Comparison
amazon-sagemaker-examples vs DeepSpeed
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.
Markdown twin · amazon-sagemaker-examples alternatives · DeepSpeed alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | amazon-sagemaker-examples | DeepSpeed |
|---|---|---|
| Maintenance | Active (7d since push) As of today · github_public_v1 | Very active (0d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of 1d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of today · osv@v1 | No lockfile (source not queried) As of 4d · 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
- 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
Stars
- amazon-sagemaker-examples
- 11k
- DeepSpeed
- 43k
Forks
- amazon-sagemaker-examples
- 7.0k
- DeepSpeed
- 4.9k
Open issues
- amazon-sagemaker-examples
- 849
- DeepSpeed
- 1.3k
Language
- amazon-sagemaker-examples
- Jupyter Notebook
- DeepSpeed
- Python
Adopt for
- amazon-sagemaker-examples
- -
- 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.
Persona
- amazon-sagemaker-examples
- -
- DeepSpeed
- -
Runtime
- amazon-sagemaker-examples
- -
- DeepSpeed
- -
License
- amazon-sagemaker-examples
- Apache-2.0
- DeepSpeed
- Apache-2.0
Last pushed
- amazon-sagemaker-examples
- Jul 7, 2026
- DeepSpeed
- Jul 13, 2026
Categories
- amazon-sagemaker-examples
- Inference & Serving, Model Training
- DeepSpeed
- Inference & Serving, Model Training
Trust and health
Maintenance
- amazon-sagemaker-examples
- Active (82%)
- DeepSpeed
- Very active (96%)
Days since push
- amazon-sagemaker-examples
- 7d
- DeepSpeed
- 0d
Open issues (now)
- amazon-sagemaker-examples
- 849
- DeepSpeed
- 1.3k
Full report
- amazon-sagemaker-examples
- Trust report
- DeepSpeed
- Trust report
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).
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.
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 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 (aws/amazon-sagemaker-examples) · observed Jul 15, 2026
- GitHub forks (aws/amazon-sagemaker-examples) · observed Jul 15, 2026
- Last push (aws/amazon-sagemaker-examples) · observed Jul 7, 2026
- License file (Apache-2.0) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (deepspeedai/DeepSpeed) · observed Jul 14, 2026
- GitHub forks (deepspeedai/DeepSpeed) · observed Jul 14, 2026
- Last push (deepspeedai/DeepSpeed) · observed Jul 13, 2026
- License file (Apache-2.0) · observed Jul 14, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: amazon-sagemaker-examples 11k · DeepSpeed 43k (synced Jul 15, 2026).
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 and DeepSpeed alternatives (amazon-sagemaker-examples markdown twin, DeepSpeed 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, 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; DeepSpeed trust report.