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# Requirements for PyNvVideoCodec Sample Applications
# This file covers dependencies for:
# - samples/basic/
# - samples/advanced/
# - samples/jupyter/ (object_detection_tutorial.ipynb, simple_decode_tutorial.ipynb)

# IMPORTANT: PyTorch must be installed separately with CUDA support!
# DO NOT install torch/torchvision from this file directly as it may install CPU-only versions.
# See installation instructions at the bottom of this file.

# Core dependencies (required for most samples)
numpy>=1.21.0
pycuda>=2021.1

# PyNvVideoCodec (assuming it's installed separately or available locally)
# PyNvVideoCodec

# For visualization in jupyter notebooks
matplotlib>=3.3.0

# For downloading sample videos in jupyter notebooks
requests>=2.25.0

# For running Jupyter notebooks
jupyter>=1.0.0
jupyterlab>=3.0.0
ipython>=7.20.0

# PyTorch with CUDA 13.0 support
# See: https://download.pytorch.org/whl/cu130/torch/
--extra-index-url https://download.pytorch.org/whl/cu130
torch==2.9.1


# Note: Standard library modules used (no installation needed):
# - json, os, sys, pathlib, argparse, time, threading, multiprocessing
# - queue, ctypes, pickle, datetime, gc, atexit


# Running Jupyter Notebooks:
#
# Option 1 - Using Jupyter Notebook (Classic):
#    jupyter notebook
#    # Then navigate to samples/jupyter/ and open the desired .ipynb file
#
# Option 2 - Using JupyterLab (Recommended):
#    jupyter lab
#    # Then navigate to samples/jupyter/ and open the desired .ipynb file
#
# Option 3 - Using specific notebook:
#    jupyter notebook samples/jupyter/object_detection_tutorial.ipynb
#    jupyter notebook samples/jupyter/simple_decode_tutorial.ipynb
#
# Note: Ensure PyNvVideoCodec is installed before running the notebooks

