Update Dockerfile

[HYLcool]

• update dockerfile to install embodied-related packages
• use uv in unit test directly
• update cuda version to 12.6.3 and ubuntu version to 24.04
• update to python 3.11

[gemini-code-assist]

Summary of Changes

Hello @HYLcool, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on a comprehensive update to the project's Docker environment. The core changes involve modernizing the underlying operating system and CUDA versions, upgrading the Python runtime, and integrating new simulation and robotics-related frameworks. These updates aim to provide a more current and capable development environment, particularly for tasks involving embodied AI or simulation, while also refining existing dependency installations.

Highlights

• Base Image and Python Upgrade: The Dockerfile has been updated to use a newer base image, upgrading from nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04 to nvidia/cuda:12.6.3-cudnn-devel-ubuntu24.04. This also includes an upgrade of the default Python version within the container from 3.10 to 3.11.
• Embodied-Related Packages Integration: Significant additions have been made to install embodied-related packages, specifically isaacsim[all,extscache]==5.1.0 and IsaacLab v2.3.0, along with necessary system dependencies and Vulkan setup for NVIDIA.
• System Dependencies and Tooling Enhancements: Various system dependencies have been expanded, including the installation of Git LFS, gcc-11/g++-11, and a more comprehensive set of graphics and development libraries. The recognize-anything package source has also been updated to a Git repository.

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Ignored Files

• Ignored by pattern: .github/workflows/** (3)
  • .github/workflows/perf-bench.yml
  • .github/workflows/unit-test-partial.yml
  • .github/workflows/unit-test.yml

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[gemini-code-assist]

Code Review

This pull request updates the Dockerfile to use newer versions of CUDA, Ubuntu, and Python, and adds several new dependencies for 'embodied-related packages' like Isaac Sim and Isaac Lab. The changes are extensive and generally improve the setup.

My review focuses on Dockerfile best practices to optimize image size and ensure build reproducibility. I've suggested:

• Combining multiple RUN commands into single layers.
• Cleaning up apt cache after installations to reduce image size.
• Pinning a git-based dependency to a specific commit to ensure reproducible builds.

These changes should help in creating a more efficient and maintainable Docker image.