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Recent GitHub Issues Activity

_{Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.}

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PR#274 - added lora fast model supportopen

3_/5

Misha Lvovsky (mishmish66)Created: 2025-02-06

The pull request introduces LoRA FAST training support, which is a potentially significant feature. However, the author admits to not fully testing it due to resource constraints, which raises concerns about its reliability. The changes are well-received by a reviewer who provided several suggestions for improvement, all of which were addressed by the author. Despite these improvements, the lack of thorough testing and potential performance issues (as indicated by the success rate drop in evaluation) prevent it from being rated higher than average. The PR is functional but requires further validation.

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PR#277 - More detailed installation instructions for dockeropen

4_/5

Jimmy Tanner (jimmyt857)Created: 2025-02-07

The pull request provides significant improvements to the Docker installation documentation by adding crucial details that were previously undocumented. These changes enhance clarity and usability, especially for users unfamiliar with Docker configurations. The PR addresses specific issues like rootless mode installation and avoiding Docker Desktop, which are important for proper setup. However, the changes are primarily documentation updates, which limits their impact on the overall project functionality. Thus, while the documentation is much improved, it is not a major code or feature enhancement.

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Quantified Commit Activity Over 14 Days

Developer	Avatar	Branches	PRs	Commits	Files	Changes
uzhilinsky		2	35/35/1	6	121	16452
Jimmy Tanner		2	0/0/0	2	2	11
Ikko Eltociear Ashimine		1	1/1/0	1	1	4
Oier Mees		1	1/1/0	1	1	2
Haohuan Wang (haohuanw)		0	10/6/4	0	0	0
Karl Pertsch (kpertsch)		0	13/11/3	0	0	0
Sergey Levine (svlevine)		0	2/2/0	0	0	0
Jimmy Tanner		0	2/1/0	0	0	0
Misha Lvovsky (mishmish66)		0	1/0/0	0	0	0
Laura Smith (lauramsmith)		0	4/3/1	0	0	0
Michael Equi (Michael-Equi)		0	3/2/1	0	0	0

_{PRs: created by that dev and opened/merged/closed-unmerged during the period}

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Recent Activity Analysis

Recent GitHub issue activity in the Physical-Intelligence/openpi repository has been quite dynamic, with multiple issues created and closed within the last few days. Notably, there are four open issues and nine recently closed ones, indicating active maintenance and community engagement.

Several issues stand out due to their complexity or significance. Issue #271 highlights a performance concern where inference takes over 10 seconds, consuming substantial GPU memory and causing CPU spikes. This is particularly significant as it impacts the usability of the model in real-time applications. The discussion around this issue suggests that JAX's just-in-time compilation and GPU memory preallocation are contributing factors, with suggestions provided for further investigation.

Issue #270 addresses minor friction points in running Docker for ALOHA simulations, pointing to potential documentation improvements needed for smoother user experience. This reflects a common theme of setup and configuration challenges, also seen in other issues like #262 and #261, which involve downloading models and running inference.

Another notable issue is #258, which discusses community engagement through platforms like Discord or WeChat. This indicates a demand for more interactive support channels beyond GitHub, reflecting a broader theme of community building around the project.

Issue Details

Open Issues

1. #273: Add LoRA FAST support

   • Priority: Not specified
   • Status: Open
   • Created: 1 day ago

2. #271: Inference takes over 10 seconds to produce output

   • Priority: High (due to performance impact)
   • Status: Open
   • Created: 1 day ago

3. #270: Minor issue running docker for ALOHA sim

   • Priority: Low
   • Status: Open
   • Created: 1 day ago
   • Updated: Edited on the same day

4. #258: Nice work! Will you open a Discord server for it or for physical intelligence? | 微信交流群

   • Priority: Low
   • Status: Open
   • Created: 3 days ago
   • Updated: Edited 1 day ago

Recently Closed Issues

1. #269: https://github.com/Physical-Intelligence/openpi/blob/openpi/docs/remote_inference.md is missing

   • Priority: Medium
   • Closed: 1 day ago

2. #268: Can't load pi0_libero ckpt

   • Priority: Medium
   • Closed: 1 day ago

3. #263: Model License

   • Priority: Low
   • Closed: 1 day ago

4. #262: Downloading s3 models fails

   • Priority: Medium
   • Closed: 2 days ago

5. #261: Run inference.ipynb and get "ValueError...

   • Priority: High (due to blocking error)
   • Closed: 2 days ago

6. #260: Release original checkpoints on Hugging Face

   • Priority: Low
   • Closed: 1 day ago

7. #257: Model checkpoints finetuned on LIBERO dataset?

   • Priority: Medium
   • Closed: 3 days ago

8. #144 & #141 (Older issues related to model loading and repository access)

   • Both were resolved over a month ago, indicating ongoing efforts to improve accessibility and usability of the project resources.

These issues reflect a mix of technical challenges, performance concerns, community engagement efforts, and documentation improvements, all crucial for the project's success and user satisfaction.

