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Improves contribution guidelines for IsaacLab (isaac-sim#3403)
I realized there were some comments I often have to repeat in my
reviewing process. I tried to add some of them into the code style page
to directly point developers to it.
- This change requires a documentation update
- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [x] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there
---------
Signed-off-by: Mayank Mittal <[email protected]>
Co-authored-by: James Tigue <[email protected]>
@@ -260,20 +243,21 @@ We provide environments for both disassembly and assembly.
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.. attention::
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CUDA is recommended for running the AutoMate environments with 570 drivers. If running with Nvidia driver 570 on Linux with architecture x86_64, we follow the below steps to install CUDA 12.8. This allows for computing rewards in AutoMate environments with CUDA. If you have a different operation system or architecture, please refer to the `CUDA installation page <https://developer.nvidia.com/cuda-12-8-0-download-archive>`_ for additional instruction.
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CUDA is required for running the AutoMate environments.
When using conda, cuda toolkit can be installed with:
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.. code-block:: bash
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conda install cudatoolkit
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With 580 drivers and CUDA 13, we are currently unable to enable CUDA for computing the rewards. The code automatically fallbacks to CPU, resulting in slightly slower performance.
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For addition instructions and Windows installation, please refer to the `CUDA installation page <https://developer.nvidia.com/cuda-12-8-0-download-archive>`_.
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* |disassembly-link|: The plug starts inserted in the socket. A low-level controller lifts the plug out and moves it to a random position. This process is purely scripted and does not involve any learned policy. Therefore, it does not require policy training or evaluation. The resulting trajectories serve as demonstrations for the reverse process, i.e., learning to assemble. To run disassembly for a specific task: ``python source/isaaclab_tasks/isaaclab_tasks/direct/automate/run_disassembly_w_id.py --assembly_id=ASSEMBLY_ID --disassembly_dir=DISASSEMBLY_DIR``. All generated trajectories are saved to a local directory ``DISASSEMBLY_DIR``.
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* |assembly-link|: The goal is to insert the plug into the socket. You can use this environment to train a policy via reinforcement learning or evaluate a pre-trained checkpoint.
@@ -990,10 +974,6 @@ inferencing, including reading from an already trained checkpoint and disabling
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