--- title: "DeepSpeed vs onepanel" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-onepanelio-onepanel" tools: ["deepspeedai-deepspeed", "onepanelio-onepanel"] --- # DeepSpeed vs onepanel *GraphCanon updated Jul 11, 2026* ## Verdict Pick DeepSpeed when deepSpeed is primarily Python; onepanel is Go; pick onepanel when onepanel is primarily Go; DeepSpeed is Python. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 4.9k forks, and 1.3k open issues, last pushed Jul 11, 2026. [onepanel](https://docs.onepanel.ai/) has 730 stars, 73 forks, and 102 open issues, last pushed Feb 25, 2023. Figures are from public GitHub metadata via [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [onepanel's repository](https://github.com/onepanelio/onepanel). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [onepanel](/tools/onepanelio-onepanel.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises. | | Stars | 42,685 | 730 | | Forks | 4,883 | 73 | | Open issues | 1,302 | 102 | | Language | Python | Go | | 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, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [onepanel](/tools/onepanelio-onepanel.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1232d | | Open issues (now) | 1.3k | 102 | | Security scan | No lockfile | 113 low (113 low) | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/onepanelio-onepanel/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… - DeepSpeed is primarily Python; onepanel is Go. - Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning. - - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters) ### Choose onepanel if… - onepanel is primarily Go; DeepSpeed is Python. - Tags unique to onepanel: ai, aiops, annotation, computer-vision. - Also covers Vector Databases. - onepanel ships Docker support for self-hosted deployment. ## 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 onepanel - Last GitHub push was 1233 days ago (dormant maintenance, Feb 25, 2023). Validate activity before betting a new project on onepanel. - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between DeepSpeed and onepanel? DeepSpeed: Deep learning optimization library for efficient distributed training and inference. onepanel: The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSpeed over onepanel? Choose DeepSpeed over onepanel when DeepSpeed is primarily Python; onepanel is Go; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters). ### When should I choose onepanel over DeepSpeed? Choose onepanel over DeepSpeed when onepanel is primarily Go; DeepSpeed is Python; Tags unique to onepanel: ai, aiops, annotation, computer-vision; Also covers Vector Databases; onepanel ships Docker support for self-hosted deployment. ### 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 onepanel? Last GitHub push was 1233 days ago (dormant maintenance, Feb 25, 2023). Validate activity before betting a new project on onepanel. 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is DeepSpeed or onepanel more popular on GitHub? DeepSpeed has more GitHub stars (42,685 vs 730). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSpeed and onepanel open source? Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, onepanel: Apache-2.0). ### Where can I find alternatives to DeepSpeed or onepanel? GraphCanon lists graph-backed alternatives at [DeepSpeed alternatives](/tools/deepspeedai-deepspeed/alternatives) and [onepanel alternatives](/tools/onepanelio-onepanel/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/alternatives.md), [onepanel markdown twin](/tools/onepanelio-onepanel/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-onepanelio-onepanel.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSpeed or onepanel? DeepSpeed: Very active. onepanel: Dormant. 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 onepanel? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSpeed trust report](/tools/deepspeedai-deepspeed/trust); [onepanel trust report](/tools/onepanelio-onepanel/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/_