GPU Setup¶

OpenBoost automatically detects and uses CUDA GPUs when available.

Verify GPU Detection¶

import openboost as ob

print(f"Backend: {ob.get_backend()}")  # "cuda" or "cpu"
print(f"Using GPU: {ob.is_cuda()}")    # True if GPU active

Manual Backend Selection¶

import openboost as ob

# Force CPU (useful for debugging or comparison)
ob.set_backend("cpu")

# Force GPU
ob.set_backend("cuda")

# Or use environment variable
# export OPENBOOST_BACKEND=cuda

GPU Performance¶

GPU acceleration provides significant speedups for larger datasets:

Dataset Size	Typical Speedup
<5K samples	~1x (CPU overhead dominates)
5K-10K	2-7x
25K+	2-3x
100K+	5-10x

Best practices for GPU

Ensure data is float32 (not float64)
Use larger datasets (GPU overhead not worth it for <5K samples)
GPU shows best speedup at 10K+ samples

Multi-GPU Training¶

import openboost as ob

# Use multiple GPUs with Ray (requires ray[default])
model = ob.GradientBoosting(n_trees=100, n_gpus=4)
model.fit(X, y)

# Or specify exact GPU devices
model = ob.GradientBoosting(n_trees=100, devices=[0, 2])
model.fit(X, y)

Requirements¶

NVIDIA GPU with CUDA Compute Capability 3.5+
CUDA Toolkit 11.0+ or 12.0+
Numba 0.60+ (pip install numba)

Troubleshooting¶

Training seems slow on GPU¶

Ensure data is float32 (not float64)
Use larger datasets (GPU overhead not worth it for <5K samples)
GPU shows best speedup at 10K+ samples

Model trained on GPU, loading on CPU machine¶

# Models are saved in a backend-agnostic format
model.save("model.joblib")

# Load on any machine (CPU or GPU)
loaded = ob.GradientBoosting.load("model.joblib")

CUDA not detected¶

Check CUDA installation: nvcc --version
Check Numba can see GPU: python -c "from numba import cuda; print(cuda.gpus)"
Ensure compatible CUDA version with Numba