Lub Software Framework rau AI yog dab tsi?

Lub Software Framework rau AI yog dab tsi?

Ib lub framework ruaj khov yuav ua rau qhov kev ntxhov siab ntawd hloov mus ua ib txoj haujlwm uas siv tau. Hauv phau ntawv qhia no, peb yuav piav qhia txog software framework rau AI, vim li cas nws thiaj tseem ceeb, thiab yuav xaiv ib qho li cas yam tsis tas yuav xav txog koj tus kheej txhua tsib feeb. Haus kas fes; qhib qhov tabs. ☕️

Cov ntawv uas koj yuav nyiam nyeem tom qab qhov no:

🔗 Kev kawm tshuab yog dab tsi vs AI
To taub qhov sib txawv tseem ceeb ntawm cov tshuab kawm thiab kev txawj ntse dag.

🔗 Dab tsi yog AI piav qhia tau
Kawm paub tias AI piav qhia tau li cas ua rau cov qauv nyuaj pom tseeb thiab nkag siab tau.

🔗 Dab tsi yog humanoid robot AI
Tshawb nrhiav cov thev naus laus zis AI uas ua rau cov neeg hlau zoo li tib neeg muaj zog thiab kev coj cwj pwm sib tham.

🔗 Lub neural network hauv AI yog dab tsi?
Tshawb nrhiav seb cov neural network ua raws li lub hlwb tib neeg li cas los ua cov ntaub ntawv.

Lub Software Framework rau AI yog dab tsi? Cov lus teb luv luv 🧩

Ib lub software framework rau AI yog ib pawg ntawm cov tsev qiv ntawv, cov khoom siv runtime, cov cuab yeej, thiab cov kev cai uas pab koj tsim, cob qhia, soj ntsuam, thiab xa cov qauv kev kawm tshuab lossis cov qauv kev kawm tob sai dua thiab ntseeg tau dua. Nws yog ntau tshaj li ib lub tsev qiv ntawv xwb. Xav txog nws zoo li lub scaffolding uas muab rau koj:

  • Cov ntsiab lus tseem ceeb rau tensors, txheej, kwv yees, lossis cov kav dej

  • Kev sib txawv tsis siv neeg thiab kev ua lej zoo tshaj plaws

  • Cov kav dej nkag mus rau cov ntaub ntawv thiab cov khoom siv ua ntej ua tiav

  • Cov voj voog kev cob qhia, cov ntsuas, thiab cov chaw kuaj xyuas

  • Kev sib koom tes nrog cov accelerators xws li GPUs thiab cov khoom siv tshwj xeeb

  • Kev ntim khoom, kev pabcuam, thiab qee zaum kev taug qab kev sim

Yog tias lub tsev qiv ntawv yog ib qho cuab yeej siv, lub moj khaum yog ib lub chaw ua haujlwm - nrog rau teeb pom kev zoo, rooj zaum, thiab lub tshuab ua daim ntawv lo koj yuav ua txuj tias koj tsis xav tau ... kom txog thaum koj xav tau. 🔧

Koj yuav pom kuv rov hais dua cov lus tseeb tias dab tsi yog software framework rau AI ob peb zaug. Qhov ntawd yog lub hom phiaj, vim tias nws yog cov lus nug uas feem ntau cov neeg ntaus thaum lawv poob rau hauv lub maze cuab yeej.

 

AI Software Framework

Dab tsi ua rau lub software framework zoo rau AI? ✅

Nov yog daim ntawv teev luv luv uas kuv xav tau yog tias kuv pib txij thaum pib:

  • Kev tsim khoom ergonomics - APIs huv si, cov qauv tsis hloov pauv, cov lus qhia yuam kev pab tau

  • Kev ua tau zoo - cov noob ceev ceev, kev sib xyaw ua ke, kev sau ua ke ntawm daim duab lossis JIT qhov twg nws pab tau

  • Qhov tob ntawm lub ecosystem - cov qauv hubs, cov lus qhia, cov qhov hnyav uas tau kawm ua ntej, kev sib koom ua ke

  • Kev yooj yim nqa mus los - kev xa tawm cov kev xws li ONNX, mobile lossis ntug runtimes, kev yooj yim rau thawv

  • Kev Soj Ntsuam - cov ntsuas, kev sau ntawv, kev txheeb xyuas, kev taug qab kev sim

  • Kev nthuav dav - ntau GPU, kev cob qhia faib tawm, kev pabcuam ywj pheej

  • Kev Tswjfwm - cov yam ntxwv kev ruaj ntseg, kev hloov kho version, keeb kwm, thiab cov ntaub ntawv uas tsis ua rau koj ntshai

  • Lub Zej Zog & lub neej ntev - cov neeg saib xyuas nquag, kev saws txais yuav hauv ntiaj teb tiag tiag, cov phiaj xwm kev ntseeg siab

Thaum cov khoom ntawd nias, koj sau cov lej nplaum tsawg dua thiab ua AI tiag tiag ntau dua. Qhov ntawd yog qhov tseem ceeb. 🙂

Cov hom frameworks uas koj yuav ntsib 🗺️

Tsis yog txhua lub moj khaum sim ua txhua yam. Xav txog hauv pawg:

  • Cov qauv kev kawm tob: tensor ops, autodiff, neural nets

    • PyTorch, TensorFlow, JAX

  • Cov qauv ML qub: cov kav dej, kev hloov pauv nta, thiab cov khoom kwv yees

    • scikit-learn, XGBoost

  • Cov chaw nruab nrab ntawm cov qauv thiab NLP stacks: cov qauv uas tau kawm ua ntej lawm, cov tokenizers, kev kho kom zoo

    • Cov Hloov Pauv Lub Ntsej Muag

  • Kev pabcuam & kev txiav txim siab lub sijhawm ua haujlwm: kev xa tawm zoo tshaj plaws

    • ONNX Runtime, NVIDIA Triton Inference Server, Ray Serve

  • MLOps & lub neej voj voog: kev taug qab, ntim khoom, cov kav dej, CI rau ML

    • MLflow, Kubeflow, Apache Airflow, Prefect, DVC

  • Ntug & txawb tau: me me, siv tau kho vajtse

    • TensorFlow Lite, Core ML

  • Cov qauv kev pheej hmoo thiab kev tswj hwm: cov txheej txheem thiab kev tswj hwm, tsis yog cov cai

    • NIST AI Risk Management Framework

Tsis muaj ib pawg twg haum rau txhua pab neeg. Tsis ua li cas.

Cov lus sib piv: cov kev xaiv nrov ntawm ib qho kev pom 📊

Muaj tej yam me me uas txawv txawv vim tias lub neej tiag tiag tsis zoo li qub. Cov nqi hloov pauv, tab sis ntau yam tseem ceeb yog qhib rau pej xeem siv.

Cuab Yeej / Pawg Zoo tshaj plaws rau Zoo li tus nqi Vim li cas nws thiaj ua haujlwm
PyTorch Cov kws tshawb nrhiav, Pythonic devs Qhib qhov chaw Cov duab kos dynamic zoo li ntuj; lub zej zog loj heev. 🙂
TensorFlow + Keras Kev tsim khoom ntawm qhov loj me, hla ntau lub platform Qhib qhov chaw Hom duab, TF Serving, TF Lite, cov cuab yeej khov kho.
JAX Cov neeg siv hluav taws xob, kev hloov pauv haujlwm Qhib qhov chaw XLA muab tso ua ke, kev xav txog lej ua ntej.
scikit-kawm Cov ntaub ntawv teev lus ML qub Qhib qhov chaw Cov kav dej, cov ntsuas, thiab cov API kwv yees tsuas yog nyem xwb.
XGBoost Cov ntaub ntawv muaj qauv, cov hauv paus yeej Qhib qhov chaw Kev txhawb nqa tsis tu ncua uas feem ntau tsuas yog yeej.
Cov Hloov Pauv Lub Ntsej Muag NLP, kev pom kev, kev sib kis nrog kev nkag mus rau hauv lub hub Feem ntau qhib Cov qauv uas tau kawm ua ntej lawm + cov tokenizers + cov ntaub ntawv, wow.
Lub Sijhawm Khiav ONNX Kev yooj yim nqa mus los, cov qauv sib xyaw Qhib qhov chaw Export ib zaug, khiav ceev ntawm ntau lub backends. [4]
MLflow Kev taug qab kev sim, kev ntim khoom Qhib qhov chaw Kev ua dua tshiab, kev sau npe qauv, APIs yooj yim.
Ray + Ray Serve Kev cob qhia faib tawm + kev pabcuam Qhib qhov chaw Ntsuas Python cov haujlwm; pabcuam micro-batching.
NVIDIA Triton Kev xam pom zoo heev Qhib qhov chaw Multi-framework, dynamic batching, GPUs.
Kubeflow Kubernetes ML pipelines Qhib qhov chaw Txij thaum pib txog thaum kawg ntawm K8s, qee zaum nyuaj siab tab sis muaj zog.
Airflow los yog Prefect Kev sib koom ua ke ntawm koj txoj kev cob qhia Qhib qhov chaw Teem sijhawm, sim dua, pom kev. Ua haujlwm zoo.

