Comparison
pytorch-lightning vs onnx-mlir
Verdict
Pick pytorch-lightning if pyTorch Lightning scales PyTorch models across GPUs with minimal code changes; pick onnx-mlir if oNNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments.
Markdown twin · pytorch-lightning alternatives · onnx-mlir alternatives
GraphCanon updated Sep 30, 2026
7views this month
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Trust & integrity
| Signal | pytorch-lightning | onnx-mlir |
|---|---|---|
| Maintenance | Very active (5d since push) As of Sep 27, 2026 · github_public_v1 | Very active (0d since push) As of Sep 30, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 27, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- pytorch-lightning
- Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
- onnx-mlir
- ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes
Stars
- pytorch-lightning
- 31k
- onnx-mlir
- 1.1k
Forks
- pytorch-lightning
- 3.8k
- onnx-mlir
- 451
Open issues
- pytorch-lightning
- 1.1k
- onnx-mlir
- 367
Language
- pytorch-lightning
- Python
- onnx-mlir
- MLIR
Adopt for
- pytorch-lightning
- PyTorch Lightning scales PyTorch models across GPUs with minimal code changes.
- onnx-mlir
- ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments.
Persona
- pytorch-lightning
- -
- onnx-mlir
- -
Runtime
- pytorch-lightning
- -
- onnx-mlir
- -
License
- pytorch-lightning
- Apache-2.0
- onnx-mlir
- Available under the Apache License Version 2.0 (Apache-2.0). Permissions granted for reproduction, distribution, etc., as per license terms.
Last pushed
- pytorch-lightning
- Sep 21, 2026
- onnx-mlir
- Sep 29, 2026
Categories
- pytorch-lightning
- Inference & Serving, Model Training
- onnx-mlir
- Inference & Serving, Model Training
Trust and health
Days since push
- pytorch-lightning
- 5d
- onnx-mlir
- 0d
Open issues (now)
- pytorch-lightning
- 1.1k
- onnx-mlir
- 367
Stars delta
- pytorch-lightning
- +96 (30d)
- onnx-mlir
- +19 (30d)
Open issues delta
- pytorch-lightning
- +30 (30d)
- onnx-mlir
- +15 (30d)
OSV dependency advisories
- pytorch-lightning
- No published findings from this source as of 2026-07-11
- onnx-mlir
- Published findings
Full report
- pytorch-lightning
- Trust report
- onnx-mlir
- Trust report
Shared compatibility
- Python · pytorch-lightning: Python runtime · onnx-mlir: Python runtime
Choose pytorch-lightning if…
- pytorch-lightning is primarily Python; onnx-mlir is MLIR.
- Tags unique to pytorch-lightning: ai, artificial-intelligence, data-science, deep-learning.
- Scalable ML model training with consistent API across single to multiple GPUs
When NOT to use pytorch-lightning
- For lightweight models requiring minimal configuration or manual control over model distribution
- Projects that target environments without access to multi-GPU setups and do not require scalability features
Choose onnx-mlir if…
- onnx-mlir is primarily MLIR; pytorch-lightning is Python.
- Tags unique to onnx-mlir: compiler, llvm, mlir, onnx.
- For users needing compile-time optimization of ONNX models to improve inference performance in a variety of language runtimes such as C++, Java, and Python
When NOT to use onnx-mlir
- When quick setup and environment management are desired without using prebuilt containers, as setting up prerequisites manually may be challenging
- For teams primarily focused on real-time inference serving with dedicated AI hardware that requires specialized frameworks not covered by ONNX-MLIR's support matrix
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Lightning-AI/pytorch-lightning) · observed Sep 27, 2026
- GitHub forks (Lightning-AI/pytorch-lightning) · observed Sep 27, 2026
- Last push (Lightning-AI/pytorch-lightning) · observed Sep 21, 2026
- License file (Apache-2.0) · observed Sep 27, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (onnx/onnx-mlir) · observed Sep 30, 2026
- GitHub forks (onnx/onnx-mlir) · observed Sep 30, 2026
- Last push (onnx/onnx-mlir) · observed Sep 29, 2026
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: pytorch-lightning 31k · onnx-mlir 1.1k (synced Sep 27, 2026).
Common questions
- What is the difference between pytorch-lightning and onnx-mlir?
- pytorch-lightning: Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.. onnx-mlir: ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes. See the comparison table for live GitHub stats and shared categories.
- When should I choose pytorch-lightning over onnx-mlir?
- Choose pytorch-lightning over onnx-mlir when pytorch-lightning is primarily Python; onnx-mlir is MLIR; Tags unique to pytorch-lightning: ai, artificial-intelligence, data-science, deep-learning; Scalable ML model training with consistent API across single to multiple GPUs.
- When should I choose onnx-mlir over pytorch-lightning?
- Choose onnx-mlir over pytorch-lightning when onnx-mlir is primarily MLIR; pytorch-lightning is Python; Tags unique to onnx-mlir: compiler, llvm, mlir, onnx; For users needing compile-time optimization of ONNX models to improve inference performance in a variety of language runtimes such as C++, Java, and Python.
- When should I avoid pytorch-lightning?
- For lightweight models requiring minimal configuration or manual control over model distribution Projects that target environments without access to multi-GPU setups and do not require scalability features
- When should I avoid onnx-mlir?
- When quick setup and environment management are desired without using prebuilt containers, as setting up prerequisites manually may be challenging For teams primarily focused on real-time inference serving with dedicated AI hardware that requires specialized frameworks not covered by ONNX-MLIR's support matrix
- Is pytorch-lightning or onnx-mlir more popular on GitHub?
- pytorch-lightning has more GitHub stars (31,363 vs 1,058). Stars measure visibility, not whether either tool fits your constraints.
- Are pytorch-lightning and onnx-mlir open source?
- Yes - both are open-source projects on GitHub (pytorch-lightning: Apache-2.0, onnx-mlir: Apache-2.0).
- Where can I find alternatives to pytorch-lightning or onnx-mlir?
- GraphCanon lists graph-backed alternatives at pytorch-lightning alternatives and onnx-mlir alternatives (pytorch-lightning markdown twin, onnx-mlir markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, pytorch-lightning or onnx-mlir?
- pytorch-lightning: Very active. onnx-mlir: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for pytorch-lightning and onnx-mlir?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: pytorch-lightning trust report; onnx-mlir trust report.