Micrograd, a minimalist automatic differentiation engine and neural network library, has seen no recent development activity from its core team, despite significant community interest and engagement.
The project, designed for educational purposes and inspired by PyTorch's API, remains popular with nearly 10,000 stars on GitHub. However, the last major contributions were made over four years ago by Andrej Karpathy. Despite this stagnation, the community continues to engage through open issues and pull requests, indicating a sustained interest in enhancing the library's functionality and usability.
Recent issues and pull requests reflect a community-driven effort to address bugs and improve features. Notable issues include #72, which questions the correctness of gradient calculations in multiplication operations, and #67, which highlights potential flaws in topological sorting. These issues suggest areas where the library's robustness could be improved to maintain its educational value.
Andrej Karpathy (karpathy):
Baptiste Pesquet (bpesquet):
Tomak Lutfu (tomaklutfu):
Community-Driven Enhancements: Despite the lack of core team activity, the community actively proposes enhancements such as adding new mathematical functions (#74) and improving code readability (#73).
Documentation Gaps: Several issues highlight the need for better documentation and type annotations (#69), crucial for maintaining usability as new features are added.
Mathematical Operations Focus: Many pull requests aim to refine mathematical functionalities, indicating their critical role in user applications (#70).
Debate on Gradient Behavior: Discussions around PR #63 reveal differing opinions on gradient accumulation practices, reflecting deeper insights into automatic differentiation frameworks.
JIT Compilation Proposal: An ambitious proposal to add JIT compilation support using MLIR (#62) suggests potential performance optimizations that could significantly enhance the library's capabilities.
Developer | Avatar | Branches | PRs | Commits | Files | Changes |
---|---|---|---|---|---|---|
None (tomaklutfu) | 0 | 1/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
Timespan | Opened | Closed | Comments | Labeled | Milestones |
---|---|---|---|---|---|
7 Days | 0 | 0 | 0 | 0 | 0 |
30 Days | 1 | 0 | 0 | 1 | 1 |
90 Days | 4 | 1 | 1 | 4 | 1 |
1 Year | 13 | 4 | 12 | 13 | 1 |
All Time | 29 | 11 | - | - | - |
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.
The recent activity on the micrograd GitHub repository shows a total of 18 open issues, indicating ongoing engagement from the community. Notably, several issues are focused on clarifying implementation details and suggesting improvements, reflecting a collaborative effort to enhance the library's functionality and usability. A recurring theme is the need for better documentation and type annotations, which suggests that while the codebase is compact and educational, users may struggle with understanding certain aspects without additional guidance.
Several issues stand out due to their implications for the project's usability and correctness. For instance, Issue #72 raises concerns about the gradient calculation in multiplication operations, hinting at potential bugs that could affect users' understanding of backpropagation. Similarly, Issue #67 discusses limitations in the topological sort implementation, which could lead to incorrect behavior if cycles are introduced in the computation graph. These issues highlight critical areas that require attention to maintain the integrity of the library.
Issue #78: Question/Idea: Automatic Gradient Clearing
Issue #76: radd
Issue #72: For addition adding incrementing grading makes sense, I can't make sense out of the incrementing it for multiplication too, potential bug?
Issue #69: type annotation lacking/ maybe also add docstrings.
Issue #68: backward member implementation question
Issue #72: For addition adding incrementing grading makes sense, I can't make sense out of the incrementing it for multiplication too, potential bug?
Issue #68: backward member implementation question
Issue #67: Topological sort - bug
Issue #65: Adjusting parameters by sign and magnitude of gradient
Issue #60: Reseting the grad of weights and biases is not enough
The analysis reveals that while there is active engagement with the project, specific issues regarding gradient calculations and documentation need immediate attention to prevent confusion among users and ensure accurate functionality in practical applications.
The analysis of the pull requests (PRs) for the karpathy/micrograd
repository reveals a total of 27 open PRs, with a focus on enhancing functionality, fixing bugs, and improving usability. The contributions range from adding mathematical functions to refining existing features, indicating active community engagement and ongoing development.
PR #74: Added the tanh
nonlinearity function to engine.py
. This addition aims to improve training performance with negative numbers, addressing a gap in the existing functionality.
