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Installation

OpenCLIP can be installed in multiple ways depending on your use case. This guide covers installation for inference, training, and development.

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Basic Installation

For inference and basic usage, install OpenCLIP via pip: 
pip install open_clip_torch
This installs the core package with dependencies for loading models and running inference.
The package name is open_clip_torch on PyPI, but you import it as open_clip in Python.

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Optional Dependencies

Some models require additional packages depending on their architecture:

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Image Encoders (timm)

Many modern models use timm-based image encoders (ConvNext, SigLIP, EVA, etc.). Install the latest timm for full model support: 
pip install timm
If you see “Unknown model” errors for the image encoder, upgrade timm to the latest version:
pip install --upgrade timm

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Tokenizers (transformers)

Models that use Hugging Face transformers tokenizers require the transformers library: 
pip install transformers

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Complete Installation

To install OpenCLIP with all optional dependencies: 
pip install open_clip_torch timm transformers

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Training Installation

For training CLIP models, install with training dependencies: 
pip install 'open_clip_torch[training]'
This includes additional packages for:
  • Distributed training
  • Data loading and augmentation
  • Logging and monitoring
  • WebDataset support
Training requires PyTorch to be installed separately. Follow the PyTorch installation guide for your specific system configuration.

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Development Installation

For contributors or those who want to modify the code:
1

Create a virtual environment

First, create and activate a virtual environment:
python3 -m venv .env
source .env/bin/activate
pip install -U pip
2

Clone the repository

Clone the OpenCLIP repository:
git clone https://github.com/mlfoundations/open_clip.git
cd open_clip
3

Install in development mode

Install the package in editable mode:
make install
Or install training dependencies:
make install-training
4

Install PyTorch

Install PyTorch according to your system:
# Visit https://pytorch.org/get-started/locally/
# Example for CUDA 11.8:
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

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Testing Installation

To verify your installation and run tests:

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Install test dependencies

make install-test

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Run all tests

make test

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Run specific tests

# Run training tests only
python -m pytest -x -s -v tests -k "training"

# Run inference tests
python -m pytest -x -s -v tests -k "inference"

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Verification

Verify your installation by loading a pretrained model: 
import open_clip
import torch

# List available pretrained models
print(open_clip.list_pretrained()[:5])  # Show first 5

# Load a model
model, _, preprocess = open_clip.create_model_and_transforms(
    'ViT-B-32',
    pretrained='laion2b_s34b_b79k'
)

print("Installation successful!")
If this runs without errors, your installation is complete.

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Platform-Specific Notes

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Linux

OpenCLIP works out of the box on most Linux distributions. For CUDA support, ensure you have:
  • NVIDIA drivers installed
  • CUDA toolkit matching your PyTorch version

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macOS

OpenCLIP works on macOS with CPU or MPS (Apple Silicon) acceleration: 
import torch
import open_clip

# Use MPS on Apple Silicon
device = "mps" if torch.backends.mps.is_available() else "cpu"
model, _, preprocess = open_clip.create_model_and_transforms(
    'ViT-B-32',
    pretrained='openai',
    device=device
)

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Windows

For Windows, we recommend using WSL2 (Windows Subsystem for Linux) for the best experience, especially for training. Native Windows installation works but may have some limitations.

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Troubleshooting

Make sure you’re importing the correct name:
import open_clip  # Correct
# NOT: import open_clip_torch
Reduce batch size or use gradient accumulation:
# Use mixed precision to save memory
model, _, preprocess = open_clip.create_model_and_transforms(
    'ViT-B-32',
    pretrained='openai',
    precision='fp16'  # or 'bf16'
)
This usually means a timm model is not found. Upgrade timm:
pip install --upgrade timm
Models are cached after first download. The cache location can be set:
model, _, preprocess = open_clip.create_model_and_transforms(
    'ViT-B-32',
    pretrained='openai',
    cache_dir='/path/to/cache'
)

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Next Steps

Now that OpenCLIP is installed, proceed to the quickstart guide to run your first example:
  • Quickstart Guide - Run zero-shot classification
  • [Model Zoo(/usage/pretrained-models) - Browse available models
  • [Training Guide(/training/overview) - Train your own CLIP models

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