Iluphi ulimi lokuhlela olusetshenziselwa i-AI

Iluphi ulimi lokuhlela olusetshenziselwa i-AI? Umhlahlandlela Owusizo.

Uma uke wazibuza ukuthi yiluphi ulimi lokuhlela olusetshenziselwa i-AI , usenkampanini enhle. Abantu bacabanga amalebhu ane-neon-lit kanye nezibalo eziyimfihlo - kodwa impendulo yangempela inobungane, ingcolile, futhi ingumuntu kakhulu. Izilimi ezihlukene zikhanya ezigabeni ezihlukene: ukwenza i-prototyping, ukuqeqeshwa, ukwenza kahle, ukunikeza, ngisho nokusebenza esipheqululini noma kufoni yakho. Kulo mhlahlandlela, sizokweqa i-fluff futhi sisebenzise ukuze ukwazi ukukhetha isitaki ngaphandle kokuqagela zonke izinqumo ezincane. Futhi yebo, sizosho ukuthi yiluphi ulimi lokuhlela olusetshenziselwa i-AI izikhathi ezingaphezu kwesisodwa ngoba lowo umbuzo osemqondweni wawo wonke umuntu. Asigiqe.

Izindatshana ongathanda ukuzifunda ngemva kwalesi:

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Qonda umehluko obalulekile futhi ufunde ukuthi ungaqala kanjani ukwakha nge-AI.

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Hlola ukuthi i-AI ilithinta kanjani ikusasa lemisebenzi yobunjiniyela besoftware.

"Yiluphi ulimi lokuhlela olusetshenziselwa i-AI?"

Impendulo emfushane: ulimi olungcono kakhulu yilo olususa embonweni uye emiphumeleni ethembekile enedrama encane. Impendulo ende:

  • Ukujula kwe-ecosystem - imitapo yolwazi ekhulile, ukwesekwa komphakathi okusebenzayo, izinhlaka ezisebenza nje.

  • Isivinini sikanjiniyela - i-syntax emfushane, ikhodi efundekayo, amabhethri afakiwe.

  • Amachamusela okuphepha okusebenza - uma udinga isivinini esingavuthiwe, yehlela ku-C++ noma kuma-GPU kernels ngaphandle kokubhala kabusha iplanethi.

  • Ukusebenzisana - ama-API ahlanzekile, i-ONNX noma amafomethi afanayo, izindlela ezilula zokuthumela.

  • Indawo okuqondiwe kuyo - isebenza kumaseva, iselula, iwebhu, kanye nomphetho anokuguqulwa okuncane.

  • I-Tooling reality - ama-debuggers, amaphrofayili, amabhukumaka, abaphathi bamaphakheji, i-CI-wonke umbukiso.

Masikhulume iqiniso: cishe uzohlanganisa izilimi. Yikhishi, hhayi imnyuziyamu. 🍳

Isinqumo esisheshayo: okuzenzakalelayo kwakho kuqala ngePython 🐍

Abantu abaningi baqala nge -Python ukuze bathole ama-prototypes, ucwaningo, ukulungisa kahle, ngisho namapayipi okukhiqiza ngoba i-ecosystem (isb, i-PyTorch) ijulile futhi inakekelwa kahle-futhi isebenzisane nge-ONNX kwenza ukudluliselwa kwezinye izikhathi ziqonde [1][2]. Ngokulungiselelwa kwedatha yezinga elikhulu kanye ne-orchestration, amaqembu avame ukuncika ku -Scala noma i-Java nge-Apache Spark [3]. Ngama-microservices amancane, asheshayo, i-Go noma i-Rust iletha okuqinile, okubambezeleka okuphansi. Futhi yebo, ungasebenzisa amamodeli esipheqululini usebenzisa i-ONNX Runtime Web uma ilingana nesidingo somkhiqizo [2].

Ngakho… yiluphi ulimi lokuhlela olusetshenziselwa i-AI ekusebenzeni? Isemishi elinobungane lePython yobuchopho, i-C++/CUDA ye-brawn, nokunye okufana ne-Go or Rust yomnyango lapho abasebenzisi bedlula khona [1][2][4].

