How To Install PyTorch on Manjaro

PyTorch has become the backbone of modern machine learning and deep learning development, powering everything from computer vision applications to natural language processing models. For Manjaro users, installing PyTorch requires careful consideration of the Arch-based distribution’s unique characteristics and rolling release nature.

This comprehensive guide walks you through multiple installation methods, ensuring you can harness PyTorch’s full potential on your Manjaro system. Whether you’re setting up a CPU-only environment for learning or configuring CUDA support for intensive GPU workloads, we’ll cover every scenario with detailed instructions and troubleshooting solutions.

Understanding PyTorch and Manjaro Compatibility

PyTorch, developed by Meta’s AI Research lab, provides dynamic computational graphs and intuitive Python APIs that make it ideal for both research and production environments. The framework’s flexibility and extensive ecosystem have made it the preferred choice for many machine learning practitioners.

Manjaro Linux, based on Arch Linux, offers cutting-edge packages through its rolling release model. This approach provides access to the latest software versions but can introduce compatibility challenges during PyTorch installation. The good news is that PyTorch officially supports Arch Linux systems with a minimum version of 2012-07-15, making modern Manjaro installations fully compatible.

The rolling release nature means system libraries and dependencies update frequently, which can occasionally break existing PyTorch installations after major system updates. Understanding this relationship helps you choose the most suitable installation method for your workflow.

System Requirements and Prerequisites

Hardware Requirements

Before installing PyTorch on Manjaro, verify your system meets the minimum hardware specifications. For CPU-only installations, any modern x86_64 processor suffices, though multi-core systems provide better performance for tensor operations.

GPU acceleration requires an NVIDIA graphics card with CUDA Compute Capability 3.5 or higher. Popular choices include GTX 1060, RTX 3070, or professional cards like the A100. AMD GPU support exists through ROCm, but NVIDIA CUDA remains the most mature option for PyTorch acceleration.

Memory requirements vary significantly based on your intended use. Plan for at least 4GB RAM for basic PyTorch development, while large-scale model training may require 16GB or more. Storage space should accommodate the PyTorch installation (typically 1-3GB) plus your datasets and model checkpoints.

Software Prerequisites

Manjaro systems require Python 3.9-3.12 for PyTorch compatibility. Most Manjaro installations include Python 3 by default, but verify your version using python --version. If Python isn’t installed or you need a different version, install it through pacman:

sudo pacman -S python python-pip

Essential development tools streamline the installation process. Install the base-devel package group for compilation tools:

sudo pacman -S base-devel git

For CUDA support, you’ll need compatible NVIDIA drivers. The proprietary nvidia driver package typically provides the best performance and stability for PyTorch workloads.

Preparation Steps

System Updates

Start with a complete system update to ensure all packages use their latest versions. This step prevents compatibility issues during PyTorch installation:

sudo pacman -Syu

Reboot your system after major kernel updates to ensure all changes take effect properly. Kernel updates can affect NVIDIA driver functionality, so verify GPU detection after rebooting.

Python Environment Setup

Create isolated Python environments to prevent dependency conflicts between projects. Virtual environments provide clean installation spaces and simplify package management:

python -m venv pytorch-env
source pytorch-env/bin/activate

Upgrade pip to the latest version within your virtual environment:

pip install --upgrade pip

This preparation ensures you have the most recent pip features and bug fixes for PyTorch installation.

Installation Method 1: Using pip (Recommended)

Pip installation offers the most straightforward approach for most Manjaro users. This method provides access to the latest PyTorch releases and supports both CPU and GPU configurations.

CPU-Only Installation

For systems without CUDA requirements or learning environments, install the CPU-only version:

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

This command downloads optimized CPU binaries that provide excellent performance for many machine learning tasks. The installation includes PyTorch core, torchvision for computer vision utilities, and torchaudio for audio processing capabilities.

CUDA-Enabled Installation

GPU acceleration dramatically improves training performance for large models. First, determine your CUDA version:

nvcc --version

Install PyTorch with matching CUDA support. For CUDA 12.1:

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

Replace cu121 with your specific CUDA version (cu118, cu124, etc.). Using the wrong CUDA version can result in torch.cuda.is_available() returning False.

