Installation

Important

Requirements: Python 3.10+ | PyTorch 2.2+ (auto-installed) Installation time: ~2 minutes

Quick Install

pip install deeptab

This installs DeepTab with all dependencies including PyTorch, Lightning, and preprocessing tools.

Verify installation:

import deeptab
print(deeptab.__version__)  # e.g., "2.0.0"

DeepTab automatically detects and uses your GPU, with no configuration needed.

Verify GPU:

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

Warning

If you have a GPU but CUDA isn’t detected, install PyTorch with CUDA support first:

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

See PyTorch installation guide for your CUDA version.

Multiple GPUs:

export CUDA_VISIBLE_DEVICES=0,1  # Use specific GPUs

For contributing or using unreleased features:

git clone https://github.com/OpenTabular/DeepTab.git
cd DeepTab
pip install -e .

Note

DeepTab uses Poetry for development. Install with poetry install to get dev tools (pytest, ruff, pyright). See the Contributing guide for details.

For 20-30% faster Mamba models, install optimized CUDA kernels:

pip install mamba-ssm

Important

Requirements: NVIDIA GPU (compute capability ≥7.0) | CUDA 11.6+ | C++ compiler

If installation fails, DeepTab automatically falls back to the default implementation. This only affects Mamba-based models.

CUDA out of memory? Reduce batch size:

from deeptab.configs import TrainerConfig
model = FTTransformerClassifier(
    trainer_config=TrainerConfig(batch_size=64)
)

Training slow? Check GPU is being used:

import torch
assert torch.cuda.is_available(), "GPU not detected"

Module not found? Verify correct environment:

which python
pip list | grep deeptab