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Analysis of Pull Requests for Physical-Intelligence/openpi

Open Pull Requests

PR #277: More detailed installation instructions for docker

• State: Open
• Created by: Jimmy Tanner (jimmyt857)
• Created: 0 days ago
• Description: This PR adds more detailed Docker installation instructions, addressing undocumented details and resolving issue #270. The changes include recommendations against using Docker Desktop and the snap version of Docker, emphasizing rootless mode installation.
• Comments: No specific reviewer comments yet.
• Commits and Changes:
  • Updates to docs/docker.md with 8 additions and 1 deletion.
  • The changes are recent and seem to address a critical gap in documentation, which could improve user experience significantly.

PR #274: added lora fast model support

• State: Open
• Created by: Misha Lvovsky (mishmish66)
• Created: 1 day ago
• Description: Introduces LoRA FAST training support. The author mentions limited resources for testing but reports successful results with their dataset.
• Comments:
  • Haohuan Wang provided several suggestions for code improvements, which Misha has addressed.
  • The discussion indicates active collaboration and refinement of the implementation.
• Commits and Changes:
  • Multiple commits within a day, indicating rapid development and iteration.
  • Significant changes across four files with a total of 94 additions and 38 deletions.

Notable Closed Pull Requests

PR #276: Document fix

• State: Closed
• Merged by: Ury Zhilinsky
• Description: A minor documentation fix in examples/aloha_real/README.md.
• Commits and Changes:
  • Simple correction with one addition and one deletion.
  • This quick fix ensures clarity in documentation, which is crucial for user guidance.

PR #275: Force pi0_libero to be re-downloaded

• State: Closed
• Merged by: Ury Zhilinsky
• Description: Ensures that the pi0_libero asset is re-downloaded, likely addressing a caching or versioning issue.
• Commits and Changes:
  • Substantial changes in src/openpi/shared/download.py with 53 additions and 35 deletions.
  • This update likely resolves issues related to outdated or incorrect assets being used.

PR #272: docs: update simple_client/README.md

• State: Closed
• Merged by: Ury Zhilinsky
• Description: Corrects a typo in the README file for the simple client example.
• Commits and Changes:
  • Minor textual correction from "specifiy" to "specify".
  • Such corrections, though small, enhance the professionalism and readability of documentation.

PR #264: Default to us-west-1 when downloading from s3://openpi-assets

• State: Closed without merging
• Description: Proposed setting the default AWS region to us-west-1 for S3 downloads.
• Comments:
  • The decision not to merge suggests alternative solutions were found or deemed unnecessary.

Observations

1. Active Development and Collaboration:

   • The repository shows active engagement from multiple contributors, with ongoing discussions on improving code quality and functionality (e.g., PR #274).

2. Focus on Documentation Improvements:

   • Several closed PRs focus on documentation fixes (#276, #272), highlighting an emphasis on maintaining clear user guidance.

3. Technical Enhancements and Bug Fixes:

   • Recent closed PRs like #275 indicate ongoing efforts to ensure technical robustness, particularly concerning asset management.

4. Unmerged PRs as Learning Points or Alternatives Considered:

   • Unmerged PRs like #264 can provide insights into alternative approaches considered during development.

Overall, the repository is actively maintained with a focus on both technical improvements and user experience enhancements through better documentation. The open pull requests suggest ongoing efforts to expand functionality (e.g., LoRA FAST support) while addressing critical setup issues (e.g., Docker instructions).

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Source Code Assessment

File: src/openpi/shared/download.py

URL: download.py

Analysis:

1. Functionality: This file is responsible for downloading files from remote sources, particularly S3, and caching them locally. It supports concurrent downloads and cache invalidation based on predefined expiration rules.

2. Structure & Organization:

   • The code is well-structured with clear separation of concerns, such as cache management, permission handling, and different download mechanisms (e.g., using boto3 or fsspec).
   • Helper functions are used effectively to encapsulate specific functionalities like _download_boto3, _ensure_permissions, etc.

3. Quality:

   • The use of logging provides good traceability for operations.
   • Error handling is robust, especially in managing permissions and file existence checks.
   • The use of environment variables for configuration (e.g., _OPENPI_DATA_HOME) adds flexibility.

4. Concurrency: The use of concurrent.futures and file locks (filelock.FileLock) ensures safe concurrent access to resources.

5. Security:

   • Permissions are managed explicitly, which is crucial for shared environments.
   • The code handles both signed and unsigned S3 URLs, which could be a potential security concern if not managed properly.

6. Performance:

   • The use of a progress bar (tqdm) for download progress is user-friendly.
   • Multi-threaded downloads via boto3 improve performance for large files.

File: src/openpi/training/data_loader.py

URL: data_loader.py

Analysis:

1. Functionality: This file defines data loading mechanisms for training models, including dataset transformations and batching using PyTorch's DataLoader.

