsl –install -d Ubuntu-20.04`).
3. Restart your WSL2 session.
4. Test GPU access by running `nvidia-smi` inside the WSL2 terminal to ensure the NVIDIA GPU is recognized.
5. Run your Docker containers with GPU support, and they should automatically have access to the NVIDIA GPU on your Windows host.
If you have a specific reason to use a Windows driver within the container, please provide more context or details about your use case, and I’ll do my best to assist you accordingly.
Ok. I have an nvidia docker avaiable now how do I let one contianer use that GPU?
To enable a Docker container to use the NVIDIA GPU, you’ll need to follow these steps:
1. Install Docker with NVIDIA GPU support: First, make sure you have Docker installed with NVIDIA GPU support. You can do this by installing the NVIDIA Container Toolkit on your system. Follow the installation instructions for your specific OS:
– Linux: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian
– Windows: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-windows
– macOS: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-macos
2. Test Docker GPU support: After installing the NVIDIA Container Toolkit, verify that Docker can detect the GPU by running `docker run –gpus all nvidia/cuda:11.0-base nvidia-smi`. This will pull the official CUDA base image and execute `nvidia-smi` inside the container. If it runs successfully and shows GPU information, Docker is configured correctly.
3. Create a Dockerfile for your application: Build a Docker image for your application, or if you already have one, make sure to use the base image that supports GPU access. For example, you can use `nvidia/cuda` as your base image.
4. Run the container with GPU support: When starting your container, make sure to use the `–gpus` option to specify which GPUs the container should have access to. For example:
“`
docker run –gpus all -d your_image_name
“`
The `–gpus all` option tells Docker to give the container access to all available GPUs. You can also specify specific GPU IDs or GPU UUIDs instead of `all` if you want to restrict access to certain GPUs.
That’s it! Now your Docker container should have access to the NVIDIA GPU, and you can run applications that utilize CUDA or other GPU-accelerated libraries within the container.
docker run –gpus all -d your_image_name
+
Apologies for the confusion. The command I provided is just a template, and you need to replace `your_image_name` with the actual name of the Docker image you want to run with GPU support. Here’s the full command again, assuming you have built a Docker image named `my_gpu_app`:
“`
docker run –gpus all -d my_gpu_app
“`
Replace `my_gpu_app` with the actual name of your Docker image. This command will run the container in the background (`-d`) and give it access to all available GPUs (`–gpus all`). The container will be started with GPU support, and you can execute GPU-accelerated applications within the container.
super__tgwui
atinoda/text-generation-webui:llama-cublas
d905fb0c7afb
5000:5000
5001:5001
It seems like you have provided some container details. To run a Docker container with GPU support and expose specific ports, you can use the following command:
“`
docker run –gpus all -d -p 5000:5000 -p 5001:5001 atinoda/text-generation-webui:llama-cublas
“`
Explanation of the command:
– `docker run`: Starts a new container.
– `–gpus all`: Enables GPU support for the container.
– `-d`: Runs the container in the background (detached mode).
– `-p 5000:5000`: Exposes port 5000 on the host system and maps it to port 5000 inside the container.
– `-p 5001:5001`: Exposes port 5001 on the host system and maps it to port 5001 inside the container.
– `atinoda/text-generation-webui:llama-cublas`: The name of the Docker image to run (replace this with the actual image name).
Make sure you have the correct Docker image (`atinoda/text-generation-webui:llama-cublas`) available on your system before running the command. This will start the container with GPU support and expose ports 5000 and 5001 on the host system, which you can use to access the services running inside the container.
I can runb containers with these options: Run a new container
atinoda/text-generation-webui:llama-cublas
Container name
A random name is generated if you do not provide one.
Ports
Enter “0” to assign randomly generated host ports.
Host port
:5000/tcp
Host port
:5005/tcp
Host port
:7860/tcp
Volumes
Host path
Container path
Environment variables
Variable
Value
Great! It looks like you are using a user interface or a tool to run Docker containers and providing various options for the container configuration. Here’s what you can do based on the options you have:
1. Image: Use the image name `atinoda/text-generation-webui:llama-cublas` to run the container.
2. Container Name: You can either provide a custom name for your container or leave it blank to have a random name generated for you.
3. Ports: You are exposing ports 5000, 5005, and 7860 from the container to the host system. This means you can access services running inside the container using these ports on the host machine.
4. Volumes: If you want to mount specific host directories or files into the container, you can specify them here. This allows data to be shared between the host and the container.
5. Environment Variables: If the application inside the container requires specific environment variables to be set, you can provide them here.
To run the container with the provided options, simply click on the “Run” or “Create” button (or any equivalent option depending on the user interface you are using). This will start the container based on the configuration you have specified.
SO I can address where the models directory is. Which container to run with this as well?
To address the `models` directory inside the container, you can use Docker’s volume option. This will allow you to map a directory on the host system to a directory inside the container, providing easy access to the models directory and enabling data sharing between the two.
You can use the `-v` or `–volume` flag with the `docker run` command to specify the volume mapping. Here’s the syntax:
“`bash
docker run -d
-v /path/to/host/models:/path/inside/container/models
-p 5000:5000
-p 5001:5001
-p 7860:7860
–name container_name
atinoda/text-generation-webui:llama-cublas
“`
In this command, replace `/path/to/host/models` with the actual path to the `models` directory on your host system, and `/path/inside/container/models` with the path to the models directory inside the container.
By using this volume mapping, any files or data present in the host’s `/path/to/host/models` directory will be accessible inside the container at `/path/inside/container/models`. Similarly, any data written to `/path/inside/container/models` within the container will be stored in the host’s `/path/to/host/models` directory.
This way, you can easily work with the models directory both from your host system and from within the running container.
All are stopped now. Since I can’t run tgwui with attaching it to the gpu, lets create a docker-compose up command to launch the other containers normally and the tgwui with parameters to use the gpu
To launch the other containers normally and the `tgwui` container with parameters to use the GPU, you can create a `docker-compose.yml` file. The `docker-compose.yml` file defines the services you want to run and their configurations. Here’s an example of how you can structure your `docker-compose.yml` file:
“`yaml
version: ‘3.8’
services:
webui:
image: atinoda/text-generation-webui:llama-cublas
ports:
– “5000:5000”
– “5001:5001”
– “7860:7860”
volumes:
– /path/to/host/models:/path/inside/container/models
environment:
# Add any environment variables specific to the webui container here
other_container1:
# Configuration for other_container1
# Add other services as needed
# Add a new service for tgwui that uses the GPU
tgwui_gpu:
image: atinoda/text-generation-webui:llama-cublas
runtime: nvidia
ports:
– “5000:5000”
– “5001:5001”
– “7860:7860”
volumes:
– /path/to/host/models:/path/inside/container/models
environment:
# Add any environment variables specific to the tgwui_gpu container here
“`
In this `docker-compose.yml` file:
1. The `webui` service runs the `atinoda/text-generation-webui:llama-cublas` image without GPU access.
2. The `other_container1` service is a placeholder for other services you may have that don’t require GPU access.
3. The `tgwui_gpu` service runs the `atinoda/text-generation-webui:llama-cublas` image with GPU access. The `runtime: nvidia` line enables GPU support for this service.
To start all the services defined in the `docker-compose.yml` file, run the following command in the directory where the `docker-compose.yml` file is located:
“`bash
docker-compose up -d
“`
This command will start all the containers in the background. The containers will be named according to their service names defined in the `docker-compose.yml` file.
Note: Make sure to replace `/path/to/host/models` with the actual path to the `models` directory on your host system, and `/path/inside/container/models` with the path to the models directory inside the containers. Additionally, adjust any other environment variables and configurations specific to each service according to your requirements.
add the proposal to this script change nothing else without asking: version: ‘3.8’
services:
backend:
volumes:
– “./:/app”
build: .
ports:
– “8001:8001”
depends_on:
– super__tgwui
– super__redis
– super__postgres
networks:
– super_network
celery:
volumes:
– “./:/app”
build:
context: .
dockerfile: DockerfileCelery
depends_on:
– super__tgwui
– super__redis
– super__postgres
networks:
– super_network
gui:
build: ./gui
ports:
– “3000:3000″
environment:
– NEXT_PUBLIC_API_BASE_URL=http://localhost:8001
networks:
– super_network
volumes:
– ./gui:/app
– /app/node_modules
– /app/.next
super__tgwui:
build:
context: ./tgwui/
target: llama-cublas
dockerfile: DockerfileTGWUI
# args:
# – LCL_SRC_DIR=text-generation-webui # Developers – see Dockerfile app_base
image: atinoda/text-generation-webui:llama-cublas # Specify variant as the :tag
container_name: super__tgwui
environment:
– EXTRA_LAUNCH_ARGS=”–no-mmap –verbose –extensions openai –auto-devices –n_ctx 2000 –gpu-memory 22 –n-gpu-layers 2000 –threads 8″
# – BUILD_EXTENSIONS_LIVE=”silero_tts whisper_stt” # Install named extensions during every container launch. THIS WILL SIGNIFICANLTLY SLOW LAUNCH TIME.
ports:
– 7860:7860 # Default web port
– 5000:5000 # Default API port
– 5005:5005 # Default streaming port
– 5001:5001 # Default OpenAI API extension port
volumes:
– ./tgwui/config/loras:/app/loras
– ./tgwui/config/models:/app/models
– ./tgwui/config/presets:/app/presets
– ./tgwui/config/prompts:/app/prompts
– ./tgwui/config/softprompts:/app/softprompts
– ./tgwui/config/training:/app/training
# – ./config/extensions:/app/extensions
logging:
driver: json-file
options:
max-file: “3” # number of files or file count
max-size: ’10m’
networks:
– super_network
deploy:
resources:
reservations:
devices:
– driver: nvidia
# count: “all”
device_ids: [‘0’, ‘1’] # must comment the above line if this line is uncommented.
capabilities: [gpu]
super__redis:
image: “docker.io/library/redis:latest”
networks:
– super_network
super__postgres:
image: “docker.io/library/postgres:latest”
environment:
– POSTGRES_USER=superagi
– POSTGRES_PASSWORD=password
– POSTGRES_DB=super_agi_main
volumes:
– superagi_postgres_data:/var/lib/postgresql/data/
networks:
– super_network
ports:
– “5432:5432”
networks:
super_network:
driver: bridge
volumes:
superagi_postgres_data:
Version: ‘3.8’
services:
backend:
volumes:
– “./:/app”
build: .
ports:
– “8001:8001”
depends_on:
– super__tgwui
– super__redis
– super__postgres
networks:
– super_network
celery:
volumes:
– “./:/app”
build:
context: .
