From ca450ee729ba9e4a11b2588b0e397f5c3d81b206 Mon Sep 17 00:00:00 2001
From: Michel Schanen <Michel.Schanen@gmail.com>
Date: Wed, 1 Nov 2023 13:03:56 -0500
Subject: [PATCH] Release fixes

---
 Project.toml | 5 ++++-
 README.md    | 2 +-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/Project.toml b/Project.toml
index 2c0e5a39..d3d48add 100644
--- a/Project.toml
+++ b/Project.toml
@@ -4,7 +4,6 @@ authors = ["Adrian Maldonado <maldonadod@anl.gov>", "Michel Schanen <mschanen@an
 version = "0.10.0"
 
 [deps]
-AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
@@ -31,7 +30,11 @@ CUDA = "4.1, 5"
 ForwardDiff = "0.10"
 KernelAbstractions = "0.9"
 Krylov = "0.9"
+LazyArtifacts = "1.9"
 LightGraphs = "1.3"
+LinearAlgebra = "1.9"
 Metis = "1"
+Printf = "1.9"
+SparseArrays = "1.9"
 SparseDiffTools = "1"
 julia = "1.9"
diff --git a/README.md b/README.md
index 881ac50b..93378bba 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@ Its main features are:
 
 ExaPF leverages [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl)
 to generate portable kernels working on different backends.
-Right now, we NVIDA's CUDA and AMD's ROCm with [Intel oneAPI](https://github.com/JuliaGPU/oneAPI.jl) in development.
+Right now, we support NVIDA's CUDA and AMD's ROCm backends with [Intel oneAPI](https://github.com/JuliaGPU/oneAPI.jl) in development.
 
 ## Quick-start
 ### How to install ExaPF?