zeekay
e037e8f7ea
The distribution called itself torus-ml while shipping a package directory named concrete, so the name a user installs and the name they import disagreed — and most of the tree already said torus.ml, which could not resolve against src/concrete. Moved src/concrete to src/torus and pointed the include at it. The dependency was Zama's concrete-python from their index. It is torus-fhe from ours, which is what luxfhe/torus already ships: that frontend declares torus-fhe and ships a torus/fhe module, so every 'from torus.fhe' in this tree was already naming our compiler and only the dependency lagged. Two distributions share one namespace, which is the shape the upstream this forked from used and the reason the split works: torus-fhe gives torus.fhe, torus-ml gives torus.ml. Swept every other spelling — identifiers, enum members, workflow variables, filenames, notebooks. LICENSE and NOTICE keep the upstream attribution a fork owes; the figures carry the old name in JPEG metadata a text sweep cannot reach.
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Deep learning examples
These examples illustrate the basic usage of Torus ML to build various types of neural networks. They use simple data-sets, focusing on the syntax and usage of Torus ML. For examples showing how to train high-accuracy models on more complex data-sets, see the Demos and Tutorials section.
FHE constraints considerations
The examples listed here make use of simulation to perform evaluation over large test sets. Since FHE execution can be slow, only a few FHE executions can be performed. The correctness guarantees of Torus ML ensure that accuracy measured with simulation is the same as that which will be obtained during FHE execution.
Some examples constrain accumulators to 7-8 bits, which can be sufficient for simple data-sets. Up to 16-bit accumulators can be used, but this introduces a slowdown of 4-5x compared to 8-bit accumulators.
List of Examples
1. Step-by-step guide to building a custom NN
This shows how to use Quantization Aware Training and pruning when starting out from a classical PyTorch network. This example uses a simple data-set and a small NN, which achieves good accuracy with low accumulator size.
2. Custom convolutional NN on the Digits data-set
Following the Step-by-step guide, this notebook implements a Quantization Aware Training convolutional neural network on the MNIST data-set. It uses 3-bit weights and activations, giving a 7-bit accumulator.