Yog tias koj xav tau cov lus teb ib kab: PyTorch rau kev tshawb fawb, TensorFlow rau kev tsim khoom ntev, scikit-learn rau tabular, ONNX Runtime rau kev yooj yim nqa, MLflow rau kev taug qab. Kuv yuav rov qab mus tom qab yog tias xav tau.

Hauv qab lub hood: yuav ua li cas cov frameworks ua haujlwm rau koj cov lej ⚙️

Feem ntau cov qauv kev kawm tob sib koom ua peb yam loj:

  1. Tensors - ntau qhov ntev arrays nrog cov cuab yeej tso thiab cov cai tshaj tawm.

  2. Autodiff - kev sib txawv ntawm hom rov qab los suav cov gradients.

  3. Txoj kev ua tiav - hom kev xav ua vs hom graphed vs JIT compilation.

  • PyTorch siv eager execution ua haujlwm thiab tuaj yeem sau cov duab nrog torch.compile los sib sau ua ke cov haujlwm thiab ua kom ceev nrooj nrog kev hloov pauv code tsawg kawg nkaus. [1]

  • TensorFlow khiav ceev ceev los ntawm lub neej ntawd thiab siv tf.function los teeb tsa Python rau hauv cov duab qhia txog cov ntaub ntawv ntws tau yooj yim, uas yog qhov yuav tsum tau rau kev xa tawm SavedModel thiab feem ntau txhim kho kev ua tau zoo. [2]

  • JAX leans rau hauv composable transforms zoo li jit, grad, vmap, thiab pmap, compiling los ntawm XLA rau kev ua kom nrawm thiab parallelism. [3]

Qhov no yog qhov uas kev ua tau zoo nyob: kernels, fusions, memory layout, mixed precision. Tsis yog khawv koob - tsuas yog engineering uas zoo li khawv koob xwb. ✨

Kev cob qhia vs kev xam pom: ob qho kev ua si sib txawv 🏃‍♀️🏁

  • Kev cob qhia hais txog kev ua haujlwm tau zoo thiab kev ruaj khov. Koj xav tau kev siv zoo, kev ntsuas qhov sib txawv, thiab cov tswv yim faib tawm.

  • Kev xam pom nrhiav kev ncua sijhawm, tus nqi, thiab kev sib koom ua ke. Koj xav tau kev sib sau ua ke, kev ntsuas kom muaj nuj nqis, thiab qee zaum kev sib koom ua ke ntawm tus neeg teb xov tooj.

Kev sib koom tes tseem ceeb ntawm no:

  • ONNX ua haujlwm ua hom kev pauv qauv sib xws; ONNX Runtime khiav cov qauv los ntawm ntau qhov chaw frameworks thoob plaws CPUs, GPUs, thiab lwm yam accelerators nrog cov lus khi rau cov khoom tsim tawm ib txwm muaj. [4]

Kev ntsuas, kev txiav, thiab kev lim dej feem ntau ua rau muaj kev yeej loj. Qee zaum loj heev - uas zoo li kev dag ntxias, txawm hais tias nws tsis yog. 😉

Lub zos MLOps: dhau ntawm lub hauv paus tseem ceeb 🏗️

Txawm tias daim duab xam zauv zoo tshaj plaws los yeej yuav tsis cawm tau lub voj voog tsis zoo. Thaum kawg koj yuav xav tau:

  • Kev taug qab kev sim & kev sau npe: pib nrog MLflow los sau cov params, metrics, thiab artifacts; txhawb nqa los ntawm kev sau npe

  • Cov kav dej thiab kev teeb tsa ua haujlwm: Kubeflow ntawm Kubernetes, lossis cov neeg siv dav dav xws li Airflow thiab Prefect

  • Kev hloov kho cov ntaub ntawv: DVC khaws cov ntaub ntawv thiab cov qauv ua ke nrog cov lej

  • Cov Thawv & kev xa tawm: Cov duab Docker thiab Kubernetes rau qhov chaw kwv yees tau, scalable

  • Cov chaw nruab nrab ntawm cov qauv: kev cob qhia ua ntej-ces-kho kom zoo dua li thaj chaw ntsuab ntau zaus dua li tsis yog

  • Kev soj ntsuam: latency, drift, thiab kev kuaj xyuas zoo thaum cov qauv tsim tawm

Ib zaj dab neeg luv luv: ib pab pawg me me ntawm e-commerce xav tau "ib qho kev sim ntxiv" txhua hnub, tom qab ntawd tsis nco qab tias qhov kev khiav twg siv cov yam ntxwv twg. Lawv ntxiv MLflow thiab txoj cai yooj yim "txhawb nqa tsuas yog los ntawm kev sau npe". Tam sim ntawd, kev tshuaj xyuas txhua lub lim tiam yog hais txog kev txiav txim siab, tsis yog kev tshawb nrhiav txog keeb kwm. Tus qauv tshwm sim txhua qhov chaw.

Kev sib koom ua ke & kev yooj yim nqa mus los: khaws koj cov kev xaiv qhib 🔁

Kev kaw tsev maj mam tshwm sim. Tiv thaiv nws los ntawm kev npaj rau:

  • Cov kev xa tawm: ONNX, SavedModel, TorchScript

  • Kev ywj pheej ntawm lub sijhawm khiav: ONNX Runtime, TF Lite, Core ML rau mobile lossis ntug

  • Kev Tsim Khoom Hauv Thawv: kev kwv yees cov kav dej tsim nrog Docker cov duab

  • Kev pabcuam tsis muaj kev cuam tshuam: kev tuav PyTorch, TensorFlow, thiab ONNX ua ke ua rau koj ncaj ncees

Kev hloov pauv ib txheej txheej lossis sau ua qauv rau lub cuab yeej me dua yuav tsum yog qhov teeb meem, tsis yog kev rov sau dua.

Kho vajtse ua kom nrawm dua & ntsuas: ua kom nrawm yam tsis muaj kua muag ⚡️

  • GPUs tswj hwm cov haujlwm kev cob qhia dav dav ua tsaug rau cov kernels zoo tshaj plaws (xav txog cuDNN).

  • Kev cob qhia faib tawm tshwm sim thaum ib qho GPU tsis tuaj yeem ua raws li: cov ntaub ntawv sib luag, qauv sib luag, cov khoom siv sib cais.

  • Kev ua kom raug sib xyaw ua ke txuag tau lub cim xeeb thiab lub sijhawm nrog qhov poob qhov tseeb tsawg kawg nkaus thaum siv kom raug.

Qee zaum cov code ceev tshaj plaws yog cov code uas koj tsis tau sau: siv cov qauv uas tau kawm ua ntej thiab kho kom zoo. Tiag tiag. 🧠

Kev tswj hwm, kev nyab xeeb, thiab kev pheej hmoo: tsis yog tsuas yog cov ntaub ntawv xwb 🛡️

Kev xa khoom AI hauv cov koom haum tiag tiag txhais tau tias xav txog:

  • Lineage: qhov twg cov ntaub ntawv los ntawm, nws tau ua tiav li cas, thiab tus qauv version twg nyob

  • Kev ua dua tshiab: kev tsim kho kom raug, kev sib txuas ntawm cov khoom siv, cov khw muag khoom cuav

  • Kev Pom Tseeb & Cov Ntaub Ntawv: Cov Qauv Daim Npav thiab Cov Lus Qhia Txog Cov Ntaub Ntawv

  • Kev tswj hwm kev pheej hmoo: NIST AI Risk Management Framework muab ib daim ntawv qhia kev siv tau rau kev kos duab, ntsuas, thiab tswj hwm cov kab ke AI uas ntseeg tau thoob plaws lub voj voog ntawm lub neej. [5]

Cov no tsis yog xaiv tau hauv cov cheeb tsam uas raug tswj hwm. Txawm tias sab nraud ntawm lawv los xij, lawv tiv thaiv kev tsis sib txuas lus thiab kev sib ntsib tsis zoo.

