Awọn ile-iṣẹ Itọsọna
Awọn iwuwo & Awọn ojuṣaaju
Weights & Biases (W&B) pèsè àwọn ohun èlò fún títẹ̀lé àwọn ìṣiṣẹ́ ML, fífún àwọn ohun èlò ní àmì àkókò, fífi àwọn tábìlì tí a wọlé wé ara wọn, àti títẹ̀lé/ṣàyẹ̀wò àwọn ìṣiṣẹ́ ohun èlò LLM.
Gbọ si itọsọna yiiAI-ṣe afihan · ~1 min
Lori iwe yi1 iseju kika
Akopọ
These tools record work and expose comparisons; they do not by themselves guarantee scientific reproducibility or correctness.
Jin Dive
W&B runs collect logged metrics and configuration. Artifacts provide versioned files and can be associated with runs as inputs or outputs. Tables support visual inspection and comparison of examples. Weave focuses on tracing and evaluating LLM applications. Each facility provides evidence about a workflow; exact reproducibility still depends on code, environment, seeds, data, and other recorded conditions.
Real-World imuse
A team compares prediction examples across model versions in a W&B Table.
A training run records an artifact version as an input so its data lineage can be inspected.
A researcher launches a Sweep to evaluate parameter configurations and compares logged metrics.
A developer inspects traces and evaluation results for an LLM application in Weave.
Ojo iwaju ti Awọn iwuwo & Awọn Irẹwẹsi
Choose tracking fields and evaluation datasets that match the question being studied. Record versions and inspect representative examples alongside aggregate metrics; product capabilities and account behavior should be checked in current documentation.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Ilana olutaja. Awọn maapu opopona olutaja ni ipa kini awọn ẹya ti ẹgbẹ rẹ le kọ ni atẹle.
- Iye owo ati isuna. Awọn ofin iṣowo ati awọn aṣayan imuṣiṣẹ ni ipa lori idiyele igba pipẹ ati eewu.
- Ewu ati ailewu. Awọn imoriya ile-iṣẹ ṣe apẹrẹ awọn abawọn ọja, iduro ailewu, ati ṣiṣi.
Ṣọra fun
- Awọn ikede ifilọlẹ le ju iduroṣinṣin lọ ni awọn iṣan-iṣẹ iṣelọpọ gidi.
- Ifowoleri API tabi awọn iyipada eto imulo le fọ awọn arosinu ni alẹ.
- Igbẹkẹle olutaja ẹyọkan ṣe alekun titiipa-inu ati awọn idiyele ijira.
Next awọn igbesẹ
- Ṣe ayẹwo awọn olupese nipa lilo awọn iṣẹ ṣiṣe tirẹ ati awọn ipilẹ data.
- Ṣe atunyẹwo asiri, aabo, ati awọn ofin ofin ṣaaju iṣọpọ.
- Ṣetọju eto ipadabọ kọja awọn awoṣe tabi awọn olutaja.
- Bojuto awọn akọsilẹ itusilẹ nitoribẹẹ awọn iyipada maapu oju-ọna ma ṣe iyalẹnu awọn ẹgbẹ.
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 Weights & Biases 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 Awọn iwuwo & Awọn Irẹwẹsi?
Weights & Biases (W&B) provides tools for tracking ML runs, versioning artifacts, comparing logged tables, and tracing/evaluating LLM application executions. These tools record work and expose comparisons; they do not by themselves guarantee scientific reproducibility or correctness.
Iṣoro akọkọ wo ni ẹkọ ẹrọ ṣe Awọn iwuwo & Ibajẹ ni akọkọ koju?
W&B jẹ pẹpẹ ti ipasẹ idanwo ti o ṣe igbasilẹ awọn metiriki, awọn hyperparameters, ati awọn ohun-ọṣọ nitoribẹẹ iṣẹ ML di atunjade ati ṣidii.
Awọn ipe Python meji wo ni aaye titẹsi aṣoju fun wíwọlé si W&B?
wandb.init() bẹrẹ ṣiṣe itopase ati wandb.log({...}) n san awọn metiriki atọka igbesẹ si dasibodu naa.
Kini idi ti W&B Sweeps?
Sweeps adaṣe adaṣe hyperparameter ti o dara ju, ifilọlẹ ati afiwe ọpọlọpọ awọn ṣiṣe lati wa iṣeto ti o dara julọ.
Kini W&B Artifacts jẹ ki o ṣe?
Awọn ohun-ọṣọ ṣe ikede awọn faili nla bi awọn ipilẹ data ati awọn iwuwo awoṣe nipa lilo hashing akoonu-adirẹsi, ti n mu ẹda gangan ṣiṣẹ.
Ile-iṣẹ wo ni o gba Awọn iwuwo & Awọn Iwaju ni Oṣu Kẹta 2025?
Olupese awọsanma GPU CoreWeave gba W&B ni 2025, sisopọ ohun elo idanwo pẹlu awọn amayederun iṣiro.
Tesiwaju kikọ
Jẹmọ awọn itọsọna
Awọn itọsọna diẹ sii ti a yan fun koko yii