Ithebula lokuqhathanisa: izilimi ze-AI ngokubuka nje 📊

Ulimi Izilaleli Inani Kungani kusebenza Amanothi e-Ecosystem
I-Python Abacwaningi, idatha bakwethu Mahhala Imitapo yolwazi emikhulu, i-prototyping esheshayo I-PyTorch, i-scikit-learn, i-JAX [1]
C++ Onjiniyela bokusebenza Mahhala Ukulawulwa kwezinga eliphansi, ukucabangela okusheshayo I-TensorRT, ama-ops wangokwezifiso, i-ONNX backends [4]
Ukugqwala Amasistimu ama-devs Mahhala Ukuphepha kwenkumbulo ngezibhamu zezinyawo ezinesivinini esincane Amakhreyithi okucabanga akhulayo
Hamba Amaqembu epulatifomu Mahhala Ukuvumelana okulula, izinsizakalo ezisebenzisekayo I-gRPC, izithombe ezincane, ama-ops alula
Scala/Java Ubunjiniyela bedatha Mahhala Amapayipi edatha enkulu, i-Spark Mllib Spark, Kafka, JVM tooling [3]
I-TypeScript Frontend, amademo Mahhala I-in-browser inconference nge-ONNX Runtime Web Izikhathi zokusebenza zewebhu/WebGPU [2]
I-Swift Izinhlelo zokusebenza ze-iOS Mahhala Incazelo yomdabu ekudivayisi I-Core ML (guqula isuka ku-ONNX/TF)
Kotlin/Java Izinhlelo zokusebenza ze-Android Mahhala Ukukhishwa kwe-Android okubushelelezi TFLite/ONNX Runtime Mobile
R Izazi zezibalo Mahhala Sula ukuhamba komsebenzi kwezibalo, ukubika caret, tidymodels
UJulia Ikhompyutha yezinombolo Mahhala Ukusebenza okuphezulu nge-syntax efundekayo Flux.jl, MLJ.jl

Yebo, ukuhlukaniswa kwetafula kufana nokuphila okuxakile. Futhi, iPython ayiyona inhlamvu yesiliva; ithuluzi nje ozolifinyelela kaningi [1].

I-Deep Dive 1: I-Python yocwaningo, i-prototyping, nokuqeqeshwa okuningi 🧪

Amandla amakhulu ePython amandla adonsela phansi e-ecosystem. Nge-PyTorch uthola amagrafu ashukumisayo, isitayela esibalulekile esihlanzekile, nomphakathi osebenzayo; Okubi kakhulu, ungakwazi ukunikeza amamodeli kwezinye izikhathi zokusebenza nge-ONNX uma sekuyisikhathi sokuhamba ngomkhumbi [1][2]. Umkhabi: uma isivinini sibalulekile, i-Python akudingeki ukuthi ihambe kancane nge-NumPy, noma ibhale ama-ops angokwezifiso awehlela ezindleleni ze-C++/CUDA ezivezwe uhlaka lwakho [4].

I-anecdote esheshayo: ithimba lombono wekhompuyutha elifanise ukutholwa kokukhubazeka ezincwadini zamanothi ze-Python, eziqinisekiswe inani lezithombe zeviki, zathunyelwa ku-ONNX, zase ziyinikeza isevisi ye-Go kusetshenziswa isikhathi sokusebenza esisheshisiwe-akukho ukuqeqeshwa kabusha noma ukubhala kabusha. Iluphu yocwaningo yahlala iqinile; ukukhiqizwa kwahlala kuyisicefe (ngendlela engcono kakhulu) [2].

I-Deep Dive 2: I-C++, i-CUDA, ne-TensorRT ngesivinini esingavuthiwe 🏎️

Ukuqeqesha amamodeli amakhulu kwenzeka ezitaki ezisheshiswe yi-GPU, futhi ama-ops abalulekile ekusebenzeni ahlala ku-C++/CUDA. Izikhathi zokusebenza ezithuthukisiwe (isb, i-TensorRT, i-ONNX Isikhathi sokusebenza esinabahlinzeki bokusebenzisa izingxenyekazi zekhompuyutha) iletha ukuwina okukhulu ngama-kernel ahlanganisiwe, ukunemba okuxubile, nokulungiselelwa kwegrafu [2][4]. Qala ngokwenza iphrofayela; ama-kernels wangokwezifiso kuphela lapho kubuhlungu khona ngempela.