Yuav xaiv li cas: daim ntawv teev cov kev txiav txim siab sai 🧭

Yog tias koj tseem tab tom ntsia tsib lub tabs, sim ua qhov no:

  1. Hom lus tseem ceeb thiab keeb kwm yav dhau los ntawm pab neeg

    • Pab pawg tshawb fawb Python-thawj zaug: pib nrog PyTorch lossis JAX

    • Kev tshawb fawb sib xyaw thiab kev tsim khoom: TensorFlow nrog Keras yog qhov kev twv txiaj nyab xeeb

    • Kev tshuaj xyuas classic lossis kev tsom mus rau cov lus: scikit-learn ntxiv rau XGBoost

  2. Lub hom phiaj ntawm kev xa tawm

    • Kev xam huab ntawm qhov ntsuas: ONNX Runtime lossis Triton, containerized

    • Txawb los yog embedded: TF Lite los yog Core ML

  3. Cov kev xav tau ntawm qhov ntsuas

    • Ib qho GPU lossis chaw ua haujlwm: txhua lub DL framework loj ua haujlwm

    • Kev cob qhia faib tawm: xyuas kom meej cov tswv yim uas twb muaj lawm lossis siv Ray Train

  4. MLOps kev loj hlob

    • Cov hnub thaum ntxov: MLflow rau kev taug qab, Docker cov duab rau kev ntim khoom

    • Pab neeg loj hlob: ntxiv Kubeflow lossis Airflow/Prefect rau cov kav dej

  5. Qhov yuav tsum tau nqa mus los tau

    • Txoj kev npaj rau kev xa tawm ONNX thiab ib txheej txheej pabcuam nruab nrab

  6. Kev pheej hmoo

    • Ua raws li NIST cov lus qhia, sau cov keeb kwm, thiab siv cov kev tshuaj xyuas [5]

Yog tias lo lus nug hauv koj lub taub hau tseem yog dab tsi yog software framework rau AI, nws yog cov kev xaiv uas ua rau cov khoom teev npe ntawd tsis lom zem. Kev tsis lom zem yog qhov zoo.

Cov lus dag thiab cov lus dab neeg me me 😬

  • Kev ntseeg: ib lub moj khaum kav lawv txhua tus. Qhov tseeb: koj yuav sib xyaw thiab phim. Qhov ntawd yog qhov zoo.

  • Kev ntseeg tsis tseeb: kev cob qhia ceev yog txhua yam. Tus nqi thiab kev ntseeg tau ntawm kev xav feem ntau tseem ceeb dua.

  • Tau txais: tsis nco qab cov kav dej ntaub ntawv. Cov ntaub ntawv tsis zoo ua rau cov qauv zoo poob. Siv cov loaders thiab kev lees paub kom raug.

  • Tau kawg: hla dhau qhov kev taug qab kev sim. Koj yuav hnov ​​​​​​qab qhov kev khiav twg zoo tshaj plaws. Yav tom ntej - koj yuav chim siab.

  • Cuav: kev yooj yim nqa mus los yog ua tau. Qee zaum kev xa tawm yuav ua tsis tau thaum ua haujlwm raws li kev cai. Sim ua ntej.

  • Tau lawm: ua MLOps ntau dhau lawm sai dhau. Ua kom yooj yim, tom qab ntawd ntxiv kev sib dhos thaum mob tshwm sim.

  • Muaj qhov piv txwv tsis zoo me ntsis: xav txog koj lub cev zoo li lub kaus mom tsheb kauj vab rau koj tus qauv. Tsis zoo nkauj? Tej zaum. Tab sis koj yuav nco nws thaum txoj kev hais nyob zoo.

Cov Lus Nug Me Me Txog Cov Qauv ❓

Q: Puas yog lub moj khaum txawv ntawm lub tsev qiv ntawv lossis lub platform?

