Building a DeepStream container can sometimes lead to frustrating errors, especially when the runtime is not found. This guide will help you resolve the error “failed to solve: runtime not found” while building a DeepStream container on an x86/64 Ubuntu machine for Jetson Nano deployment.
Understanding the DeepStream Docker Error
When you try to build the DeepStream container using the command:
bashCopy codedocker build --network=host -t deepstream_image:jetson
You might encounter the following error:
bashCopy codeERROR: failed to solve: nvcr.io/nvidia/l4t-tensorrt:r8.5.2.2-runtime: nvcr.io/nvidia/l4t-tensorrt:r8.5.2.2-runtime: not found
This error indicates that Docker cannot find the specified TensorRT runtime image (r8.5.2.2-runtime) on NVIDIA’s NGC registry.
Causes of the Error
The primary reasons for this error are:
- Image Version Updates: NVIDIA might have updated the image versions after the release, causing the specified version to be unavailable.
- Incorrect Image Version: Using an incorrect or outdated version of the TensorRT image.
Steps to Resolve the DeepStream Docker Error
Follow these steps to resolve the issue and successfully build your DeepStream container:
1. Verify the Image Version
First, ensure you are using the correct and latest version of the TensorRT image. NVIDIA occasionally updates their image versions, which might not be reflected in older guides.
- Visit NVIDIA NGC TensorRT Container Repository to find the latest version.
- Update your Dockerfile to use the correct version.
2. Update Your Dockerfile
Modify your Dockerfile to use the latest TensorRT image version. For example, if the latest version is r8.5.3.0-runtime, update your Dockerfile accordingly:
DockerfileCopy codeFROM nvcr.io/nvidia/l4t-tensorrt:r8.5.3.0-runtime
3. Clear Docker Cache
Sometimes, Docker’s cached data can cause issues. Clear the Docker build cache to ensure you’re fetching the latest metadata:
bashCopy codedocker builder prune
4. Explicitly Specify VPI Version
If your Dockerfile includes Visual Programming Interface (VPI) installation, specify the VPI version explicitly to avoid compatibility issues:
DockerfileCopy codeRUN apt-get update && apt-get install -y \
vpi2-dev=<specific-version> \
libnvvpi2=<specific-version> \
vpi2-samples=<specific-version>
Use the apt-cache policy command to find available versions:
bashCopy codeapt-cache policy vpi2-dev
5. Rebuild the Docker Image
After making these changes, rebuild your Docker image:
bashCopy codedocker build --network=host -t deepstream_image:jetson .
Example Dockerfile
Here’s an example Dockerfile after making the necessary changes:
DockerfileCopy code# Use the latest TensorRT runtime version
FROM nvcr.io/nvidia/l4t-tensorrt:r8.5.3.0-runtime
# Install VPI
RUN apt-get update && apt-get install -y \
vpi2-dev=2.1.0 \
libnvvpi2=2.1.0 \
vpi2-samples=2.1.0
# Additional setup commands...
Conclusion
By following these steps, you should be able to resolve the “failed to solve: runtime not found” error when building your DeepStream Docker container. Always ensure you are using the correct image versions and explicitly specify any dependencies to avoid compatibility issues.
Remember: Troubleshooting Docker and DeepStream can be complex, but patience and careful attention to detail will help you overcome these challenges. Enjoy your journey with DeepStream and Docker!
For more detailed troubleshooting and community support, consider visiting the NVIDIA Developer Forums and joining relevant discussions.
By following these steps, you should be able to resolve the “failed to solve: runtime not found” error and successfully build your DeepStream Docker container. Always keep your dependencies up-to-date and consult the latest documentation for the best results.