PR #73: Introduced a more readable text representation for objects like Neuron, Layer, and MLP. This enhancement improves code clarity and usability for users interacting with these classes.
PR #71: Fixed an inheritance bug related to the micrograd.Value
class. This PR addresses issues with hard-coded class constructors in overload methods, improving extensibility.
PR #70: Modified the __pow__()
method to calculate derivatives for both the exponent and base. This change enhances the mathematical capabilities of the library.
PR #63: Attempted to fix an issue where gradients were incorrectly accumulated during multiple calls to backward()
. The discussion highlighted differing opinions on whether this was a bug or a feature.
PR #62: Proposed adding JIT compilation support using MLIR. This ambitious enhancement aims to improve performance by optimizing computation graphs.
PR #61: Updated the README to include paths for notebooks, improving documentation clarity.
PR #56: Fixed issues with reversed subtraction and division operations. Ensures correct behavior in mathematical operations involving Value
objects.
PR #55: Introduced significant speed improvements by reusing expression trees instead of recreating them, optimizing performance during calculations.
PR #49: Added a missing import statement in the README, ensuring users can correctly utilize the library.
PR #48: Improved code readability by inserting empty lines, contributing to better maintainability.
PR #43: Conducted housekeeping on .gitignore
, ensuring unnecessary files are excluded from version control.
PR #41: Added support for exponentiation between two Value
objects, expanding mathematical capabilities.
PR #39: Optimized operations by avoiding unnecessary calls to reversed addition and multiplication methods.
PR #36: Updated power methods (__pow__
and __rpow__
) to handle both base and exponent as Value
objects.
PR #34: Simplified backpropagation by removing unnecessary sequence traversal, streamlining the algorithm.
PR #29: Attempted to add tanh
and sigmoid
functions but faced criticism regarding numerical stability.
The closed PRs include various attempts at improvements, bug fixes, and feature additions that were ultimately not merged due to various reasons such as lack of consensus on implementation details or failure to meet project requirements. Notably:
PR #64 aimed at simplifying backpropagation but was closed after discussions revealed potential issues with its implementation.
PR #59 was focused on creating a companion notebook for video lectures but did not lead to any significant changes in the main codebase.
The current state of open pull requests in the karpathy/micrograd
repository reflects an active community that is continuously seeking to enhance the library's functionality while also addressing bugs and usability issues. The diversity of contributions indicates that users are not only engaged with the core functionalities but are also interested in extending capabilities such as JIT compilation (as seen in PR #62) and adding new mathematical functions (e.g., PRs #74 and #70).
A notable theme is the focus on mathematical operations within the library. Several PRs aim to expand or refine how mathematical functions are implemented—such as adding non-linearities like tanh
, improving power operations, and fixing inheritance issues. This suggests that contributors are keenly aware of how critical these functionalities are for users who may be applying micrograd in educational or experimental contexts.
The discussions around PRs like #63 highlight an interesting dynamic within the community regarding what constitutes a bug versus expected behavior. The differing opinions on gradient accumulation practices illustrate a deeper understanding of how automatic differentiation frameworks operate—an essential aspect for users looking to implement complex models.
Moreover, there is a clear emphasis on documentation improvements (e.g., PRs #61 and #49) which is crucial for maintaining usability as new features are added. Clear documentation helps lower barriers for new users who may be learning about automatic differentiation through micrograd.
However, some PRs have been closed without being merged due to concerns about stability or implementation quality (e.g., PRs #64 and #29). This reflects a healthy level of scrutiny within the community, ensuring that only robust contributions make it into the main codebase. It also indicates that contributors are willing to engage in discussions about best practices, which can lead to better overall quality in future contributions.
In conclusion, while there is significant activity surrounding feature enhancements and bug fixes within micrograd's pull requests, it is essential for maintainers to balance this enthusiasm with rigorous testing and validation processes. As the project evolves, maintaining clarity in documentation and fostering constructive discussions will be key factors in sustaining community engagement and ensuring high-quality contributions.
Andrej Karpathy (karpathy):
setup.py
for package registration.-, /, **
) and corresponding unit tests.Baptiste Pesquet (bpesquet):
Tomak Lutfu (tomaklutfu):