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# Google Colab-like requirements.txt for local Python environments
# Optimized for NVIDIA GPU usage and unlimited local resources
# Based on Google Colab runtime versions 2025.07-2025.10
#
# IMPORTANT: For CUDA support, PyTorch packages must be installed separately:
# pip install "torch>=2.6,<2.7" "torchvision>=0.21,<0.22" "torchaudio>=2.6,<2.7" \
# --index-url https://download.pytorch.org/whl/cu124
# (Use cu124 for CUDA 12.4; for CUDA 11.8 use cu118 and matching versions.)
# Then install remaining packages with: pip install -r requirements.txt
# =============================================================================
# CORE DATA SCIENCE STACK
# =============================================================================
numpy>=2.0.0
pandas>=2.0.0
matplotlib>=3.7.0
seaborn>=0.12.0
scipy>=1.11.0
scikit-learn>=1.3.0
# =============================================================================
# DEEP LEARNING FRAMEWORKS (CUDA/GPU versions)
# =============================================================================
# PyTorch with CUDA support - CANNOT be installed via requirements.txt
# Install separately to mitigate CVE-2025-32434 (requires torch>=2.6):
# pip install "torch>=2.6,<2.7" "torchvision>=0.21,<0.22" "torchaudio>=2.6,<2.7" \
# --index-url https://download.pytorch.org/whl/cu124
# Note: Use cu118 for CUDA 11.8; keep versions mutually compatible
# Exclude torch/torchvision/torchaudio from pip install -r requirements.txt
# TensorFlow with GPU support (requires CUDA toolkit and cuDNN installed)
tensorflow[and-cuda]>=2.13.0
keras>=2.13.0
tf_keras>=2.13.0
# JAX with CUDA support - specify CUDA version based on your setup
# For CUDA 12: jax[cuda12_local]>=0.4.0
# For CUDA 11: jax[cuda11_local]>=0.4.0
# Or use: pip install jax[cuda]>=0.4.0 jaxlib>=0.4.0 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
jax[cuda12_local]>=0.4.0
jaxlib>=0.4.0
# =============================================================================
# JUPYTER ECOSYSTEM
# =============================================================================
jupyter>=1.0.0
jupyterlab>=4.0.0
ipython>=8.0.0
ipywidgets>=8.0.0
notebook>=7.0.0
# =============================================================================
# VISUALIZATION LIBRARIES
# =============================================================================
plotly>=5.15.0
bokeh>=3.0.0
altair>=5.0.0
folium>=0.14.0
# =============================================================================
# COMPUTER VISION
# =============================================================================
# Note: For OpenCV with CUDA support, you'll need to build from source or use
# a pre-built wheel. Standard opencv-python does not include CUDA support.
# See: https://github.com/opencv/opencv-python#frequently-asked-questions
opencv-python>=4.8.0
opencv-contrib-python>=4.8.0
pillow>=10.0.0
imageio>=2.31.0
scikit-image>=0.21.0
# =============================================================================
# NATURAL LANGUAGE PROCESSING
# =============================================================================
nltk>=3.8.0
spacy>=3.6.0
transformers>=4.21.0
tokenizers>=0.13.0
datasets>=2.14.0
# =============================================================================
# DATA PROCESSING & WEB SCRAPING
# =============================================================================
requests>=2.31.0
beautifulsoup4>=4.12.0
lxml>=4.9.0
openpyxl>=3.1.0
xlrd>=2.0.0
sqlalchemy>=2.0.0
pymongo>=4.5.0
# =============================================================================
# STATISTICAL ANALYSIS
# =============================================================================
statsmodels>=0.14.0
pingouin>=0.5.0
# =============================================================================
# SCIENTIFIC COMPUTING & OPTIMIZATION
# =============================================================================
sympy>=1.12
numba>=0.58.0
# =============================================================================
# WEB FRAMEWORKS & DEPLOYMENT
# =============================================================================
flask>=2.3.0
fastapi>=0.103.0
streamlit>=1.25.0
gradio>=3.40.0
# =============================================================================
# UTILITIES & DEVELOPMENT TOOLS
# =============================================================================
tqdm>=4.66.0
joblib>=1.3.0
psutil>=5.9.0
pyyaml>=6.0.0
jsonschema>=4.19.0
# Strongly recommended for safe model loading (avoid pickle):
safetensors>=0.4.5
# =============================================================================
# GOOGLE CLOUD INTEGRATION (like in Colab)
# =============================================================================
google-cloud-storage>=2.10.0
google-auth>=2.22.0
google-auth-oauthlib>=1.0.0
gspread>=5.10.0
# =============================================================================
# AUDIO PROCESSING
# =============================================================================
librosa>=0.10.0
soundfile>=0.12.0
# =============================================================================
# TIME SERIES & FORECASTING
# =============================================================================
prophet>=1.1.0
# =============================================================================
# ADDITIONAL ML LIBRARIES (Gradient Boosting with GPU support)
# =============================================================================
# These libraries support GPU acceleration when CUDA is available
xgboost>=1.7.0 # Enable GPU with: model = XGBClassifier(tree_method="hist", device="cuda")
lightgbm>=4.0.0 # Enable GPU with: LGBMClassifier(..., device="gpu")
catboost>=1.2.0 # Enable GPU with: CatBoostClassifier(..., task_type="GPU")
# =============================================================================
# NETWORKING & SECURITY
# =============================================================================
urllib3>=2.0.0
certifi>=2023.7.22