{"id":27160325,"url":"https://github.com/pelayo-felgueroso/tensorflow-gpu-setup","last_synced_at":"2026-02-17T21:36:11.968Z","repository":{"id":282688434,"uuid":"949363335","full_name":"pelayo-felgueroso/tensorflow-gpu-setup","owner":"pelayo-felgueroso","description":"Step-by-step guide to installing TensorFlow with GPU support on Conda.","archived":false,"fork":false,"pushed_at":"2025-04-15T10:11:24.000Z","size":8,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-15T11:25:53.290Z","etag":null,"topics":["artificial-intelligence","cuda","deep-learning","gpu","machine-learning","nvidia","nvidia-gpu","setup-guide","tensorflow"],"latest_commit_sha":null,"homepage":"","language":null,"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/pelayo-felgueroso.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":"2025-03-16T09:36:09.000Z","updated_at":"2025-04-15T10:11:28.000Z","dependencies_parsed_at":"2025-03-16T22:32:48.104Z","dependency_job_id":null,"html_url":"https://github.com/pelayo-felgueroso/tensorflow-gpu-setup","commit_stats":null,"previous_names":["itsmatchesmalone/tensorflow-gpu-setup","pelayo-felgueroso/tensorflow-gpu-setup"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/pelayo-felgueroso/tensorflow-gpu-setup","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pelayo-felgueroso%2Ftensorflow-gpu-setup","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pelayo-felgueroso%2Ftensorflow-gpu-setup/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pelayo-felgueroso%2Ftensorflow-gpu-setup/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pelayo-felgueroso%2Ftensorflow-gpu-setup/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pelayo-felgueroso","download_url":"https://codeload.github.com/pelayo-felgueroso/tensorflow-gpu-setup/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pelayo-felgueroso%2Ftensorflow-gpu-setup/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29558694,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-17T20:52:40.164Z","status":"ssl_error","status_checked_at":"2026-02-17T20:48:10.325Z","response_time":100,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["artificial-intelligence","cuda","deep-learning","gpu","machine-learning","nvidia","nvidia-gpu","setup-guide","tensorflow"],"created_at":"2025-04-08T23:41:27.509Z","updated_at":"2026-02-17T21:36:06.745Z","avatar_url":"https://github.com/pelayo-felgueroso.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# tensorflow-gpu-setup\nStep-by-step guide to installing TensorFlow with GPU support on Conda.\n\n**After days of struggling, I finally found a solution that works.**  \nI've seen countless Reddit and YouTube posts from people saying that **TensorFlow won’t run on their GPU**, and that tutorials don’t work due to **version conflicts**. Many guides are outdated or miss crucial details, leading to frustration.\n\nAfter experiencing the same issues, I found a solution using **Python virtual environments**. This ensures TensorFlow runs in an **isolated** setup, fully compatible with **CUDA and cuDNN**, while preventing conflicts with other projects.\n\n**My specs:**\n\n* **OS:** Windows 11\n* **CPU:** Intel Core i7-11800H\n* **GPU:** Nvidia GeForce RTX 3060 Laptop GPU\n* **Driver Version:** 572.16\n* **RAM:** 16GB\n* **Python Version:** 3.12.6 (global) but using **Python 3.10** in Conda\n* **CUDA Version:** 12.3 (global) but using **CUDA 11.2** in Conda\n* **cuDNN Version:** 8.1\n\n# Step-by-Step Installation:\n\n# 1. Install Miniconda (if you don’t have it)\n\nDownload `.exe` file:  \n[Miniconda3 Windows 64-bit](https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe)  \nOr Download the Miniconda installer by yourself here:  \n[Miniconda installer link](https://docs.anaconda.com/miniconda/)  \nDuring installation, DO NOT check \"Add Miniconda to PATH\" to avoid conflicts with other Python versions.  \nComplete the installation and restart your computer.\n\nAfter installing Miniconda, open **CMD** or **PowerShell** and run:\n\n    conda --version\n\nIf you see something like:\n\n    conda 25.1.1\n\nMiniconda is installed correctly.\n\n# 2. Create a Virtual Environment with Python 3.10\n\nOpen **Anaconda Prompt** or **PowerShell** and run:\n\n    conda create --name tf-2.10 python=3.10\n\nOnce created, initiate it:\n\n    conda init\n\nOnce initiated, close and reopen **PowerShell,** then activate it:\n\n    conda activate tf-2.10\n\n# 3. Fix NumPy Version to Avoid Import Errors\n\nTensorFlow 2.10 does **not** support NumPy 2.x. If you installed it already, downgrade it:\n\n    pip install numpy==1.23.5\n\n# 4. Install TensorFlow 2.10 (Compatible with GPU)\n\n    pip install tensorflow==2.10\n\n**Note:** Newer TensorFlow versions **(2.11+) dropped support for CUDA 11**, so **2.10 is the last version that supports it!**\n\n# 5. Install Correct CUDA and cuDNN Versions\n\nTensorFlow 2.10 **requires CUDA 11.2 and cuDNN 8.1**. Install them inside Conda:\n\n    conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1\n\n# 6. Verify Installation\n\nRun this in Python:\n\n    import tensorflow as tf\n    print(\"TensorFlow version:\", tf.__version__)\n    print(\"GPUs available:\", tf.config.list_physical_devices('GPU'))\n\nExpected Output:\n\n    TensorFlow version: 2.10.0\n    GPUs available: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n\nIf the **GPU list is empty (**`[]`**)**, TensorFlow is running on the **CPU**. Try restarting your terminal and running again.\n\n# 7. (Optional) Set Up TensorFlow in PyCharm\n\nIf you're using **PyCharm**, you need to manually add the Conda environment:\n\n1. Go to **File \u003e Settings \u003e Project: \u003cYourProject\u003e \u003e Python Interpreter**.\n2. Click **Add Interpreter \u003e Add Local Interpreter**.\n3. Select **Existing Environment** and browse to: C:\\\\Users\\\\\u003cyour\\_username\u003e\\\\miniconda3\\\\envs\\\\tf-2.10\\\\python.exe\n4. Select your **Environment** : tf-2.10\n5. Click **OK**.\n\n# Done!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpelayo-felgueroso%2Ftensorflow-gpu-setup","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpelayo-felgueroso%2Ftensorflow-gpu-setup","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpelayo-felgueroso%2Ftensorflow-gpu-setup/lists"}