How to Download and Install PyTorch

Key Takeaways

• PyTorch can be easily installed via pip or conda with official commands.
• Selecting the correct OS, package manager, and compute platform ensures proper installation.
• Verifying your setup with a simple Python test confirms successful installation.

Introduction

PyTorch is a leading open-source deep learning framework developed by Meta. It provides powerful tools like tensor operations, dynamic computation graphs, and GPU acceleration for tasks in computer vision, NLP, and more .

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Step 1: Choose Your Installation Method 🎯

Visit the official “Get Started” page and select the options matching your system:

• PyTorch build: Typically the latest stable release (e.g., 2.7.0) .
• OS: Linux, macOS, or Windows.
• Package manager: Pip or Conda.
• Language: Python (most common).
• Compute platform: CPU-only, CUDA (GPU), or ROCm (AMD GPUs).

Then copy the generated install command.

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Step 2: Install via Pip

Ensure Python 3.9+ and pip are installed. For pip:

CPU-only:

pip3 install torch torchvision torchaudio

With NVIDIA CUDA:

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu<XX>

Replace <XX> with your CUDA version (e.g. cu118) .

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Step 3: Install via Conda

Anaconda simplifies dependency handling:

CPU-only:

conda install pytorch torchvision torchaudio cpuonly -c pytorch

With CUDA:

conda install pytorch torchvision torchaudio cudatoolkit=<XX> -c pytorch

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Step 4: Alternative Methods

• Manual .whl file: Download directly from download.pytorch.org/whl/cu118, then install with:

  pip install torch‑<version>‑<scheme>.whl
  pip install torchvision torchaudio
  

  Helpful if your connection breaks during pip fetch .

• Build from source: Clone the PyTorch repo and use python setup.py develop after installing prerequisites like CUDA, cuDNN, cmake, ninja .

• Docker container:

  docker pull pytorch/pytorch:latest
  docker run -it --rm --gpus all pytorch/pytorch:latest bash
  

  Great for isolated setups .

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Step 5: Verify Installation

Open Python and run:

import torch
x = torch.rand(5, 3)
print(x)
print("CUDA available:", torch.cuda.is_available())

You should see a random tensor and a boolean confirmation for GPU access .

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Helpful Video

Here’s a clear demonstration of installing PyTorch on Windows using pip:

Install PyTorch 2.0 on Windows | Pip | PyTorch 2.0

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Troubleshooting Tips

• Virtual environments: Always activate venv or conda env before installing. Using the wrong shell can install PyTorch globally .
• Large downloads: If pip installation fails repeatedly, download the .whl file manually and install using pip for resumption support .

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Summary Table

FAQs

What is the easiest way to install PyTorch?

Use the official pip or conda commands from the PyTorch website.

How do I check if PyTorch installed correctly?

Import torch in Python and run a tensor creation test.

What should I do if pip installation fails?

Download the PyTorch .whl file manually and install with pip.

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