{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/jaxpruner-a-concise-library-for-sparsity","title":"JaxPruner: A concise library for sparsity research","arxiv_id":"2304.14082","date":"2023-04-27","proceeding":null,"authors":["Joo Hyung Lee","Wonpyo Park","Nicole Mitchell","Jonathan Pilault","Johan Obando-Ceron","Han-Byul Kim","Namhoon Lee","Elias Frantar","Yun Long","Amir Yazdanbakhsh","Shivani Agrawal","Suvinay Subramanian","Xin Wang","Sheng-Chun Kao","Xingyao Zhang","Trevor Gale","Aart Bik","Woohyun Han","Milen Ferev","Zhonglin Han","Hong-Seok Kim","Yann Dauphin","Gintare Karolina Dziugaite","Pablo Samuel Castro","Utku Evci"],"abstract":"This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research. JaxPruner aims to accelerate research on sparse neural networks by providing concise implementations of popular pruning and sparse training algorithms with minimal memory and latency overhead. Algorithms implemented in JaxPruner use a common API and work seamlessly with the popular optimization library Optax, which, in turn, enables easy integration with existing JAX based libraries. We demonstrate this ease of integration by providing examples in four different codebases: Scenic, t5x, Dopamine and FedJAX and provide baseline experiments on popular benchmarks.","url_abs":"https://arxiv.org/abs/2304.14082v3","url_pdf":"https://arxiv.org/pdf/2304.14082v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"jaxpruner-a-concise-library-for-sparsity","repo_url":"https://github.com/google-research/jaxpruner","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[],"methods":[{"method_slug":null,"method_name":"Library"},{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2304.14082","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.14082"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/google-research/jaxpruner","reach":null}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"c6a6436e3a9a3b4d","entry":"eval_step","repo":"google-research/jaxpruner","repo_kind":"official","path":"jaxpruner/baselines/scenic/classification_trainer.py","file_url":"https://github.com/google-research/jaxpruner/blob/HEAD/jaxpruner/baselines/scenic/classification_trainer.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c6a6436e3a9a3b4d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}