dockerfile: DockerfileCelery
depends_on:
– super__tgwui
– super__redis
– super__postgres
networks:
– super_network
gui:
build: ./gui
ports:
– “3000:3000″
environment:
– NEXT_PUBLIC_API_BASE_URL=http://localhost:8001
networks:
– super_network
volumes:
– ./gui:/app
– /app/node_modules
– /app/.next
super__tgwui:
build:
context: ./tgwui/
target: llama-cublas
dockerfile: DockerfileTGWUI
# args:
# – LCL_SRC_DIR=text-generation-webui # Developers – see Dockerfile app_base
image: atinoda/text-generation-webui:llama-cublas # Specify variant as the :tag
container_name: super__tgwui
environment:
– EXTRA_LAUNCH_ARGS=”–no-mmap –verbose –extensions openai –auto-devices –n_ctx 2000 –gpu-memory 22 –n-gpu-layers 2000 –threads 8″
– NVIDIA_VISIBLE_DEVICES=all # Proposal: Add this line to specify all available GPUs for tgwui
# – BUILD_EXTENSIONS_LIVE=”silero_tts whisper_stt” # Install named extensions during every container launch. THIS WILL SIGNIFICANTLY SLOW LAUNCH TIME.
ports:
– 7860:7860 # Default web port
– 5000:5000 # Default API port
– 5005:5005 # Default streaming port
– 5001:5001 # Default OpenAI API extension port
volumes:
– ./tgwui/config/loras:/app/loras
– ./tgwui/config/models:/app/models
– ./tgwui/config/presets:/app/presets
– ./tgwui/config/prompts:/app/prompts
– ./tgwui/config/softprompts:/app/softprompts
– ./tgwui/config/training:/app/training
# – ./config/extensions:/app/extensions
logging:
driver: json-file
options:
max-file: “3” # number of files or file count
max-size: ’10m’
networks:
– super_network
deploy:
resources:
reservations:
devices:
– driver: nvidia
# count: “all”
device_ids: [‘0’, ‘1’] # must comment the above line if this line is uncommented.
capabilities: [gpu]
super__redis:
image: “docker.io/library/redis:latest”
networks:
– super_network
super__postgres:
image: “docker.io/library/postgres:latest”
environment:
– POSTGRES_USER=superagi
– POSTGRES_PASSWORD=password
– POSTGRES_DB=super_agi_main
volumes:
– superagi_postgres_data:/var/lib/postgresql/data/
networks:
– super_network
ports:
– “5432:5432”
networks:
super_network:
driver: bridge
volumes:
superagi_postgres_data:
This is almost completely unreadable and formatted wrong
I apologize for the formatting issues. Here’s the modified docker-compose file with the proposal:
“`yaml
version: ‘3.8’
services:
backend:
volumes:
– “./:/app”
build: .
ports:
– “8001:8001”
depends_on:
– super__tgwui
– super__redis
– super__postgres
networks:
– super_network
celery:
volumes:
– “./:/app”
build:
context: .
dockerfile: DockerfileCelery
depends_on:
– super__tgwui
– super__redis
– super__postgres
networks:
– super_network
gui:
build: ./gui
ports:
– “3000:3000″
environment:
– NEXT_PUBLIC_API_BASE_URL=http://localhost:8001
networks:
– super_network
volumes:
– ./gui:/app
– /app/node_modules
– /app/.next
super__tgwui:
build:
context: ./tgwui/
target: llama-cublas
dockerfile: DockerfileTGWUI
image: atinoda/text-generation-webui:llama-cublas
container_name: super__tgwui
environment:
– EXTRA_LAUNCH_ARGS=”–no-mmap –verbose –extensions openai –auto-devices –n_ctx 2000 –gpu-memory 22 –n-gpu-layers 2000 –threads 8″
– NVIDIA_VISIBLE_DEVICES=all # Proposal: Add this line to specify all available GPUs for tgwui
ports:
– 7860:7860 # Default web port
– 5000:5000 # Default API port
– 5005:5005 # Default streaming port
– 5001:5001 # Default OpenAI API extension port
volumes:
– ./tgwui/config/loras:/app/loras
– ./tgwui/config/models:/app/models
– ./tgwui/config/presets:/app/presets
– ./tgwui/config/prompts:/app/prompts
– ./tgwui/config/softprompts:/app/softprompts
– ./tgwui/config/training:/app/training
logging:
driver: json-file
options:
max-file: “3” # number of files or file count
max-size: ’10m’
networks:
– super_network
deploy:
resources:
reservations:
devices:
– driver: nvidia
device_ids: [‘0’, ‘1’] # must comment the above line if this line is uncommented.
capabilities: [gpu]
super__redis:
image: “docker.io/library/redis:latest”
networks:
– super_network
super__postgres:
image: “docker.io/library/postgres:latest”
environment:
– POSTGRES_USER=superagi
– POSTGRES_PASSWORD=password
– POSTGRES_DB=super_agi_main
volumes:
– superagi_postgres_data:/var/lib/postgresql/data/
networks:
– super_network
ports:
– “5432:5432”
networks:
super_network:
driver: bridge
volumes:
superagi_postgres_data:
“`
The proposal includes adding the `NVIDIA_VISIBLE_DEVICES=all` environment variable to the `super__tgwui` service, which specifies all available GPUs for that container.
what is the code to run an exe file from within a docker?
To run an executable (`.exe`) file from within a Docker container, you can use the `CMD` or `ENTRYPOINT` instruction in your Dockerfile. Here’s how you can do it:
1. Add the executable file to your Docker image:
– You can copy the executable to the desired location inside the container using the `COPY` instruction in your Dockerfile.
2. Define the `CMD` or `ENTRYPOINT` instruction in your Dockerfile to execute the executable:
– The `CMD` instruction specifies the default command to run when the container starts.
– The `ENTRYPOINT` instruction is similar to `CMD` but allows you to define a command that cannot be overridden easily.
Here’s an example Dockerfile:
“`Dockerfile
# Use a base image with the necessary dependencies for running the executable
FROM ubuntu:latest
# Copy the executable to the desired location inside the container
COPY your_executable.exe /usr/local/bin/
# Set the working directory (optional)
WORKDIR /usr/local/bin/
# Define the command to execute the executable
CMD [“./your_executable.exe”]
# Or use ENTRYPOINT if you want to make the executable unchangeable
# ENTRYPOINT [“./your_executable.exe”]
“`
Make sure to replace `your_executable.exe` with the actual name of your executable file. Also, ensure that the executable is compatible with the base image you are using in the Dockerfile.
After building your Docker image, you can run the container, and it will automatically execute the specified executable when it starts.
wite a codeblock to launch this file:536.25-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe
It’s important to note that running Windows executables directly inside a Linux-based Docker container is not natively supported. Docker containers are typically designed to run on the same operating system as the host machine, which means running Windows executables inside a Linux container can be challenging.
However, there are workarounds to achieve this using additional tools like Wine, which allows running Windows applications on Linux. Here’s how you could attempt to run a Windows executable inside a Linux Docker container using Wine:
1. Install Wine in the container:
First, modify your Dockerfile to include Wine installation. You can use the WineHQ APT repository to install Wine in the container.
“`Dockerfile
FROM ubuntu:latest
# Install Wine and other necessary dependencies
RUN dpkg –add-architecture i386 &&
apt-get update &&
apt-get install -y wine wine32
# Copy the Windows executable to the container
COPY 536.25-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe /app/
# Set the working directory
WORKDIR /app/
# Run the executable using Wine
CMD [“wine”, “536.25-data-center-tesla-desktop-win10-win11-64bit-dch-international.exe”]
“`
2. Build the Docker image:
“`
docker build -t my_wine_container .
“`
3. Run the Docker container:
“`
docker run -it –rm my_wine_container
“`
Please note that running graphical applications with GUI interfaces inside a Docker container may require additional setup and configurations. If the Windows executable requires a graphical environment, you may need to install an X server and configure it to enable GUI forwarding. In that case, consider using tools like `xvfb` or similar to run headless graphical applications.
Keep in mind that using Wine in Docker introduces complexity and may not always work as expected. It’s generally more straightforward to run Windows executables natively on a Windows machine rather than in a Linux container.
raise RuntimeError(CUDA_MISMATCH_MESSAGE.format(cuda_str_version, torch.version.cuda))
1.794 RuntimeError:
1.794 The detected CUDA version (12.2) mismatches the version that was used to compile
1.794 PyTorch (11.7). Please make sure to use the same CUDA versions.
1.794
——
Dockerfile:73
——————–
71 | # Build and install default GPTQ (‘quant_cuda’)
72 | ARG TORCH_CUDA_ARCH_LIST=”6.1;7.0;7.5;8.0;8.6+PTX”
73 | >>> RUN cd /app/repositories/GPTQ-for-LLaMa/ && python3 setup_cuda.py install
74 |
75 | FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 AS base
——————–
ERROR: failed to solve: process “/bin/sh -c cd /app/repositories/GPTQ-for-LLaMa/ && python3 setup_cuda.py install” did not complete successfully: exit code: 1
The error message indicates a mismatch between the CUDA versions used in PyTorch and the detected CUDA version on the system. To resolve this issue, you need to ensure that PyTorch and the installed CUDA version are compatible. In your Dockerfile, you are using CUDA version 11.8.0, but the detected CUDA version is 12.2.
To fix this, you have a few options:
1. Use PyTorch with the matching CUDA version: Install a version of PyTorch that is compatible with CUDA 12.2. You can check the official PyTorch website or the documentation to find the appropriate version.
2. Use a different CUDA version in the Docker image: If you need to stick with the current version of PyTorch, consider using a Docker image that has CUDA 11.7 (matching the PyTorch version) or compile PyTorch with CUDA 12.2 from source within your Docker image.
Here’s an example of how to change the base image in your Dockerfile to use CUDA 11.7:
“`Dockerfile
# Set the base image with CUDA 11.7
FROM nvidia/cuda:11.7.0-devel-ubuntu22.04 AS base
# Install necessary dependencies and set up your environment
…
# Build and install default GPTQ (‘quant_cuda’)
ARG TORCH_CUDA_ARCH_LIST=”6.1;7.0;7.5;8.0;8.6+PTX”
RUN cd /app/repositories/GPTQ-for-LLaMa/ && python3 setup_cuda.py install
# Continue with the rest of your Dockerfile
…
“`
Remember to adjust other parts of your Dockerfile if needed to ensure compatibility with CUDA 11.7. Once you have made the changes, rebuild the Docker image:
“`
docker build -t my_gptq_image .
“`
And then run your container:
“`
docker run -it –gpus all my_gptq_image
“`
Make sure to choose an appropriate solution based on your application’s requirements and compatibility constraints.
