ivis
The facts below are what the maker publishes about ivis, checked and dated. Our editors have not written about it yet.
The facts
Each one read from ivis's own pages, with the date it was checked.
What it does
- Compute options
- The Python installation page documents CPU installation and a GPU option for users with CUDA installed.checked 11 Oct 2026
- Dashboard type
- interactivechecked 23 Sep 2026
- Data inputs
- The library supports arbitrary datasets, including N-dimensional arrays, image files on disk, and custom data connectors.checked 12 Oct 2026
- Dependencies
- The Python package documentation lists Python 3.5+, TensorFlow, NumPy, tqdm, and Annoy as dependencies.checked 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.checked 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.checked 11 Oct 2026
- Integration example
- A single-cell analysis example uses Scanpy for data import, preprocessing, and normalization before applying ivis.checked 11 Oct 2026
- Integrations
- The documentation lists callbacks and TensorBoard integration.checked 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.checked 11 Oct 2026
- Learning modes
- ivis supports unsupervised, semi-supervised, and fully supervised dimensionality reduction.checked 12 Oct 2026
- License
- The GitHub repository identifies its license as Apache-2.0.checked 11 Oct 2026
- New data
- A parametric mapping function can add new data points to existing embeddings.checked 12 Oct 2026
- Processing
- ivis supports in-memory and out-of-memory data processing.checked 12 Oct 2026
- Purpose
- ivis is a machine learning library for reducing the dimensionality of very large datasets using Siamese Neural Networks.checked 12 Oct 2026
- Python and R
- The documentation provides Python and R package sections, and Python installation instructions use pip.checked 11 Oct 2026
- Python installation
- The documentation provides CPU and GPU installation options through pip; the GPU option is for users with CUDA installed.checked 12 Oct 2026
- Scale
- The library states that it scales linearly to millions of observations.checked 12 Oct 2026
- Structure preservation
- The library preserves global data structures in a low-dimensional space.checked 12 Oct 2026
- Support
- The documentation asks users to report bugs through the GitHub issue tracker and says a reproducible example is helpful.checked 12 Oct 2026
- Training
- Training can be resumed, and the library supports arbitrary neural network backbones.checked 12 Oct 2026
- Use case
- The documentation includes an example applying ivis to visualise single-cell experiments.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
The company
- Maker
- The repository credits Bering Limited; the maker's site lists its address as 54 Portland Place, London, United Kingdom.checked 11 Oct 2026
Where it runs
- Platform
- webchecked 23 Sep 2026
