add threading support

Project.toml

@@ -1,7 +1,7 @@
 name = "Hyperopt"
 uuid = "93e5fe13-2215-51db-baaf-2e9a34fb2712"
 author = ["Fredrik Bagge Carlson <[email protected]>"]
-version = "0.4.3"
+version = "0.4.4"
 
 [deps]
 BayesianOptimization = "4c6ed407-134f-591c-93fa-e0f7c164a0ec"
@@ -15,6 +15,7 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+ThreadPools = "b189fb0b-2eb5-4ed4-bc0c-d34c51242431"
 
 [compat]
 BayesianOptimization = "0.2"
@@ -23,6 +24,7 @@ Juno = "0.7, 0.8"
 LatinHypercubeSampling = "1.2"
 MacroTools = "0.5"
 RecipesBase = "0.7, 0.8, 1.0"
+ThreadPools = "1"
 julia = "1.5"
 
 [extras]

README.md

@@ -206,4 +206,5 @@ end
 ```
 
 # Parallel execution
-The macro `@phyperopt` works in the same way as `@hyperopt` but distributes all computation on available workers. The usual caveats apply, code must be loaded on all workers etc.
+- The macro `@phyperopt` works in the same way as `@hyperopt` but distributes all computation on available workers. The usual caveats apply, code must be loaded on all workers etc.
+- The macro `@thyperopt` uses `ThreadPools.tmap` to evaluate the objective on all available threads. Beware of high memory consumption if your objective allocates a lot of memory.

src/Hyperopt.jl

@@ -1,6 +1,6 @@
 module Hyperopt
 
-export Hyperoptimizer, @hyperopt, @phyperopt, printmin, printmax
+export Hyperoptimizer, @hyperopt, @phyperopt, @thyperopt, printmin, printmax
 export RandomSampler, BlueNoiseSampler, LHSampler, CLHSampler, Continuous, Categorical, GPSampler, Max, Min, Hyperband
 
 using Base.Threads: threadid, nthreads
@@ -12,6 +12,7 @@ using RecipesBase
 using Distributed
 using LatinHypercubeSampling
 using BayesianOptimization, GaussianProcesses
+using ThreadPools
 
 const HO_RNG = [MersenneTwister(rand(1:1000)) for _ in 1:nthreads()]
 
@@ -138,7 +139,7 @@ macro hyperopt(ex)
     end
 end
 
-function pmacrobody(ex, params, ho_)
+function pmacrobody(ex, params, ho_, pmap=pmap)
     quote
         function workaround_function()
             ho = $(ho_)
@@ -147,7 +148,7 @@ function pmacrobody(ex, params, ho_)
             # reassign the original array and then append the new history. If a new array is used, the change will not be visible in the original hyperoptimizer
             hist = ho.history
             ho.history = []
-            res = pmap(1:ho.iterations) do i
+            res = $(pmap)(1:ho.iterations) do i
                 $(Expr(:tuple, esc.(params)...)),_ = iterate(ho,i)
                 res = $(esc(ex.args[2])) # ex.args[2] = Body of the For loop
 
@@ -162,13 +163,26 @@ function pmacrobody(ex, params, ho_)
     end
 end
 
+"""
+Same as `@hyperopt` but uses `Distributed.pmap` for parallel evaluation of the cost function.
+"""
 macro phyperopt(ex)
     pre = preprocess_expression(ex)
     pre[3].args[1] === :GPSampler && error("We currently do not support running the GPSampler in parallel. If this is an issue, open an issue ;)")
     ho_ = create_ho(pre...)
     pmacrobody(ex, pre[1], ho_)
 end
 
+"""
+Same as `@hyperopt` but uses `ThreadPools.tmap` for multithreaded evaluation of the cost function.
+"""
+macro thyperopt(ex)
+    pre = preprocess_expression(ex)
+    pre[3].args[1] === :GPSampler && error("We currently do not support running the GPSampler in parallel. If this is an issue, open an issue ;)")
+    ho_ = create_ho(pre...)
+    pmacrobody(ex, pre[1], ho_, ThreadPools.tmap)
+end
+
 function Base.minimum(ho::Hyperoptimizer)
     m,i = findmin(replace(ho.results, NaN => Inf))
     m

test/runtests.jl

@@ -263,6 +263,20 @@ f(a,b=true;c=10) = sum(@. 100 + (a-3)^2 + (b ? 10 : 20) + (c-100)^2) # This func
         @info "Testing Parallel"
 
         rmprocs(workers())
+
+
+        horp = @thyperopt for i=300, sampler=RandomSampler(), a = LinRange(1,5,50), b = [true, false], c = exp10.(LinRange(-1,3,50))
+            # println(i, "\t", a, "\t", b, "\t", c)
+            f(a,b,c=c)
+        end
+        @test minimum(horp) < 300
+        @test length(horp.history) == 300
+        @test length(horp.results) == 300
+        @test all(1:300) do i
+            f(horp.history[i][1:2]..., c=horp.history[i][3]) == horp.results[i]
+        end
+
+
         horp = @phyperopt for i=300, sampler=RandomSampler(), a = LinRange(1,5,50), b = [true, false], c = exp10.(LinRange(-1,3,50))
             # println(i, "\t", a, "\t", b, "\t", c)
             f(a,b,c=c)