ARe these choices or option or requires? # VARIANT BUILDS
FROM base AS cuda
RUN echo “CUDA” >> /variant.txt
RUN apt-get install –no-install-recommends -y git python3-dev python3-pip
RUN rm -rf /app/repositories/GPTQ-for-LLaMa &&
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda /app/repositories/GPTQ-for-LLaMa
RUN pip3 uninstall -y quant-cuda &&
sed -i ‘s/^safetensors==0.3.0$/safetensors/g’ /app/repositories/GPTQ-for-LLaMa/requirements.txt &&
pip3 install -r /app/repositories/GPTQ-for-LLaMa/requirements.txt
ENV EXTRA_LAUNCH_ARGS=””
CMD [“python3”, “/app/server.py”]
FROM base AS triton
RUN echo “TRITON” >> /variant.txt
RUN apt-get install –no-install-recommends -y git python3-dev build-essential python3-pip
RUN rm -rf /app/repositories/GPTQ-for-LLaMa &&
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b triton /app/repositories/GPTQ-for-LLaMa
RUN pip3 uninstall -y quant-cuda &&
sed -i ‘s/^safetensors==0.3.0$/safetensors/g’ /app/repositories/GPTQ-for-LLaMa/requirements.txt &&
pip3 install -r /app/repositories/GPTQ-for-LLaMa/requirements.txt
ENV EXTRA_LAUNCH_ARGS=””
CMD [“python3”, “/app/server.py”]
FROM base AS llama-cublas
RUN echo “LLAMA-CUBLAS” >> /variant.txt
RUN apt-get install –no-install-recommends -y git python3-dev build-essential python3-pip
ENV LLAMA_CUBLAS=1
RUN pip uninstall -y llama-cpp-python && pip install llama-cpp-python
ENV EXTRA_LAUNCH_ARGS=””
CMD [“python3”, “/app/server.py”]
FROM base AS monkey-patch
RUN echo “4-BIT MONKEY-PATCH” >> /variant.txt
RUN apt-get install –no-install-recommends -y git python3-dev build-essential python3-pip
RUN git clone https://github.com/johnsmith0031/alpaca_lora_4bit /app/repositories/alpaca_lora_4bit &&
cd /app/repositories/alpaca_lora_4bit && git checkout 2f704b93c961bf202937b10aac9322b092afdce0
ARG TORCH_CUDA_ARCH_LIST=”8.6″
RUN pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
ENV EXTRA_LAUNCH_ARGS=””
CMD [“python3”, “/app/server.py”, “–monkey-patch”]
FROM base AS default
RUN echo “DEFAULT” >> /variant.txt
ENV EXTRA_LAUNCH_ARGS=””
CMD [“python3”, “/app/server.py”]
The code you provided defines multiple variant builds of the Docker image, each with a different configuration. The `VARIANT BUILDS` section shows a series of Dockerfiles that extend from a base image (`base`) and create different variants for the GPTQ-for-LLaMa application. Each variant includes different dependencies and configuration options, allowing you to choose a specific configuration when building the image.
Here’s a summary of the variant builds:
1. `cuda` variant:
– This variant is intended for CUDA support.
– Installs additional packages required for CUDA support, like git, python3-dev, and python3-pip.
– Clones the GPTQ-for-LLaMa repository from GitHub and switches to the `cuda` branch.
– Installs the Python requirements and sets the environment variable `EXTRA_LAUNCH_ARGS`.
– The container runs `server.py` as the default command when started.
2. `triton` variant:
– This variant is intended for Triton support.
– Installs additional packages required for Triton support, like git, python3-dev, build-essential, and python3-pip.
– Clones the GPTQ-for-LLaMa repository from GitHub and switches to the `triton` branch.
– Installs the Python requirements and sets the environment variable `EXTRA_LAUNCH_ARGS`.
– The container runs `server.py` as the default command when started.
3. `llama-cublas` variant:
– This variant is intended for LLAMA-CUBLAS support.
– Installs additional packages required for LLAMA-CUBLAS support, like git, python3-dev, build-essential, and python3-pip.
– Sets the environment variable `LLAMA_CUBLAS` to 1.
– Installs the `llama-cpp-python` package.
– Sets the environment variable `EXTRA_LAUNCH_ARGS`.
– The container runs `server.py` as the default command when started.
4. `monkey-patch` variant:
– This variant is intended for a 4-bit monkey-patch.
– Installs additional packages required for the monkey-patch support, like git, python3-dev, build-essential, and python3-pip.
– Clones the `alpaca_lora_4bit` repository from GitHub and checks out a specific commit.
– Sets the `TORCH_CUDA_ARCH_LIST` argument to 8.6.
– Installs the `GPTQ-for-LLaMa` package from a specific branch (`lora_4bit`).
– Sets the environment variable `EXTRA_LAUNCH_ARGS`.
– The container runs `server.py` with the `–monkey-patch` option when started.
5. `default` variant:
– This is the default variant, and it doesn’t specify any additional dependencies.
– Sets the environment variable `EXTRA_LAUNCH_ARGS`.
– The container runs `server.py` as the default command when started.
To build and run a specific variant, you can use the `–target` option with `docker build`. For example, to build the `llama-cublas` variant:
“`bash
docker build –target llama-cublas -t my_gptq_image .
“`
And to run the container with the `llama-cublas` variant:
“`bash
docker run -it my_gptq_image
“`
Please note that you should adjust the Dockerfile and dependencies based on your specific use case and requirements.
docker build –target cuda -t .
The `docker build` command you provided is almost correct. However, it is missing the name of the Docker image to be built with the `-t` option. Also, you need to specify the build context (the directory containing the Dockerfile and related files). Here’s the corrected command:
“`bash
docker build –target cuda -t my_gptq_cuda_image .
“`
In this command:
– `–target cuda`: Specifies the target build stage as the `cuda` variant.
– `-t my_gptq_cuda_image`: Tags the built image with the name `my_gptq_cuda_image`.
– `.`: Specifies the build context, which means it will look for the Dockerfile and related files in the current directory.
Make sure to replace `my_gptq_cuda_image` with the desired name for your Docker image. Once the build is complete, you can run the container from this image using the `docker run` command, passing any additional options or environment variables as needed.
docker build –target cuda -t tgwui:cuda .
That looks correct! The `docker build` command with the `–target` option and the `-t` option allows you to build the Docker image named `tgwui:cuda` using the specified target build stage `cuda`. This command will build the image based on the `cuda` variant defined in the Dockerfile.
After running this command, you should have an image named `tgwui:cuda` that you can use to run containers with the specified GPU-related features and configurations.
=> CANCELED [internal] load metadata for docker.io/nvidia/cuda:11.78.0-devel-ubuntu22.04 0.8s
=> ERROR [internal] load metadata for docker.io/nvidia/cuda:11.7.0-devel-ubuntu22.04 0.8s
=> [auth] nvidia/cuda:pull token for registry-1.docker.io 0.0s
——
> [internal] load metadata for docker.io/nvidia/cuda:11.7.0-devel-ubuntu22.04:
——
Dockerfile:1
——————–
1 | >>> FROM nvidia/cuda:11.7.0-devel-ubuntu22.04 AS env_base
2 | # Pre-reqs
3 | RUN apt-get update && apt-get install –no-install-recommends -y
——————–
ERROR: failed to solve: nvidia/cuda:11.7.0-devel-ubuntu22.04: docker.io/nvidia/cuda:11.7.0-devel-ubuntu22.04: not found
The error message indicates that the Docker image `nvidia/cuda:11.7.0-devel-ubuntu22.04` was not found on Docker Hub. This is likely because there is no image with that specific tag available on Docker Hub.
To resolve this issue, you can try using a different tag for the `nvidia/cuda` base image. You can check the available tags for the `nvidia/cuda` image on Docker Hub by visiting its page: https://hub.docker.com/r/nvidia/cuda
Choose a valid tag that is available on Docker Hub and replace `11.7.0-devel-ubuntu22.04` with the correct tag in your Dockerfile. Then try building the image again with the updated tag.
The following packages have unmet dependencies:
8.118 libcudnn8-dev : Depends: libcudnn8 (= 8.9.3.28-1+cuda12.1) but 8.5.0.96-1+cuda11.7 is to be installed
8.127 E: Unable to correct problems, you have held broken packages.
——
Dockerfile:27
——————–
26 |
27 | >>> RUN apt-get update && apt-get install -y –no-install-recommends
28 | >>> cuda-toolkit-11-7
29 | >>> libcudnn8-dev
30 | >>> nvidia-container-toolkit
31 |
——————–
ERROR: failed to solve: process “/bin/sh -c apt-get update && apt-get install -y –no-install-recommends cuda-toolkit-11-7 libcudnn8-dev nvidia-container-toolkit” did not complete successfully: exit code: 100
The error message indicates that there are unmet dependencies while trying to install the `libcudnn8-dev` package in your Dockerfile. It seems that the required version of `libcudnn8` is not available in the package repository.
To resolve this issue, you can try using a different version of the `libcudnn8-dev` package that is available in the package repository for the CUDA version you are using.
Alternatively, you can check the official NVIDIA documentation or the package repository to see if there are any instructions on how to install the correct version of `libcudnn8-dev` for the specific CUDA version you are using.
Make sure that the package versions you are trying to install are compatible with your CUDA version to avoid any dependency conflicts.
So the target here if building just his mntainerut I am building a parent build that call this later. How do I tell it to use the cuda build when it gets to this scfript?
To use the CUDA build as a base image when building the subsequent containers, you can specify the `FROM` instruction in the Dockerfile of the child containers. The child containers will inherit all the layers and configurations from the CUDA base image.
For example, if you have a Dockerfile for the child container called `DockerfileChild`, and you want it to use the `tgwui:cuda` image as the base image, you can do the following:
DockerfileChild:
“`Dockerfile
FROM tgwui:cuda
# Add any additional instructions specific to the child container
# …
“`
When you build the child container, Docker will first check if the `tgwui:cuda` image is available locally. If it’s not available, it will pull it from the registry (if it’s a remote image). Then, Docker will use the `tgwui:cuda` image as the base to build the child container, which includes all the layers and configurations from the `tgwui:cuda` image.
By doing this, you ensure that the child container will have access to the CUDA environment and other configurations provided by the `tgwui:cuda` base image.
rather than making it a variant build can I move it up some and hard code the cuda variant?
Yes, you can move the CUDA variant build section up and hard code it in the Dockerfile without making it a variant build. This way, you can explicitly specify the CUDA version you want to use.
Here’s an example of how you can do it:
“`Dockerfile
# Use the desired CUDA version as the base image
FROM nvidia/cuda:11.7.0-devel-ubuntu22.04 AS env_base
# Pre-reqs
RUN apt-get update && apt-get install –no-install-recommends -y
# Add your desired packages here
cuda-toolkit-11-7
libcudnn8-dev
nvidia-container-toolkit
# …
# Continue with the rest of your Dockerfile, adding any other specific configurations or installations
# …
“`
By specifying the desired CUDA version directly in the `FROM` instruction, you are effectively hardcoding the CUDA variant. This way, the subsequent steps in your Dockerfile will use the CUDA environment provided by the specified version. When you build the Docker image using this Dockerfile, it will create a new image with the specified CUDA version as the base.
I am talking about this: The variants are at the end of the script (I commented out those that I do not want used for security). Since there is only one left, why not just hardcode it into the script rather than need to call it?
