Yini i-Software Framework ye-AI?

Yini i-Software Framework ye-AI?

Uhlaka oluqinile luguqula leyo siphithiphithi ibe ukuhamba komsebenzi okusebenzisekayo. Kulo mhlahlandlela, sizokhipha ukuthi yiluphi uhlaka lwesofthiwe ye-AI , kungani ibalulekile, nokuthi ungalukhetha kanjani olulodwa ngaphandle kokuziqagela njalo ngemizuzu emihlanu. Thatha ikhofi; gcina amathebhu evuliwe. ☕️

Izindatshana ongathanda ukuzifunda ngemva kwalesi:

🔗 Kuyini ukufunda komshini vs AI
Qonda umehluko oyinhloko phakathi kwezinhlelo zokufunda zomshini nobuhlakani bokwenziwa.

🔗 Yini echazwe yi-AI
Funda ukuthi i-AI echazekayo yenza kanjani amamodeli ayinkimbinkimbi abe sobala futhi aqondakale.

🔗 Iyini irobhothi le-humanoid AI
Hlola ubuchwepheshe be-AI obunika amandla amarobhothi afana nabantu kanye nokuziphatha okusebenzisanayo.

🔗 Iyini inethiwekhi ye-neural ku-AI
Thola ukuthi amanethiwekhi e-neural alingisa kanjani ubuchopho bomuntu ukucubungula ulwazi.

Yini i-Software Framework ye-AI? Impendulo emfushane 🧩

Uhlaka lwesofthiwe ye-AI luyinqwaba ehlelekile yamalabhulali, izingxenye zesikhathi sokusebenza, amathuluzi, nezimiso ezikusiza ukuthi wakhe, uqeqeshe, uhlole, futhi usebenzise ukufundwa komshini noma amamodeli okufunda ajulile ngokushesha nangokwethembeke nakakhulu. Ingaphezu komtapo wolwazi owodwa. Kucabange njenge-scaffolding enemibono ekunikeza:

  • Okushunqayo okubalulekile kwama-tensor, izendlalelo, izilinganiso, noma amapayipi

  • Ukwehlukanisa okuzenzakalelayo kanye nezinhlamvu zezibalo ezithuthukisiwe

  • Amapayipi okokufaka idatha kanye nezinsiza zokucubungula kusengaphambili

  • Ukuqeqesha amalophu, amamethrikhi, nokukhomba

  • Hlangana nama-accelerator afana nama-GPU nezingxenyekazi zekhompuyutha ezikhethekile

  • Ukupakisha, ukuphakela, futhi kwesinye isikhathi ukuhlolwa kokulandelela

Uma umtapo wolwazi kuyikhithi yamathuluzi, uhlaka luyishabhu-nokukhanya, amabhentshi, nomakhi wamalebula uzokwenza sengathi awuwadingi… uze wenze kanjalo. 🔧

Uzongibona ngiphinda umusho oqondile ukuthi luyini uhlaka lwesoftware ye-AI izikhathi ezimbalwa. Lokho kwenziwa ngamabomu, ngoba umbuzo abantu abaningi abawuthayiphayo uma belahlekile ku-maze yamathuluzi.

Yini eyenza uhlaka lwesoftware oluhle lwe-AI? ✅

Nalu uhlu olufushane engingalufuna uma bengiqala ekuqaleni:

  • I-ergonomics ekhiqizayo - ama-API ahlanzekile, okuzenzakalelayo okunengqondo, imilayezo yephutha ewusizo

  • Ukusebenza - izinhlamvu ezisheshayo, ukunemba okuxubile, ukuhlanganiswa kwegrafu noma i-JIT lapho kusiza khona

  • Ukujula kwe-Ecosystem - amahabhu wamamodeli, okokufundisa, izisindo eziqeqeshwe kusengaphambili, ukuhlanganiswa

  • Ukuphatheka - izindlela zokuthekelisa ezifana ne-ONNX, izikhathi zokusebenza zeselula noma ezisemaphethelweni, ubungane beziqukathi

  • Ukuqaphela - amamethrikhi, ukugawulwa kwemithi, ukwenza iphrofayela, ukulandelela ukuhlolwa

  • I-Scalability - i-multi-GPU, ukuqeqeshwa okusabalalisiwe, ukukhonza okunwebekayo

  • Ukuphatha - izici zokuphepha, inguqulo, uhlu, namadokhumenti angakuphoqi

  • Umphakathi nempilo ende - abalondolozi abasebenzayo, ukutholwa komhlaba wangempela, amamephu emigwaqo athembekile

Lapho lezo zingcezu zichofoza, ubhala ikhodi yeglue encane futhi wenze i-AI yangempela eyengeziwe. Okuyiphuzu. 🙂

Izinhlobo zezinhlaka ozobhekana nazo 🗺️

Akuwona wonke uhlaka oluzama ukwenza yonke into. Cabanga ngezigaba:

  • Izinhlaka zokufunda ezijulile : i-tensor ops, i-autodiff, amanetha e-neural

    • I-PyTorch, i-TensorFlow, i-JAX

  • Izinhlaka ze-ML zakudala : amapayipi, ukuguqulwa kwesici, izilinganiso

    • scikit-learn, XGBoost

  • Amahabhu amamodeli nezitaki ze-NLP : amamodeli aqeqeshwe kusengaphambili, amathokheniza, ukulungisa kahle

    • Ama-Face Transformers Okwanga

  • Izikhathi zokusebenza neziqondiso : ukusetshenziswa okuthuthukisiwe

    • I-ONNX Runtime, i-NVIDIA Triton Inference Server, iRay Serve

  • Ama-MLOps nomjikelezo wokuphila : ukulandelela, ukupakishwa, amapayipi, i-CI ye-ML

    • MLflow, Kubeflow, Apache Airflow, Prefect, DVC

  • I-Edge neselula : izinyathelo ezincane, i-hardware-friendly

    • I-TensorFlow Lite, i-Core ML

  • Ubungozi nohlaka lokuphatha : inqubo nezilawuli, hhayi ikhodi

    • I-NIST AI Uhlaka Lokulawulwa Kwengozi

Asikho isitaki esisodwa esilingana neqembu ngalinye. Kulungile.

Ithebula lokuqhathanisa: izinketho ezidumile lapho uthi nhla 📊

Izinto ezincane ezifakiwe zifakiwe ngoba impilo yangempela ingcolile. Amanani ayashintsha, kodwa izingcezu eziningi eziyinhloko ziwumthombo ovulekile.

Ithuluzi / Isitaki Kuhle kakhulu Inani-ish Kungani kusebenza
I-PyTorch Abacwaningi, ama-Pythonic devs Umthombo ovulekile Amagrafu anamandla azwakala engokwemvelo; umphakathi omkhulu. 🙂
I-TensorFlow + Keras Ukukhiqizwa ngezinga, cross-platform Umthombo ovulekile Imodi yegrafu, i-TF Serving, i-TF Lite, ithuluzi eliqinile.
I-JAX Abasebenzisi bamandla, umsebenzi uyashintsha Umthombo ovulekile Ukuhlanganiswa kwe-XLA, i-math-first vibe ehlanzekile.
scikit-funda I-ML yakudala, idatha yethebula Umthombo ovulekile Amapayipi, amamethrikhi, i-estimator API ivele ichofoze.
XGBoost Idatha ehleliwe, izisekelo eziwinayo Umthombo ovulekile Ukuthuthukisa okuvamile okuvame ukuwina nje.
Ama-Face Transformers Okwanga I-NLP, umbono, ukusabalalisa ngokufinyelela kwehabhu Ikakhulukazi evuliwe Amamodeli aqeqeshwe kusengaphambili + ama-tokenizer + amadokhumenti, wow.
Isikhathi sokusebenza se-ONNX Ukuphatheka, izinhlaka ezixubile Umthombo ovulekile Thumela kanye, gijima ngokushesha kuma-backend amaningi. [4]
I-MLflow Ukulandelela ukuhlola, ukupakisha Umthombo ovulekile Ukukhiqiza kabusha, ukubhaliswa kwemodeli, ama-API alula.
Ray + Ray Khonza Ukuqeqeshwa okusabalalisiwe + ukukhonza Umthombo ovulekile Imithwalo yemisebenzi ye-Scales Python; inikeza i-micro-batching.
I-NVIDIA Triton Ukuchazwa komphumela ophezulu Umthombo ovulekile I-Multi-framework, i-dynamic batching, ama-GPU.
Kubeflow Amapayipi e-Kubernetes ML Umthombo ovulekile Ukuphela kokuphela kuma-K8, kwesinye isikhathi kuyaxaka kodwa kunamandla.
Ukugeleza komoya noma i-Prefect I-orchestration ezungeze ukuqeqeshwa kwakho Umthombo ovulekile Ukuhlela, ukuzama futhi, ukubonakala. Isebenza kahle.

