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Imọ-ẹrọ
Imọ Itọsọna
Awoṣe Pruning
Pireje awoṣe n dinku nẹtiwọọki nkankikan nipa yiyọ awọn iwuwo tabi gbogbo awọn ẹya ti o ṣe alabapin diẹ si iṣelọpọ rẹ.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Lori iwe yi2 min ka
Akopọ
It cuts size, memory, and compute cost while aiming to keep accuracy nearly intact.
Jin Dive
Trained neural networks are typically over-parameterized: many connections carry tiny weights that barely affect predictions. Pruning identifies and removes these, leaving a leaner model. Pirege ti a ko ṣeto ṣe jade awọn iwọnwọn kọọkan, ti n ṣe awọn matiriki fọnka ti o le ni fisinuirindigbindigbin gaan ṣugbọn nilo ohun elo pataki tabi awọn ile-ikawe lati yara yara nitootọ. Pirogi ti a tunṣe yọkuro gbogbo awọn ẹya kuro - awọn neuronu, awọn ori akiyesi, awọn ikanni, tabi awọn fẹlẹfẹlẹ - ti nso awoṣe ipon kekere ti o yara yiyara lori ohun elo lasan. Ohunelo ti o wọpọ ni lupu aṣetunṣe: ọkọ oju irin, piruni awọn aye pataki ti o kere julọ nipasẹ awọn ami-ami (nigbagbogbo iwuwo iwuwo), lẹhinna tunne-fifẹ lati bọsipọ deede ti o sọnu, tun ṣe titi iwọn tabi ibi-afẹde iyara yoo pade. Pruning pairs naturally with quantization and distillation in deployment pipelines.
Real-World imuse
Titari awoṣe ede nla kan lati ṣiṣẹ lori GPU olumulo kan dipo iṣupọ olupin kan.
Slimming awoṣe iran kan ki o baamu laarin iranti ti foonuiyara tabi kamẹra ti a fi sinu.
Yiyọ awọn olori akiyesi laiṣe kuro lati Amunawa kan pẹlu idinku iwọnwọn kekere ni didara.
Idinku agbara inference ati lairi fun awọn iṣẹ iṣowo-giga lati dinku awọn idiyele awọsanma.
Ojo iwaju ti Awoṣe Pruning
Pruning is increasingly applied to large language models, where structured methods can remove supported attention heads, neurons, or layers to fit models onto smaller GPUs and edge devices. Some hardware and kernels accelerate specific supported sparsity patterns, such as NVIDIA 2:4 structured sparsity; arbitrary unstructured zeros do not automatically run faster. Expect pruning to be combined with quantization and distillation in compression pipelines measured against concrete latency, energy, memory, and quality targets.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Iye owo ati isuna. Awọn ipinnu faaji ṣe awakọ iṣẹ ati idiyele iṣẹ fun awọn ọdun.
- Awọn ipinnu diẹ sii. Ẹkọ imọ-ẹrọ ṣe iranlọwọ fun awọn ẹgbẹ lati yan akopọ to tọ, kii ṣe ọkan tuntun nikan.
- Iṣakoso didara. Awọn yiyan imọ-ẹrọ to dara julọ dinku awọn iṣẹlẹ igbẹkẹle ni iṣelọpọ.
Ṣọra fun
- Ṣiṣepe ala-ilẹ kan le tọju awọn ailagbara eto ti o gbooro.
- Awọn ohun elo amayederun ati awọn idiyele itọju nigbagbogbo ni aibikita.
- Aabo ati awọn ela akiyesi le dagba bi awọn eto ṣe di eka sii.
Next awọn igbesẹ
- Ṣetumo lairi, didara, ati awọn ibi-afẹde idiyele ṣaaju imuse.
- Aṣepari labẹ ẹru ojulowo ati awọn ipo data.
- Abojuto ohun elo fun awọn aṣiṣe, fiseete, ati ipa olumulo.
- Mura ipadasẹhin pada ati awọn ipa ọna esi iṣẹlẹ ṣaaju iwọn.
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the Model Pruning quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
What is Model Pruning?
Pireje awoṣe n dinku nẹtiwọọki nkankikan nipa yiyọ awọn iwuwo tabi gbogbo awọn ẹya ti o ṣe alabapin diẹ si iṣelọpọ rẹ. O ge iwọn, iranti, ati iye owo iṣiro lakoko ti o n pinnu lati jẹ ki deede wa nitosi.
Kini pruning awoṣe yọ kuro lati inu nẹtiwọọki oṣiṣẹ kan?
Pireje nilokulo ju-parameterization nipa gige awọn iwọn idasi kekere tabi awọn iwọn lati dinku awoṣe lakoko titọju pupọ julọ ti deede rẹ.
Kini o ṣe iyatọ si pruning ti a ṣeto si pruning ti a ko ṣeto?
Pipa ti eleto yọ gbogbo awọn paati, ti nso kere ipon si dede ti o nṣiṣẹ yiyara lori boṣewa hardware, nigba ti unstructured pruning zeroes olukuluku òṣuwọn, ṣiṣẹda sparsity.
Kini idi ti pruning ti ko ni ipilẹ nigbagbogbo kuna lati yara yara awoṣe kan lori ohun elo lasan?
Awọn odo ti tuka ko ṣe itumọ laifọwọyi si awọn iṣiro diẹ; ohun elo ipon tun ṣe ilana wọn ayafi ti awọn kernel fọnka amọja tabi awọn accelerators ti lo.
Ohun ti o jẹ aṣoju aṣetunṣe pruning ilana?
Itumọ pruning alternates laarin yiyọ kekere-pataki sile ati itanran-yiyi lati bọsipọ išedede, diėdiė de iwọn tabi iyara afojusun.
Kini Irosọ Tikẹti Lotiri sọ?
Isọyesi naa ṣe akiyesi pe iṣẹ-ọna asopọ fọnka ti a yan daradara ('tiketi ti o bori'), ti ikẹkọ lati ipilẹṣẹ atilẹba rẹ, le de deede ti nẹtiwọọki ipon ni kikun.
Tesiwaju kikọ
Jẹmọ awọn itọsọna
Awọn itọsọna diẹ sii ti a yan fun koko yii