Merge pull request #9 from LAMPSPUC/feature/ci

Fix ci pipe

Author: Giovanni3A

.github/workflows/ci.yml

@@ -25,7 +25,6 @@ jobs:
         version:
           - '1.11'
           - '1.6'
-          - 'pre'
         os:
           - ubuntu-latest
         arch:

Project.toml

@@ -1,14 +1,16 @@
+name = "ApplicationDrivenLearning"
+uuid = "0856f1c8-ef17-4e14-9230-2773e47a789e"
+authors = ["giovanni "]
+version = "0.1.0"
+
 [deps]
 BilevelJuMP = "485130c0-026e-11ea-0f1a-6992cd14145c"
-DiffOpt = "930fe3bc-9c6b-11ea-2d94-6184641e85e7"
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
-JobQueueMPI = "32d208e1-246e-420c-b6ff-18b71b410923"
+DiffOpt = "930fe3bc-9c6b-11ea-2d94-6184641e85e7"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
-MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
-NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 ParametricOptInterface = "0ce4ce61-57bf-432b-a095-efac525d185e"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+JobQueueMPI = "32d208e1-246e-420c-b6ff-18b71b410923"
+MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"

src/simulation.jl

@@ -1,5 +1,3 @@
-using Distributed
-
 function compute_single_step_cost(
     model::Model,
     y::Vector{<:Real},
@@ -48,7 +46,7 @@ function compute_single_step_gradient(
 end
 
 """
-    compute_cost(model, X, Y, with_gradients=false, n_workers=1)
+    compute_cost(model, X, Y, with_gradients=false)
 
 Compute the cost function (C) based on the model predictions and the true values.
 
@@ -67,7 +65,6 @@ function compute_cost(
     X::Matrix{<:Real},
     Y::Matrix{<:Real},
     with_gradients::Bool = false,
-    n_workers::Int = 1,
 )
 
     # data size assertions
@@ -96,32 +93,11 @@ function compute_cost(
     # get predictions
     Yhat = model.forecast(X')'  # size=(T, output_size)
 
-    # main loop - sequential
-    if n_workers == 1
-        for t = 1:T
-            result = _compute_step(Y[t, :], Yhat[t, :])
-            C += result[1] ./ T
-            dC .+= result[2] ./ T
-        end
-
-        # main loop - parallel
-    elseif n_workers > 1
-
-        # add workers and import package on first call
-        if nprocs() < n_workers
-            error(
-                "Please add workers and import the AppDrivenLearning module @everywhere before calling `train!` function.",
-            )
-        end
-
-        # parallel computation
-        result =
-            pmap(_compute_step, [Y[t, :] for t = 1:T], [Yhat[t, :] for t = 1:T])
-        C = sum([r[1] for r in result])
-        dC = sum([r[2] for r in result])
-
-    else
-        error("Invalid number of workers")
+    # main loop to compute cost
+    for t = 1:T
+        result = _compute_step(Y[t, :], Yhat[t, :])
+        C += result[1] ./ T
+        dC .+= result[2] ./ T
     end
 
     if with_gradients

test/Project.toml

@@ -0,0 +1,15 @@
+[deps]
+BilevelJuMP = "485130c0-026e-11ea-0f1a-6992cd14145c"
+DiffOpt = "930fe3bc-9c6b-11ea-2d94-6184641e85e7"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
+JobQueueMPI = "32d208e1-246e-420c-b6ff-18b71b410923"
+JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
+MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+ParametricOptInterface = "0ce4ce61-57bf-432b-a095-efac525d185e"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"