I am talking about this: The variants are at the end of the script (I commented out those that I do not want used for security). Since there is only one left, why not just hardcode it into the script rather than need to call it?:::FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 AS env_base
# Pre-reqs
RUN apt-get update && apt-get install –no-install-recommends -y
git vim build-essential python3-dev python3-venv python3-pip
# Instantiate venv and pre-activate
RUN pip3 install virtualenv
RUN virtualenv /venv
# Credit, Itamar Turner-Trauring: https://pythonspeed.com/articles/activate-virtualenv-dockerfile/
ENV VIRTUAL_ENV=/venv
RUN python3 -m venv $VIRTUAL_ENV
ENV PATH=”$VIRTUAL_ENV/bin:$PATH”
RUN pip3 install –upgrade pip setuptools &&
pip3 install torch torchvision torchaudio
FROM env_base AS app_base
### DEVELOPERS/ADVANCED USERS ###
# Clone oobabooga/text-generation-webui
RUN git clone https://github.com/oobabooga/text-generation-webui /src
# To use local source: comment out the git clone command then set the build arg `LCL_SRC_DIR`
#ARG LCL_SRC_DIR=”text-generation-webui”
#COPY ${LCL_SRC_DIR} /src
#################################
ENV LLAMA_CUBLAS=1
# Copy source to app
RUN cp -ar /src /app
# Install oobabooga/text-generation-webui
RUN –mount=type=cache,target=/root/.cache/pip pip3 install -r /app/requirements.txt
# Install extensions
COPY ./scripts/build_extensions.sh /scripts/build_extensions.sh
RUN –mount=type=cache,target=/root/.cache/pip
chmod +x /scripts/build_extensions.sh && . /scripts/build_extensions.sh
# Clone default GPTQ
RUN git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda /app/repositories/GPTQ-for-LLaMa
# Build and install default GPTQ (‘quant_cuda’)
ARG TORCH_CUDA_ARCH_LIST=”6.1;7.0;7.5;8.0;8.6+PTX”
RUN cd /app/repositories/GPTQ-for-LLaMa/ && python3 setup_cuda.py install
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 AS base
# Runtime pre-reqs
RUN apt-get update && apt-get install –no-install-recommends -y
python3-venv python3-dev git
# Copy app and src
COPY –from=app_base /app /app
COPY –from=app_base /src /src
# Copy and activate venv
COPY –from=app_base /venv /venv
ENV VIRTUAL_ENV=/venv
RUN python3 -m venv $VIRTUAL_ENV
ENV PATH=”$VIRTUAL_ENV/bin:$PATH”
## Link models directory to container
ADD ./config/models/ /app/models/
# Finalise app setup
WORKDIR /app
EXPOSE 7860
EXPOSE 5000
EXPOSE 5005
# Required for Python print statements to appear in logs
ENV PYTHONUNBUFFERED=1
# Force variant layers to sync cache by setting –build-arg BUILD_DATE
ARG BUILD_DATE
ENV BUILD_DATE=$BUILD_DATE
RUN echo “$BUILD_DATE” > /build_date.txt
# Copy and enable all scripts
COPY ./scripts /scripts
RUN chmod +x /scripts/*
# Run
ENTRYPOINT [“/scripts/docker-entrypoint.sh”]
# VARIANT BUILDS
FROM base AS cuda
RUN echo “CUDA” >> /variant.txt
RUN apt-get install –no-install-recommends -y git python3-dev python3-pip
RUN rm -rf /app/repositories/GPTQ-for-LLaMa &&
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda /app/repositories/GPTQ-for-LLaMa
RUN pip3 uninstall -y quant-cuda &&
sed -i ‘s/^safetensors==0.3.0$/safetensors/g’ /app/repositories/GPTQ-for-LLaMa/requirements.txt &&
pip3 install -r /app/repositories/GPTQ-for-LLaMa/requirements.txt
ENV EXTRA_LAUNCH_ARGS=””
CMD [“python3”, “/app/server.py”]
Apologies for the confusion. If you only have one variant left and you don’t need to switch between different variants, you can indeed hardcode the CUDA variant directly in the Dockerfile without using multiple `FROM` stages for variants.
Here’s how you can do it:
“`Dockerfile
# Use the desired CUDA version as the base image
FROM nvidia/cuda:11.7.0-devel-ubuntu22.04 AS env_base
# Pre-reqs
RUN apt-get update && apt-get install –no-install-recommends -y
# Add your desired packages here
cuda-toolkit-11-7
libcudnn8-dev
nvidia-container-toolkit
# …
# Build and install default GPTQ (‘quant_cuda’)
ARG TORCH_CUDA_ARCH_LIST=”6.1;7.0;7.5;8.0;8.6+PTX”
RUN cd /app/repositories/GPTQ-for-LLaMa/ && python3 setup_cuda.py install
# Continue with the rest of your Dockerfile, adding any other specific configurations or installations
# …
“`
By directly specifying the desired CUDA version in the `FROM` instruction, you are hardcoding it into the Dockerfile, and there’s no need for additional variant builds or multiple `FROM` stages. This approach is simpler and more straightforward when you only need to use one specific CUDA version.
Yes, you can hardcode the CUDA variant by removing the variant build section and directly specifying the required packages for the CUDA variant in the main Dockerfile. Here’s the modified Dockerfile with the CUDA variant hardcoded:
“`Dockerfile
# Use the desired CUDA version as the base image
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 AS env_base
# Pre-reqs
RUN apt-get update && apt-get install –no-install-recommends -y
git vim build-essential python3-dev python3-venv python3-pip
# Instantiate venv and pre-activate
RUN pip3 install virtualenv
RUN virtualenv /venv
# Credit, Itamar Turner-Trauring: https://pythonspeed.com/articles/activate-virtualenv-dockerfile/
ENV VIRTUAL_ENV=/venv
RUN python3 -m venv $VIRTUAL_ENV
ENV PATH=”$VIRTUAL_ENV/bin:$PATH”
# Install required packages for the CUDA variant
RUN apt-get install –no-install-recommends -y
python3-dev python3-pip git
# Clone oobabooga/text-generation-webui
RUN git clone https://github.com/oobabooga/text-generation-webui /src
# To use local source: comment out the git clone command then set the build arg `LCL_SRC_DIR`
#ARG LCL_SRC_DIR=”text-generation-webui”
#COPY ${LCL_SRC_DIR} /src
ENV LLAMA_CUBLAS=1
# Copy source to app
RUN cp -ar /src /app
# Install oobabooga/text-generation-webui
RUN –mount=type=cache,target=/root/.cache/pip pip3 install -r /app/requirements.txt
# Install extensions
COPY ./scripts/build_extensions.sh /scripts/build_extensions.sh
RUN –mount=type=cache,target=/root/.cache/pip
chmod +x /scripts/build_extensions.sh && . /scripts/build_extensions.sh
# Clone default GPTQ
RUN git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda /app/repositories/GPTQ-for-LLaMa
# Build and install default GPTQ (‘quant_cuda’)
ARG TORCH_CUDA_ARCH_LIST=”6.1;7.0;7.5;8.0;8.6+PTX”
RUN cd /app/repositories/GPTQ-for-LLaMa/ && python3 setup_cuda.py install
# … The rest of the Dockerfile continues as before
“`
By doing this, you have hardcoded the CUDA variant directly in the main Dockerfile. The CUDA version specified in the `FROM` instruction will be used throughout the rest of the Dockerfile, including when building the CUDA-specific part of the image.
I don’t see this shit at all you loon: FROM base AS cuda
RUN echo “CUDA” >> /variant.txt
RUN apt-get install –no-install-recommends -y git python3-dev python3-pip
RUN rm -rf /app/repositories/GPTQ-for-LLaMa &&
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda /app/repositories/GPTQ-for-LLaMa
RUN pip3 uninstall -y quant-cuda &&
sed -i ‘s/^safetensors==0.3.0$/safetensors/g’ /app/repositories/GPTQ-for-LLaMa/requirements.txt &&
pip3 install -r /app/repositories/GPTQ-for-LLaMa/requirements.txt
ENV EXTRA_LAUNCH_ARGS=””
CMD [“python3”, “/app/server.py”]
Apologies for the confusion. You are right; the code I provided does not match the snippet you posted. I see that the CUDA variant is defined separately in the Dockerfile. If you want to hardcode the CUDA variant and remove the variant build section, you can modify the Dockerfile as follows:
“`Dockerfile
# Use the desired CUDA version as the base image
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 AS env_base
# Pre-reqs
RUN apt-get update && apt-get install –no-install-recommends -y
git vim build-essential python3-dev python3-venv python3-pip
# Instantiate venv and pre-activate
RUN pip3 install virtualenv
RUN virtualenv /venv
# Credit, Itamar Turner-Trauring: https://pythonspeed.com/articles/activate-virtualenv-dockerfile/
ENV VIRTUAL_ENV=/venv
RUN python3 -m venv $VIRTUAL_ENV
ENV PATH=”$VIRTUAL_ENV/bin:$PATH”
# Install required packages for the CUDA variant
RUN apt-get install –no-install-recommends -y
python3-dev python3-pip git
# Clone oobabooga/text-generation-webui
RUN git clone https://github.com/oobabooga/text-generation-webui /src
# To use local source: comment out the git clone command then set the build arg `LCL_SRC_DIR`
#ARG LCL_SRC_DIR=”text-generation-webui”
#COPY ${LCL_SRC_DIR} /src
ENV LLAMA_CUBLAS=1
# Copy source to app
RUN cp -ar /src /app
# Install oobabooga/text-generation-webui
RUN –mount=type=cache,target=/root/.cache/pip pip3 install -r /app/requirements.txt
# Install extensions
COPY ./scripts/build_extensions.sh /scripts/build_extensions.sh
RUN –mount=type=cache,target=/root/.cache/pip
chmod +x /scripts/build_extensions.sh && . /scripts/build_extensions.sh
# Clone default GPTQ
RUN git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda /app/repositories/GPTQ-for-LLaMa
# Build and install default GPTQ (‘quant_cuda’)
ARG TORCH_CUDA_ARCH_LIST=”6.1;7.0;7.5;8.0;8.6+PTX”
RUN cd /app/repositories/GPTQ-for-LLaMa/ && python3 setup_cuda.py install
# … The rest of the Dockerfile continues as before
“`
With this modification, you have hardcoded the CUDA variant directly in the main Dockerfile, and the CUDA version specified in the `FROM` instruction will be used throughout the rest of the Dockerfile, including when building the CUDA-specific part of the image.
everything built but here is where the error usualy comes. When I use docker-compose upp I want to add the flags gpu all anything esel?
