Installation

SEAM requires Python 3.7.2 or later. We recommend using Anaconda to manage dependencies.

Standard Install (CPU)

With Anaconda sourced, create a new environment with Python 3.8 or later:

conda create --name seam python=3.9
conda activate seam
pip install seam-nn

When you are done using the environment, always exit via conda deactivate.

Note

Some specialized workflows (e.g., the SEAM GUI, certain example scripts, or model-specific pipelines) may require older Python or package versions. Review the installation notes at the top of any relevant script before creating your environment.

Note

GPU access is not required to use SEAM. pip install seam-nn installs TensorFlow as a dependency, but GPU acceleration is only used by optional code paths—primarily Attributer (attribution maps) and Clusterer (hierarchical clustering distance matrices). Compiler, MetaExplainer, and Identifier do not require a GPU. All GPU-enabled paths fall back to CPU when no GPU is detected; attribution and hierarchical clustering on large sequence libraries are the steps most noticeably slower without one.

GPU Support (Optional)

SEAM uses TensorFlow for GPU acceleration in Attributer and in Clusterer hierarchical clustering (distance-matrix computation). To utilize GPU acceleration, your environment must have a strictly matched combination of Python, TensorFlow, CUDA, and cuDNN.

Installing seam-nn via pip pulls in TensorFlow, but does not guarantee the correct CUDA/cuDNN runtime libraries are installed. If you see the warning Could not find cuda drivers on your machine, GPU will not be used, your GPU is present but the CUDA runtime is missing or mismatched.

Recommended: Python 3.9+ with TensorFlow 2.16+

TensorFlow 2.16+ bundles the required NVIDIA libraries via pip. You do not need to install CUDA via Conda. Use Python 3.9 or later and install GPU-enabled TensorFlow before SEAM:

conda create --name seam-gpu python=3.9
conda activate seam-gpu
pip install "tensorflow[and-cuda]"
pip install seam-nn

On Python 3.9+, pip install seam-nn alone may also enable GPU support (as it pulls a recent TensorFlow), but installing tensorflow[and-cuda] first is the most reliable approach.

Legacy GPU Environments (Python 3.8 or TensorFlow < 2.16)

If you use Python 3.8, pip installs TensorFlow 2.13, which does not support tensorflow[and-cuda]. You must manually install the exact CUDA Toolkit and cuDNN versions that match your TensorFlow version. For example, TensorFlow 2.12–2.14 requires CUDA 11.8 and cuDNN 8.6:

conda install -c conda-forge cudatoolkit=11.8 cudnn=8.6
export LD_LIBRARY_PATH=$CONDA_PREFIX/lib:$LD_LIBRARY_PATH
pip install seam-nn

If cudnn=8.6 is unavailable on your platform, try conda install -c conda-forge cudnn without a version pin. You may need to add the LD_LIBRARY_PATH export to your shell profile or job script so it persists across sessions.

Verify GPU detection with:

import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))

Warning

If you are managing dependencies manually, consult the official TensorFlow Tested Build Configurations Table to find the exact CUDA and cuDNN versions required for your specific version of TensorFlow and Python.

Getting Help

If you have any issues installing SEAM, please see:

• https://seam-nn.readthedocs.io/en/latest/installation.html

• https://github.com/evanseitz/seam-nn/issues

For issues installing SQUID, the package used for sequence generation and inference, please see:

• https://squid-nn.readthedocs.io/en/latest/installation.html

• https://github.com/evanseitz/squid-nn/issues

Dependencies

Core Dependencies

• numpy

• matplotlib >= 3.6.0

• pandas

• tqdm

• psutil

• biopython

• tensorflow >= 2.0.0

• scipy >= 1.7.0

• squid-nn

• seaborn

Optional Dependencies

• umap-learn (for UMAP)

• phate (for PHATE)

• openTSNE (for t-SNE)

• scikit-learn (for PCA, K-means, DBSCAN)

• cuml (for GPU-accelerated UMAP, t-SNE, and PCA)

• kmeanstf (for GPU-accelerated K-means)

• shap (for DeepSHAP)

Development

For development installation:

git clone https://github.com/evanseitz/seam-nn.git
cd seam-nn
pip install -e .[dev]

Notes

• SEAM has been tested on Mac and Linux operating systems

• Installation typically takes less than 1 minute

• For issues with SQUID installation, see: https://squid-nn.readthedocs.io/

• DNNs trained with TF1.x may require separate environments for TF1.x and TF2.x