I-Deep Dive 3: Rust and Go ukuthola izinsiza ezithembekile, ezingabambeki kancane 🧱

Uma i-ML ihlangana nokukhiqiza, ingxoxo isuka kusivinini sika-F1 iye kumaveni amancane angaphuki. I-Rust and Go ikhanya lapha: ukusebenza okuqinile, amaphrofayli enkumbulo abikezelwayo, nokuthunyelwa okulula. Empeleni, amaqembu amaningi aziqeqesha nge-Python, athumele ku-ONNX, futhi asebenze ngemuva kokuhlukaniswa kokukhathazeka okuhlanzekile kwe-Rust noma i-Go,, umthwalo wokuqonda omncane wama-ops [2].

I-Deep Dive 4: I-Scala ne-Java yamapayipi edatha nezitolo zesici 🏗️

I-AI ayenzeki ngaphandle kwedatha enhle. Ku-ETL yezinga elikhulu, ukusakaza, nobunjiniyela besici, i-Scala noma i-Java ene-Apache Spark ihlala ingamahhashi okusebenza, iqoqo elihlanganisayo nokusakaza ngaphansi kophahla olulodwa futhi isekela izilimi eziningi ukuze amaqembu abambisane kahle [3].

I-Deep Dive 5: I-TypeScript ne-AI kusiphequluli 🌐

Ukugijima kwamamodeli ngaphakathi kwesiphequluli akuselona iqhinga lephathi. I-ONNX Runtime Web ingakwazi ukusebenzisa amamodeli ohlangothini lweklayenti, inikeze amandla okucabanga okuyimfihlo nokuzenzakalelayo kumademo amancane namawijethi asebenzisanayo ngaphandle kwezindleko zeseva [2]. Ilungele ukuphindaphindwa komkhiqizo ngokushesha noma ukuzizwisa okushumekiwe.

I-Deep Dive 6: I-Mobile AI ene-Swift, i-Kotlin, namafomethi aphathekayo 📱

I-AI ekudivayisi ithuthukisa ukubambezeleka nobumfihlo. Indlela evamile: isitimela nge-Python, thekelisa ku-ONNX, guqulela okuqondiwe (isb, i-Core ML noma i-TFLite), futhi uyixhume ngentambo ku -Swift noma -Kotlin . Ubuciko bulinganisa usayizi wemodeli, ukunemba, nempilo yebhethri; usizo lwe-quantization kanye ne-hardware-aware ops [2][4].

Isitaki somhlaba wangempela: hlanganisa uphinde ufanise ngaphandle kwamahloni 🧩

Isistimu ye-AI evamile ingase ibukeke kanje:

  • Ucwaningo lwemodeli - Izincwadi zokubhalela zePython ezinePyTorch.

  • Amapayipi edatha - Spark ku-Scala noma i-PySpark ukuze kube lula, kuhlelwe nge-Airflow.

  • Ukuthuthukisa - Thumela ku-ONNX; sheshisa nge-TensorRT noma i-ONNX Runtime EPs.

  • Ukukhonza - Rust or Go microservice enesendlalelo esincane se-gRPC/HTTP, esikalwe ngokuzenzakalela.

  • Amaklayenti - Uhlelo lokusebenza lwewebhu ku-TypeScript; izinhlelo zokusebenza zeselula ku-Swift noma ku-Kotlin.

  • Ukuqaphela - amamethrikhi, amalogi ahlelekile, ukutholwa kwe-drift, kanye nedeshi yamadeshibhodi.

Ingabe yonke iphrojekthi idinga konke lokho? Vele akunjalo. Kodwa ukuba nomzila owenziwe imephu kukusiza ukuthi wazi ukuthi iyiphi ijika okufanele uyithathe ngokulandelayo [2][3][4].

Amaphutha avamile lapho ukhetha ukuthi yiluphi ulimi lokuhlela olusetshenziselwa i-AI 😬

  • Ukuthuthukisa kakhulu kusenesikhathi - bhala i-prototype, fakazela inani, bese ujaha ama-nanoseconds.

  • Ukukhohlwa impokophelo yokusebenzisa - uma kufanele isebenze kusiphequluli noma kudivayisi, hlela uchungechunge lwamathuluzi ngosuku lokuqala [2].

  • Ukuziba amapayipi edatha - imodeli enhle ezicini ezidwetshiwe ifana nendlu enkulu esihlabathini [3].

  • Ukucabanga kwe-Monolith - ungagcina iPython ukuze uyifanise futhi usebenze nge-Go noma Rust nge-ONNX.

  • Ukujaha izinto ezintsha - izinhlaka ezintsha zipholile; ukwethembeka kupholile.