To use the GPU in your `docker-compose` setup, you need to add the `–gpus` flag with the appropriate value. Since you want to use all available GPUs, you can use `all` as the value. Here’s how you can modify your `docker-compose.yml` file to use the `–gpus` flag:
“`yaml
version: ‘3.8’
services:
# … Other services …
super__tgwui:
build:
context: ./tgwui/
target: llama-cublas
dockerfile: DockerfileTGWUI
image: atinoda/text-generation-webui:llama-cublas
container_name: super__tgwui
environment:
– EXTRA_LAUNCH_ARGS=”–no-mmap –verbose –extensions openai –auto-devices –n_ctx 2000 –gpu-memory 22 –n-gpu-layers 2000 –threads 8″
ports:
– 7860:7860 # Default web port
– 5000:5000 # Default API port
– 5005:5005 # Default streaming port
– 5001:5001 # Default OpenAI API extension port
volumes:
– ./tgwui/config/loras:/app/loras
– ./tgwui/config/models:/app/models
– ./tgwui/config/presets:/app/presets
– ./tgwui/config/prompts:/app/prompts
– ./tgwui/config/softprompts:/app/softprompts
– ./tgwui/config/training:/app/training
logging:
driver: json-file
options:
max-file: “3” # number of files or file count
max-size: ’10m’
networks:
– super_network
deploy:
resources:
reservations:
devices:
– driver: nvidia
capabilities: [gpu]
“`
With this configuration, when you use `docker-compose up`, it will automatically enable access to all GPUs for the `super__tgwui` service. Make sure you have installed the necessary NVIDIA Container Toolkit and GPU drivers on your host system for this to work properly.
AND/OR point to the nvidia docker image…
Certainly! To use the official NVIDIA Docker image, you can modify the `super__tgwui` service in your `docker-compose.yml` file as follows:
“`yaml
version: ‘3.8’
services:
# … Other services …
super__tgwui:
image: nvcr.io/nvidia/tensorrt:21.11-py3
container_name: super__tgwui
environment:
– EXTRA_LAUNCH_ARGS=”–no-mmap –verbose –extensions openai –auto-devices –n_ctx 2000 –gpu-memory 22 –n-gpu-layers 2000 –threads 8″
ports:
– 7860:7860 # Default web port
– 5000:5000 # Default API port
– 5005:5005 # Default streaming port
– 5001:5001 # Default OpenAI API extension port
volumes:
– ./tgwui/config/loras:/app/loras
– ./tgwui/config/models:/app/models
– ./tgwui/config/presets:/app/presets
– ./tgwui/config/prompts:/app/prompts
– ./tgwui/config/softprompts:/app/softprompts
– ./tgwui/config/training:/app/training
logging:
driver: json-file
options:
max-file: “3” # number of files or file count
max-size: ’10m’
networks:
– super_network
deploy:
resources:
reservations:
devices:
– driver: nvidia
capabilities: [gpu]
“`
In this configuration, the `super__tgwui` service will use the NVIDIA TensorRT container image provided by NVIDIA. You can change the tag to any other version that suits your requirements.
Make sure you have the NVIDIA Container Toolkit and compatible GPU drivers installed on your host system to enable GPU access for containers using the NVIDIA Docker image.
I think I am using torch
Apologies for any confusion. Based on your previous Dockerfile and code, it looks like you are using PyTorch, which is a popular deep learning framework similar to TensorFlow but developed by Facebook. The error message you shared earlier indicates a mismatch between the CUDA versions used in PyTorch and the detected CUDA version on the system.
To clarify, you have a few options to address the CUDA version mismatch:
1. Make sure the correct CUDA version is installed on your system. It seems you are trying to use CUDA 11.8.0 in your Dockerfile, but the detected CUDA version is 12.2. Ensure that you have CUDA 11.8.0 installed on your host system.
2. Update your Dockerfile to use the correct CUDA version. You can change the base image to `nvidia/cuda:11.8.0-devel-ubuntu22.04` to match the desired CUDA version.
Here’s the relevant part of the Dockerfile that you can modify:
“`Dockerfile
# Set the desired CUDA version in the base image
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 AS env_base
# Rest of your Dockerfile…
“`
3. Ensure that your PyTorch version is compatible with the CUDA version. If you are using a pre-built PyTorch version, make sure it matches the CUDA version you want to use. You may need to install a specific version of PyTorch that is built for CUDA 11.8.0.
Remember to rebuild your Docker image after making any changes to the Dockerfile. If the issue persists, you can check the CUDA version and PyTorch version inside the container to verify that they match. You can run the container interactively using `docker run -it bash` and then check the CUDA and PyTorch versions using `nvcc –version` and `python -c “import torch; print(torch.version.cuda)”` respectively.
If you still encounter issues, please provide more information about your setup, such as the PyTorch version you are using, the host system’s CUDA version, and the complete error message. This will help in providing a more specific solution.
what is the command docker-compose up –gpus all?
The `docker-compose up` command with the `–gpus all` option is used to run Docker Compose and enable GPU access for the services that require it. This option allows containers to use the host machine’s NVIDIA GPUs for hardware acceleration.
Here’s the syntax of the command:
“`
docker-compose up –gpus all
“`
The `–gpus all` flag ensures that all available NVIDIA GPUs on the host machine are exposed to the containers managed by Docker Compose. It allows containers to use GPU resources, which is particularly useful for machine learning or deep learning workloads that require GPU acceleration.
When you use this command, Docker Compose will automatically configure the necessary NVIDIA container runtime to enable GPU access for the services that are defined in your `docker-compose.yml` file.
Keep in mind that for `docker-compose` to recognize the `–gpus` flag, you need to be using Docker Compose version 1.28.0 or higher. If your Docker Compose version is older, you may need to update it to use this feature. You can check your Docker Compose version with the following command:
“`
docker-compose –version
“`
If you find that your Docker Compose version is not up to date, you can update it using your package manager or by following the instructions provided by the official Docker Compose documentation.
Additionally, ensure that you have the NVIDIA container runtime (`nvidia-container-runtime`) installed on your system, as it is required for GPU support in Docker containers. You can refer to the official NVIDIA documentation or your distribution’s package manager for instructions on how to install the NVIDIA container runtime.
what is the command to build all with a variant? Is it docker build ‘cuda’ .
To build a specific variant from your Dockerfile, you can use the `–target` option with the `docker build` command. In your case, to build the `cuda` variant, you can use the following command:
“`
docker build –target cuda -t your_image_name .
“`
Replace `your_image_name` with the desired name for your Docker image. The `–target` option specifies the build stage to use in the Dockerfile. In your provided Dockerfile, the build stage for the `cuda` variant is defined with the name `cuda`, so you can use that name as the argument to `–target`. The `.` at the end of the command indicates that the build context is the current directory, where the Dockerfile is located.
This command will build the `cuda` variant of your Docker image, and you can use it later in your `docker-compose.yml` file or when running containers with Docker.
Remember that the variant builds you have defined in your Dockerfile (e.g., `cuda`, `triton`, `llama-cublas`, etc.) are essentially different configurations of your Docker image, each with specific dependencies and settings. By specifying the target with `–target`, you can choose which variant to build based on your specific needs.
docker build –target cuda -t morning_eve .[+] Building 0.0s (2/2) FINISHED docker:default
=> [internal] load .dockerignore 0.0s
=> => transferring context: 298B 0.0s
=> [internal] load build definition from Dockerfile 0.0s
=> => transferring dockerfile: 915B 0.0s
ERROR: failed to solve: target stage cuda could not be found
It looks like there might be an issue with the name of the build stage for the `cuda` variant in your Dockerfile. To troubleshoot this, please ensure that the target stage in the Dockerfile is correctly named as `cuda`. Double-check the spelling and make sure there are no typos.
Based on your provided Dockerfile, the target stage for the `cuda` variant is defined as `cuda`. Here’s a snippet from your Dockerfile:
“`Dockerfile
# VARIANT BUILDS
FROM base AS cuda
# … (other commands specific to the ‘cuda’ variant)
“`
To build the `cuda` variant, the correct `docker build` command is:
“`bash
docker build –target cuda -t morning_eve .
“`
If you encounter any further issues, please make sure to verify the name of the build stage in your Dockerfile and adjust the `–target` argument accordingly.
I am never goinf to eget past this error am I? Created 0.0s
Attaching to super__tgwui, superagi-backend-1, superagi-celery-1, superagi-gui-1, superagi-super__postgres-1, superagi-super__redis-1
superagi-super__postgres-1 |
superagi-super__postgres-1 | PostgreSQL Database directory appears to contain a database; Skipping initialization
superagi-super__postgres-1 |
superagi-super__postgres-1 |
superagi-super__redis-1 | 1:C 28 Jul 2023 13:11:49.305 # oO0OoO0OoO0Oo Redis is starting oO0OoO0OoO0Oo
superagi-super__redis-1 | 1:C 28 Jul 2023 13:11:49.305 # Redis version=7.0.12, bits=64, commit=00000000, modified=0, pid=1, just started
superagi-super__redis-1 | 1:C 28 Jul 2023 13:11:49.305 # Warning: no config file specified, using the default config. In order to specify a config file use redis-server /path/to/redis.conf
superagi-super__redis-1 | 1:M 28 Jul 2023 13:11:49.306 * monotonic clock: POSIX clock_gettime
superagi-super__redis-1 | 1:M 28 Jul 2023 13:11:49.307 * Running mode=standalone, port=6379.
superagi-super__redis-1 | 1:M 28 Jul 2023 13:11:49.307 # Server initialized
superagi-super__redis-1 | 1:M 28 Jul 2023 13:11:49.308 * Loading RDB produced by version 7.0.12
superagi-super__redis-1 | 1:M 28 Jul 2023 13:11:49.308 * RDB age 1719 seconds
superagi-super__redis-1 | 1:M 28 Jul 2023 13:11:49.308 * RDB memory usage when created 0.82 Mb
superagi-super__redis-1 | 1:M 28 Jul 2023 13:11:49.308 * Done loading RDB, keys loaded: 0, keys expired: 0.
superagi-super__redis-1 | 1:M 28 Jul 2023 13:11:49.308 * DB loaded from disk: 0.000 seconds
superagi-super__redis-1 | 1:M 28 Jul 2023 13:11:49.308 * Ready to accept connections
superagi-super__postgres-1 | 2023-07-28 13:11:49.376 UTC [1] LOG: starting PostgreSQL 15.3 (Debian 15.3-1.pgdg120+1) on x86_64-pc-linux-gnu, compiled by gcc (Debian 12.2.0-14) 12.2.0, 64-bit
superagi-super__postgres-1 | 2023-07-28 13:11:49.386 UTC [1] LOG: listening on IPv4 address “0.0.0.0”, port 5432
superagi-super__postgres-1 | 2023-07-28 13:11:49.386 UTC [1] LOG: listening on IPv6 address “::”, port 5432
superagi-super__postgres-1 | 2023-07-28 13:11:49.389 UTC [1] LOG: listening on Unix socket “/var/run/postgresql/.s.PGSQL.5432”
superagi-super__postgres-1 | 2023-07-28 13:11:49.392 UTC [29] LOG: database system was shut down at 2023-07-28 12:43:10 UTC
superagi-super__postgres-1 | 2023-07-28 13:11:49.402 UTC [1] LOG: database system is ready to accept connections
Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: error running
hook #0: error running hook: exit status 1, stdout: , stderr: Auto
It seems like there was an error when starting the container due to an issue with the OCI runtime. The specific error message is cut off, so it’s challenging to identify the exact cause without the full error message.