2. Structure & Organization:

   • Protocols are used to define interfaces (Dataset, DataLoader), promoting a clean architecture.
   • The TransformedDataset class encapsulates dataset transformation logic, enhancing modularity.

3. Quality:

   • The code is well-documented with docstrings explaining the purpose and usage of classes and functions.
   • Type hints are extensively used, improving readability and maintainability.

4. Performance:

   • The implementation leverages JAX for efficient computation and sharding, although multi-process data loading is not supported.
   • The use of PyTorch's DataLoader with multiprocessing options allows for scalable data loading.

5. Flexibility & Extensibility:

   • Supports various configurations through the create_data_loader function, allowing customization of sharding, normalization, and batching.
   • The design allows easy integration of new datasets by implementing the Dataset protocol.

File: examples/aloha_real/README.md

URL: README.md

Analysis:

1. Clarity & Completeness:

   • Provides comprehensive instructions for setting up and running experiments with ALOHA robots.
   • Includes both Docker-based and non-Docker setups, catering to different user preferences.

2. Usefulness:

   • Lists prerequisites clearly, ensuring users understand dependencies before proceeding.
   • Offers detailed scene setup guidelines with images to aid in replicating experimental conditions accurately.

3. Organization:

   • Sections are logically organized, guiding users step-by-step from setup to execution.
   • Use of headings and bullet points enhances readability.

4. Accuracy & Relevance:

   • Checkpoint paths and prompts are specified for different tasks, providing essential information for users to replicate experiments.

File: examples/simple_client/README.md

URL: README.md

Analysis:

1. Clarity & Simplicity:

   • Offers a straightforward guide to running a simple client for testing inference without a robot.
   • Instructions are concise, focusing on key steps necessary to execute the example.

2. Usefulness:

   • Provides both Docker-based and manual execution options, accommodating various user environments.

3. Completeness:

   • Could benefit from additional context or troubleshooting tips for common issues users might encounter during setup.

File: .github/workflows/test.yml

URL: test.yml

Analysis:

1. Functionality: Defines a CI/CD workflow triggered on pull requests to run tests using pytest.

2. Structure & Clarity:

   • Clearly structured with distinct steps for checkout, environment setup, installation, and test execution.

3. Efficiency & Coverage:

   • Uses a specific runner (openpi-verylarge), which should be verified for availability across environments.
   • Ensures that tests marked as "manual" are excluded from automated runs, focusing on relevant automated tests only.

4. Maintainability & Flexibility:

   • Relies on external actions (actions/checkout, astral-sh/setup-uv), which should be monitored for updates or deprecations.

File: docs/docker.md

URL: docker.md

Analysis:

1. Clarity & Usefulness:

   • Provides clear instructions on setting up Docker environments, emphasizing its benefits over traditional installations.

2. Completeness & Relevance:

   • Includes links to official Docker documentation and NVIDIA toolkit setup for GPU support, ensuring users have access to comprehensive resources.

3. Organization & Accessibility:

   • Well-organized with concise language that facilitates quick understanding and implementation by users at various skill levels.

Overall, the files demonstrate a high level of quality in terms of structure, clarity, and functionality. They provide essential components for downloading data efficiently, loading datasets for training robustly, setting up environments using Docker effectively, and ensuring continuous integration through GitHub Actions workflows.

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Development Team and Recent Activity

Team Members and Activities

• Ury Zhilinsky (uzhilinsky)

  • Recent activities include fixing documentation, updating download scripts to force re-downloads, setting default regions for downloads, and adding links to public data in README files. Ury has been active across multiple branches, contributing significantly with 6 commits involving 16,452 changes across 121 files.

• Ikko Eltociear Ashimine (eltociear)

  • Made a documentation correction in the simple_client/README.md file, with a single commit involving 4 changes.

• Jimmy Tanner (jimmyt857)

  • Worked on updating GitHub workflows by using a new runner. Made two commits with 11 changes across two files. Also contributed to more detailed Docker installation instructions.

• Oier Mees (mees)

  • Added a link to public ALOHA data in the README file with one commit involving 2 changes.

Patterns and Themes

• Documentation and Readability Improvements: Several recent commits focus on improving documentation, such as fixing typos and adding links to external resources. This indicates an effort to enhance the usability and accessibility of the repository for users.

• Infrastructure and Configuration Updates: Changes to download scripts and GitHub workflows suggest ongoing efforts to streamline development processes and ensure reliable access to necessary resources.

• Collaboration: Ury Zhilinsky appears to be the most active contributor, frequently collaborating with other team members like Jimmy Tanner. The presence of multiple contributors working on similar files indicates collaborative efforts in maintaining and improving the project.

• Focus on Defaults and Usability: Setting defaults for data parallel sharding and download regions highlights a focus on making the software easier to use out of the box, which is crucial for user adoption.

Overall, the recent activities reflect a balanced focus on enhancing both the technical robustness and user-friendliness of the openpi project.