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Accéder à WSL\n###  Pour démarrer l'environnement WSL :\n\n1. Ouvrir PowerShell ou CMD :\n\n```\nwsl -d Ubuntu-22.04\n```\n- Ca connectera à l'instance WSL nommée Ubuntu-22.04\n\n2. Vérifier votre version de Linux (facultatif) :\n\n```\nlsb_release -a\n```\n##  2. Activer l’environnement virtuel Python\n\n### Étape 1 : Créer ou copier l’environnement virtuel et les fichiers nécessaires\n\n1. Créer un nouvel environnement virtuel (si besoin):\n\nSi vous n’avez pas encore d’environnement virtuel pour le projet, créez-en un dans votre dossier projet :\n\n```\npython3.10 -m venv .venv\n```\n\n2. Télécharger les fichiers nécessaires à PyTorch avec support GPU ROCm :\nExécutez les commandes suivantes pour télécharger les fichiers .whl requis :\n\n```\nwget https://repo.radeon.com/rocm/manylinux/rocm-rel-6.1.3/torch-2.1.2%2Brocm6.1.3-cp310-cp310-linux_x86_64.whl\nwget https://repo.radeon.com/rocm/manylinux/rocm-rel-6.1.3/torchvision-0.16.1%2Brocm6.1.3-cp310-cp310-linux_x86_64.whl\nwget https://repo.radeon.com/rocm/manylinux/rocm-rel-6.1.3/pytorch_triton_rocm-2.1.0%2Brocm6.1.3.4d510c3a44-cp310-cp310-linux_x86_64.whl\n```\n\n3. Copier un .venv existant (optionnel) :\n\nSi vous avez déjà un environnement virtuel configuré pour le GPU dans un autre projet, copiez-le dans votre nouveau projet :\n\nexemple : \n\n```\ncp -r ~/\u003cnom_du_modèle\u003e_model/.venv ./\n```\n\n4. Installer PyTorch et ses dépendances :\n\nSi vous avez téléchargé les fichiers ```.whl```, installez-les dans votre environnement virtuel :\n\n```\npip install torch-2.1.2+rocm6.1.3-cp310-cp310-linux_x86_64.whl \\\n            torchvision-0.16.1+rocm6.1.3-cp310-cp310-linux_x86_64.whl \\\n            pytorch_triton_rocm-2.1.0+rocm6.1.3.4d510c3a44-cp310-cp310-linux_x86_64.whl\n```\n\n\n### Étape 2 : Activez l’environnement virtuel Python :\n\n```\nsource .venv/bin/activate\n```\n\nVous saurez que l'environnement est activé si vous voyez (.venv) dans l'invite de commande.\n\n##  3. Vérifier l'état du GPU\n\n1. Tester si PyTorch détecte le GPU :\n\n```\npython -c \"import torch; print(torch.cuda.is_available())\"\n```\n\n2. Affichez le nom du GPU détecté :\n\n```\npython -c \"import torch; print(torch.cuda.get_device_name(0))\"\n```\n\nRésultat attendu pour la config:\n```\nTrue\n\u003cnom du gpu\u003e\n```\n\n##  4. Désactiver l’environnement virtuel\n\nLorsque vous avez terminé votre session de travail :\n\n1. Désactivez l’environnement virtuel :\n\n```\ndeactivate\n```\n\n2. Quittez WSL :\n\n```\nexit\n```\n\n3. Arrêtez WSL pour libérer les ressources :\n\n```\nwsl --shutdown\n```\n\n\n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fantoinebendafischulmann%2Flinux-windows-dualboot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fantoinebendafischulmann%2Flinux-windows-dualboot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fantoinebendafischulmann%2Flinux-windows-dualboot/lists"}