User vs System Installation

The --user flag installs packages in your home directory, avoiding system-wide modifications that might conflict with package manager-installed software:

pip install --user torch torchvision torchaudio

This approach aligns with Arch Linux best practices and reduces permission-related issues.

Installation Method 2: Using Conda/Mamba

Conda provides robust dependency management and environment isolation. While PyTorch has deprecated some Anaconda support, conda installations remain viable for Manjaro users.

Installing Conda on Manjaro

Download Miniconda from the official website or install through AUR:

yay -S miniconda3

Initialize conda for your shell:

conda init bash

Restart your terminal or source your shell configuration to activate conda functionality.

Creating Conda Environment

Create a dedicated environment for PyTorch development:

conda create --name pytorch-env python=3.11
conda activate pytorch-env

This environment isolation prevents conflicts with system Python packages and other projects.

Installing PyTorch via Conda

Install PyTorch using the conda-forge channel for reliable packages:

conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

Conda automatically resolves dependencies and ensures compatibility between PyTorch, CUDA, and supporting libraries.

Installation Method 3: Using Pacman (Manjaro Native)

Manjaro’s package manager provides system-integrated PyTorch installations through official repositories and AUR packages.

Available Packages

The main repositories include python-pytorch for CPU-only installations:

sudo pacman -S python-pytorch

For GPU support, install the CUDA-enabled variant:

sudo pacman -S python-pytorch-cuda

AUR Installation

AUR helpers like yay or paru simplify AUR package installation:

yay -S python-pytorch-cuda

This method integrates PyTorch with Manjaro’s package management system, providing automatic updates and dependency tracking.

Advantages and Limitations

System package installation offers several benefits: integration with Manjaro’s update system, automatic dependency resolution, and consistent library versions. However, AUR packages may lag behind the latest PyTorch releases, and customization options are limited compared to pip installations.

Installation Method 4: Building from Source

Source builds provide maximum customization and optimization for specific hardware configurations. This method requires additional time and system resources but offers the best performance potential.

Prerequisites

Install development dependencies for source compilation:

sudo pacman -S cmake ninja gcc cuda

Clone the PyTorch repository:

git clone --recursive https://github.com/pytorch/pytorch
cd pytorch

Build Configuration

Set environment variables for optimal compilation:

export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
export TORCH_CUDA_ARCH_LIST="6.0;6.1;7.0;7.5;8.0;8.6"

The TORCH_CUDA_ARCH_LIST variable specifies GPU architectures for optimization. Include only architectures matching your hardware to reduce build time.

Compilation Process

Install Python dependencies and initiate the build:

pip install -r requirements.txt
python setup.py develop

Source compilation can take 30-60 minutes depending on your system specifications and selected optimizations. Ensure adequate RAM (8GB+) and disk space during compilation.

CUDA Support Configuration

CUDA support enables GPU acceleration for PyTorch operations, dramatically improving performance for deep learning workloads.

NVIDIA Driver Installation

Install proprietary NVIDIA drivers for optimal performance:

sudo pacman -S nvidia nvidia-utils

For DKMS support that automatically rebuilds drivers after kernel updates:

sudo pacman -S nvidia-dkms

CUDA Toolkit Installation

Install the CUDA development toolkit:

sudo pacman -S cuda cudnn

Add CUDA binaries to your PATH by adding these lines to your .bashrc or .zshrc:

export PATH="/opt/cuda/bin:$PATH"
export LD_LIBRARY_PATH="/opt/cuda/lib64:$LD_LIBRARY_PATH"

Verification

Test CUDA detection in PyTorch:

import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"CUDA version: {torch.version.cuda}")
print(f"GPU count: {torch.cuda.device_count()}")

If CUDA returns False, verify driver installation and restart your system.

Verification and Testing

Comprehensive testing ensures your PyTorch installation functions correctly across all intended use cases.

Basic Installation Verification

Import PyTorch and create a test tensor:

import torch
import torchvision
import torchaudio

# Create random tensor
x = torch.rand(5, 3)
print(f"Random tensor:\n{x}")

# Check versions
print(f"PyTorch version: {torch.__version__}")
print(f"Torchvision version: {torchvision.__version__}")

CUDA Functionality Testing

Verify GPU operations work correctly:

import torch

if torch.cuda.is_available():
    device = torch.device("cuda")
    x = torch.ones(5, 3, device=device)
    y = torch.ones(5, 3, device=device)
    z = x + y
    print(f"GPU computation result:\n{z}")
    print(f"GPU memory allocated: {torch.cuda.memory_allocated()} bytes")
else:
    print("CUDA not available")

Performance Benchmarking

Compare CPU and GPU performance with a simple benchmark:

import torch
import time

size = 10000
iterations = 100

# CPU benchmark
x_cpu = torch.randn(size, size)
start_time = time.time()
for _ in range(iterations):
    torch.mm(x_cpu, x_cpu)
cpu_time = time.time() - start_time

# GPU benchmark (if available)
if torch.cuda.is_available():
    x_gpu = torch.randn(size, size, device='cuda')
    torch.cuda.synchronize()
    start_time = time.time()
    for _ in range(iterations):
        torch.mm(x_gpu, x_gpu)
    torch.cuda.synchronize()
    gpu_time = time.time() - start_time
    
    print(f"CPU time: {cpu_time:.2f}s")
    print(f"GPU time: {gpu_time:.2f}s")
    print(f"Speedup: {cpu_time/gpu_time:.2f}x")

Troubleshooting Common Issues

Import Errors and Solutions

Missing dependencies often cause import failures. Install missing packages:

pip install numpy pillow

Path issues can prevent PyTorch detection. Verify installation location:

import torch
print(torch.__file__)

CUDA-Specific Problems

The most common issue involves CUDA detection returning False. This typically results from:

1. Driver incompatibility: Ensure NVIDIA driver version matches CUDA requirements
2. Version mismatches: Verify PyTorch CUDA version matches system CUDA installation
3. Missing libraries: Install nvidia-utils and cuda packages

After kernel updates, NVIDIA drivers may need reinstallation:

sudo pacman -S nvidia nvidia-utils
sudo reboot

Manjaro-Specific Challenges

Rolling release updates can break PyTorch installations. If issues arise after system updates:

1. Reinstall PyTorch in virtual environments
2. Update CUDA toolkit and drivers
3. Check AUR package compatibility

Memory issues during compilation require increasing swap space:

sudo fallocate -l 4G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

Package Manager Conflicts

Mixing pip and pacman PyTorch installations can create conflicts. Choose one installation method and remove others:

# Remove pip installation
pip uninstall torch torchvision torchaudio

# Remove pacman installation
sudo pacman -R python-pytorch python-pytorch-cuda

Best Practices and Optimization

Environment Management

Use virtual environments consistently to isolate projects and dependencies. Name environments descriptively:

python -m venv pytorch-cv-project
python -m venv pytorch-nlp-research

Document environment requirements in requirements.txt files for reproducibility:

pip freeze > requirements.txt

Update Strategies

Monitor PyTorch releases and update regularly, but test compatibility with your projects first. Use staging environments for update validation before applying changes to production workflows.

For CUDA compatibility, update drivers before PyTorch to ensure proper support for new features and optimizations.

Performance Optimization

Configure PyTorch for optimal performance on your hardware:

import torch

# Set number of threads for CPU operations
torch.set_num_threads(4)

# Enable TensorFloat-32 for faster training on Ampere GPUs
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True

Monitor GPU memory usage and adjust batch sizes accordingly to maximize hardware utilization while avoiding out-of-memory errors.

Alternative Approaches and Advanced Topics

Docker Installation

Docker provides consistent environments across different systems. Use official PyTorch images:

docker run -it --gpus all pytorch/pytorch:latest

This approach isolates PyTorch completely from your system, preventing conflicts and ensuring reproducible environments.

Development Environment Integration

Configure popular IDEs for PyTorch development. For VS Code, install the Python extension and configure the Python interpreter to use your PyTorch environment.

Jupyter notebook integration enhances interactive development:

pip install jupyter
jupyter notebook

Multi-Version Management

Advanced users may require multiple PyTorch versions for different projects. Use conda environments or virtual environments with specific PyTorch versions:

conda create -n pytorch-1.12 python=3.9
conda activate pytorch-1.12
pip install torch==1.12.0 torchvision==0.13.0

Congratulations! You have successfully installed PyTorch. Thanks for using this tutorial for installing the PyTorch on Manjaro Linux system. For additional help or useful information, we recommend you check the official PyTorch website.