How to install PyTorch with pip

PyTorch is usually installed inside a project-specific Python environment so training, inference, and notebook code can import torch without changing the system interpreter. A pip install is the direct path for local development when the project needs the official PyTorch wheels rather than a distro package.

An isolated virtual environment and the official CPU wheel index make a narrow default for Linux development hosts that do not need CUDA or ROCm acceleration. The same environment keeps torch, torchvision, torchaudio, and their Python dependencies together for one project.

Use the PyTorch install selector before choosing an accelerator-specific wheel index. After installation, python -m pip show torch should point inside the active environment, and a small tensor operation should run without import errors.

1. Open a terminal where Python 3.10 through 3.14 is available.

   Current PyTorch stable wheels require a supported Python 3 release. If the project already uses a virtual environment, activate that environment instead of creating a new one.

2. Create an isolated Python environment for PyTorch.

   $ python3 -m venv ~/venvs/torch-cpu

   Replace ~/venvs/torch-cpu with the project environment path when another location is preferred.

3. Activate the environment.

   $ source ~/venvs/torch-cpu/bin/activate
   (torch-cpu) $

4. Upgrade pip inside the active environment.

   (torch-cpu) $ python -m pip install --upgrade pip
   Requirement already satisfied: pip in ~/venvs/torch-cpu/lib/python3.14/site-packages (25.1.1)
   Collecting pip
   ##### snipped #####
   Successfully installed pip-26.1.2

5. Install PyTorch, torchvision, and torchaudio from the official CPU wheel index.

   (torch-cpu) $ python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
   Looking in indexes: https://download.pytorch.org/whl/cpu
   Collecting torch
   Collecting torchvision
   Collecting torchaudio
   ##### snipped #####
   Successfully installed torch-2.12.1+cpu torchaudio-2.11.0+cpu torchvision-0.27.1+cpu

   The --index-url value selects the CPU wheel index. Use the PyTorch install selector for CUDA or ROCm wheels, then install the selected command into the same active environment.
   Related: How to enable CUDA in PyTorch
   Related: How to enable ROCm in PyTorch

6. Confirm that torch is installed inside the active environment.

   (torch-cpu) $ python -m pip show torch
   Name: torch
   Version: 2.12.1+cpu
   Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration
   Home-page: https://pytorch.org
   License: BSD-3-Clause
   Location: ~/venvs/torch-cpu/lib/python3.14/site-packages
   Requires: filelock, fsspec, jinja2, networkx, setuptools, sympy, typing-extensions
   Required-by: torchvision

   The Location value should point inside the environment that will run the project code.

7. Run a PyTorch tensor smoke test.

   (torch-cpu) $ python -c 'import torch; print(torch.__version__); print(torch.tensor([1.0, 2.0, 3.0]) * 2); print("cuda_available=" + str(torch.cuda.is_available())); print("cuda_runtime=" + str(torch.version.cuda))'
   2.12.1+cpu
   tensor([2., 4., 6.])
   cuda_available=False
   cuda_runtime=None

   The CPU wheel reports cuda_available=False and cuda_runtime=None. Use a CUDA, ROCm, or MPS setup when the project must run tensors on a GPU.
   Related: How to enable MPS in PyTorch
   Related: How to select a device in PyTorch