Installation

Deep LVPM now targets Keras 3 (multi-backend). You install one backend (TensorFlow or PyTorch) via a pip extra, then force the backend if necessary with KERAS_BACKEND. The current release has been exercised on Python 3.10–3.12; Python 3.12 is recommended for fresh environments.

We strongly suggest creating a clean conda environment (or venv/micromamba etc) before installing. The commands below match the instructions in README.md.

Choose a backend

DLVPM provides extras so you only pull the runtime you need. Pick exactly one of the following:

• TensorFlow: tf-cpu (portable CPU build), tf-gpu (Linux + NVIDIA CUDA wheel), tf-apple (Apple Silicon).

• PyTorch: torch-cpu (CPU builds), torch-apple (Apple Silicon), torch-gpu (install CUDA PyTorch first, then use the empty extra to avoid CPU wheels).

Note

DLVPM is now compatible with both TensorFlow and PyTorch backends through Keras 3. Set KERAS_BACKEND=tensorflow or KERAS_BACKEND=torch if you have multiple runtimes installed and want to force a specific backend.

Conda environment

To create a conda environment and install the package from the keras3 branch:

conda create -n dlvpm-k3 python=3.12 -y
conda activate dlvpm-k3

# TensorFlow backends -------------------------------------------------
# CPU (Linux/Windows/Intel Mac)
pip install "git+https://github.com/alexjamesing/Deep_LVPM.git@keras3#egg=deep-lvpm[tf-cpu]"

# NVIDIA GPU (Linux, bundled CUDA wheel)
pip install "git+https://github.com/alexjamesing/Deep_LVPM.git@keras3#egg=deep-lvpm[tf-gpu]"

# Apple Silicon (M-series)
pip install "git+https://github.com/alexjamesing/Deep_LVPM.git@keras3#egg=deep-lvpm[tf-apple]"

# PyTorch backends ----------------------------------------------------
# CPU (Linux/Windows/macOS Intel)
pip install "git+https://github.com/alexjamesing/Deep_LVPM.git@keras3#egg=deep-lvpm[torch-cpu]"

# Apple Silicon (M-series)
pip install "git+https://github.com/alexjamesing/Deep_LVPM.git@keras3#egg=deep-lvpm[torch-apple]"

# NVIDIA GPU (CUDA) – install CUDA-enabled PyTorch first, then:
pip install "git+https://github.com/alexjamesing/Deep_LVPM.git@keras3#egg=deep-lvpm[torch-gpu]"

Virtualenv

To use Python’s venv module instead of conda:

python3 -m venv dlvpm-k3
source dlvpm-k3/bin/activate           # Windows: dlvpm-k3\Scripts\activate

# Use the same pip install commands shown in the conda section above,
# selecting exactly one backend extra (tf-* or torch-*).

Verifying the backend

After installing, confirm which backend Keras selected:

python -c "import keras, os; print('KERAS_BACKEND=', os.getenv('KERAS_BACKEND')); print('Detected backend:', keras.backend.backend())"

Set the backend explicitly if you have both runtimes available:

export KERAS_BACKEND=tensorflow   # or: torch

Additional notes

• tf-gpu is Linux-only and installs tensorflow[and-cuda] (no extra CUDA toolkit needed).

• torch-gpu intentionally does not install PyTorch; install the CUDA wheel from pytorch.org first, then use the extra.

• Tutorials in deep_lvpm.tutorial default to TensorFlow but can run on PyTorch by setting KERAS_BACKEND=torch before launching.