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chore: Numpy, not NumPy.

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Benoit Chevallier-Mames
2022-01-05 19:49:02 +01:00
committed by Benoit Chevallier
parent 721bc06eb7
commit ef74f6b5f6
1 changed files with 2 additions and 2 deletions
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docs/dev/explanation/compilation.md
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@@ -17,7 +17,7 @@ import concrete.numpy as hnp
def f(x, y):
return (2 * x) + y
# Create a NumPy FHE Compiler
# Create a Numpy FHE Compiler
compiler = hnp.NPFHECompiler(f, {"x": "encrypted", "y": "encrypted"})
# Compile an FHE Circuit using an inputset
@@ -57,7 +57,7 @@ Here is the visual representation of the pipeline:
Compiling a torch Module is pretty straightforward.
The torch Module is first converted to a NumPy equivalent we call `NumpyModule` if all the layers in the torch Module are supported.
The torch Module is first converted to a Numpy equivalent we call `NumpyModule` if all the layers in the torch Module are supported.
Then the module is quantized post-training to be compatible with our compiler which only works on integers. The post training quantization uses the provided dataset for calibration.