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Installation Guide

WorldEngine consists of two main subsystems that require separate conda environments due to different Python version requirements:

  • SimEngine (simengine env, Python 3.9) - Closed-loop simulation with photorealistic rendering
  • AlgEngine (algengine env, Python 3.9) - End-to-end model training and evaluation

Note: Scene Reconstruction uses the same environment as SimEngine since it's based on MTGS and shares dependencies.

System Requirements

Hardware Requirements

Minimum:

  • GPU: NVIDIA GPU with 8GB VRAM (e.g., RTX 2080)
  • RAM: 32GB
  • Storage: 500GB SSD
  • CPU: 8 cores

Recommended:

  • GPU: NVIDIA GPU with 24GB+ VRAM (e.g., RTX 3090, A100)
  • RAM: 64GB+
  • Storage: 5TB+ SSD
  • CPU: 16+ cores

Software Requirements

  • OS: Linux (Ubuntu 20.04/22.04 recommended)
  • CUDA: 11.8
  • Conda/Miniconda: Latest version

Environment 1: SimEngine (simengine)

This environment is used for:

  • Closed-loop simulation
  • Photorealistic rendering
  • Behavior world model (coming soon)

Step-by-Step Installation

1. Create Conda Environment

conda create --name simengine python=3.9 -y
conda activate simengine

2. Install CUDA Toolkit

conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit -y

3. Install PyTorch

pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118

Verify PyTorch installation:

python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"
# Expected: PyTorch: 2.0.1+cu118, CUDA: True

4. Install gsplat (Gaussian Splatting Library)

pip install ninja   # For build acceleration
pip install git+https://github.com/nerfstudio-project/gsplat.git@v1.4.0 \
--no-build-isolation

5. Install SimEngine Dependencies

cd projects/SimEngine
pip install -r requirements.txt

6. Verify Installation

conda activate simengine
python -c "
import torch
import ray
import hydra
import gsplat
print('✓ All SimEngine dependencies OK')
print(f'✓ PyTorch {torch.__version__}')
print(f'✓ CUDA available: {torch.cuda.is_available()}')
"

Environment 2: AlgEngine (algengine)

This environment is used for:

  • End-to-end model training
  • End-to-end model testing
  • Fine-tuning with rare cases

Step-by-Step Installation

1. Create Conda Environment

conda create --name algengine python=3.9 -y
conda activate algengine

2. Install PyTorch

pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118

Verify PyTorch:

python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"

3. Install MMCV (Build from Source - Required!)

MMCV must be built from source to include custom CUDA operators:

# Clone MMCV repository
git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
git checkout v1.6.2

# Build with custom ops (this will take 10-15 minutes)
# You may downgrade setuptools if errors occur. (Recommend 75.1.0)
MMCV_WITH_OPS=1 pip install -v -e .

# Verify installation
python .dev_scripts/check_installation.py
cd ..

Verify MMCV:

python -c "import mmcv; print(f'MMCV: {mmcv.__version__}')"
# Expected: MMCV: 1.6.2

4. Install OpenMMLab Ecosystem

pip install mmcls==0.25.0
pip install mmdet==2.25.3
pip install mmdet3d==1.0.0rc6
pip install mmsegmentation==0.29.1

Verify MMDetection3D:

python -c "import mmdet3d; print(f'MMDetection3D: {mmdet3d.__version__}')"
# Expected: MMDetection3D: 1.0.0rc6

5. Install AlgEngine Dependencies

cd projects/AlgEngine
pip install -r requirements.txt
pip install shapely==2.0.4

6. Verify Installation

conda activate algengine
python -c "
import torch
import mmcv
import mmdet
import mmdet3d
import numpy
import hydra
print('✓ All AlgEngine dependencies OK')
print(f'✓ PyTorch {torch.__version__}')
print(f'✓ MMCV {mmcv.__version__}')
print(f'✓ MMDetection3D {mmdet3d.__version__}')
print(f'✓ CUDA available: {torch.cuda.is_available()}')
"

Environment Variables

we rely on NAVSIM devkit, please git clone and switch to v1.1 branch:

git clone -b v1.1 https://github.com/autonomousvision/navsim.git

Add to your ~/.bashrc or ~/.zshrc:

# AlgEngine Environment Variables
export NAVSIM_DEVKIT_ROOT="/path/to/your/navsim/v1.1"
export WORLDENGINE_ROOT="/path/to/WorldEngine"
export SIMENGINE_ROOT="${WORLDENGINE_ROOT}/projects/SimEngine"
export ALGENGINE_ROOT="${WORLDENGINE_ROOT}/projects/AlgEngine"
export NUPLAN_MAPS_ROOT="${WORLDENGINE_ROOT}/data/raw/nuplan/maps"

PYTHONPATH=$WORLDENGINE_ROOT:$SIMENGINE_ROOT:$ALGENGINE_ROOT:$NAVSIM_DEVKIT_ROOT:$PYTHONPATH

Apply changes:

source ~/.bashrc  # or source ~/.zshrc

Environment Summary

Feature SimEngine (simengine) AlgEngine (algengine)
Python Version 3.9 3.9
PyTorch 2.0.1+cu118 2.0.1+cu118
Key Libs gsplat, ray MMCV, MMDet3D, MMCls
Use Cases Simulation, Rendering, BWM Training, Evaluation
Disk Space ~10 GB ~15 GB
Install Time ~30 min ~45 min (MMCV build)