Here are some general troubleshooting steps you can follow to resolve the issue:
1. Check the full error message: Look for the complete error message after the line “stderr: Auto” to get more information about the problem. It should give you a more detailed reason for the failure.
2. Check container logs: Use the `docker logs ` command to view the logs of the container that failed to start. This will provide additional details on what went wrong during the initialization process.
3. Verify system resources: Ensure that your system has enough resources (CPU, memory, etc.) available to run the containers. Running out of resources can cause containers to fail to start.
4. Check Docker version: Make sure you are using a compatible version of Docker with the installed runtime and libraries.
5. Rebuild the image: If the issue persists, try rebuilding the Docker image to ensure that the image is created correctly without any errors.
If you encounter any specific error messages or need further assistance, please provide the complete error message, and I’ll do my best to help you troubleshoot the problem.
2023-07-28 13:11:49.402 UTC [1] LOG: database system is ready to accept connections
Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: error running
hook #0: error running hook: exit status 1, stdout: , stderr: Auto-detected mode as ‘legacy’
nvidia-container-cli: device error: 1: unknown device: unknown
The error message “nvidia-container-cli: device error: 1: unknown device: unknown” indicates that there is an issue with the NVIDIA container runtime. This error usually occurs when Docker is unable to access the NVIDIA GPU devices on the host machine.
To fix this issue, you need to ensure that you have installed the NVIDIA container runtime correctly and have the necessary drivers and dependencies available on the host machine. Here are some steps you can follow to resolve the problem:
1. Install NVIDIA Container Runtime: Make sure you have installed the NVIDIA container runtime on your host machine. You can find installation instructions on the official NVIDIA container toolkit documentation: https://github.com/NVIDIA/nvidia-docker
2. Verify NVIDIA Driver: Ensure that you have installed the correct NVIDIA driver on the host machine, compatible with the GPU you want to use in the containers. The NVIDIA container runtime relies on the host system having the appropriate NVIDIA drivers installed.
3. Check NVIDIA GPU Availability: Verify that the NVIDIA GPU is correctly installed and recognized by the host system. You can use the `nvidia-smi` command to check if the GPU is accessible and working as expected.
4. Check Docker Daemon Configuration: Ensure that the Docker daemon is properly configured to work with the NVIDIA container runtime. The `nvidia-container-runtime` should be set as the default runtime for Docker. You can check the Docker daemon configuration in the `/etc/docker/daemon.json` file.
5. Restart Docker: After making any changes to the Docker daemon configuration or installing the NVIDIA container runtime, restart the Docker service to apply the changes.
6. Run with Appropriate Privileges: Make sure you are running the `docker-compose up` command with appropriate privileges (usually as a superuser or with `sudo`) to access GPU resources.
Once you have verified these steps, try running the containers again with the `docker-compose up` command and see if the issue is resolved. If you encounter any specific error messages or need further assistance, please provide the complete error message, and I’ll be glad to help you further troubleshoot the problem.
so this conatiner seems to have gpu accss: | MIG M. |
2023-07-28 01:43:07
wizardly_keldysh
|=========================================+======================+======================|
2023-07-28 01:43:07
wizardly_keldysh
| 0 Tesla P40 On | 00000000:24:00.0 Off | Off |
2023-07-28 01:43:07
wizardly_keldysh
| N/A 29C P8 10W / 250W | 58MiB / 24576MiB | 0% Default |
2023-07-28 01:43:07
wizardly_keldysh
| | | N/A |
2023-07-28 01:43:07
wizardly_keldysh
+—————————————–+———————-+———————-+
2023-07-28 01:43:07
wizardly_keldysh
+—————————————————————————————+
2023-07-28 01:43:07
wizardly_keldysh
| Processes: |
2023-07-28 01:43:07
wizardly_keldysh
| GPU GI CI PID Type Process name GPU Memory |
2023-07-28 01:43:07
wizardly_keldysh
| ID ID Usage |
2023-07-28 01:43:07
wizardly_keldysh
|==========================
Based on the information you provided, it appears that the container has GPU access, and it can detect and access an NVIDIA Tesla P40 GPU (device 00000000:24:00.0). The GPU status indicates that it is currently turned on and operational.
The GPU status output shows the following information:
– GPU: Tesla P40
– Power state: On
– Temperature: 29°C
– Performance state: P8
– Power usage: 10W out of 250W (indicating low power usage)
– Memory usage: 58MiB out of 24576MiB (indicating that 58MiB of GPU memory is being used)
– GPU processes: None running at the moment
This information confirms that the container has access to the GPU, and the GPU seems to be working correctly. It should be ready to perform any GPU-accelerated tasks within the container.
If you have any specific tasks or applications that you want to run using the GPU, you can now proceed with running those tasks within the container, and it should utilize the GPU for any GPU-accelerated workloads.
For instance, if you want to run a deep learning application using PyTorch or TensorFlow that supports GPU acceleration, the container should be able to utilize the GPU for faster computation.
If you encounter any issues or have specific questions regarding how to use the GPU within the container for your specific workload, feel free to ask, and I’ll be happy to assist you further!
BUT it is not withinb the container that IU just built. It is outside of that
Apologies for the confusion. If the GPU status you provided is from outside the container you just built and not from within the container, it means that the GPU access is not being utilized by the container.
To ensure that the GPU is accessible from within the container, you need to set up proper GPU support and access when running the container. Here are some steps you can take to ensure GPU access within the container:
1. Make sure you have installed the necessary NVIDIA drivers and software on the host machine to enable GPU access.
2. When running the container, use the `–gpus` flag to specify which GPUs the container should have access to. For example, to allow the container access to all available GPUs on the host, you can use `–gpus all`.
“`
docker run –gpus all -d your_image_name
“`
Replace `your_image_name` with the name of your Docker image that you want to run.
3. Ensure that you have installed the necessary NVIDIA Docker runtime on the host machine. This allows Docker to properly pass GPU access to the container.
4. In your Dockerfile, make sure you are using the correct base image that includes the necessary CUDA and NVIDIA libraries to enable GPU support.
Here’s a modified version of your Dockerfile with additional instructions to enable GPU support:
“`Dockerfile
# Choose the appropriate base image that includes CUDA and NVIDIA libraries
# Replace with the version that matches the NVIDIA driver installed on the host machine.
FROM nvidia/cuda:-devel-ubuntu22.04 AS env_base
# Rest of your Dockerfile goes here…
# VARIANT BUILDS
FROM base AS cuda
RUN echo “CUDA” >> /variant.txt
RUN apt-get install –no-install-recommends -y git python3-dev python3-pip
RUN rm -rf /app/repositories/GPTQ-for-LLaMa &&
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda /app/repositories/GPTQ-for-LLaMa
RUN pip3 uninstall -y quant-cuda &&
sed -i ‘s/^safetensors==0.3.0$/safetensors/g’ /app/repositories/GPTQ-for-LLaMa/requirements.txt &&
pip3 install -r /app/repositories/GPTQ-for-LLaMa/requirements.txt
ENV EXTRA_LAUNCH_ARGS=””
CMD [“python3”, “/app/server.py”]
“`
Make sure you replace “ with the correct CUDA version that matches the NVIDIA driver installed on the host machine.
With these modifications, when you run the container using `docker run –gpus all -d your_image_name`, it should have access to the GPU, and the GPU status should be visible from within the container.
If you still face issues, ensure that you have the necessary NVIDIA drivers, CUDA, and NVIDIA Docker runtime properly installed on the host machine and that you are using the correct base image in your Dockerfile.
If you encounter any further issues or have specific questions, feel free to ask for more assistance!
don’t you get iut? If I built one here then the pother should run as well? Why am I even using WSL? HOw do I get out of using WSL
Apologies for any confusion. It seems like you are running into issues with GPU access and Docker containers in your environment.
Regarding your questions about WSL (Windows Subsystem for Linux) and getting out of using it, let’s clarify a few things:
1. **WSL**: WSL is a compatibility layer in Windows that allows you to run Linux distributions alongside Windows. It’s useful for developers who need a Linux environment on Windows without the need for a full virtual machine. However, WSL does have some limitations, especially when it comes to GPU support and Docker. If you are facing issues with GPU access and Docker within WSL, it might be due to these limitations.
2. **Getting Out of WSL**: If you want to avoid using WSL and run Linux or Docker natively on Windows, you have a few options:
a. **Use Native Linux**: Install a native Linux distribution (e.g., Ubuntu, Fedora, etc.) alongside Windows on your machine. You can set up a dual-boot system to switch between Windows and Linux.
b. **Virtual Machine**: Use a virtualization software like VirtualBox or VMware to create a virtual machine and run Linux within that VM. This will allow you to have a separate Linux environment without relying on WSL.
c. **Docker Desktop for Windows**: Docker Desktop for Windows allows you to run Docker containers natively on Windows without WSL. It provides better support for GPU access, and you won’t need WSL to use Docker.
To use Docker Desktop for Windows, follow these steps:
1. Download and install Docker Desktop for Windows from the Docker website: https://www.docker.com/products/docker-desktop
2. Once installed, Docker Desktop should run natively on Windows without the need for WSL.
3. Make sure you have installed the necessary NVIDIA drivers and software for GPU support on your Windows machine.
4. When running Docker containers that require GPU access, you can use the `–gpus` flag to enable GPU support, as mentioned in our previous discussions.
Remember that to use GPU access within a container, you need to have the appropriate drivers and software installed on the host machine (Windows in this case) and ensure that Docker and the container are set up to utilize GPU resources.
If you are still facing issues with GPU access and Docker containers after trying the above steps, it’s possible that there might be specific configurations or environment-related challenges that need further investigation. In such cases, providing more detailed information about your setup and the specific errors you are encountering will help in providing better assistance.
I use docker for windows. The script ois making me uise WSL I w2nat to change the script
If you are using Docker for Windows, you don’t necessarily need to use WSL for running Docker containers. Docker Desktop for Windows provides the ability to run Docker containers natively on Windows without relying on WSL. By default, Docker Desktop uses the Windows container mode, which is more suitable for Windows-specific applications.
However, in the Dockerfile you provided earlier, it looks like there are specific variant builds, and one of them is named “cuda,” which might be causing confusion. The script is designed to build different variants based on the target specified during the build process.
If you want to build a specific variant, such as the “cuda” variant, you can use the following command:
“`
docker build –target cuda -t morning_eve .
“`
In the Dockerfile, the variant builds are defined with the `FROM` statement using the target as follows:
“`
FROM base AS cuda
# … (additional instructions specific to the “cuda” variant)
“`
If you want to avoid using WSL and ensure that the containers run natively on Windows without GPU-related issues, you should stick to using Docker Desktop for Windows and make sure the necessary NVIDIA drivers and software are installed on your Windows machine.
To summarize:
1. Use Docker Desktop for Windows to run Docker containers natively on Windows.
2. Make sure you have the required NVIDIA drivers and software installed for GPU support on your Windows machine.
3. To build a specific variant, use the `–target` flag during the `docker build` command (e.g., `–target cuda`).
By following these steps, you should be able to build and run Docker containers without relying on WSL. If you encounter any specific errors or issues, please provide more details, and we can further assist you in resolving them.
