Hvilken CUDA-versjon for TensorFlow?
Maskinvarekrav. Merk: TensorFlow-binærfiler bruker AVX-instruksjoner som kanskje ikke kjører på eldre prosessorer. Følgende GPU-aktiverte enheter støttes: NVIDIA® GPU-kort med CUDA®-arkitekturer3,5, 5,0, 6,0, 7,0, 7,5, 8,0 og høyere.
- Trinn 1 – Bestem versjoner for CUDA, cuDNN og Visual Studio. ...
- Trinn 2 – Last ned CUDA Toolkit. ...
- Trinn 3 – Last ned cuDNN. ...
- Trinn 4 – Last ned Visual Studio 2019 Community. ...
- Trinn 5 – Pakk ut og slå sammen filer. ...
- Trinn 6 – Sjekk vellykket installasjon av CUDA. ...
- Trinn 7 – Opprett et conda-miljø og installer TensorFlow.
- Bekreft at systemet har en CUDA-kompatibel GPU.
- Last ned NVIDIA CUDA Toolkit.
- Installer NVIDIA CUDA Toolkit.
- Test at den installerte programvaren kjører riktig og kommuniserer med maskinvaren.
Minimum driverversjon som kreves er450.80.02. Hva med nye funksjoner introdusert i mindre utgivelser av CUDA?
Å installere tensorflow uten CUDA er bare for å komme raskt i gang. Men etter at du ønsker å bli seriøs med tensorflow, bør du installere CUDA selv slik at flere tensorflow-miljøer kan gjenbruke den samme CUDA-installasjonen, og den lar deg installere den nyeste tensorflow-versjonen som tensorflow 2.0.
- Den beste måten er muligens å teste en fil. Kjør cat /usr/local/cuda/version.txt. ...
- En annen løsning er gjennom cuda-toolkit-kommandoen nvcc. nvcc –versjon.
- Den andre måten er ved hjelp av NVIDIA-driverens nvidia-smi-kommando du kanskje har installert. Bare kjør nvidia-smi.
Bruktf. test. er_bygget_med_cudafor å validere om TensorFlow ble bygget med CUDA-støtte.
Som standard er CUDA SDK Toolkit installert under/usr/local/cuda/. nvcc-kompilatordriveren er installert i /usr/local/cuda/bin, og CUDA 64-biters kjøretidsbiblioteker er installert i /usr/local/cuda/lib64.
- Trinn 0: Sjekk CUDA-versjonen. Sjekk hvilken versjon av CUDA du har. ...
- Trinn 1: Finn systemmiljøvariabler. Åpne opp miljøvariablene dine. ...
- Trinn 2: Endre systemvariabler. ...
- Trinn 3: Endre systembaner. ...
- Trinn 4: Sjekk om du lykkes.
Du kan sjekke vianvcc --version kommandohvis CUDA virkelig er installert. Merk - Noen ganger installerer CUDA via noen metoder (.run-fil) som standard også en NVIDIA-driver eller erstatter den eksisterende installerte driveren, og mange blir forvirret angående dette.
Har jeg CUDA installert Windows?
Du kan bekrefte at du har en CUDA-kompatibel GPU gjennom delen Skjermadaptere i Windows Enhetsbehandling. Her finner du leverandørens navn og modell for ditt(e) grafikkort. Hvis du har et NVIDIA-kort som er oppført i http:// developer.nvidia.com/cuda-gpus, er den GPU-en CUDA-kompatibel.
Kan jeg installere og bruke CUDA uten å installere NVIDIA grafikkdriver først? Takk. Et forenklet svar på dette er "nei". CUDA-driveren er avhengig av funksjonalitet på lavt nivå som enkelte komponenter i grafikkdriveren gir.
CUDA fungerer med alle Nvidia GPUer fra G8x-serien og utover, inkludert GeForce, Quadro og Tesla-linjen. CUDA er kompatibel med de fleste standard operativsystemer.
