Comparison
horovod vs pytorch-lightning
Verdict
Pick horovod if simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes; pick pytorch-lightning if pyTorch Lightning scales PyTorch models across GPUs with minimal code changes.
Markdown twin · horovod alternatives · pytorch-lightning alternatives
GraphCanon updated Sep 27, 2026
10views this month
vs
Trust & integrity
| Signal | horovod | pytorch-lightning |
|---|---|---|
| Maintenance | Archived (8d since push) As of Sep 26, 2026 · github_public_v1 | Very active (5d since push) As of Sep 27, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 26, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 27, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No published findings from this source as of 2026-07-11 As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | No lockfile (source not queried) As of Aug 2, 2026 · deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Published findings As of Jul 26, 2026 · openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- horovod
- Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
- pytorch-lightning
- Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Stars
- horovod
- 15k
- pytorch-lightning
- 31k
Forks
- horovod
- 2.2k
- pytorch-lightning
- 3.8k
Open issues
- horovod
- 407
- pytorch-lightning
- 1.1k
Language
- horovod
- Python
- pytorch-lightning
- Python
Adopt for
- horovod
- Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes.
- pytorch-lightning
- PyTorch Lightning scales PyTorch models across GPUs with minimal code changes.
Persona
- horovod
- -
- pytorch-lightning
- -
Runtime
- horovod
- -
- pytorch-lightning
- -
License
- horovod
- Other
- pytorch-lightning
- Apache-2.0
Last pushed
- horovod
- Sep 17, 2026
- pytorch-lightning
- Sep 21, 2026
Categories
- horovod
- Model Training
- pytorch-lightning
- Inference & Serving, Model Training
Trust and health
Maintenance
- horovod
- Archived (8%)
- pytorch-lightning
- Very active (96%)
Days since push
- horovod
- 8d
- pytorch-lightning
- 5d
Archived on GitHub
- horovod
- Yes
- pytorch-lightning
- No
Open issues (now)
- horovod
- 407
- pytorch-lightning
- 1.1k
Stars delta
- horovod
- -13 (30d)
- pytorch-lightning
- +96 (30d)
Open issues delta
- horovod
- +1 (30d)
- pytorch-lightning
- +30 (30d)
OSV dependency advisories
- horovod
- No lockfile (source not queried)
- pytorch-lightning
- No published findings from this source as of 2026-07-11
deps.dev advisories
- horovod
- No lockfile (source not queried)
- pytorch-lightning
- Not queried
OpenSSF Scorecard
- horovod
- Published findings
- pytorch-lightning
- Not queried
Full report
- horovod
- Trust report
- pytorch-lightning
- Trust report
Choose horovod if…
- License: horovod is Other, pytorch-lightning is Apache-2.0.
- Tags unique to horovod: distributed training, keras, mxnet, tensorflow.
- When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts.
When NOT to use horovod
- Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility.
- Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch.
Choose pytorch-lightning if…
- License: pytorch-lightning is Apache-2.0, horovod is Other.
- Tags unique to pytorch-lightning: ai, artificial-intelligence, data-science, machine-learning.
- Also covers Inference & Serving.
- 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (horovod/horovod) · observed Sep 26, 2026
- GitHub forks (horovod/horovod) · observed Sep 26, 2026
- Last push (horovod/horovod) · observed Sep 17, 2026
- License file (Other) · observed Sep 26, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: horovod 15k · pytorch-lightning 31k (synced Sep 26, 2026).
Common questions
- What is the difference between horovod and pytorch-lightning?
- horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.. pytorch-lightning: Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.. See the comparison table for live GitHub stats and shared categories.
- When should I choose horovod over pytorch-lightning?
- Choose horovod over pytorch-lightning when License: horovod is Other, pytorch-lightning is Apache-2.0; Tags unique to horovod: distributed training, keras, mxnet, tensorflow; When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts.
- When should I choose pytorch-lightning over horovod?
- Choose pytorch-lightning over horovod when License: pytorch-lightning is Apache-2.0, horovod is Other; Tags unique to pytorch-lightning: ai, artificial-intelligence, data-science, machine-learning; Also covers Inference & Serving; Scalable ML model training with consistent API across single to multiple GPUs.
- When should I avoid horovod?
- Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility. Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch.
- 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
- Is horovod or pytorch-lightning more popular on GitHub?
- pytorch-lightning has more GitHub stars (31,363 vs 14,682). Stars measure visibility, not whether either tool fits your constraints.
- Are horovod and pytorch-lightning open source?
- Yes - both are open-source projects on GitHub (horovod: Other, pytorch-lightning: Apache-2.0).
- Where can I find alternatives to horovod or pytorch-lightning?
- GraphCanon lists graph-backed alternatives at horovod alternatives and pytorch-lightning alternatives (horovod markdown twin, pytorch-lightning 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, horovod or pytorch-lightning?
- horovod: Archived. pytorch-lightning: 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 horovod and pytorch-lightning?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: horovod trust report; pytorch-lightning trust report.