Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
C-Rank Grade: A (Welcoming) - 64/100
External PR Merge Rate: 76%
Response Time: 1d
First Timer Success: 63%
tracel-ai/burn has a recorded first-timer success rate of 62.8%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in tracel-ai/burn respond to incoming external pull requests in approximately 30.6 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 63.9 places tracel-ai/burn in the A tier.
The external contributor pull request merge rate for tracel-ai/burn is 75.8%, based on public PR activity from non-core contributors.
tracel-ai/burn does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Highly welcoming maintainers in tracel-ai/burn. Prompt code reviews with positive guidance for new contributors.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/tracel-ai/burn?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 14:00 UTC (14:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 13:00 to 17:00 UTC (13:00 - 17:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~20 hours (95% approval). Large PRs (>500 lines) take ~148 hours (34% approval). Keep your diff compact for rapid review.
Describe the bug tui panics during training of mnist example To Reproduce cargo run --example mnist --release --features cuda Expected behavior No panic Screenshots Desktop (please complete the following information): OS: nixos 25.11 Additional context ... 2026-04-13T22:40:59.020414Z WARN burn_train::renderer::tui::renderer: Failed to send TUI event: sending on a closed channel 2026-04-13T22:40:59.031327Z WARN burn_train::renderer::tui::renderer: Failed to send TUI event: sending on a closed channel 2026-04-13T22:40:59.031334Z WARN burn_train::renderer::tui::renderer: Failed to send TUI event: sending on a closed channel 2026-04-13T22:40:59.031346Z ERROR burn_train::learner::application_logger: PANIC => panicked at crates/burn-train/src/renderer/tui/renderer.rs:235:45: called `Option::unwrap()` on a `None` value
Feature description Bring back the RL examples from the burn-rl crate, disabled by default from builds here burn/Cargo.toml Line 19 in ed72d2b "examples/dqn-agent", # gym-rs This is disabled because of the outdated dependency MathisWellmann/gym-rs#14 This does not build on modern linux e.g. arch linux, where CMAKE and SDL included by sdl-sys are very out of date and do not compile. Feature motivation Build on RL example with more implementations e.g. https://github.com/yunjhongwu/burn-rl-examples (Optional) Suggest a Solution Remove rendering by removing sdl feature, e.g. yunjhongwu/burn-rl-examples#54 or change dependency to modern fork e.g. MathisWellmann/gym-rs#14 I have done this in a fork main...johnny-smitherson:burn:rl-examples-fix
Feature description Add metrics for assessing image quality, including deep learning metrics. L1/MAE loss, torch, torch-image-metrics Closed via #4341 L2/MSE loss, torch, torch-image-metrics Closed via #4341 PSNR, Wikipedia, scikit-image, PyTorch-ignite, torch-image-metrics Closed via #4379 SSIM, Wikipedia, scikit-image, PyTorch-ignite, torch-image-metrics Closed via #4396 MS-SSIM, lightning.ai, pytorch-msssim Closed via #4555 Smooth L1, torch Closed via #4547 Gram Matrix Loss, torch tutorial, piq Closed via #4595 FID, lightning.ai, pytorch-fid, torch-image-metrics Closed via #4644 LPIPS, lightning.ai, torch-image-metrics Closed via #4403 DISTS, lightning.ai Closed via #4574 A-FINE, IQA-PyTorch Feature motivation When training visual AI models the loss function may include various image metrics. Users would have to re-implement these image metrics unless readily available. Examples Take for example the impressive recent ml-sharp which uses a
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When evaluating whether to contribute to tracel-ai/burn, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of tracel-ai/burn acknowledge new external contributions in approximately 1 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 75.8% were successfully merged into the primary branch.
tracel-ai/burn has a recorded first-timer success rate of 62.8%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~1 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 63.9 places tracel-ai/burn in the Welcoming tier.
The external pull request merge rate is 75.8%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
tracel-ai/burn does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for tracel-ai/burn are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.