Bump CI to Julia 1.12

.github/workflows/ci.yml

@@ -17,15 +17,13 @@ jobs:
       labels: cuda
     strategy:
       matrix:
-        julia-version: ['1.11']
+        julia-version: ['1']
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
       - uses: julia-actions/install-juliaup@v2
         with:
           channel: ${{ matrix.julia-version }}
-      - name: Cache Julia artifacts
-        uses: actions/cache@v3
       - name: Run tests
         shell: julia --project=. --color=yes {0}
         run: |
@@ -37,5 +35,5 @@ jobs:
           directories: src
       - uses: codecov/codecov-action@v5
         with:
-          file: lcov.info
+          files: lcov.info
           token: ${{ secrets.CODECOV_TOKEN }}

.github/workflows/docs.yml

@@ -13,14 +13,12 @@ jobs:
       labels: cuda
     strategy:
       matrix:
-        julia-version: ['1.11']
+        julia-version: ['1']
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/install-juliaup@v2
         with:
           channel: ${{ matrix.julia-version }}
-      - name: Cache Julia artifacts
-        uses: actions/cache@v3
       - name: Instantiate the dependencies
         shell: julia --project=docs/ --color=yes {0}
         run: |

docs/Project.toml

@@ -3,6 +3,7 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244"
 ExaModels = "1037b233-b668-4ce9-9b63-f9f681f55dd2"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+CUDSS = "45b445bb-4962-46a0-9369-b4df9d0f772e"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6"
 MadNLPGPU = "d72a61cc-809d-412f-99be-fd81f4b8a598"

docs/src/mpopf_demo.jl

@@ -4,7 +4,7 @@
 
 # Some of the models in this portion of the tutorial involve using external files. While we provide the necessary code to access these additional files, you may view them __[here](https://github.com/mit-shin-group/multi-period-opf-data)__ as well.
 # We will start with the simplest way to model the MPOPF, which also does not require the user to have any data already downloaded. Instead, the user specifies a demand curve for the system. The demand curve is a vector of ratios from 0 to 1 which indicate the scaling of demand compared to the demand indicated by the static OPF file. In this model of the MPOPF, every consuming bus has the same scaling in power demand for each point in time. A corrective action ratio, which limits the ramp rate of generators, can also be inputted. It is set to 0.1 as a default. The adjustable coordinate system and backend that were present for the static OPF are also available for all MPOPF models.
-using ExaModelsPower, CUDA, MadNLP, MadNLPGPU, ExaModels
+using ExaModelsPower, CUDA, MadNLP, MadNLPGPU, CUDSS, ExaModels
 model, vars, cons = mpopf_model(
     "pglib_opf_case118_ieee.m", # static network data
     [.64, .60, .58, .56, .56, .58, .64, .76, .87, .95, .99, 1.0, .99, 1.0, 1.0,

docs/src/opf_demo.jl

@@ -5,8 +5,8 @@
 # The latest version of ExaModelsPower can be installed in julia as so. Additionally, in order to develop models that can be solved on the GPU, CUDA is required. 
 using ExaModelsPower, CUDA
 
-# In order to solve the ExaModels developed by ExaModelsPower, an NLP solver is required. ExaModels is compatible with MadNLP and Ipopt, but this tutorial will focus on MadNLP to demonstrate GPU solving capabilities.
-using MadNLP, MadNLPGPU #, NLPModelsIpopt 
+# In order to solve the ExaModels developed by ExaModelsPower, an NLP solver is required. ExaModels is compatible with MadNLP and Ipopt, but this tutorial will focus on MadNLP to demonstrate GPU solving capabilities. CUDSS is loaded so MadNLP's CUDA extension activates and the default GPU linear solver becomes available.
+using MadNLP, MadNLPGPU, CUDSS #, NLPModelsIpopt
 
 # Finally, we install ExaModels to allow solved models to be unpacked.
 using ExaModels