Uma ulangazelela izimpendulo zomugqa owodwa: I-PyTorch yocwaningo, i-TensorFlow yokukhiqiza okude, i-scikit-learn ye-tabular, i-ONNX Runtime yokuphatheka, i-MLflow yokulandelela. Ngizobuyela emuva uma kudingeka.

Ngaphansi kwe-hood: ukuthi izinhlaka zisebenza kanjani izibalo zakho ⚙️

Iningi lezinhlaka zokufunda ezijulile zihlanganisa izinto ezintathu ezinkulu:

  1. Ama-tensors - ama-multi-dimensional array anokubekwa kwedivayisi nemithetho yokusakaza.

  2. I-Autodiff - umehluko wemodi yokuhlehla ukuze ubale ama-gradient.

  3. Isu lokwenza - imodi yokulangazelela iqhudelana nemodi yegrafu vs ukuhlanganiswa kwe-JIT.

  • I-PyTorch izenzakalela ekwenzeni ngokuzimisela futhi ingahlanganisa amagrafu ne -torch.compile ukuze ihlanganise ama-ops futhi isheshise izinto ngoshintsho oluncane lwekhodi. [1]

  • I-TensorFlow isebenza ngokulangazela ngokuzenzakalelayo futhi isebenzisa i-tf.function ukuze ifake i-Python esigabeni samagrafu okugeleza kwedatha ephathekayo, adingekayo ekuthumeleni i-SavedModel futhi ngokuvamile ithuthukisa ukusebenza. [2]

  • I-JAX incike ekuguquleni okuhlanganisekayo njenge -jit , grad , vmap , kanye ne-pmap , ihlanganisa nge-XLA ukuze kusheshiswe nokufana. [3]

Kulapho ukusebenza kuhlala khona: ama-kernel, ama-fusions, ukwakheka kwememori, ukunemba okuxubile. Hhayi umlingo - ubunjiniyela nje obubukeka buwumlingo. ✨

Ukuqeqeshwa uma kuqhathaniswa nokucabangayo: imidlalo emibili ehlukene 🏃♀️🏁

  • Ukuqeqeshwa kugcizelela ukusebenza nokuzinza. Ufuna ukusetshenziswa okuhle, ukukala kwe-gradient, namasu asabalalisiwe.

  • Imibono ijaha ukubambezeleka, izindleko, nokuvumelana. Ufuna i-batching, i-quantization, futhi ngezinye izikhathi ukuhlanganiswa komsebenzisi.

Ukusebenzisana kubalulekile lapha:

  • I-ONNX isebenza njengefomethi yokushintshisana eyimodeli evamile; I-ONNX Runtime isebenzisa amamodeli asuka kuzinhlaka zemithombo eminingi kuwo wonke ama-CPU, ama-GPU, nezinye izisheshisi ezinesibopho solimi sezitaki zokukhiqiza ezijwayelekile. [4]

I-quantization, ukuthena, kanye ne-distillation kuvame ukuletha impumelelo enkulu. Kwesinye isikhathi kukhulu ngendlela ehlekisayo - okuzwakala njengokukopela, nakuba kungenjalo. 😉