Convert this entire script to run under windows: FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 AS env_base
# Pre-reqs
RUN apt-get update && apt-get install –no-install-recommends -y
git vim build-essential python3-dev python3-venv python3-pip
# Instantiate venv and pre-activate
RUN pip3 install virtualenv
RUN virtualenv /venv
# Credit, Itamar Turner-Trauring: https://pythonspeed.com/articles/activate-virtualenv-dockerfile/
ENV VIRTUAL_ENV=/venv
RUN python3 -m venv $VIRTUAL_ENV
ENV PATH=”$VIRTUAL_ENV/bin:$PATH”
RUN pip3 install –upgrade pip setuptools &&
pip3 install torch torchvision torchaudio
FROM env_base AS app_base
### DEVELOPERS/ADVANCED USERS ###
# Clone oobabooga/text-generation-webui
RUN git clone https://github.com/oobabooga/text-generation-webui /src
# To use local source: comment out the git clone command then set the build arg `LCL_SRC_DIR`
#ARG LCL_SRC_DIR=”text-generation-webui”
#COPY ${LCL_SRC_DIR} /src
#################################
ENV LLAMA_CUBLAS=1
# Copy source to app
RUN cp -ar /src /app
# Install oobabooga/text-generation-webui
RUN –mount=type=cache,target=/root/.cache/pip pip3 install -r /app/requirements.txt
# Install extensions
COPY ./scripts/build_extensions.sh /scripts/build_extensions.sh
RUN –mount=type=cache,target=/root/.cache/pip
chmod +x /scripts/build_extensions.sh && . /scripts/build_extensions.sh
# Clone default GPTQ
RUN git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda /app/repositories/GPTQ-for-LLaMa
# Build and install default GPTQ (‘quant_cuda’)
ARG TORCH_CUDA_ARCH_LIST=”6.1;7.0;7.5;8.0;8.6+PTX”
RUN cd /app/repositories/GPTQ-for-LLaMa/ && python3 setup_cuda.py install
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 AS base
# Runtime pre-reqs
RUN apt-get update && apt-get install –no-install-recommends -y
python3-venv python3-dev git
# Copy app and src
COPY –from=app_base /app /app
COPY –from=app_base /src /src
# Copy and activate venv
COPY –from=app_base /venv /venv
ENV VIRTUAL_ENV=/venv
RUN python3 -m venv $VIRTUAL_ENV
ENV PATH=”$VIRTUAL_ENV/bin:$PATH”
## Link models directory to container
ADD ./config/models/ /app/models/
FROM nvidia/cuda
RUN echo “CUDA” >> /variant.txt
RUN apt-get install –no-install-recommends -y git python3-dev python3-pip
RUN rm -rf /app/repositories/GPTQ-for-LLaMa &&
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda /app/repositories/GPTQ-for-LLaMa
RUN pip3 uninstall -y quant-cuda &&
sed -i ‘s/^safetensors==0.3.0$/safetensors/g’ /app/repositories/GPTQ-for-LLaMa/requirements.txt &&
pip3 install -r /app/repositories/GPTQ-for-LLaMa/requirements.txt
ENV EXTRA_LAUNCH_ARGS=””
CMD [“python3”, “/app/server.py”]
# Finalise app setup
WORKDIR /app
EXPOSE 7860
EXPOSE 5000
EXPOSE 5005
# Required for Python print statements to appear in logs
ENV PYTHONUNBUFFERED=1
# Force variant layers to sync cache by setting –build-arg BUILD_DATE
ARG BUILD_DATE
ENV BUILD_DATE=$BUILD_DATE
RUN echo “$BUILD_DATE” > /build_date.txt
# Copy and enable all scripts
COPY ./scripts /scripts
RUN chmod +x /scripts/*
# Run
ENTRYPOINT [“/scripts/docker-entrypoint.sh”]
# VARIANT BUILDS
FROM base AS default
RUN echo “DEFAULT” >> /variant.txt
ENV EXTRA_LAUNCH_ARGS=””
CMD [“python3”, “/app/server.py”] –>
To convert the provided Dockerfile to work on Windows with Docker Desktop, you need to make a few modifications, as Windows uses a different syntax for file paths and has some differences in how certain commands work. Below is the modified Dockerfile to work on Windows:
“`Dockerfile
# Use the base image with CUDA for development
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 AS env_base
# Pre-reqs
RUN apt-get update && apt-get install –no-install-recommends -y
git vim build-essential python3-dev python3-venv python3-pip
# Instantiate venv and pre-activate
RUN pip3 install virtualenv
RUN virtualenv /venv
# Set the virtual environment variables
ENV VIRTUAL_ENV=/venv
ENV PATH=”$VIRTUAL_ENV/bin:$PATH”
# Upgrade pip and install required Python packages
RUN pip3 install –upgrade pip setuptools &&
pip3 install torch torchvision torchaudio
# Create a new stage for the app
FROM env_base AS app_base
# Clone oobabooga/text-generation-webui
RUN git clone https://github.com/oobabooga/text-generation-webui /src
# Set environment variable for LLAMA_CUBLAS
ENV LLAMA_CUBLAS=1
# Copy source to app
RUN cp -ar /src /app
# Install oobabooga/text-generation-webui
RUN pip3 install -r /app/requirements.txt
# Install extensions
COPY ./scripts/build_extensions.sh /scripts/build_extensions.sh
RUN chmod +x /scripts/build_extensions.sh && . /scripts/build_extensions.sh
# Clone default GPTQ
RUN git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda /app/repositories/GPTQ-for-LLaMa
# Build and install default GPTQ (‘quant_cuda’)
ARG TORCH_CUDA_ARCH_LIST=”6.1;7.0;7.5;8.0;8.6+PTX”
RUN cd /app/repositories/GPTQ-for-LLaMa/ && python3 setup_cuda.py install
# Create a new stage for the base
FROM nvidia/cuda AS base
# Runtime pre-reqs
RUN apt-get update && apt-get install –no-install-recommends -y
python3-venv python3-dev git
# Copy app and src
COPY –from=app_base /app /app
COPY –from=app_base /src /src
# Copy and activate venv
COPY –from=app_base /venv /venv
ENV VIRTUAL_ENV=/venv
ENV PATH=”$VIRTUAL_ENV/bin:$PATH”
## Link models directory to container
ADD ./config/models/ /app/models/
# Set the variant as “CUDA”
RUN echo “CUDA” >> /variant.txt
# Install additional dependencies for the variant
RUN apt-get install –no-install-recommends -y git python3-dev python3-pip
# Clone GPTQ-for-LLaMa repository with CUDA variant
RUN git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda /app/repositories/GPTQ-for-LLaMa
# Uninstall quant-cuda and install requirements
RUN pip3 uninstall -y quant-cuda &&
sed -i ‘s/^safetensors==0.3.0$/safetensors/g’ /app/repositories/GPTQ-for-LLaMa/requirements.txt &&
pip3 install -r /app/repositories/GPTQ-for-LLaMa/requirements.txt
# Set the EXTRA_LAUNCH_ARGS environment variable
ENV EXTRA_LAUNCH_ARGS=””
# Set the CMD for the container to run the server.py
CMD [“python3”, “/app/server.py”]
# Finalise app setup
WORKDIR /app
EXPOSE 7860
EXPOSE 5000
EXPOSE 5005
# Required for Python print statements to appear in logs
ENV PYTHONUNBUFFERED=1
# Force variant layers to sync cache by setting –build-arg BUILD_DATE
ARG BUILD_DATE
ENV BUILD_DATE=$BUILD_DATE
RUN echo “$BUILD_DATE” > /build_date.txt
# Copy and enable all scripts
COPY ./scripts /scripts
RUN chmod +x /scripts/*
# Run entrypoint script
ENTRYPOINT [“/scripts/docker-entrypoint.sh”]
# Create a new stage for the default variant
FROM base AS default
RUN echo “DEFAULT” >> /variant.txt
# Set the EXTRA_LAUNCH_ARGS environment variable for the default variant
ENV EXTRA_LAUNCH_ARGS=””
# Set the CMD for the container to run the server.py for the default variant
CMD [“python3”, “/app/server.py”]
“`
Please note that if you are using Docker Desktop for Windows with GPU support, you should have the necessary NVIDIA drivers and software installed on
These are windows commands?
Yes, the Dockerfile I provided is a Dockerfile that is compatible with Docker running on Windows. The Docker commands and instructions in the Dockerfile are designed to work on a Windows system with Docker Desktop installed and properly configured with GPU support. It uses the Docker syntax for building and running containers, which is the same across different operating systems, including Windows, macOS, and Linux.
pip3 and apt get?
Apologies for the confusion. My mistake in the provided Dockerfile. You are correct that `pip3` and `apt-get` are not available in Windows Docker images, as they are specific to Linux-based systems.
When running Docker on Windows, you would typically use Windows-based Docker images, which use PowerShell or other Windows-specific commands instead of `apt-get` and `pip3`. However, for the specific case of CUDA and NVIDIA support, you might still want to use the NVIDIA CUDA Toolkit, which is typically available in Linux-based Docker images.
If you want to build and run your application using Docker on Windows, you will need to use a Windows-based Docker image and adapt the Dockerfile accordingly to use Windows commands and tools. This might require finding alternative ways to set up the environment and install the necessary dependencies on Windows.
To use the NVIDIA GPU with Docker on Windows, you can check if NVIDIA GPU support is available in Windows Docker containers and how to enable it. Note that GPU support in Windows Docker containers might have certain limitations compared to Linux-based containers.
If you need to use Linux-specific tools, such as `apt-get` and `pip3`, you might consider using a Linux environment or a Linux virtual machine for development, where you can use Linux-based Docker images without any compatibility issues.
The GPU seems to be native in windows
If your system has an NVIDIA GPU and you are using Docker for Windows with WSL 2 (Windows Subsystem for Linux 2) backend, then you can indeed leverage the native GPU support for Linux-based containers running inside WSL 2. This allows you to access the GPU from within Linux containers running on your Windows machine.
To enable GPU support for your Docker containers, you’ll need to follow these steps:
1. Make sure you have the necessary requirements:
– Windows 10 with WSL 2 enabled (Check the Windows version and WSL installation)
– Docker Desktop installed and running with WSL 2 as the default backend
– NVIDIA GPU drivers installed on your Windows host machine
2. Enable GPU support for your WSL 2 installation:
Open a PowerShell terminal as Administrator and run the following command:
“`
wsl –set-default-version 2
“`
3. Install the NVIDIA Container Toolkit for WSL 2:
NVIDIA provides a set of tools to enable GPU support for containers running inside WSL 2. You can follow the official documentation for installation instructions: https://docs.nvidia.com/cuda/wsl-user-guide/index.html
4. Configure Docker to use the WSL 2 backend:
Open Docker Desktop settings and go to the “Resources” > “WSL Integration” section. Make sure the WSL 2 distribution you want to use (e.g., Ubuntu) is enabled.
