# Goldfish Base GPU - Pre-installed ML libraries with CUDA support
# Version pinned for reproducibility
#
# NOTE: PyTorch is installed from cu126 index (see Dockerfile.gpu)
# This file is for non-PyTorch dependencies only

# Core scientific computing
numpy>=1.24,<2.0
pandas>=2.0,<3.0
scipy>=1.11,<2.0

# Machine learning
scikit-learn>=1.3,<2.0
xgboost>=2.0,<3.0

# NOTE: torch, torchvision, torchaudio installed separately in Dockerfile
# from https://download.pytorch.org/whl/cu126 for CUDA 12.6 compatibility

# Hugging Face ecosystem
transformers>=4.35,<5.0
datasets>=2.15,<3.0
accelerate>=0.24,<1.0
safetensors>=0.4,<1.0

# Data handling
pyarrow>=14.0,<16.0
fastparquet>=2023.0

# Visualization (for notebooks/debugging)
matplotlib>=3.8,<4.0
seaborn>=0.13,<1.0

# Utilities
tqdm>=4.66,<5.0
pyyaml>=6.0,<7.0
requests>=2.31,<3.0

# Jupyter support
jupyterlab>=4.0,<5.0
ipykernel>=6.25,<7.0

# Weights & Biases for experiment tracking
wandb>=0.16,<1.0

# Claude Agent SDK for SVS during-run AI reviews
claude-agent-sdk>=0.1.0