Dessverre,du kan ikke bruke CUDA uten et Nvidia grafikkort. CUDA er et rammeverk utviklet av Nvidia som lar personer med et Nvidia-grafikkkort bruke GPU-akselerasjon når det kommer til dyp læring, og å ikke ha et Nvidia-grafikkort beseirer det formålet.
Ja, hvis du vil håndtere det siste. Med en bedre GPU kan du gå med Machine Learning (AI) som trenger CUDA (Nvidia GPU). Machine Learning har et stort omfang fremover.
- Forutsetninger.
- Installer Python 3.6.
- Installer Tensorflow GPU 1.5.
- Installer CUDA Toolkit 9.0.
- Kopier cudnn64_7.dll fra cuDNN-biblioteket.
- Legg til CUDA-mappen til PATH.
- Bekreft Tensorflow-installasjonen.
- importer tensorflow som tf.
- if tf.test.gpu_device_name():
- print('Standard GPU-enhet:
- {}'.format(tf.test.gpu_device_name()))
- ellers:
- print("Vennligst installer GPU-versjonen av TF")
- Åpne en terminal eller ledetekst på systemet ditt.
- Type pip freeze | grep tensorflow (for Linux eller macOS) eller pip freeze | findstr tensorflow (for Windows) og trykk enter.
- Utdataene skal vise versjonen av TensorFlow som er installert på systemet ditt.
- Først last ned og installer Miniconda fra https://docs.conda.io/en/latest/miniconda.html.
- For å installere CUDA på maskinen din trenger du: CUDA-kompatibel GPU, ...
- Nå skal vi laste ned NVIDIA cuDNN, https://developer.nvidia.com/cudnn. Sjekk versjonskoden fra TensorFlow-siden. ...
- Gjør miljøet klart:
tensorflow fungerer ikke med CUDA 12 på WSL2· Utgave #59413 · tensorflow/tensorflow · GitHub.
Fungerer TensorFlow 1.15 med CUDA 11?
Dessverre, den offisielle tensorflow 1.15xstøtter ikke CUDA 11.
Du kan installere CUDA 11.2 og cuDNN 8.0. 4 (den nyeste versjonen som støtter CUDA 11.2) for TensorFlow, og beholdCUDA 11.6 og cuDNN 11.0 for PyTorch.
Bruk tf. test. er_bygget_med_cudafor å validere om TensorFlow ble bygget med CUDA-støtte.
- Offisiell bygg fra kilde: Det første trinnet er å besøke det offisielle TensorFlow-nettstedet for å sjekke ut den nyeste versjonen av TensorFlow som er tilgjengelig for øyeblikket. ...
- Last ned Microsoft Visual Studio: ...
- Installere CUDA-verktøysettet: ...
- Installerer CuDNN: ...
- Siste kommandoinstallasjon:
Velkommen til Tensorflow-forumet! Merk:Alle de eksisterende Tensorflow nightlies og Tensorflow 2.12 (ennå ikke utgitt) er kompatible med CUDA 11.8-støtte.
0med Cuda 11.0 og CuDnn 8 ved å bruke Anaconda.
Hvis du installerer PyTorch via binærfilene (f.eks. pip-hjul eller conda), kommer den allerede med CUDA og cuDNN ferdigpakket. Det eneste tilfellet hvor du trenger å installere CUDA & cuDNN selv er når du kompilerer det fra kilden.
- Flytte tensorer med til()-funksjonen. Hver Tensor du oppretter er tildelt en to()-medlemsfunksjon. ...
- Flytte tensorer med cuda()-funksjonen. Du kan også bruke cuda() for å plassere tensorer. ...
- Sørg for å bruke samme enhet for tensorer.
Avslutningsvis støtter PyTorch CUDA-versjoner9.2, 10.1, 10.2, 11.0 og 11.1. PyTorch støtter dessverre ikke CUDA 3.0. Hvis du bruker en GPU med CUDA 3.0, må du bruke en eldre versjon av PyTorch for å sikre kompatibilitet.
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