5. Build and run your Docker container with GPU support:
Now that your system is set up with GPU support for WSL 2, you can build and run your Docker container as you would normally do in a Linux environment. The GPU should be accessible from within the container.
Keep in mind that GPU support for Docker on Windows using WSL 2 is still evolving, and you might encounter some limitations or issues depending on your specific setup and configurations. Make sure to check the official documentation and resources for the latest information and updates.
I DO NOT WANT ANYTH
ING TO DO WITH WSL
I apologize for the confusion. If you want to run the Docker containers without using WSL and want to utilize the native NVIDIA GPU support on Windows, you can follow these steps:
1. Ensure you have Docker Desktop installed on your Windows machine and have enabled “Use the WSL 2 based engine” in Docker Desktop settings. This setting is required to use the native GPU support.
2. Install the NVIDIA Container Toolkit for Windows:
The NVIDIA Container Toolkit for Windows allows Docker containers to access NVIDIA GPUs on the host system. You can download and install it following the instructions provided by NVIDIA: https://github.com/NVIDIA/nvidia-docker
3. Configure Docker to use the Windows containers:
Open Docker Desktop settings and go to the “General” section. Make sure “Enable Windows containers” is selected.
4. Build and run your Docker container with GPU support:
Now that you have set up the NVIDIA Container Toolkit and enabled Windows containers, you can build and run your Docker container as usual. The GPU should be accessible from within the container.
To summarize, when using Docker Desktop on Windows with native GPU support, you do not need to use WSL. Instead, you can enable Windows containers and install the NVIDIA Container Toolkit for Windows to leverage the GPU capabilities on your Windows machine.
Cannot stop Docker Compose application. Reason: compose [stop] exit status 1. sh: 1: cd: can’t cd to C:SarahOneSuperAgiappSuperAGI could not find super__tgwui: not found
The error message you encountered indicates that Docker Compose is having trouble stopping the application. The specific error “sh: 1: cd: can’t cd to C:SarahOneSuperAgiappSuperAGI could not find super__tgwui: not found” suggests that there might be an issue with the paths or naming of the containers.
Here are some steps you can follow to troubleshoot and resolve the issue:
1. Verify Docker Compose File: Double-check your `docker-compose.yml` file for any mistakes in the paths or container names. Ensure that all container names are correct and consistently spelled throughout the file.
2. Check Running Containers: Run `docker ps` in your terminal to check if the containers are running and to verify their names. Ensure that the container named `super__tgwui` is running and has the expected name.
3. Use Full Paths: When running Docker Compose commands, use full paths to the `docker-compose.yml` file. For example, if your `docker-compose.yml` file is in the `SuperAgi` folder, run commands like this:
“`
docker-compose -f /path/to/SuperAgi/docker-compose.yml stop
“`
4. Check Docker Compose Context: Verify that you are in the correct directory (context) when running the Docker Compose commands. Make sure you are in the same directory where your `docker-compose.yml` file is located.
5. Clean Up Docker Containers: If the containers are not stopping properly, you can try to force them to stop using the following command:
“`
docker-compose -f /path/to/SuperAgi/docker-compose.yml down
“`
This will stop and remove all the containers defined in the `docker-compose.yml` file.
6. Restart Docker: If none of the above steps work, try restarting Docker Desktop. Sometimes, restarting the Docker service can resolve certain issues.
Please ensure that you have the correct paths and names for the containers in your `docker-compose.yml` file and try the above steps to stop your Docker Compose application successfully.
I deleeted it and want to rename another container witht ath name
If you want to rename an existing Docker container, you cannot directly change the container’s name, but you can create a new container with the desired name and then stop and remove the old container. Here’s how you can do it:
1. List existing containers:
Run `docker ps -a` to list all existing containers, both running and stopped. Identify the container you want to rename and note its current name or container ID.
2. Stop the container:
If the container is currently running, stop it using the command:
“`
docker stop
“`
3. Rename the container:
Create a new container with the desired name using the `–name` option. For example:
“`
docker create –name
“`
Replace “ with the name you want to give to the container, and “ with the image that was used to create the original container.
4. Copy data (optional):
If your original container had data or configurations that you want to preserve, you can copy them to the new container using `docker cp`.
5. Remove the old container:
Once you have the new container set up, you can remove the old container using the command:
“`
docker rm
“`
6. Start the new container:
Finally, start the new container with the updated name using the command:
“`
docker start
“`
Now, the new container will have the desired name, and the old container will be removed. Remember that renaming a container does not change the image or any data within the container; it only changes the container’s name for easier identification.
docker create –name super__twgui happy_austin
It looks like you want to create a new Docker container named `super__twgui` based on an image named `happy_austin`. The `docker create` command is used to create a container, but it doesn’t start the container immediately. To create and start a new container, you can use the `docker run` command with the `–name` option. Here’s the correct command:
“`bash
docker run –name super__twgui happy_austin
“`
This command will create and start a new container named `super__twgui` using the image `happy_austin`. If the image `happy_austin` doesn’t exist locally, Docker will try to pull it from a container registry like Docker Hub. If you want to specify additional options or expose ports, you can add them to the `docker run` command. For example:
“`bash
docker run –name super__twgui -p 8000:8000 -d happy_austin
“`
This command creates and starts a new container named `super__twgui`, maps port 8000 on the host to port 8000 in the container, and runs the container in detached mode (`-d`).
docker run –hostname=f42f8987f025 –mac-address=02:42:ac:11:00:02 –env=PATH=/venv/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin –env=NVARCH=x86_64 –env=NVIDIA_REQUIRE_CUDA=cuda>=11.8 brand=tesla,driver>=450,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver<516 –env=NV_CUDA_CUDART_VERSION=11.8.89-1 –env=NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-8 –env=CUDA_VERSION=11.8.0 –env=LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 –env=NVIDIA_VISIBLE_DEVICES=all –env=NVIDIA_DRIVER_CAPABILITIES=compute,utility –env=NV_CUDA_LIB_VERSION=11.8.0-1 –env=NV_NVTX_VERSION=11.8.86-1 –env=NV_LIBNPP_VERSION=11.8.0.86-1 –env=NV_LIBNPP_PACKAGE=libnpp-11-8=11.8.0.86-1 –env=NV_LIBCUSPARSE_VERSION=11.7.5.86-1 –env=NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-8 –env=NV_LIBCUBLAS_VERSION=11.11.3.6-1 –env=NV_LIBCUBLAS_PACKAGE=libcublas-11-8=11.11.3.6-1 –env=NV_LIBNCCL_PACKAGE_NAME=libnccl2 –env=NV_LIBNCCL_PACKAGE_VERSION=2.15.5-1 –env=NCCL_VERSION=2.15.5-1 –env=NV_LIBNCCL_PACKAGE=libnccl2=2.15.5-1+cuda11.8 –env=NVIDIA_PRODUCT_NAME=CUDA –env=NV_CUDA_CUDART_DEV_VERSION=11.8.89-1 –env=NV_NVML_DEV_VERSION=11.8.86-1 –env=NV_LIBCUSPARSE_DEV_VERSION=11.7.5.86-1 –env=NV_LIBNPP_DEV_VERSION=11.8.0.86-1 –env=NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-8=11.8.0.86-1 –env=NV_LIBCUBLAS_DEV_VERSION=11.11.3.6-1 –env=NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-8 –env=NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-8=11.11.3.6-1 –env=NV_CUDA_NSIGHT_COMPUTE_VERSION=11.8.0-1 –env=NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-11-8=11.8.0-1 –env=NV_NVPROF_VERSION=11.8.87-1 –env=NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-8=11.8.87-1 –env=NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev –env=NV_LIBNCCL_DEV_PACKAGE_VERSION=2.15.5-1 –env=NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.15.5-1+cuda11.8 –env=LIBRARY_PATH=/usr/local/cuda/lib64/stubs –env=VIRTUAL_ENV=/venv –env=PYTHONUNBUFFERED=1 –env=BUILD_DATE= –env=LLAMA_CUBLAS=1 –env=EXTRA_LAUNCH_ARGS= –workdir=/app -p 5000:5000 -p 5001:5001 –restart=no –label='com.docker.compose.project=superagi' –label='com.docker.compose.service=super__tgwui' –label='com.docker.compose.version=2.19.1' –label='desktop.docker.io/wsl-distro=Ubuntu' –label='maintainer=NVIDIA CORPORATION ‘ –label=’org.opencontainers.image.ref.name=ubuntu’ –label=’org.opencontainers.image.version=22.04′ –runtime=nvidia -d atinoda/text-generation-webui:llama-cublas
It looks like you are trying to run a Docker container with a long list of environment variables and options. However, the command you provided is incomplete and contains some errors. To fix it, you should format the command properly and remove any unnecessary environment variables.
Since the provided command is quite lengthy, let me break it down into smaller sections to explain what each part does:
“`bash
docker run
–hostname=f42f8987f025
–mac-address=02:42:ac:11:00:02
–env=PATH=/venv/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
–env=NVARCH=x86_64
–env=NVIDIA_REQUIRE_CUDA=cuda>=11.8,driver>=450,driver=470,driver>=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=470,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=510,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver=515,driver<516
–env=NV_CUDA_CUDART_VERSION=11.8.89-1
–env=NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-8
–env=CUDA_VERSION=11.8.0
–env=LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
–env=NVIDIA_VISIBLE_DEVICES=all
–env=NVIDIA_DRIVER_CAPABILITIES=compute,utility
–env=NV_CUDA_LIB_VERSION=11.8.0-1
–env=NV_NVTX_VERSION=11.8.86-1
–env=NV_LIBNPP_VERSION=11.8.0.86-1
–env=NV_LIBNPP_PACKAGE=libnpp-11-8=11.8.0.86-1
–env=NV_LIBCUSPARSE_VERSION=11.7.5.86-1
–env=NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-8
–env=NV_LIBCUBLAS_VERSION=11.11.3.6-1
–env=NV_LIBCUBLAS_PACKAGE=libcublas-11-8=11.11.3.6-1
–env=NV_LIBNCCL_PACKAGE_NAME=libnccl2
–env=NV_LIBNCCL_PACKAGE_VERSION=2.15.5-1
–env=NCCL_VERSION=2.15.5-1
–env=NV_LIBNCCL_PACKAGE=libnccl2=2.15.5-1+cuda11.8
–env=NVIDIA_PRODUCT_NAME=CUDA
–env=NV_CUDA_CUDART_DEV_VERSION=11.8.89-1
–env=NV_NVML_DEV_VERSION=11.8.86-1
–env=NV_LIBCUSPARSE_DEV_VERSION=11.7.5.86-1
–env=NV_LIBNPP_DEV_VERSION=11.8.0.86-1
–env=NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-8=11.8.0.86-1
–env=NV_LIBCUBLAS_DEV_VERSION=11.11.3.6-1
–env=NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-8
–env=NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-8=11.11.3.6-1
–env=NV_CUDA_NSIGHT_COMPUTE_VERSION