diff --git a/docs/Project.toml b/docs/Project.toml
index 9b87ffd8ec..90863935db 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -52,6 +52,7 @@ StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
+StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
 SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
 SymbolicRegression = "8254be44-1295-4e6a-a16d-46603ac705cb"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
@@ -107,6 +108,7 @@ StableRNGs = "1"
 StaticArrays = "1"
 Statistics = "1"
 StatsPlots = "0.15"
+StochasticDiffEq = "6"
 SymbolicIndexingInterface = "0.3"
 SymbolicRegression = "1"
 Symbolics = "6, 7"
diff --git a/docs/src/showcase/gpu_spde.md b/docs/src/showcase/gpu_spde.md
index 40441b2760..932d919e0c 100644
--- a/docs/src/showcase/gpu_spde.md
+++ b/docs/src/showcase/gpu_spde.md
@@ -474,8 +474,9 @@ end
 Now we just define and solve the system of SDEs:
 
 ```@example spde
-prob = DE.SDEProblem(f, g, u0, (0.0, 100.0))
-@time sol = DE.solve(prob, DE.SRIW1());
+import StochasticDiffEq as SDE
+prob = SDE.SDEProblem(f, g, u0, (0.0, 100.0))
+@time sol = SDE.solve(prob, SDE.SRIW1());
 ```
 
 ```@example spde
diff --git a/docs/src/showcase/massively_parallel_gpu.md b/docs/src/showcase/massively_parallel_gpu.md
index e645cf3c15..4d3ebb97d9 100644
--- a/docs/src/showcase/massively_parallel_gpu.md
+++ b/docs/src/showcase/massively_parallel_gpu.md
@@ -83,7 +83,7 @@ Now, from this problem, we build an `EnsembleProblem` as per the DifferentialEqu
 specification. A `prob_func` jiggles the parameters and we solve 10_000 trajectories:
 
 ```@example diffeqgpu
-prob_func = (prob, ctx) -> ODE.remake(prob, p = (StaticArrays.@SVector rand(Float32, 3)) .* p)
+prob_func = (prob, i, repeat) -> ODE.remake(prob, p = (StaticArrays.@SVector rand(Float32, 3)) .* p)
 monteprob = DiffEqGPU.EnsembleProblem(prob, prob_func = prob_func, safetycopy = false)
 sol = ODE.solve(monteprob, ODE.Tsit5(), DiffEqGPU.EnsembleThreads(), trajectories = 10_000, saveat = 1.0f0)
 ```
diff --git a/docs/src/showcase/optimization_under_uncertainty.md b/docs/src/showcase/optimization_under_uncertainty.md
index f433d650d1..bfad4e7568 100644
--- a/docs/src/showcase/optimization_under_uncertainty.md
+++ b/docs/src/showcase/optimization_under_uncertainty.md
@@ -69,7 +69,7 @@ import Distributions
 cor_dist = Distributions.truncated(Distributions.Normal(0.9, 0.02), 0.9 - 3 * 0.02, 1.0)
 trajectories = 100
 
-prob_func(prob, ctx) = DE.remake(prob, p = [p[1], rand(cor_dist)])
+prob_func(prob, i, repeat) = DE.remake(prob, p = [p[1], rand(cor_dist)])
 ensemble_prob = DE.EnsembleProblem(prob, prob_func = prob_func)
 ensemblesol = DE.solve(ensemble_prob, DE.Tsit5(), DE.EnsembleThreads(), trajectories = trajectories,
     callback = cbs)