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Installation

This guide will help you install BatteryML and set up your development environment.

Prerequisites

  • Python: 3.8 or higher
  • CUDA: (Optional) For GPU acceleration with PyTorch models
  • Git: For cloning the repository

Installation Steps

1. Clone the Repository

git clone https://github.com/yourusername/battery-ml.git
cd battery-ml

2. Create a Virtual Environment

We recommend using a virtual environment to isolate dependencies:

# Using venv (Python 3.8+)
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On Linux/Mac:
source venv/bin/activate

3. Install Dependencies

Install the required packages:

pip install -r requirements.txt

4. (Optional) Install Development Dependencies

For running tests and contributing:

pip install pytest pytest-cov

5. Verify Installation

Test that everything is installed correctly:

import torch
import lightgbm
import hydra
from src.pipelines.sample import Sample

print("✓ All imports successful!")

Dependencies Overview

Core Dependencies

Package Purpose Version
torch Deep learning framework ≥2.0
numpy Numerical computing ≥1.24
pandas Data manipulation ≥2.0
scipy Scientific computing ≥1.10
scikit-learn Machine learning utilities ≥1.3

Configuration

Package Purpose Version
hydra-core Configuration management ≥1.3
omegaconf Configuration objects ≥2.3
pydantic Data validation ≥2.0

Models

Package Purpose Version
lightgbm Gradient boosting ≥4.0
torchdiffeq Neural ODE solvers ≥0.2.3

Tracking & Visualization

Package Purpose Version
tensorboard Training visualization ≥2.14
mlflow Experiment tracking ≥2.5
shap Model interpretability ≥0.42
matplotlib Plotting ≥3.7
seaborn Statistical visualization ≥0.12
plotly Interactive plots Latest

GPU Setup (Optional)

For GPU acceleration with PyTorch models:

CUDA Installation

  1. Install CUDA Toolkit from NVIDIA
  2. Verify CUDA installation:
nvidia-smi

PyTorch with CUDA

Install PyTorch with CUDA support:

# For CUDA 11.8
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# For CUDA 12.1
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Verify GPU availability:

import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"CUDA device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'N/A'}")

Troubleshooting

Common Issues

Issue: ImportError for torch or other packages

  • Solution: Ensure virtual environment is activated and packages are installed

Issue: CUDA not detected

  • Solution: Verify CUDA installation and PyTorch CUDA version matches your CUDA version

Issue: Permission errors on Windows

  • Solution: Run terminal as administrator or use user installation: pip install --user -r requirements.txt

Getting Help

If you encounter issues not covered here, check the Troubleshooting section or open an issue on GitHub.

Next Steps

Once installation is complete, proceed to: