Fix numbering in the docs

.buildkite/pipeline.yml

@@ -206,6 +206,7 @@ steps:
  cuda: "*"
  artifact_paths:
  - "tutorial_deps/*"
+ - "docs/build/**/*"
  env:
  DATADEPS_ALWAYS_ACCEPT: true
  JULIA_DEBUG: "Documenter"

docs/src/index.md

@@ -5,8 +5,8 @@ layout: home
 
 hero:
  name: LuxDL Docs
- text: Elegant & Performant Deep Learning in JuliaLang
- tagline: A Pure Julia Deep Learning Framework putting Correctness and Performance First
+ text: Elegant & Performant Scientific Machine Learning in JuliaLang
+ tagline: A Pure Julia Deep Learning Framework designed for Scientific Machine Learning
  actions:
  - theme: brand
  text: Tutorials

docs/src/manual/distributed_utils.md

@@ -10,16 +10,16 @@ DDP Training using `Lux.DistributedUtils` is a spiritual successor to
 
 ## Guide to Integrating DistributedUtils into your code
 
-1. Initialize the respective backend with [`DistributedUtils.initialize`](@ref), by passing
-  in a backend type. It is important that you pass in the type, i.e. `NCCLBackend` and not
-  the object `NCCLBackend()`.
+* Initialize the respective backend with [`DistributedUtils.initialize`](@ref), by passing
+ in a backend type. It is important that you pass in the type, i.e. `NCCLBackend` and not
+ the object `NCCLBackend()`.
 
 ```julia
 DistributedUtils.initialize(NCCLBackend)
 ```
 
-2. Obtain the backend via [`DistributedUtils.get_distributed_backend`](@ref) by passing in
-  the type of the backend (same note as last point applies here again).
+* Obtain the backend via [`DistributedUtils.get_distributed_backend`](@ref) by passing in
+ the type of the backend (same note as last point applies here again).
 
 ```julia
 backend = DistributedUtils.get_distributed_backend(NCCLBackend)
@@ -28,36 +28,36 @@ backend = DistributedUtils.get_distributed_backend(NCCLBackend)
 It is important that you use this function instead of directly constructing the backend,
 since there are certain internal states that need to be synchronized.
 
-3. Next synchronize the parameters and states of the model. This is done by calling
-  [`DistributedUtils.synchronize!!`](@ref) with the backend and the respective input.
+* Next synchronize the parameters and states of the model. This is done by calling
+ [`DistributedUtils.synchronize!!`](@ref) with the backend and the respective input.
 
 ```julia
 ps = DistributedUtils.synchronize!!(backend, ps)
 st = DistributedUtils.synchronize!!(backend, st)
 ```
 
-4. To split the data uniformly across the processes use
-  [`DistributedUtils.DistributedDataContainer`](@ref). Alternatively, one can manually
-  split the data. For the provided container to work
-  [`MLUtils.jl`](https://github.com/JuliaML/MLUtils.jl) must be installed and loaded.
+* To split the data uniformly across the processes use
+ [`DistributedUtils.DistributedDataContainer`](@ref). Alternatively, one can manually
+ split the data. For the provided container to work
+ [`MLUtils.jl`](https://github.com/JuliaML/MLUtils.jl) must be installed and loaded.
 
 ```julia
 data = DistributedUtils.DistributedDataContainer(backend, data)
 ```
 
-5. Wrap the optimizer in [`DistributedUtils.DistributedOptimizer`](@ref) to ensure that the
-  optimizer is correctly synchronized across all processes before parameter updates. After
-  initializing the state of the optimizer, synchronize the state across all processes.
+* Wrap the optimizer in [`DistributedUtils.DistributedOptimizer`](@ref) to ensure that the
+ optimizer is correctly synchronized across all processes before parameter updates. After
+ initializing the state of the optimizer, synchronize the state across all processes.
 