Idolobhana le-MLOps: ngale kohlaka oluyisisekelo 🏗️

Ngisho negrafu yekhompyutha engcono kakhulu ngeke ikhulule umjikelezo wokuphila ongcolile. Ekugcineni uzofuna:

  • Ukulandelela kokuhlola nokubhalisa : qala nge-MLflow ukuze ungene kumapharamu, amamethrikhi, nama-artifacts; phromotha ngerejista

  • Amapayipi ne-orchestration yokuhamba komsebenzi : Kubeflow ku-Kubernetes, noma ama-generalists afana ne-Airflow ne-Prefect

  • Ukuguqulwa kwedatha : I-DVC igcina idatha namamodeli enguqulo ehambisana nekhodi

  • Iziqukathi nokuthunyelwa : Izithombe ze-Docker kanye ne-Kubernetes yezindawo ezibikezelwayo, ezingabazekayo

  • Amahabhu emodeli : pretrain-then-fine-tune beats greenfield kaningi kunalokho

  • Ukuqapha : i-latency, drift, kanye nokuhlolwa kwekhwalithi uma amamodeli efika ekukhiqizeni

I-anecdote yenkundla esheshayo: ithimba elincane le-e-commerce lalifuna “ukuhlolwa okukodwa futhi” nsuku zonke, lapho-ke alikwazanga ukukhumbula ukuthi yikuphi ukuqalisa okusebenzise iziphi izici. Bangeze i-MLflow kanye nomthetho olula “wokuphromotha kuphela kusuka kurejista”. Ngokungazelelwe, ukubuyekezwa kwamasonto onke kwakuphathelene nezinqumo, hhayi imivubukulo. Iphethini ibonakala yonke indawo.

Ukusebenzisana nokuphatheka: gcina izinketho zakho zivuliwe 🔁

I-Lock-in ingena ngokuthula. Kugweme ngokuhlelela:

  • Izindlela zokuthekelisa : ONNX, SavedModel, TorchScript

  • Ukuguquguquka kwesikhathi sokusebenza : Isikhathi sokusebenza se-ONNX, i-TF Lite, i-Core ML yeselula noma umphetho

  • I-Containerization : amapayipi okwakha angabikezelwa anezithombe ze-Docker

  • Ukukhonza ukungathathi hlangothi : ukusingathwa kwe-PyTorch, i-TensorFlow, ne-ONNX eceleni-by-side kukugcina uthembekile

Ukushintsha isendlalelo sokuphakela noma ukuhlanganisa imodeli yedivayisi encane kufanele kube inkathazo, hhayi ukubhala kabusha.

Ukusheshisa kwezingxenyekazi zekhompuyutha nesikali: kwenze kusheshe ngaphandle kwezinyembezi ⚡️

  • Ama-GPU abusa umthwalo ojwayelekile wokuqeqeshwa ngenxa yezinhlamvu ezithuthukiswe kakhulu (cabanga nge-cuDNN).

  • Ukuqeqeshwa okusabalalisiwe kubonakala lapho i-GPU eyodwa ingakwazi ukuqhubeka: ukufana kwedatha, ukufana kwemodeli, izithuthukisi ezishiyiwe.

  • Ukunemba okuxutshiwe konga inkumbulo nesikhathi ngokulahleka kokunemba okuncane uma kusetshenziswa kahle.

Kwesinye isikhathi ikhodi eshesha kakhulu ikhodi ongayibhalanga: sebenzisa amamodeli aqeqeshwe kusengaphambili futhi ucule kahle. Ngokujulile. 🧠

Ukubusa, ukuphepha, kanye nobungozi: hhayi nje amaphepha 🛡️

Ukuthumela i-AI ezinhlanganweni zangempela kusho ukucabanga ngalokhu:

  • Uzalo : idatha ivela kuphi, ukuthi yacutshungulwa kanjani, nokuthi iyiphi inguqulo yemodeli ebukhoma

  • Ukukhiqiza kabusha : Izakhiwo ezinqunyiwe, ukuncika okuphiniwe, izitolo ze-artifact

  • Ukungafihli nokubhala : amakhadi emodeli kanye nezitatimende zedatha

  • Ukulawulwa kobungozi : I-NIST AI Risk Management Framework inikeza umgwaqo osebenzayo wokwenza imephu, ukulinganisa, nokulawula amasistimu we-AI athembekile kuwo wonke umjikelezo wempilo. [5]

Lokhu akukona ukuzikhethela ezizindeni ezilawulwayo. Ngisho nangaphandle kwabo, bavimbela ukuphuma okudidayo nemihlangano ewubuqaba.