Ukukhetha okusheshayo ngokwesimo 🧭

  • Iqala ku-zero - Python ngePyTorch. Engeza i-scikit-learn ye-ML yakudala.

  • I-Edge noma i-latency-critical - Python ukuqeqesha; I-C++/CUDA kanye ne-TensorRT noma Isikhathi sokusebenza se-ONNX ukuze uthole inkomba [2][4].

  • Ubunjiniyela besici sedatha enkulu - I-Spark ene-Scala noma i-PySpark.

  • Izinhlelo zokusebenza zokuqala kuwebhu noma amademo asebenzisanayo - I-TypeScript ene-ONNX Runtime Web [2].

  • I-iOS ne-Android yokuthunyelwa - I-Swift enemodeli eguqulelwe yi-Core-ML noma i-Kotlin enemodeli ye-TFLite/ONNX [2].

  • Izinsizakalo ezibaluleke kakhulu kumishini - Khonza ku-Rust noma Go; gcina ama-artifact emodeli ephathekayo nge-ONNX [2].

I-FAQ: ngakho… yiluphi ulimi lokuhlela olusetshenziselwa i-AI, futhi? ❓

  • Iluphi ulimi lokuhlela olusetshenziselwa i-AI ocwaningweni?
    I-Python-ke ngezinye izikhathi ibe yi-JAX noma i-PyTorch-tooling ethize, ene-C++/CUDA ngaphansi kwesivalo ngesivinini [1][4].

  • Kuthiwani ngokukhiqiza?
    Isitimela ngePython, thekelisa nge-ONNX, sebenza nge-Rust/Go noma i-C++ lapho ushefa izindaba zama-millisecond [2][4].

  • Ingabe i-JavaScript yanele ku-AI?
    Kumademo, amawijethi asebenzisanayo, nokunye okucatshangelwayo kokukhiqiza ngezikhathi zokusebenza zewebhu, yebo; ukuqeqeshwa okukhulu, hhayi ngempela [2].

  • Ingabe u-R uphelelwe yisikhathi?
    Cha. Kuhle kakhulu ngezibalo, ukubika, nokugeleza komsebenzi okuthile kwe-ML.

  • Ngabe uJulia uzongena esikhundleni sePython?
    Mhlawumbe ngolunye usuku, mhlawumbe akunjalo. Amajika okwamukela athatha isikhathi; sebenzisa ithuluzi elikuvulelayo namuhla.

TL;DR🎯

  • Qala ku -Python ukuze uthole isivinini nokunethezeka kwe-ecosystem.

  • Sebenzisa i-C++/CUDA nezikhathi zokusebenza ezilungiselelwe lapho udinga ukusheshisa.

  • Khonza nge -Rust noma Hamba ukuze uthole ukuzinza okuphansi.

  • Gcina amapayipi edatha esesimweni esihle nge- Scala/Java ku-Spark.

  • Ungakhohlwa isiphequluli nezindlela zeselula uma ziyingxenye yendaba yomkhiqizo.

  • Ngaphezu kwakho konke, khetha inhlanganisela eyehlisa ukungqubuzana kusuka embonweni kuya kumthelela. Leyo yimpendulo yangempela yokuthi yiluphi ulimi lokuhlela olusetshenziselwa i-AI -hhayi ulimi olulodwa, kodwa i-orchestra encane efanele. 🎻

Izithenjwa

  1. I-Stack Overflow Survey 2024 - ukusetshenziswa kolimi namasiginali we-ecosystem
    https://survey.stackoverflow.co/2024/

  2. I-ONNX Runtime (amadokhumenti asemthethweni) - inference yeplatform (ifu, umphetho, iwebhu, iselula), ukusebenzisana kohlaka
    https://onnxruntime.ai/docs/

  3. I-Apache Spark (isayithi elisemthethweni) - injini yezilimi eziningi yobunjiniyela bedatha/isayensi kanye ne-ML esikalini
    https://spark.apache.org/

  4. I-NVIDIA CUDA Toolkit (amadokhumenti asemthethweni) - Amalabhulali asheshiswe yi-GPU, izihlanganisi, namathuluzi e-C/C++ nezitaki zokufunda ezijulile
    https://docs.nvidia.com/cuda/

  5. I-PyTorch (isayithi elisemthethweni) - uhlaka lokufunda olujulile olusetshenziswa kabanzi ucwaningo nokukhiqiza
    https://pytorch.org/

Thola i-AI yakamuva esitolo esisemthethweni somsizi we-AI

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