PyPI Package: torch

Install and configure PyTorch (torch), a Python package for tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system.

What This Skill Does

This skill guides you through installing PyTorch from PyPI, verifying the installation, checking GPU availability, and running a basic tensor operation to confirm everything works correctly.

Instructions

Follow these steps to install and verify PyTorch:

Step 1: Check Python Version

Verify you have Python 3.10 or later installed:

```bash

python --version

```

If Python is not installed or the version is too old, install or upgrade Python first.

Step 2: Install PyTorch via pip

Install the latest stable version of PyTorch from PyPI:

```bash

pip install torch

```

**For specific configurations:**

```bash

pip install torch --index-url https://download.pytorch.org/whl/cpu

```

```bash

pip install torch --index-url https://download.pytorch.org/whl/cu121

```

```bash

pip install torch --index-url https://download.pytorch.org/whl/cu118

```

Visit [pytorch.org/get-started/locally](https://pytorch.org/get-started/locally/) for the most up-to-date installation commands for your platform.

Step 3: Verify Installation

Create a Python script or open a Python REPL and run:

```python

import torch

print(f"PyTorch version: {torch.__version__}")

print(f"CUDA available: {torch.cuda.is_available()}")

if torch.cuda.is_available():

print(f"CUDA version: {torch.version.cuda}")

print(f"GPU device: {torch.cuda.get_device_name(0)}")

```

This confirms PyTorch is installed and shows whether GPU acceleration is available.

Step 4: Run a Basic Test

Test basic tensor operations:

```python

import torch

Create a random tensor

x = torch.rand(3, 3)

print("Random tensor:")

print(x)

Move to GPU if available

if torch.cuda.is_available():

x = x.cuda()

print("\nTensor moved to GPU")

print(x)

Basic operation

y = x + 2

print("\nTensor + 2:")

print(y)

```

If this runs without errors, PyTorch is correctly installed and functional.

Step 5: Optional Dependencies

For additional functionality, install optional packages:

```bash

For vision tasks (datasets, transforms, models)

pip install torchvision

For audio processing

pip install torchaudio

For text/NLP

pip install torchtext

```

Key Features

Common Use Cases

Troubleshooting

Resources

Notes