On the recordRead 12 Oct 202625 facts
ivis
On the record
- Compute options
- The Python installation page documents CPU installation and a GPU option for users with CUDA installed.beringresearch.github.io · read 11 Oct 2026
- Dashboard type
- interactiveberingresearch.github.io · read 23 Sep 2026
- Data inputs
- The library supports arbitrary datasets, including N-dimensional arrays, image files on disk, and custom data connectors.beringresearch.github.io · read 12 Oct 2026
- Dependencies
- The Python package documentation lists Python 3.5+, TensorFlow, NumPy, tqdm, and Annoy as dependencies.beringresearch.github.io · read 12 Oct 2026
- Embeddings
- It preserves global data structures in a low-dimensional space and can add new data points to existing embeddings using a parametric mapping function.beringresearch.github.io · read 11 Oct 2026
- Input data
- The documentation lists N-dimensional arrays, image files on disk, and custom data connectors; the repository README also lists NumPy arrays, sparse matrices, and HDF5 files.beringresearch.github.io · read 11 Oct 2026
- Integration example
- A single-cell analysis example uses Scanpy for data import, preprocessing, and normalization before applying ivis.beringresearch.github.io · read 11 Oct 2026
- Integrations
- The documentation lists callbacks and TensorBoard integration.beringresearch.github.io · read 12 Oct 2026
- Large-data workflow
- For out-of-memory training, the docs demonstrate training from HDF5 files and describe custom dataset loaders for other storage, including databases.beringresearch.github.io · read 11 Oct 2026
- Learning modes
- ivis supports unsupervised, semi-supervised, and fully supervised dimensionality reduction.beringresearch.github.io · read 12 Oct 2026
- License
- The GitHub repository identifies its license as Apache-2.0.github.com · read 11 Oct 2026
- Maker
- The repository credits Bering Limited; the maker's site lists its address as 54 Portland Place, London, United Kingdom.beringresearch.com · read 11 Oct 2026
- New data
- A parametric mapping function can add new data points to existing embeddings.beringresearch.github.io · read 12 Oct 2026
- Platform
- webberingresearch.github.io · read 23 Sep 2026
- Processing
- ivis supports in-memory and out-of-memory data processing.beringresearch.github.io · read 12 Oct 2026
- Purpose
- ivis is a machine learning library for reducing the dimensionality of very large datasets using Siamese Neural Networks.beringresearch.github.io · read 12 Oct 2026
- Python and R
- The documentation provides Python and R package sections, and Python installation instructions use pip.beringresearch.github.io · read 11 Oct 2026
- Python installation
- The documentation provides CPU and GPU installation options through pip; the GPU option is for users with CUDA installed.beringresearch.github.io · read 12 Oct 2026
