{"id":21001242,"url":"https://github.com/andrew2077/tensorflow-pytorch-gpu","last_synced_at":"2026-04-09T05:31:18.462Z","repository":{"id":63379262,"uuid":"567453785","full_name":"Andrew2077/TensorFlow-Pytorch-GPU","owner":"Andrew2077","description":"Tutorial for installing TF and Pytorch on local windows machine ","archived":false,"fork":false,"pushed_at":"2023-02-24T22:57:45.000Z","size":93,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-31T23:56:39.683Z","etag":null,"topics":["conda","pytorch-installation","tensorflow-installation"],"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/Andrew2077.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":"2022-11-17T20:33:16.000Z","updated_at":"2024-12-16T20:28:00.000Z","dependencies_parsed_at":"2025-01-20T09:42:03.123Z","dependency_job_id":"4ef02808-9e63-4026-b349-ea66e5870217","html_url":"https://github.com/Andrew2077/TensorFlow-Pytorch-GPU","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Andrew2077/TensorFlow-Pytorch-GPU","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Andrew2077%2FTensorFlow-Pytorch-GPU","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Andrew2077%2FTensorFlow-Pytorch-GPU/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Andrew2077%2FTensorFlow-Pytorch-GPU/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Andrew2077%2FTensorFlow-Pytorch-GPU/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Andrew2077","download_url":"https://codeload.github.com/Andrew2077/TensorFlow-Pytorch-GPU/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Andrew2077%2FTensorFlow-Pytorch-GPU/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31587757,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"online","status_checked_at":"2026-04-09T02:00:06.848Z","response_time":112,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["conda","pytorch-installation","tensorflow-installation"],"created_at":"2024-11-19T08:14:21.679Z","updated_at":"2026-04-09T05:31:18.441Z","avatar_url":"https://github.com/Andrew2077.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\n## table of contents\n- [TensorFlow GPU Installation](#tensorflow-gpu-installation)\n  - [installation for windows](#installation-for-windows)\n  - [Set up the environment](#set-up-the-environment)\n- [PyTorch GPU installation](#pytorch-gpu-installation)\n  - [Installation for windows](#installation-for-windows-1)\n\n# TensorFlow GPU Installation\n## installation for windows\n\n[pip_install](https://www.tensorflow.org/install/pip)\n\n1- install [anaconda](https://docs.anaconda.com/anaconda/install/windows/) \n\n2-open anaconda prompt, use conda's prompt as it directly  points towards the base environment of conda \n\n3- create your own Environment run the following command\n\n```bash\nconda create --name tfgpu python=3.10\nconda activate tfgpu \n```\n4- installing cudatoolkit package, this will take time depending on you connection speed\n\n\n```bash\nconda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0\n```\nyou can install another version of CUDA but it has to be compatible with CUDNN\n\n5- install tensorflow \n\n```bash \npython -m pip install tensorflow==2.10\n```\n\n6- make sure it's running \n```bash \npython -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\n```\nif it worked properly you should see something like this \n```bash \n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n```\nif it installed CPU version of tensorflow you should see something like this \n```bash \n[]\n```\n\n7- install all the packages needed inside tools.yml \n \u003e you can edit the file at your own prefrence\nmove tools.yml to the root directory of your conda environment \nfor example \n\u003e C:\\Users\\DELL\u003e\n```bash \nconda activate tfgpu\nconda env update --file tools.yml\n```\n\n## Set up the environment \nfor VSCODE \n- change kernel to environment kernel [tfgpu python3.10.8] \n- ![](img2.png)\n- it will ask for ipython and other dependencies, just allow all their installation and you're ready to go \n- in case if you want to to install other libraraies\n  \n```bash\nconda activate tfgpu\npip install \u003cpackage\u003e\n\n```\n\nfor jupyter \n- install jupyter from Anaconda Navigator \n- ![](img1.png)\n- load ipython kernel and you're ready to go \n- to install package just do it normally in a cell while tfpgu kernel is running\n```bash\npip install \u003cpackage\u003e\n```\n\n# PyTorch GPU installation\n## Installation for windows \n\n\u003e Keep in mind that this PyTorch version is only compatible with python 3.7 to 3.9\n\n\n1.  Considering that you have installed Conda already\n2. run the conda prompt and the write the follwoing commands \n3. create different environment for pytorch and activate it\n```bash\nconda create -n torch python=3.8\nconda activate torch\n```\n4. install pytorch using conda, this will take time depending on your connection speed\n```bash\nconda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch\n```\n5. now your enviroment is ready to use pytorch, just make sure to select the environment kernel in your IDE\n6. run the following command to ensure that GPU(cuda) is working \n```python\nimport torch \ntorch.cuda.is_available() # true \ntorch.cuda.device_count() #1 \ntorch.cuda.get_device_name(device='cuda') # GPU device name\n```\n7. you should see the commented output if everything is working fine\n8. install your favorite packages using pip \n```bash\npip install \u003cpackage\u003e\n```\n9. you can also install packages using conda install but i don't recoomend that \n10. you can have both Tensorflow and pytorch on the same environment following the torch installation steps and adding this line below\n```bash\npip install tensorflow==2.10\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrew2077%2Ftensorflow-pytorch-gpu","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandrew2077%2Ftensorflow-pytorch-gpu","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrew2077%2Ftensorflow-pytorch-gpu/lists"}