# DMML notebook environment
#
# Python 3.11, 3.12 or 3.13. (3.10 is too old: pandas, numpy and scikit-learn
# all require >=3.11 at these versions.)
#
# Install with:
#   python -m pip install -r requirements.txt
#
# Versions are PINNED, not floors. This is deliberate for a taught course:
# everyone gets the same environment, the Gradescope autograder matches your
# laptop, and a notebook that worked in September still works in December.
# If you need to move a pin, change it here so the whole class moves together.
#
# For GPU PyTorch, install torch/torchvision from the official PyTorch selector
# for your CUDA version first, then run this file.


# Jupyter
jupyterlab==4.5.6
notebook==7.5.5
ipykernel==7.2.0
ipywidgets==8.1.8

# Core scientific Python
numpy==2.4.2
pandas==3.0.2
matplotlib==3.10.8
seaborn==0.13.2

# Classical machine learning and statistics
scikit-learn==1.8.0
statsmodels==0.14.6
shap==0.51.0

# Neural networks and computer vision
torch==2.12.0
torchvision==0.27.0

# HuggingFace datasets and transformers
datasets==4.8.5
transformers==5.9.0
accelerate==1.13.0

# Running a notebook as a batch job on the cluster needs no extra package:
# nbconvert ships with jupyterlab. See cluster/submit_notebook.slurm.