
Welcome to BeyondML’s documentation!
BeyondML is a Python package which enables creating sparse multitask artificial neural networks (MANNs) compatible with TensorFlow and PyTorch. This package contains custom layers and utilities to facilitate the training and optimization of models using the Reduction of Sub-Network Neuroplasticity (RSN2) training procedure developed by AI Squared, Inc.
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Installation
This package is available through Pypi and can be installed by running the following command:
pip install beyondml
Alternatively, the latest version of the software can be installed directly from GitHub using the following command:
pip install git+https://github.com/beyond-ml-labs/beyondml
Documentation:
Changelog
- Version 0.1.0
Refactored existing MANN repository to rename to BeyondML
- Version 0.1.1
- Added the SparseDense, SparseConv, SparseMultiDense, and SparseMultiConv layers to
beyondml.tflow.layers, giving users the functionality to utilize sparse tensors during inference
- Version 0.1.2
Added the MaskedMultiHeadAttention, MaskedTransformerEncoderLayer, and MaskedTransformerDecoderLayer layers to beyondml.pt.layers to add pruning to the transformer architecture
Added MaskedConv3D, MultiMaskedConv3D, MultiConv3D, MultiMaxPool3D, SparseConv3D, and SparseMultiConv3D layers to beyondml.tflow.layers
Added MaskedConv3D, MultiMaskedConv3D, MultiConv3D, MultiMaxPool3D, SparseConv3D, SparseMultiConv3D, and MultiMaxPool2D layers to beyondml.pt.layers
- Version 0.1.3
Added beyondml.pt compatibility with more native PyTorch functionality for using models on different devices and datatypes
Added train_model function to beyondml.tflow.utils
Added MultitaskNormalization layer to beyondml.tflow.layers and beyondml.pt.layers
- Version 0.1.4
Updated documentation to use Sphinx
- Version 0.1.5
Updated requirements to use newer version of TensorFlow
Fixed errors with changes to types of input_shape in TensorFlow Keras layers
Fixed errors resulting from model/configuration changes with TensorFlow