--- title: "DeepSpeed vs keras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-keras-team-keras" tools: ["deepspeedai-deepspeed", "keras-team-keras"] --- # DeepSpeed vs keras *GraphCanon updated Jul 12, 2026* ## 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. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 4.9k forks, and 1.3k open issues, last pushed Jul 11, 2026. [keras](http://keras.io/) has 64k stars, 20k forks, and 228 open issues, last pushed Jul 7, 2026. Figures are from public GitHub metadata via [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [keras's repository](https://github.com/keras-team/keras). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [keras](/tools/keras-team-keras.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | Deep Learning for humans | | Stars | 42,685 | 64,191 | | Forks | 4,883 | 19,752 | | Open issues | 1,302 | 228 | | Language | Python | Python | | Adopt for | 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. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Model Training, Inference & Serving | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [keras](/tools/keras-team-keras.md) | | --- | --- | --- | | Days since push | 0d | 4d | | Open issues (now) | 1.3k | 228 | | Security scan | No lockfile | No criticals | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/keras-team-keras/trust.md) | ## Decision facts: DeepSpeed - **Adopt for:** 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. ## Choose when ### 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) ### Choose keras if… - Tags unique to keras: data-science, neural-networks, python, pytorch. - More GitHub stars (64k vs 43k) - visibility, not fit. ## 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 ## When NOT to use keras - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## 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](/tools/deepspeedai-deepspeed/alternatives) and [keras alternatives](/tools/keras-team-keras/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/alternatives.md), [keras markdown twin](/tools/keras-team-keras/alternatives.md)), 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](/compare/deepspeedai-deepspeed-vs-keras-team-keras.md) 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](/tools/deepspeedai-deepspeed/trust); [keras trust report](/tools/keras-team-keras/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepspeedai-deepspeed`](/api/graphcanon/graph?tool=deepspeedai-deepspeed) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_