--- title: Benchmarking machine learning frameworks | daily.dev description: MLBench is an open-source benchmarking system developed by Collabora for evaluating machine learning frameworks across different backends and hardware boards.... canonical: https://daily.dev/posts/benchmarking-machine-learning-frameworks-4w4grrfwv twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Benchmarking machine learning frameworks | daily.dev og:description: MLBench is an open-source benchmarking system developed by Collabora for evaluating machine learning frameworks across different backends and hardware boards.... og:url: https://daily.dev/posts/benchmarking-machine-learning-frameworks-4w4grrfwv og:image: https://api.daily.dev/og/posts/4W4GrRFwV.png og:image:alt: Benchmarking machine learning frameworks og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Benchmarking machine learning frameworks **[Collabora Blog](https://daily.dev/sources/collabora)** · 5 min read · 0 upvotes · 0 comments ## Summary MLBench is an open-source benchmarking system developed by Collabora for evaluating machine learning frameworks across different backends and hardware boards. Built on top of MLPerf results, it automates the collection of runtime performance, memory usage, power consumption, CPU/GPU utilization, and precision metrics. It uses wrapper scripts to standardize interfaces across frameworks written in different languages, and a configuration file to define models, datasets, and libraries. MLBench integrates with CI pipelines for low-maintenance continuous benchmarking and provides a dashboard for side-by-side comparisons — demonstrated with Inception V4 across Jetson Nano (TensorRT, onnxruntime), Rockpi RK3399 (tflite), and Coral TPU (tflite for EdgeTPU). The code is publicly available on GitHub. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What metrics does MLBench collect when benchmarking ML frameworks? MLBench collects runtime performance, memory usage, power consumption, CPU/GPU utilization, temperature, and precision metrics. It stores all combinations of parameters and datasets in a database, enabling side-by-side comparisons across frameworks and hardware boards. The dashboard demonstrates this with Inception V4 running on Jetson Nano with TensorRT and onnxruntime, Rockpi RK3399 with tflite, and Coral TPU with tflite for EdgeTPU. _ML engineers benchmarking across edge hardware track tools like MLBench on daily.dev._ ### How does MLBench handle different ML frameworks written in different languages? MLBench uses wrapper scripts to standardize the interfaces of frameworks regardless of the language they are written in. For each method to be benchmarked, a wrapper script handles running the framework and collecting metrics consistently. A configuration file then maps models and datasets to each library, enabling meaningful cross-framework comparisons. _Developers integrating diverse ML frameworks into CI pipelines find relevant tooling discussions on daily.dev._ ## Similar posts on daily.dev - [ReviewBench: An open benchmark for AI code review](https://daily.dev/posts/reviewbench-an-open-benchmark-for-ai-code-review-kpedezcgk) · GitHub Blog · 1 upvotes · 0 comments - [MLPerf Client 1.5 Introduces Experimental Linux Support](https://daily.dev/posts/mlperf-client-1-5-introduces-experimental-linux-support-fupn6gamj) · Phoronix · 0 upvotes · 0 comments - [Elevating AI-assisted Android development and improving LLMs with Android Bench](https://daily.dev/posts/elevating-ai-assisted-android-development-and-improving-llms-with-android-bench-vcrh8h3bs) · Android Developers Blog · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning) [View this post on daily.dev](https://daily.dev/posts/benchmarking-machine-learning-frameworks-4w4grrfwv) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Benchmarking machine learning frameworks","url":"https://daily.dev/posts/benchmarking-machine-learning-frameworks-4w4grrfwv","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/benchmarking-machine-learning-frameworks-4w4grrfwv"},"datePublished":"2026-08-11T08:55:09.274Z","dateModified":"2026-08-11T09:19:59.929Z","description":"MLBench is an open-source benchmarking system developed by Collabora for evaluating machine learning frameworks across different backends and hardware boards....","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/8cf82fb0dccb4473337fb6be60273e06?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/8cf82fb0dccb4473337fb6be60273e06?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Collabora Blog","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Collabora Blog","logo":"https://media.daily.dev/image/upload/s--ZGZ0FjqS--/f_auto,q_auto/v1786438306/logos/collabora?_a=BAMAMicg0","url":"https://daily.dev/sources/collabora"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/benchmarking-machine-learning-frameworks-4w4grrfwv","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning","timeRequired":"PT5M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Collabora Blog","item":"https://daily.dev/sources/collabora"},{"@type":"ListItem","position":3,"name":"Benchmarking machine learning frameworks"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/benchmarking-machine-learning-frameworks-4w4grrfwv#faq","mainEntity":[{"@type":"Question","name":"What metrics does MLBench collect when benchmarking ML frameworks?","acceptedAnswer":{"@type":"Answer","text":"MLBench collects runtime performance, memory usage, power consumption, CPU/GPU utilization, temperature, and precision metrics. 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