  • Tsev Qiv Ntawv: cov haujlwm tshwj xeeb lossis cov qauv uas koj hu.

  • Lub moj khaum: txhais cov qauv thiab lub voj voog ntawm lub neej, ntsaws rau hauv cov tsev qiv ntawv.

  • Platform: ib puag ncig dav dua nrog infra, UX, kev them nqi, thiab kev tswj hwm cov kev pabcuam.

Q: Kuv puas tuaj yeem tsim AI yam tsis muaj lub moj khaum?

Yog lawm. Qhov tseeb, nws zoo li sau koj tus kheej lub compiler rau ib qho blog post. Koj ua tau, tab sis yog vim li cas.

Q: Kuv puas xav tau ob qho kev cob qhia thiab cov qauv kev pabcuam?

Feem ntau yog. Qhia hauv PyTorch lossis TensorFlow, xa tawm mus rau ONNX, pab nrog Triton lossis ONNX Runtime. Cov seams nyob ntawd rau lub hom phiaj. [4]

Q: Cov kev coj ua zoo tshaj plaws uas muaj cai nyob qhov twg?

NIST's AI RMF rau kev coj ua pheej hmoo; cov ntaub ntawv muag khoom rau kev tsim vaj tsev; cov lus qhia ML ntawm cov neeg muab kev pabcuam huab yog cov kev kuaj xyuas uas pab tau. [5]

Kev piav qhia luv luv ntawm cov lus tseem ceeb kom meej meej📌

Cov neeg feem ntau tshawb nrhiav seb lub software framework rau AI yog dab tsi vim lawv tab tom sim txuas cov ntsiab lus ntawm kev tshawb fawb code thiab qee yam uas siv tau. Yog li, lub software framework rau AI hauv kev xyaum yog dab tsi? Nws yog cov khoom siv suav, kev rho tawm, thiab cov kev cai uas cia koj cob qhia, soj ntsuam, thiab xa cov qauv nrog tsawg dua qhov xav tsis thoob, thaum ua si zoo nrog cov kav dej ntaub ntawv, kho vajtse, thiab kev tswj hwm. Muaj, hais nws peb zaug. 😅

Cov Lus Kawg - Ntev Dhau Kuv Tsis Tau Nyeem Nws 🧠➡️🚀

  • Ib lub software framework rau AI muab rau koj cov scaffolding opinions: tensors, autodiff, training, deployment, thiab tooling.

  • Xaiv los ntawm hom lus, lub hom phiaj xa tawm, qhov ntsuas, thiab qhov tob ntawm lub ecosystem.

  • Xav kom sib xyaw cov stacks: PyTorch lossis TensorFlow los cob qhia, ONNX Runtime lossis Triton los pab, MLflow los taug qab, Airflow lossis Prefect los tswj hwm. [1][2][4]

  • Ci hauv kev yooj yim nqa mus los, kev soj ntsuam, thiab kev coj ua pheej hmoo thaum ntxov. [5]

  • Thiab yog, txais tos cov qhov chaw tho txawv. Kev tho txawv yog qhov ruaj khov, thiab cov nkoj ruaj khov.

Cov qauv zoo tsis tshem tawm qhov nyuaj. Lawv tswj nws kom koj pab neeg tuaj yeem txav mus sai dua nrog tsawg lub sijhawm oops. 🚢

Cov ntaub ntawv siv los ua piv txwv

[1] PyTorch - Kev Taw Qhia rau torch.compile (cov ntaub ntawv raug cai): nyeem ntxiv

[2] TensorFlow - Kev ua tau zoo dua nrog tf.function (phau ntawv qhia raug cai): nyeem ntxiv

[3] JAX - Pib Sai: Yuav Ua Li Cas Xav Hauv JAX (cov ntaub ntawv raug cai): nyeem ntxiv

[4] ONNX Runtime - ONNX Runtime rau Kev Nkag Siab (cov ntaub ntawv raug cai): nyeem ntxiv

[5] NIST - AI Risk Management Framework (AI RMF 1.0): nyeem ntxiv

Nrhiav cov AI tshiab kawg ntawm lub khw muag khoom AI Assistant Official

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