Indlela yokukhetha: uhlu lokuhlola izinqumo ezisheshayo 🧭

Uma usagqolozele amathebhu amahlanu, zama lokhu:

  1. Ulimi oluyisisekelo nesizinda seqembu

    • Ithimba locwaningo lokuqala lwePython: qala nge-PyTorch noma i-JAX

    • Ucwaningo oluxubile nokukhiqiza: I-TensorFlow ne-Keras ukubheja okuphephile

    • Izibalo zakudala noma ukugxila kwethebula: i-scikit-learn plus XGBoost

  2. Impokophelo yokusebenzisa

    • I-Cloud inference esikalini: I-ONNX Runtime noma i-Triton, ifakwe esitsheni

    • Iselula noma eshumekiwe: I-TF Lite noma i-Core ML

  3. Izidingo zesikali

    • I-GPU eyodwa noma indawo yokusebenza: noma yiluphi uhlaka lwe-DL olukhulu luyasebenza

    • Ukuqeqeshwa okusatshalalisiwe: qinisekisa amasu akhelwe ngaphakathi noma sebenzisa i-Ray Train

  4. Ukuvuthwa kwe-MLOps

    • Izinsuku zakuqala: I-MLflow yokulandelela, izithombe ze-Docker zokupakishwa

    • Ithimba elikhulayo: engeza i-Kubeflow noma i-Airflow/Prefect yamapayipi

  5. Imfuneko yokuphatheka

    • Hlela ukuthunyelwa kwe-ONNX kanye nesendlalelo sokuphakela esimaphakathi

  6. Ukuma kwengozi

    • Qondanisa nesiqondiso se-NIST, uhlu lwamadokhumenti, gcizelela ukubuyekezwa [5]

Uma umbuzo osekhanda lakho uhlala ukuthi luyini uhlaka lwesofthiwe ye-AI , isethi yezinketho ezenza lezo zinto zohlu lokuhlola zibe yisicefe. Ukubhoreka kuhle.

I-gotchas evamile kanye nezinganekwane ezithambile 😬

  • Inganekwane: uhlaka olulodwa lubusa zonke. Iqiniso: uzoxuba futhi ufanise. Kunempilo lokho.

  • Inganekwane: isivinini sokuqeqesha siyikho konke. Izindleko zokucatshangelwa nokuthembeka kuvame ukuba nendaba kakhulu.

  • Gotcha: ukukhohlwa amapayipi edatha. Okokufaka okungalungile kucwilisa amamodeli amahle. Sebenzisa izilayishi ezifanele nokuqinisekisa.

  • I-Gotcha: ukweqa ukulandelela ukuhlolwa. Uzokhohlwa ukuthi yikuphi ukugijima obekungcono kakhulu. Ikusasa-uzocasuka.

  • Inganekwane: ukuphatheka kuyazenzakalela. Ukuthunyelwa kwamanye amazwe ngezinye izikhathi kuphulwa ngama-ops wangokwezifiso. Hlola kusenesikhathi.

  • I-Gotcha: ama-MLOps asetshenziswe ngokweqile maduze. Kugcine kulula, bese wengeza i-orchestration lapho ubuhlungu buvela.

  • Isingathekiso esinamaphutha kancane : cabanga ngohlaka lwakho njengesigqoko sebhayisikili semodeli yakho. Akusona isitayela? Kungenzeka. Kodwa uzophuthelwa lapho i-pavement ithi sawubona.

I-FAQ emincane mayelana nezinhlaka ❓

Umbuzo: Ingabe uhlaka luhlukile kumtapo wolwazi noma inkundla?

