Comparison
DeepSpeed vs keras
Verdict
Pick DeepSpeed when tags unique to DeepSpeed: gpu, compression, billion-parameters, mixture-of-experts; pick keras when tags unique to keras: data-science, neural-networks, python, pytorch.
Markdown twin · DeepSpeed alternatives · keras alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | DeepSpeed | keras |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Very active (4d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No criticals As of today · osv@v1 |
Tagline
- DeepSpeed
- Deep learning optimization library for efficient distributed training and inference
- keras
- Deep Learning for humans
Stars
- DeepSpeed
- 43k
- keras
- 64k
Forks
- DeepSpeed
- 4.9k
- keras
- 20k
Open issues
- DeepSpeed
- 1.3k
- keras
- 228
Language
- DeepSpeed
- Python
- keras
- Python
Adopt for
- DeepSpeed
- Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression.
- keras
- -
Persona
- DeepSpeed
- -
- keras
- -
Runtime
- DeepSpeed
- -
- keras
- -
License
- DeepSpeed
- Apache-2.0
- keras
- Apache-2.0
Last pushed
- DeepSpeed
- Jul 11, 2026
- keras
- Jul 7, 2026
Categories
- DeepSpeed
- Model Training, Inference & Serving
- keras
- Model Training
Trust and health
Days since push
- DeepSpeed
- 0d
- keras
- 4d
Open issues (now)
- DeepSpeed
- 1.3k
- keras
- 228
Security scan
- DeepSpeed
- No lockfile
- keras
- No criticals
Full report
- DeepSpeed
- Trust report
- keras
- Trust report
Choose DeepSpeed if…
- Tags unique to DeepSpeed: gpu, compression, billion-parameters, mixture-of-experts.
- Also covers Inference & Serving.
- - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters)
When NOT to use DeepSpeed
- - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs
- - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (deepspeedai/DeepSpeed) · observed Jul 11, 2026
- GitHub forks (deepspeedai/DeepSpeed) · observed Jul 11, 2026
- Last push (deepspeedai/DeepSpeed) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (keras-team/keras) · observed Jul 11, 2026
- GitHub forks (keras-team/keras) · observed Jul 11, 2026
- Last push (keras-team/keras) · observed Jul 7, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: DeepSpeed 43k · keras 64k (synced Jul 11, 2026).
Common questions
- What is the difference between DeepSpeed and keras?
- DeepSpeed: Deep learning optimization library for efficient distributed training and inference. keras: Deep Learning for humans. See the comparison table for live GitHub stats and shared categories.
- When should I choose DeepSpeed over keras?
- Choose DeepSpeed over keras when Tags unique to DeepSpeed: gpu, compression, billion-parameters, mixture-of-experts; Also covers Inference & Serving; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters).
- When should I choose keras over DeepSpeed?
- Choose keras over DeepSpeed when Tags unique to keras: data-science, neural-networks, python, pytorch; More GitHub stars (64k vs 43k) - visibility, not fit.
- When should I avoid DeepSpeed?
- - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
- When should I avoid keras?
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Is DeepSpeed or keras more popular on GitHub?
- keras has more GitHub stars (64,191 vs 42,685). Stars measure visibility, not whether either tool fits your constraints.
- Are DeepSpeed and keras open source?
- Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, keras: Apache-2.0).
- Where can I find alternatives to DeepSpeed or keras?
- GraphCanon lists graph-backed alternatives at DeepSpeed alternatives and keras alternatives (DeepSpeed markdown twin, keras 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, DeepSpeed or keras?
- DeepSpeed: Very active. keras: 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 DeepSpeed and keras?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSpeed trust report; keras trust report.