{"id":22520502,"url":"https://github.com/lruizap/testcuda","last_synced_at":"2026-05-12T07:34:31.278Z","repository":{"id":266735986,"uuid":"899201445","full_name":"lruizap/testCuda","owner":"lruizap","description":"Guide to install and use cuda for programming","archived":false,"fork":false,"pushed_at":"2024-12-05T20:09:49.000Z","size":6,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-28T03:18:30.226Z","etag":null,"topics":["cuda","cudnn","nvidia","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lruizap.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-05T20:05:34.000Z","updated_at":"2024-12-05T20:09:52.000Z","dependencies_parsed_at":"2024-12-05T21:30:17.955Z","dependency_job_id":null,"html_url":"https://github.com/lruizap/testCuda","commit_stats":null,"previous_names":["lruizap/testcuda"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lruizap%2FtestCuda","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lruizap%2FtestCuda/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lruizap%2FtestCuda/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lruizap%2FtestCuda/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lruizap","download_url":"https://codeload.github.com/lruizap/testCuda/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245960835,"owners_count":20700783,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cuda","cudnn","nvidia","pytorch"],"created_at":"2024-12-07T05:07:35.924Z","updated_at":"2026-05-12T07:34:31.249Z","avatar_url":"https://github.com/lruizap.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# README: Verificar Instalación de CUDA\n\nEste README guía paso a paso cómo comprobar que CUDA está correctamente instalado y funcionando en tu sistema, después de haber seguido los pasos de instalación.\n\n---\n\n## **Pasos Previos**\nPara instalar CUDA y las bibliotecas necesarias, sigue estos pasos:\n\n1. **Descargar y actualizar dependencias de la gráfica**\n   - Asegúrate de tener los drivers de la gráfica actualizados. Puedes descargarlos desde los siguientes enlaces:\n     - [Descargar CUDA](https://developer.nvidia.com/cuda-downloads)\n     - [Descargar cuDNN](https://developer.nvidia.com/cudnn-downloads)\n\n2. **Instalar PyTorch con soporte CUDA**\n   - Sigue la guía oficial de instalación de PyTorch:\n     - [Guía de instalación de PyTorch](https://pytorch.org/get-started/locally/#windows-pip)\n\n---\n\n## **Prueba de CUDA**\nUna vez completada la instalación, verifica que CUDA esté funcionando correctamente con el siguiente script:\n\n1. **Abrir un entorno Python**\n   Si usas un entorno virtual, actívalo primero:\n   ```bash\n   source .venv/bin/activate    # Linux/Mac\n   .venv\\Scripts\\activate      # Windows\n   ```\n\n2. **Ejecutar el siguiente código en Python:**\n   ```python\n   import torch\n\n   # Verifica si CUDA está disponible\n   print(\"¿CUDA está disponible?:\", torch.cuda.is_available())\n\n   # Obtén el nombre de la GPU si está disponible\n   if torch.cuda.is_available():\n       print(\"Nombre de la GPU:\", torch.cuda.get_device_name(0))\n   else:\n       print(\"CUDA no está disponible. Revisa la instalación.\")\n   ```\n\n3. **Salida esperada:**\n   Si CUDA está configurado correctamente, deberías obtener una salida como esta:\n   ```\n   ¿CUDA está disponible?: True\n   Nombre de la GPU: NVIDIA GeForce RTX 3060\n   ```\n\n---\n\n## **Resolución de Problemas**\nSi el script indica que CUDA no está disponible:\n1. Revisa si los drivers de la GPU están actualizados.\n2. Asegúrate de que las versiones de CUDA, cuDNN y PyTorch sean compatibles entre sí.\n3. Consulta la documentación oficial de NVIDIA o PyTorch para solucionar problemas específicos:\n   - [NVIDIA CUDA Documentation](https://docs.nvidia.com/cuda/)\n   - [PyTorch Troubleshooting](https://pytorch.org/docs/stable/troubleshooting.html)\n\n---\n\nEste README está diseñado para confirmar que la instalación de CUDA es exitosa antes de proceder con otros proyectos.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flruizap%2Ftestcuda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flruizap%2Ftestcuda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flruizap%2Ftestcuda/lists"}