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Hugging Face Yara fun Ikẹkọ Multi-GPU
Imọ-ẹrọ
Imọ Itọsọna
Multi-Apeere GPU Partitioning
NVIDIA Multi-Instance GPU partitions supported GPUs into instances with assigned compute and memory resources.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Lori iwe yi2 min ka
Akopọ
It can improve sharing for smaller workloads, but profile support and isolation boundaries depend on the GPU and configuration.
Jin Dive
MIG was introduced with the NVIDIA A100 and is supported on selected later GPUs. A GPU Instance groups compute and dedicated memory resources, including assigned paths through cache and memory hardware. On an A100 40 GB, supported layouts can provide up to seven small GPU Instances or fewer larger ones. Compute Instances can further divide compute resources inside a GPU Instance while sharing its memory resources. This differs from ordinary software time slicing, but does not imply immunity from every device-level fault or interference in the wider host and service. Workload capacity, supported profiles, and operational constraints should be tested on the actual GPU.
Real-World imuse
Olupese awọsanma pin A100 kan si awọn iṣẹlẹ meje nitoribẹẹ awọn alabara meje kọọkan gba iṣeduro kan, ege GPU ti o ya sọtọ fun itọkasi.
Iṣupọ iwadii ile-ẹkọ giga kan fun ọmọ ile-iwe PhD kọọkan ni apẹẹrẹ 10GB MIG fun ṣiṣe adaṣe dipo monopolizing gbogbo awọn kaadi.
Iṣẹ ifọkasi n ṣe akopọ ede kekere pupọ ati awọn awoṣe iran lori H100 kan, ọkọọkan ni ipin tirẹ pẹlu airotẹlẹ asọtẹlẹ.
Iṣupọ Kubernetes kan n polowo awọn iṣẹlẹ MIG gẹgẹbi awọn orisun eto eto ki awọn pods beere 'nvidia.com/mig-1g.5gb' bii eyikeyi orisun miiran.
Ojo iwaju ti Multi-Apeere GPU Partitioning
Bi awọn GPU ṣe ndagba si 80GB, 141GB, ati kọja, ipinpin di iwunilori diẹ sii nitori awọn awoṣe kọọkan ko ṣọwọn nilo gbogbo kaadi fun itọkasi. Reti Kubernetes tighter ati isọpọ awọsanma, ipadasẹhin ti o ni agbara laisi ṣiṣan oju ipade, ati awọn profaili ti o dara julọ. Awọn olutaja ti o ni idije n lepa iru SR-IOV-ara-ara GPU agbara, ati awọn iru ẹrọ aibikita olupin ti npọ si igbẹkẹle ipin lati gbe ọpọlọpọ awọn awoṣe lọpọlọpọ ati ge egbin aiṣiṣẹ.
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 Multi-Instance GPU Partitioning 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
Kini Multi-Instance GPU Partitioning?
NVIDIA Multi-Instance GPU partitions supported GPUs into instances with assigned compute and memory resources. It can improve sharing for smaller workloads, but profile support and isolation boundaries depend on the GPU and configuration.
Which resource boundary distinguishes separate MIG GPU Instances from software time slicing?
MIG assigns hardware resource slices. Compute Instances within a shared GPU Instance have a different boundary, and device-level faults are not universally eliminated.
Lori kini faaji NVIDIA ti ṣe afihan MIG akọkọ?
MIG debuted pẹlu Ampere-orisun A100 ati ki o tẹsiwaju lori H100 ati nigbamii data aarin GPUs.
On an A100 40 GB using supported small-instance profiles, how many GPU Instances can a layout provide?
The A100 supports layouts of up to seven GPU Instances. Maximum counts and profiles should not be generalized to all GPU models.
Ninu profaili MIG kan bi '1g.5gb', kini '5gb' ṣe aṣoju?
Profaili naa ṣe koodu awọn ege oniṣiro (1g) ati iranti igbẹhin (5GB) ti a fun apẹẹrẹ.
Iru iṣẹ wo ni MIG paapaa dara julọ fun?
MIG nmọlẹ nigbati ọpọlọpọ awọn kekere, awọn ẹru iṣẹ ti o ya sọtọ (bii itọkasi) pin kaadi kan pẹlu didara iṣẹ ti iṣeduro.
Tesiwaju kikọ
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