  • Umtapo wolwazi : imisebenzi ethile noma amamodeli owashayelayo.

  • Uhlaka : luchaza ukwakheka kanye nomjikelezo wempilo, amapulagi emitapo yolwazi.

  • I-Platform : indawo ebanzi ene-infra, UX, inkokhiso, namasevisi aphethwe.

Umbuzo: Ngingakwazi ukwakha i-AI ngaphandle kohlaka?

Ngobuchwepheshe yebo. Empeleni, kufana nokubhala iqoqo lakho lokuthunyelwe kwebhulogi. Ungakwazi, kodwa kungani.

Umbuzo: Ingabe ngidinga kokubili ukuqeqeshwa kanye nezinhlaka zokuhlinzeka?

Ngokuvamile yebo. Isitimela nge-PyTorch noma i-TensorFlow, thumela ku-ONNX, sebenzisa ne-Triton noma i-ONNX Runtime. Imithungo ikhona ngamabomu. [4]

Q: Ihlala kuphi imikhuba emihle egunyaziwe?

I-NIST's AI RMF yezenzo zobungozi; amadokhumenti omthengisi wezakhiwo; imihlahlandlela ye-ML yabahlinzeki befu iwusizo ekuhloleni okuphambene. [5]

Isifinyezo esisheshayo somushwana ongukhiye ukuze kucace 📌

Abantu bavame ukucinga ukuthi luyini uhlaka lwesofthiwe ye-AI ngoba bazama ukuxhuma amachashazi phakathi kwekhodi yocwaningo nento engasebenziseka. Ngakho-ke, luyini uhlaka lwesoftware lwe-AI ekusebenzeni? Yinqwaba ekhethiwe yokubala, okufinyeziwe, nezimiso ezikuvumela ukuthi uqeqeshe, uhlole, futhi usebenzise amamodeli anezimanga ezimbalwa, kuyilapho udlala kahle ngamapayipi edatha, izingxenyekazi zekhompuyutha, kanye nokuphatha. Lapho, washo kathathu. 😅

Amazwi Okugcina - Kude Kakhulu Angizange Ngikufunde 🧠➡️🚀

  • Uhlaka lwesoftware ye-AI lukunikeza isikafula esinombono: ama-tensor, i-autodiff, ukuqeqeshwa, ukuthunyelwa, kanye nokusetshenziswa kwamathuluzi.

  • Khetha ngolimi, okuqondiwe kokusetshenziswa, isikali, nokujula kwe-ecosystem.

  • Lindela ukuhlanganisa izitaki: I-PyTorch noma i-TensorFlow ukuze uqeqeshe, i-ONNX Runtime noma i-Triton isebenze, i-MLflow ukulandelela, i-Airflow noma i-Prefect ukuze i-orchestrate. [1][2][4]

  • Bhaka ngendlela ephathekayo, ebonakalayo, kanye nezinqubo zobungozi kusenesikhathi. [5]

  • Futhi yebo, yamukela izingxenye eziyisicefe. Imikhumbi eyisicefe izinzile, futhi izinzile.

Izinhlaka ezinhle azisusi ubunkimbinkimbi. Bayihlanganisa ukuze iqembu lakho lihambe ngokushesha ngemizuzwana embalwa. 🚢

Izithenjwa

[1] I-PyTorch - Isingeniso ku -torch.compile (amadokhumenti asemthethweni): funda kabanzi

[2] I-TensorFlow - Ukusebenza okungcono nge -tf.function (umhlahlandlela osemthethweni): funda kabanzi

[3] I-JAX - Quickstart: Ungacabanga kanjani nge-JAX (amadokhumenti asemthethweni): funda kabanzi

[4] Isikhathi sokusebenza se-ONNX - Isikhathi sokusebenza se-ONNX Sokuphenya (amadokhumenti asemthethweni): funda kabanzi

[5] NIST - I-AI Risk Management Framework (AI RMF 1.0) : funda kabanzi

Thola i-AI yakamuva esitolo esisemthethweni somsizi we-AI

Mayelana NATHI

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