 ```julia
 opt = DistributedUtils.DistributedOptimizer(backend, opt)
 opt_state = Optimisers.setup(opt, ps)
 opt_state = DistributedUtils.synchronize!!(backend, opt_state)
-```
+ ```
 
-6. Finally change all logging and serialization code to trigger on
-  `local_rank(backend) == 0`. This ensures that only the master process logs and serializes
-  the model.
+* Finally change all logging and serialization code to trigger on
+ `local_rank(backend) == 0`. This ensures that only the master process logs and serializes
+ the model.
 
 ## [GPU-Aware MPI](@id gpu-aware-mpi)
 
@@ -108,4 +108,4 @@ And that's pretty much it!
 1. Currently we don't run tests with CUDA or ROCM aware MPI, use those features at your own
  risk. We are working on adding tests for these features.
 2. AMDGPU support is mostly experimental and causes deadlocks in certain situations, this is
- being investigated. If you have a minimal reproducer for this, please open an issue.
+ being investigated. If you have a minimal reproducer for this, please open an issue.

docs/src/tutorials/index.md

@@ -6,7 +6,7 @@ layout: page
 <script setup>
 import { VPTeamPage, VPTeamPageTitle, VPTeamMembers, VPTeamPageSection } from 'vitepress/theme'
 
-const githubSvg = '<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 640 512"><path d="M392.8 1.2c-17-4.9-34.7 5-39.6 22l-128 448c-4.9 17 5 34.7 22 39.6s34.7-5 39.6-22l128-448c4.9-17-5-34.7-22-39.6zm80.6 120.1c-12.5 12.5-12.5 32.8 0 45.3L562.7 256l-89.4 89.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l112-112c12.5-12.5 12.5-32.8 0-45.3l-112-112c-12.5-12.5-32.8-12.5-45.3 0zm-306.7 0c-12.5-12.5-32.8-12.5-45.3 0l-112 112c-12.5 12.5-12.5 32.8 0 45.3l112 112c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L77.3 256l89.4-89.4c12.5-12.5 12.5-32.8 0-45.3z"/></svg>';
+const codeSvg = '<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 640 512"><path d="M392.8 1.2c-17-4.9-34.7 5-39.6 22l-128 448c-4.9 17 5 34.7 22 39.6s34.7-5 39.6-22l128-448c4.9-17-5-34.7-22-39.6zm80.6 120.1c-12.5 12.5-12.5 32.8 0 45.3L562.7 256l-89.4 89.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l112-112c12.5-12.5 12.5-32.8 0-45.3l-112-112c-12.5-12.5-32.8-12.5-45.3 0zm-306.7 0c-12.5-12.5-32.8-12.5-45.3 0l-112 112c-12.5 12.5-12.5 32.8 0 45.3l112 112c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L77.3 256l89.4-89.4c12.5-12.5 12.5-32.8 0-45.3z"/></svg>';
 
 const beginners = [
  {
@@ -16,7 +16,7 @@ const beginners = [
  links: [
  {
  icon: {
- svg: githubSvg,
+ svg: codeSvg,
  },
  link: 'beginner/1_Basics' }
  ]
@@ -28,7 +28,7 @@ const beginners = [
  links: [
  {
  icon: {
- svg: githubSvg,
+ svg: codeSvg,
  },
  link: 'beginner/2_PolynomialFitting' }
  ]
@@ -40,7 +40,7 @@ const beginners = [
  links: [
  {
  icon: {
- svg: githubSvg,
+ svg: codeSvg,
  },
  link: 'beginner/3_SimpleRNN' }
  ]
@@ -52,7 +52,7 @@ const beginners = [
  links: [
  {
  icon: {
- svg: githubSvg,
+ svg: codeSvg,
  },
  link: 'beginner/4_SimpleChains' }
  ]
@@ -67,7 +67,7 @@ const intermediate = [
  links: [
  {
  icon: {
- svg: githubSvg,
+ svg: codeSvg,
  },
  link: 'intermediate/1_NeuralODE' }
  ]
@@ -79,7 +79,7 @@ const intermediate = [
  links: [
  {
  icon: {
- svg: githubSvg,
+ svg: codeSvg,
  },
  link: 'intermediate/2_BayesianNN' }
  ]
@@ -92,7 +92,7 @@ const intermediate = [
  links: [
  {
  icon: {
- svg: githubSvg,
+ svg: codeSvg,
  },
  link: 'intermediate/3_HyperNet' }
  ]
@@ -107,7 +107,7 @@ const advanced = [
  links: [
  {
  icon: {
- svg: githubSvg,
+ svg: codeSvg,
  },
  link: 'advanced/1_GravitationalWaveForm' }
  ]