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docs: update the documentation

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refs #1085
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Benoit Chevallier-Mames
2021-12-07 16:10:46 +01:00
committed by Benoit Chevallier
parent 8db49f79f3
commit 7ce7d1e10b
3 changed files with 25 additions and 10 deletions
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27
docs/dev/explanation/TERMINOLOGY_AND_STRUCTURE.md
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@@ -1,7 +1,3 @@
```{warning}
FIXME(Benoit): to check this is still valid
```
# Terminology and Structure
## Terminology
@@ -28,11 +24,34 @@ In this section, we will discuss the module structure of **concretefhe** briefly
- concrete
- common: types and utilities that can be used by multiple frontends (e.g., numpy, torch)
- bounds_measurement: utilities for determining bounds of intermediate representation
- common_helpers: various utilities
- compilation: type definitions related to compilation (e.g., compilation config, compilation artifacts)
- data_types: type definitions of typing information of intermediate representation
- debugging: utilities for printing/displaying intermediate representation
- extensions: utilities that provide special functionality to our users
- fhe_circuit: class to hold the result of the compilation
- helpers: various helpers
- mlir: MLIR conversion module
- operator_graph: code to wrap and make manipulating networkx graphs easier
- optimization: optimization and simplification
- representation: type definitions of intermediate representation
- tracing: utilities for generic function tracing used during intermediate representation creation
- values: define the different types we use, including tensors and scalar, encrypted or clear
- numpy: numpy frontend of the package
- compile: compilation of a numpy function
- np_dtypes_helpers: utilities about types
- np_fhe_compiler: main API for compilation of numpy functions
- np_indexing_helpers: utilities for indexing
- np_inputset_helpers: utilities for inputsets
- np_mlir_converter: utilities for MLIR conversion
- tracing: tracing of numpy functions
- quantization: tools to quantize networks
- post_training: post training quantization
- quantized_activations: management of quantization in activations
- quantized_array: utilities for quantization
- quantized_layers: management of quantization of neural network layers
- quantized_module: main API for quantization
- torch: torch compilation and conversion
- compile: compilation of a torch module, including quantization
- numpy_module: conversion tools to turn a torch module into a numpy function

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docs/user/explanation/FHE_AND_FRAMEWORK_LIMITS.md
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@@ -23,7 +23,7 @@ Since this is an early version of the product, not everything is done, to say th
### Currently executing locally
```{warning}
FIXME(Benoit): we'll see later if this is still a valid limit
FIXME(Benoit): we'll see later if this is still a valid limit, #1111
```
As of today, the execution of the FHE program is done locally. Notably, in the current version, there is no client (on which we encrypt the private data, or decrypt the returned result) or server (on which the computation is done completely over encrypted data), but a single host. As explained in [this section](FUTURE_FEATURES.md), this limit will be removed in the next version, such that the **Concrete Framework** can be used in production.

6
docs/user/explanation/WHAT_IS_FHE.md
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@@ -9,8 +9,4 @@ You can learn more about FHE using the following links:
- [quick overview](https://6min.zama.ai/)
- [monthly technical FHE.org meetup](https://www.meetup.com/fhe-org/)
- [videos and resources](http://fhe.org/)
```{warning}
FIXME(Alex/Jeremy): should we link to Zama blogposts or not?
FIXME(Benoit): if yes, I'll do it
```
- [Homomorphic Encryption 101](https://medium.com/zama-ai/homomorphic-encryption-101-c1524fb76013)