Update docs

README.md

@@ -1,6 +1,6 @@
 # Lux 🔥
 
-[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Latest Docs](https://img.shields.io/badge/docs-latest-blue.svg)](http://lux.csail.mit.edu/dev/) [![Stable Docs](https://img.shields.io/badge/docs-stable-blue.svg)](http://lux.csail.mit.edu/stable/) [![CI](https://github.com/avik-pal/Lux.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/avik-pal/Lux.jl/actions/workflows/CI.yml) [![codecov](https://codecov.io/gh/avik-pal/Lux.jl/branch/main/graph/badge.svg?token=IMqBM1e3hz)](https://codecov.io/gh/avik-pal/Lux.jl) [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
+[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Latest Docs](https://img.shields.io/badge/docs-latest-blue.svg)](http://lux.csail.mit.edu/dev/) [![Stable Docs](https://img.shields.io/badge/docs-stable-blue.svg)](http://lux.csail.mit.edu/stable/) [![CI](https://github.com/avik-pal/Lux.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/avik-pal/Lux.jl/actions/workflows/CI.yml) [![codecov](https://codecov.io/gh/avik-pal/Lux.jl/branch/main/graph/badge.svg?token=IMqBM1e3hz)](https://codecov.io/gh/avik-pal/Lux.jl) [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac) [![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)
 
 
 The 🔥 Deep Learning Framework
@@ -21,15 +21,8 @@ rng = Random.default_rng()
 Random.seed!(rng, 0)
 
 # Construct the layer
-model = Chain(
-    BatchNorm(128),
-    Dense(128, 256, tanh),
-    BatchNorm(256),
-    Chain(
-        Dense(256, 1, tanh),
-        Dense(1, 10)
-    )
-)
+model = Chain(BatchNorm(128), Dense(128, 256, tanh), BatchNorm(256),
+              Chain(Dense(256, 1, tanh),Dense(1, 10)))
 
 # Parameter and State Variables
 ps, st = Lux.setup(rng, model) .|> gpu
@@ -54,9 +47,7 @@ Look in the [examples](/examples/) directory for self-contained usage examples.
 
 ## Ecosystem
 
-### Prebuilt Deep Learning Models
-
-See [Boltz](lib/Boltz/) for pre-built deep learning models with pretrained weights for popular datasets.
+Checkout our [Ecosystem](http://lux.csail.mit.edu/dev/introduction/ecosystem/) page for more details. 
 
 ## Getting Help
 

docs/src/examples.md

@@ -1,10 +1,11 @@
 !!! warning
     These were not written in the form of tutorials but standalone scripts/packages for people to use
 
-## Packages
-
-* [Deep Equilibrium Models](https://github.com/SciML/FastDEQ.jl)
-
 ## Scipts
 
 * [ImageNet Classification using Metalhead.jl Models](https://github.com/avik-pal/Lux.jl/tree/main/examples/ImageNet)
+
+
+## Packages
+
+See [Ecosystem](introduction/ecosystem.md) for more details

docs/src/introduction/ecosystem.md

@@ -1,12 +1,67 @@
-# Extended Deep Learning Ecosystem
+# Ecosystem
+
+## Frameworks extending Lux
+
+- [Boltz.jl](https://github.com/avik-pal/Lux.jl/tree/main/lib/Boltz) -- Prebuilt deep learning models for image classification tasks
+- [DeepEquilibriumNetworks.jl](https://github.com/SciML/DeepEquilibriumNetworks.jl) -- Continuous and Discrete Deep Equilibrium Networks
+- [DiffEqFlux.jl](https://github.com/SciML/DiffEqFlux.jl) -- Neural Differential Equations, Continuous Normalizing Flows, etc.
+
+## Extended Julia Ecosystem
 
 As you might have noticed we don't do much apart from Neural Networks. All other parts of the DL training/evaluation pipeline should be offloaded to:
 
-* Data Manipulation/Loading -- [Augmentor.jl](https://evizero.github.io/Augmentor.jl/stable/), [DataLoaders.jl](https://lorenzoh.github.io/DataLoaders.jl/docs/dev/), [Images.jl](https://juliaimages.org/stable/), [DataAugmentation.jl](https://lorenzoh.github.io/DataAugmentation.jl/dev/README.md.html)
-* Optimisation -- [Optimisers.jl](https://github.com/FluxML/Optimisers.jl), [ParameterSchedulers.jl](https://darsnack.github.io/ParameterSchedulers.jl/dev/README.html)
-* Automatic Differentiation -- [Zygote.jl](https://github.com/FluxML/Zygote.jl) _(Default AD)_, [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) _(Experimental Support)_
-* Parameter Manipulation -- [Functors.jl](https://fluxml.ai/Functors.jl/stable/)
-* Model Checkpointing -- [Serialization.jl](https://docs.julialang.org/en/v1/stdlib/Serialization/)
-* Activation Functions / Common Neural Network Primitives -- [NNlib.jl](https://fluxml.ai/Flux.jl/stable/models/nnlib/)
-* Distributed DataParallel Training -- [FluxMPI.jl](https://github.com/avik-pal/FluxMPI.jl)
-* Training Visualization -- [Wandb.jl](https://github.com/avik-pal/Wandb.jl), [TensorBoardLogger.jl](https://github.com/PhilipVinc/TensorBoardLogger.jl)
+
+### Automatic Differentiation
+
+- [Zygote.jl](https://github.com/FluxML/Zygote.jl) -- Currently the default and recommended AD library
+- [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl) -- Experimental Support (but will most likely become the future default)
+- [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) -- For forward mode AD support
+- [ReverseDiff.jl](https://github.com/JuliaDiff/ReverseDiff.jl) -- Tape based reverse mode AD (mostly untested)
+
+
+### Data Manipulation and Loading
+
+-  [Augmentor.jl](https://evizero.github.io/Augmentor.jl/stable/)
+-  [DataLoaders.jl](https://lorenzoh.github.io/DataLoaders.jl/docs/dev/)
+-  [Images.jl](https://juliaimages.org/stable/)
+-  [DataAugmentation.jl](https://lorenzoh.github.io/DataAugmentation.jl/dev/README.md.html)
+
+
+### Distributed DataParallel Training
+
+- [FluxMPI.jl](https://github.com/avik-pal/FluxMPI.jl)
+
+
+### Neural Network Primitives
+
+- [NNlib.jl](https://fluxml.ai/Flux.jl/stable/models/nnlib/)
+
+
+### Optimisation 
+
+- [Optimisers.jl](https://github.com/FluxML/Optimisers.jl)
+- [ParameterSchedulers.jl](https://darsnack.github.io/ParameterSchedulers.jl/dev/README.html)
+- [Optimization.jl](http://optimization.sciml.ai/stable/)
+
+
+### Parameter Manipulation
+
+- [Functors.jl](https://fluxml.ai/Functors.jl/stable/)
+
+
+### Serialization
+
+- [Serialization.jl](https://docs.julialang.org/en/v1/stdlib/Serialization/)
+- [JLD2.jl](https://github.com/JuliaIO/JLD2.jl)
+
+
+### Testing Utilities
+
+- [FiniteDifferences.jl](https://github.com/JuliaDiff/FiniteDifferences.jl) -- Finite Differencing. Useful for testing gradient correctness
+- [JET.jl](https://aviatesk.github.io/JET.jl/dev/)
+
+
+### Training Visualization & Logging
+
+- [Wandb.jl](https://github.com/avik-pal/Wandb.jl)
+- [TensorBoardLogger.jl](https://github.com/PhilipVinc/TensorBoardLogger.jl)