Comparison
amazon-sagemaker-examples vs ColossalAI
Verdict
Pick amazon-sagemaker-examples when amazon-sagemaker-examples is primarily Jupyter Notebook; ColossalAI is Python; pick ColossalAI when colossalAI is primarily Python; amazon-sagemaker-examples is Jupyter Notebook.
Markdown twin · amazon-sagemaker-examples alternatives · ColossalAI alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | amazon-sagemaker-examples | ColossalAI |
|---|---|---|
| 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.
- ColossalAI
- Making large AI models cheaper, faster and more accessible
Stars
- amazon-sagemaker-examples
- 11k
- ColossalAI
- 41k
Forks
- amazon-sagemaker-examples
- 7.0k
- ColossalAI
- 4.5k
Open issues
- amazon-sagemaker-examples
- 849
- ColossalAI
- 499
Language
- amazon-sagemaker-examples
- Jupyter Notebook
- ColossalAI
- Python
Adopt for
- amazon-sagemaker-examples
- -
- ColossalAI
- ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models.
Persona
- amazon-sagemaker-examples
- -
- ColossalAI
- -
Runtime
- amazon-sagemaker-examples
- -
- ColossalAI
- -
License
- amazon-sagemaker-examples
- Apache-2.0
- ColossalAI
- Apache-2.0
Last pushed
- amazon-sagemaker-examples
- Jul 7, 2026
- ColossalAI
- Jul 13, 2026
Categories
- amazon-sagemaker-examples
- Inference & Serving, Model Training
- ColossalAI
- Inference & Serving, Model Training
Trust and health
Maintenance
- amazon-sagemaker-examples
- Active (82%)
- ColossalAI
- Very active (96%)
Days since push
- amazon-sagemaker-examples
- 7d
- ColossalAI
- 0d
Open issues (now)
- amazon-sagemaker-examples
- 849
- ColossalAI
- 499
Full report
- amazon-sagemaker-examples
- Trust report
- ColossalAI
- Trust report
Choose amazon-sagemaker-examples if…
- amazon-sagemaker-examples is primarily Jupyter Notebook; ColossalAI is Python.
- Tags unique to amazon-sagemaker-examples: aws, data-science, examples, inference.
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 ColossalAI if…
- ColossalAI is primarily Python; amazon-sagemaker-examples is Jupyter Notebook.
- Tags unique to ColossalAI: ai, big model, data-parallelism, distributed-computing.
- You require handling extremely large AI models with massive context windows, such as over 2M tokens.
When NOT to use ColossalAI
- You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems.
- Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series).
- You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7.
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 (hpcaitech/ColossalAI) · observed Jul 14, 2026
- GitHub forks (hpcaitech/ColossalAI) · observed Jul 14, 2026
- Last push (hpcaitech/ColossalAI) · 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 · ColossalAI 41k (synced Jul 15, 2026).
Common questions
- What is the difference between amazon-sagemaker-examples and ColossalAI?
- amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.. ColossalAI: Making large AI models cheaper, faster and more accessible. See the comparison table for live GitHub stats and shared categories.
- When should I choose amazon-sagemaker-examples over ColossalAI?
- Choose amazon-sagemaker-examples over ColossalAI when amazon-sagemaker-examples is primarily Jupyter Notebook; ColossalAI is Python; Tags unique to amazon-sagemaker-examples: aws, data-science, examples, inference.
- When should I choose ColossalAI over amazon-sagemaker-examples?
- Choose ColossalAI over amazon-sagemaker-examples when ColossalAI is primarily Python; amazon-sagemaker-examples is Jupyter Notebook; Tags unique to ColossalAI: ai, big model, data-parallelism, distributed-computing; You require handling extremely large AI models with massive context windows, such as over 2M tokens.
- 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 ColossalAI?
- You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7.
- Is amazon-sagemaker-examples or ColossalAI more popular on GitHub?
- ColossalAI has more GitHub stars (41,413 vs 10,971). Stars measure visibility, not whether either tool fits your constraints.
- Are amazon-sagemaker-examples and ColossalAI open source?
- Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, ColossalAI: Apache-2.0).
- Where can I find alternatives to amazon-sagemaker-examples or ColossalAI?
- GraphCanon lists graph-backed alternatives at amazon-sagemaker-examples alternatives and ColossalAI alternatives (amazon-sagemaker-examples markdown twin, ColossalAI 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 ColossalAI?
- amazon-sagemaker-examples: Active. ColossalAI: 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 ColossalAI?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: amazon-sagemaker-examples trust report; ColossalAI trust report.