{"id":"https://openalex.org/W4385966040","doi":"https://doi.org/10.48550/arxiv.2308.08500","title":"InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models","display_name":"InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models","publication_year":2023,"publication_date":"2023-08-13","ids":{"openalex":"https://openalex.org/W4385966040","doi":"https://doi.org/10.48550/arxiv.2308.08500"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2308.08500","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.08500","pdf_url":"https://arxiv.org/pdf/2308.08500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2308.08500","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029753236","display_name":"Kabir Nagrecha","orcid":"https://orcid.org/0000-0002-0214-4812"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nagrecha, Kabir","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010053455","display_name":"Lingyi Liu","orcid":"https://orcid.org/0000-0001-5601-221X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Lingyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114985924","display_name":"Pablo Delgado","orcid":"https://orcid.org/0009-0009-7783-9035"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Delgado, Pablo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103241906","display_name":"Prasanna Padmanabhan","orcid":"https://orcid.org/0009-0001-0570-6796"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Padmanabhan, Prasanna","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5029753236"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9847999811172485,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8324118852615356},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.738317608833313},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6107742786407471},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5159459710121155},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5067535042762756},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48945868015289307},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4728931486606598},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4616265594959259},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.4475579261779785},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.43148452043533325}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8324118852615356},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.738317608833313},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6107742786407471},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5159459710121155},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5067535042762756},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48945868015289307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4728931486606598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4616265594959259},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4475579261779785},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.43148452043533325},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2308.08500","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.08500","pdf_url":"https://arxiv.org/pdf/2308.08500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2308.08500","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2308.08500","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2308.08500","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.08500","pdf_url":"https://arxiv.org/pdf/2308.08500","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385966040.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2357124094","https://openalex.org/W2387399993","https://openalex.org/W2389739210","https://openalex.org/W2348924972","https://openalex.org/W2365736347","https://openalex.org/W2047454415","https://openalex.org/W2070040999","https://openalex.org/W2387293848","https://openalex.org/W2375218795","https://openalex.org/W2393010557"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"recommender":[2,13],"models":[3],"(DLRMs)":[4],"have":[5],"become":[6],"an":[7,198],"essential":[8],"component":[9],"of":[10,78,112,173,223],"many":[11],"modern":[12],"systems.":[14],"Several":[15],"companies":[16],"are":[17,44,52],"now":[18],"building":[19],"large":[20],"compute":[21,103],"clusters":[22],"reserved":[23],"only":[24,204],"for":[25,153],"DLRM":[26,62,87,96,179],"training,":[27],"driving":[28],"new":[29,151],"interest":[30],"in":[31,41,61,119,239],"cost-":[32],"and":[33,83,91,115,148,188,208,221,242,252],"time-":[34],"saving":[35],"optimizations.":[36],"The":[37],"systems":[38],"challenges":[39],"faced":[40],"this":[42,71,79],"setting":[43],"unique;":[45],"while":[46,270],"typical":[47],"deep":[48],"learning":[49,162],"training":[50,63,88,215],"jobs":[51],"dominated":[53],"by":[54,260],"model":[55,240],"execution,":[56],"the":[57,75,109,170,219],"most":[58],"important":[59],"factor":[60],"performance":[64,110],"is":[65],"often":[66],"online":[67,113,228],"data":[68,80,97,154,180,186,200,229,257,267],"ingestion.":[69],"In":[70],"paper,":[72],"we":[73],"explore":[74],"unique":[76],"characteristics":[77],"ingestion":[81,114,230,258],"problem":[82],"provide":[84],"insights":[85],"into":[86,213],"pipeline":[89,121,155,181,201,268],"bottlenecks":[90],"challenges.":[92],"We":[93,123,245],"study":[94],"real-world":[95,250],"processing":[98],"pipelines":[99],"taken":[100],"from":[101],"our":[102,249],"cluster":[104,138],"at":[105],"Netflix":[106],"to":[107,116,140,146,165,168,182,248],"observe":[108],"impacts":[111],"identify":[117],"shortfalls":[118],"existing":[120,214,233],"optimizers.":[122],"find":[124,253],"that":[125,194,254],"current":[126],"tooling":[127],"either":[128],"yields":[129],"sub-optimal":[130],"performance,":[131],"frequent":[132],"crashes,":[133],"or":[134],"else":[135],"requires":[136],"impractical":[137],"re-organization":[139],"adopt.":[141],"Our":[142,191],"studies":[143],"lead":[144],"us":[145],"design":[147],"build":[149,197],"a":[150,160,174,178,205],"solution":[152],"optimization,":[156],"InTune.":[157],"InTune":[158,195,225,247],"employs":[159],"reinforcement":[161],"(RL)":[163],"agent":[164],"learn":[166],"how":[167],"distribute":[169],"CPU":[171,274],"resources":[172],"trainer":[175],"machine":[176],"across":[177],"more":[183],"effectively":[184],"parallelize":[185],"loading":[187],"improve":[189],"throughput.":[190],"experiments":[192],"show":[193],"can":[196,209],"optimized":[199],"configuration":[202],"within":[203],"few":[206],"minutes,":[207],"easily":[210],"be":[211],"integrated":[212],"workflows.":[216],"By":[217],"exploiting":[218],"responsiveness":[220],"adaptability":[222],"RL,":[224],"achieves":[226],"higher":[227],"rates":[231],"than":[232],"optimizers,":[234],"thus":[235],"reducing":[236],"idle":[237],"times":[238],"execution":[241],"increasing":[243],"efficiency.":[244],"apply":[246],"cluster,":[251],"it":[255],"increases":[256],"throughput":[259],"as":[261,263],"much":[262],"2.29X":[264],"versus":[265],"state-of-the-art":[266],"optimizers":[269],"also":[271],"improving":[272],"both":[273],"&":[275],"GPU":[276],"utilization.":[277]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2023-08-18T00:00:00"}