{"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/predicting-the-computational-cost-of-deep","title":"Predicting the Computational Cost of Deep Learning Models","arxiv_id":"1811.11880","date":"2018-11-28","proceeding":null,"authors":["Daniel Justus","John Brennan","Stephen Bonner","Andrew Stephen McGough"],"abstract":"Deep learning is rapidly becoming a go-to tool for many artificial\nintelligence problems due to its ability to outperform other approaches and\neven humans at many problems. Despite its popularity we are still unable to\naccurately predict the time it will take to train a deep learning network to\nsolve a given problem. This training time can be seen as the product of the\ntraining time per epoch and the number of epochs which need to be performed to\nreach the desired level of accuracy. Some work has been carried out to predict\nthe training time for an epoch -- most have been based around the assumption\nthat the training time is linearly related to the number of floating point\noperations required. However, this relationship is not true and becomes\nexacerbated in cases where other activities start to dominate the execution\ntime. Such as the time to load data from memory or loss of performance due to\nnon-optimal parallel execution. In this work we propose an alternative approach\nin which we train a deep learning network to predict the execution time for\nparts of a deep learning network. Timings for these individual parts can then\nbe combined to provide a prediction for the whole execution time. This has\nadvantages over linear approaches as it can model more complex scenarios. But,\nalso, it has the ability to predict execution times for scenarios unseen in the\ntraining data. Therefore, our approach can be used not only to infer the\nexecution time for a batch, or entire epoch, but it can also support making a\nwell-informed choice for the appropriate hardware and model.","url_abs":"http://arxiv.org/abs/1811.11880v1","url_pdf":"http://arxiv.org/pdf/1811.11880v1.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":"predicting-the-computational-cost-of-deep","repo_url":"https://github.com/CDECatapult/ml-performance-prediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.11880","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11880"}},"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/CDECatapult/ml-performance-prediction","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"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":"a543194ec482c10d","entry":"generate_devlist","repo":"CDECatapult/ml-performance-prediction","repo_kind":"official","path":"benchmark/benchmark.py","file_url":"https://github.com/CDECatapult/ml-performance-prediction/blob/HEAD/benchmark/benchmark.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a543194ec482c10d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}