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Do you want to accelerate your deep learning projects with GPU support on PyTorch and TensorFlow? The best way to do this is by installing **CUDA** with **Conda**. Setting up CUDA manually can be tricky and error-prone, but Conda makes the process much easier by handling dependencies and ensuring compatibility between CUDA, cuDNN, and your machine learning libraries.
In this step-by-step tutorial, I’ll guide you through the process of installing CUDA on your system using Conda, so you can take full advantage of your NVIDIA GPU for faster training and inference. By the end of this video, you’ll have a fully functional deep learning environment with GPU acceleration enabled.
Here’s what you’ll learn in this video:
* Why Conda is the easiest way to set up CUDA for machine learning projects
* How to create a new Conda environment for PyTorch or TensorFlow
* Installing the correct versions of CUDA and cuDNN with Conda
* How to install PyTorch with GPU support using `conda install pytorch torchvision torchaudio cudatoolkit`
* How to install TensorFlow with GPU support using Conda or pip inside your Conda environment
* Verifying that your GPU is recognized by PyTorch (`torch.cuda.is_available()`)
* Checking TensorFlow GPU setup (`tf.config.list_physical_devices('GPU')`)
* Tips for avoiding version mismatches and driver issues
* How to keep your CUDA setup updated without breaking your environment
With Conda, you don’t need to worry about downloading CUDA or cuDNN separately from
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2025/09/15
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