--- title: "amazon-sagemaker-examples vs DeepSpeed" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-amazon-sagemaker-examples-vs-deepspeedai-deepspeed" tools: ["aws-amazon-sagemaker-examples", "deepspeedai-deepspeed"] --- # amazon-sagemaker-examples vs DeepSpeed *GraphCanon updated Jul 15, 2026* ## Verdict Pick amazon-sagemaker-examples when amazon-sagemaker-examples is primarily Jupyter Notebook; DeepSpeed is Python; pick DeepSpeed when deepSpeed is primarily Python; amazon-sagemaker-examples is Jupyter Notebook. [amazon-sagemaker-examples](https://sagemaker-examples.readthedocs.io) reports 11k GitHub stars, 7.0k forks, and 849 open issues, last pushed Jul 7, 2026. [DeepSpeed](https://www.deepspeed.ai/) has 43k stars, 4.9k forks, and 1.3k open issues, last pushed Jul 13, 2026. Figures are from public GitHub metadata via [amazon-sagemaker-examples's repository](https://github.com/aws/amazon-sagemaker-examples) and [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed). | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | | --- | --- | --- | | Tagline | Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker. | Deep learning optimization library for efficient distributed training and inference | | Stars | 10,971 | 42,700 | | Forks | 6,969 | 4,881 | | Open issues | 849 | 1,299 | | Language | Jupyter Notebook | 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 | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 7d | 0d | | Open issues (now) | 849 | 1.3k | | Full report | [trust report](/tools/aws-amazon-sagemaker-examples/trust.md) | [trust report](/tools/deepspeedai-deepspeed/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 amazon-sagemaker-examples if… - amazon-sagemaker-examples is primarily Jupyter Notebook; DeepSpeed is Python. - Tags unique to amazon-sagemaker-examples: aws, data-science, examples, jupyter-notebook. - Leaner open-issue backlog (849). ### Choose DeepSpeed if… - DeepSpeed is primarily Python; amazon-sagemaker-examples is Jupyter Notebook. - Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu. - - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters) ## When NOT to use amazon-sagemaker-examples - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## 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 ## Common questions ### What is the difference between amazon-sagemaker-examples and DeepSpeed? amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.. DeepSpeed: Deep learning optimization library for efficient distributed training and inference. See the comparison table for live GitHub stats and shared categories. ### When should I choose amazon-sagemaker-examples over DeepSpeed? Choose amazon-sagemaker-examples over DeepSpeed when amazon-sagemaker-examples is primarily Jupyter Notebook; DeepSpeed is Python; Tags unique to amazon-sagemaker-examples: aws, data-science, examples, jupyter-notebook; Leaner open-issue backlog (849). ### When should I choose DeepSpeed over amazon-sagemaker-examples? Choose DeepSpeed over amazon-sagemaker-examples when DeepSpeed is primarily Python; amazon-sagemaker-examples is Jupyter Notebook; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters). ### When should I avoid amazon-sagemaker-examples? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### 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 ### Is amazon-sagemaker-examples or DeepSpeed more popular on GitHub? DeepSpeed has more GitHub stars (42,700 vs 10,971). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-sagemaker-examples and DeepSpeed open source? Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, DeepSpeed: Apache-2.0). ### Where can I find alternatives to amazon-sagemaker-examples or DeepSpeed? GraphCanon lists graph-backed alternatives at [amazon-sagemaker-examples alternatives](/tools/aws-amazon-sagemaker-examples/alternatives) and [DeepSpeed alternatives](/tools/deepspeedai-deepspeed/alternatives) ([amazon-sagemaker-examples markdown twin](/tools/aws-amazon-sagemaker-examples/alternatives.md), [DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/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/aws-amazon-sagemaker-examples-vs-deepspeedai-deepspeed.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, amazon-sagemaker-examples or DeepSpeed? amazon-sagemaker-examples: Active. DeepSpeed: 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 amazon-sagemaker-examples and DeepSpeed? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-sagemaker-examples trust report](/tools/aws-amazon-sagemaker-examples/trust); [DeepSpeed trust report](/tools/deepspeedai-deepspeed/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aws-amazon-sagemaker-examples`](/api/graphcanon/graph?tool=aws-amazon-sagemaker-examples) - 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/_