--- title: "bigcode-evaluation-harness vs scalene" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-plasma-umass-scalene" tools: ["bigcode-project-bigcode-evaluation-harness", "plasma-umass-scalene"] --- # bigcode-evaluation-harness vs scalene *GraphCanon updated Aug 5, 2026* ## Verdict Pick bigcode-evaluation-harness if bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments; pick scalene if scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations. [bigcode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness) reports 1.1k GitHub stars, 261 forks, and 96 open issues, last pushed Jul 22, 2025. [scalene](https://github.com/plasma-umass/scalene) has 13k stars, 435 forks, and 151 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [scalene's repository](https://github.com/plasma-umass/scalene). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [scalene](/tools/plasma-umass-scalene.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | High-performance CPU, GPU, and memory profiler for Python with AI-powered optimization | | Stars | 1,055 | 13,485 | | Forks | 261 | 435 | | Open issues | 96 | 151 | | Language | Python | Python | | Adopt for | bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. | Scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [scalene](/tools/plasma-umass-scalene.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 378d | 2d | | Open issues (now) | 96 | 151 | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/plasma-umass-scalene/trust.md) | ## Decision facts: bigcode-evaluation-harness - **Requirements:** Users must have Docker installed to leverage the isolated execution environments for model output evaluation. - **Adopt for:** bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. - **License detail:** bigcode-evaluation-harness is distributed under the Apache-2.0 license. ## Decision facts: scalene - **Adopt for:** Scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations. ## Choose when ### Choose bigcode-evaluation-harness if… - Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.. - Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python. - bigcode-evaluation-harness ships Docker support for self-hosted deployment. - When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments. ### Choose scalene if… - Tags unique to scalene: cpu-profiling, gpu-programming, memory-allocation, profiler. - When you need precise profiling of both CPU and GPU performance in Python applications - More GitHub stars (13k vs 1.1k) - visibility, not fit. ## When NOT to use bigcode-evaluation-harness - When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. - If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity. ## When NOT to use scalene - If your project does not involve Python, as Scalene is specific to this language - Avoid if your system lacks necessary dependencies like Visual C++ Redistributable on Windows ## Common questions ### What is the difference between bigcode-evaluation-harness and scalene? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. scalene: High-performance CPU, GPU, and memory profiler for Python with AI-powered optimization. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over scalene? Choose bigcode-evaluation-harness over scalene when Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.; Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python; bigcode-evaluation-harness ships Docker support for self-hosted deployment; When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments. ### When should I choose scalene over bigcode-evaluation-harness? Choose scalene over bigcode-evaluation-harness when Tags unique to scalene: cpu-profiling, gpu-programming, memory-allocation, profiler; When you need precise profiling of both CPU and GPU performance in Python applications; More GitHub stars (13k vs 1.1k) - visibility, not fit. ### When should I avoid bigcode-evaluation-harness? When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity. ### When should I avoid scalene? If your project does not involve Python, as Scalene is specific to this language Avoid if your system lacks necessary dependencies like Visual C++ Redistributable on Windows ### Is bigcode-evaluation-harness or scalene more popular on GitHub? scalene has more GitHub stars (13,485 vs 1,055). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and scalene open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, scalene: Apache-2.0). ### Where can I find alternatives to bigcode-evaluation-harness or scalene? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [scalene alternatives](/tools/plasma-umass-scalene/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [scalene markdown twin](/tools/plasma-umass-scalene/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/bigcode-project-bigcode-evaluation-harness-vs-plasma-umass-scalene.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, bigcode-evaluation-harness or scalene? bigcode-evaluation-harness: Dormant. scalene: 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 bigcode-evaluation-harness and scalene? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [bigcode-evaluation-harness trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust); [scalene trust report](/tools/plasma-umass-scalene/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness`](/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness) - 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/_