Edit documentation

README.md

@@ -17,6 +17,14 @@ Conflux.jl is a toolkit designed to enable data parallelism for [Flux.jl](https:
 
 See the documentation for more details, examples, and important caveats.
 
+## Installation
+
+The package can be installed with the Julia package manager. From the Julia REPL, type `]` to enter the Pkg REPL mode and run:
+
+```julia
+pkg> add https://github.com/MurrellGroup/Conflux.jl#main
+```
+
 ## Example usage
 
 ```julia

docs/make.jl

@@ -7,13 +7,16 @@ makedocs(;
     modules=[Conflux],
     authors="Anton Oresten <anton.oresten42@gmail.com>",
     sitename="Conflux.jl",
+    doctest=false,
     format=Documenter.HTML(;
         edit_link="main",
         assets=String[],
     ),
     pages=[
         "Home" => "index.md",
+        "API Reference" => "API.md",
     ],
+    checkdocs=:all,
 )
 
 deploydocs(

docs/src/index.md

@@ -4,11 +4,80 @@ CurrentModule = Conflux
 
 # Conflux
 
-Documentation for [Conflux](https://github.com/anton083/Conflux.jl).
+[![Latest Release](https://img.shields.io/github/release/MurrellGroup/Conflux.jl.svg)](https://github.com/MurrellGroup/Conflux.jl/releases/latest)
+[![MIT license](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/license/MIT)
+[![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://MurrellGroup.github.io/Conflux.jl/stable/)
+[![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://MurrellGroup.github.io/Conflux.jl/dev/)
+[![Status](https://github.com/MurrellGroup/Conflux.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/MurrellGroup/Conflux.jl/actions/workflows/CI.yml?query=branch%3Amain)
+[![Coverage](https://codecov.io/gh/MurrellGroup/Conflux.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/MurrellGroup/Conflux.jl)
 
-```@index
-```
+Conflux.jl is a toolkit designed to enable data parallelism for [Flux.jl](https://github.com/FluxML/Flux.jl) models by simplifying the process of replicating them across multiple GPUs on a single node, and by leveraging [NCCL.jl](https://github.com/JuliaGPU/NCCL.jl) for efficient inter-GPU communication. This package aims to provide a straightforward and intuitive interface for multi-GPU training, requiring minimal changes to existing code and training loops.
+
+## Features
+
+- Easy replication of objects across multiple GPUs with the **replicate** function
+- Efficient synchronization of models and averaging of gradients with the **allreduce!** function, which takes an operator (e.g. `+`, `*`, `avg`) and a set of replicas, and reduces all their parameters with the given operator, leaving the replicas identical.
+- A **withdevices** function that allows you to run code on each device asynchronously.
+
+See the documentation for more details, examples, and important caveats.
+
+## Installation
 
-```@autodocs
-Modules = [Conflux]
+The package can be installed with the Julia package manager. From the Julia REPL, type `]` to enter the Pkg REPL mode and run:
+
+```julia
+pkg> add https://github.com/MurrellGroup/Conflux.jl#main
 ```
+
+## Example usage
+
+```julia
+# Specify the default devices to use
+ENV["CUDA_VISIBLE_DEVICES"] = "0,1"
+
+using Conflux
+
+using Flux, Optimisers
+
+model = Chain(Dense(1 => 256, tanh), Dense(256 => 512, tanh), Dense(512 => 256, tanh), Dense(256 => 1))
+
+# This will use the available devices. If you want to use a specific device, you can pass them in a second argument.
+models = replicate(model)
+
+opt = Optimisers.Adam(0.001f0)
+
+# Instantiate the optimiser states on each device
+states = Conflux.withdevices() do (i, device)
+    Optimisers.setup(opt, model) |> device
+end
+
+# A single batch, stored on CPU. Could use a more sophisticated mechanism to distribute multiple batches.
+X = rand(1, 16)
+Y = X .^ 2
+
+loss(y, Y) = sum(abs2, y .- Y)
+
+losses = []
+for epoch in 1:10
+    # Get the gradients for each batch on each device
+    ∇models = Conflux.withdevices() do (i, device)
+        x, y = device(X), device(Y)
+        # The second return value is a tuple because `Flux.withgradient` takes `args...`, and the model is the first argument.
+        l, (∇model,) = Flux.withgradient(m -> loss(m(x), y), models[i])
+        push!(losses, l)
+        ∇model
+    end
+
+    # Average the gradients across devices
+    allreduce!(avg, ∇models...)
+
+    # Update the models on each device
+    Conflux.withdevices() do (i, device)
+        Optimisers.update!(states[i], models[i], ∇models[i])
+    end
+
+    # Optionally synchronize the models and optimiser states, in case the parameters diverge
+    #allreduce!(avg, models...)
+    #allreduce!(avg, states...)
+end
+```

docs/src/reference.md

@@ -0,0 +1,5 @@
+# Reference
+
+```@autodocs
+Modules = [Conflux]
+```