diff --git a/src/rulesets/LinearAlgebra/structured.jl b/src/rulesets/LinearAlgebra/structured.jl
index 245335774..4de6b9a5b 100644
--- a/src/rulesets/LinearAlgebra/structured.jl
+++ b/src/rulesets/LinearAlgebra/structured.jl
@@ -1,6 +1,5 @@
 # Structured matrices
 using LinearAlgebra: AbstractTriangular
-using SparseInverseSubset
 
 # Matrix wrapper types that we know are square and are thus potentially invertible. For
 # these we can use simpler definitions for `/` and `\`.
diff --git a/src/rulesets/SparseArrays/sparsematrix.jl b/src/rulesets/SparseArrays/sparsematrix.jl
index 06de7a135..0f16fd6db 100644
--- a/src/rulesets/SparseArrays/sparsematrix.jl
+++ b/src/rulesets/SparseArrays/sparsematrix.jl
@@ -50,94 +50,99 @@ function rrule(::typeof(findnz), v::AbstractSparseVector)
     return (I, V), findnz_pullback
 end
 
-if VERSION < v"1.7"
-    #=
-    The method below for `logabsdet(F::UmfpackLU)` is required to calculate the (log) 
-    determinants of sparse matrices, but was not defined prior to Julia v1.7. In order
-    for the rrules for the determinants of sparse matrices below to work, they need to be
-    able to compute the primals as well, so this import from the future is included. For
-    more recent versions of Julia, this definition lives in:
-    julia/stdlib/SuiteSparse/src/umfpack.jl
-    =#
-    using SuiteSparse.UMFPACK: UmfpackLU
-
-    # compute the sign/parity of a permutation
-    function _signperm(p)
-        n = length(p)
-        result = 0
-        todo = trues(n)
-        while any(todo)
-            k = findfirst(todo)
-            todo[k] = false
-            result += 1 # increment element count
-            j = p[k]
-            while j != k
+if Base.USE_GPL_LIBS # Don't define rrules for sparse determinants if we don't have CHOLMOD from SuiteSparse.jl
+        
+    if VERSION < v"1.7"
+        #=
+        The method below for `logabsdet(F::UmfpackLU)` is required to calculate the (log) 
+        determinants of sparse matrices, but was not defined prior to Julia v1.7. In order
+        for the rrules for the determinants of sparse matrices below to work, they need to be
+        able to compute the primals as well, so this import from the future is included. For
+        more recent versions of Julia, this definition lives in:
+        julia/stdlib/SuiteSparse/src/umfpack.jl
+        =#
+        using SuiteSparse.UMFPACK: UmfpackLU
+    
+        # compute the sign/parity of a permutation
+        function _signperm(p)
+            n = length(p)
+            result = 0
+            todo = trues(n)
+            while any(todo)
+                k = findfirst(todo)
+                todo[k] = false
                 result += 1 # increment element count
-                todo[j] = false
-                j = p[j]
+                j = p[k]
+                while j != k
+                    result += 1 # increment element count
+                    todo[j] = false
+                    j = p[j]
+                end
+                result += 1 # increment cycle count
             end
-            result += 1 # increment cycle count
+            return ifelse(isodd(result), -1, 1)
         end
-        return ifelse(isodd(result), -1, 1)
-    end
 
-    function LinearAlgebra.logabsdet(F::UmfpackLU{T, TI}) where {T<:Union{Float64,ComplexF64},TI<:Union{Int32, Int64}} 
-        n = checksquare(F)
-        issuccess(F) || return log(zero(real(T))), zero(T)
-        U = F.U
-        Rs = F.Rs
-        p = F.p
-        q = F.q
-        s = _signperm(p)*_signperm(q)*one(real(T))
-        P = one(T)
-        abs_det = zero(real(T))
-        @inbounds for i in 1:n
-            dg_ii = U[i, i] / Rs[i]
-            P *= sign(dg_ii)
-            abs_det += log(abs(dg_ii))
+        using SparseInverseSubset
+    
+        function LinearAlgebra.logabsdet(F::UmfpackLU{T, TI}) where {T<:Union{Float64,ComplexF64},TI<:Union{Int32, Int64}} 
+            n = checksquare(F)
+            issuccess(F) || return log(zero(real(T))), zero(T)
+            U = F.U
+            Rs = F.Rs
+            p = F.p
+            q = F.q
+            s = _signperm(p)*_signperm(q)*one(real(T))
+            P = one(T)
+            abs_det = zero(real(T))
+            @inbounds for i in 1:n
+                dg_ii = U[i, i] / Rs[i]
+                P *= sign(dg_ii)
+                abs_det += log(abs(dg_ii))
+            end
+            return abs_det, s * P
         end
-        return abs_det, s * P
     end
-end
-
-
-function rrule(::typeof(logabsdet), x::SparseMatrixCSC)
-    F = cholesky(x)
-    L, D, U, P = SparseInverseSubset.get_ldup(F)
-    Ω = logabsdet(D)
-    function logabsdet_pullback(ΔΩ)
-        (Δy, Δsigny) = ΔΩ
-        (_, signy) = Ω
-        f = signy' * Δsigny
-        imagf = f - real(f)
-        g = real(Δy) + imagf
-        Z, P = sparseinv(F, depermute=true)
-        ∂x = g * Z'
-        return (NoTangent(), ∂x)
+    
+    
+    function rrule(::typeof(logabsdet), x::SparseMatrixCSC)
+        F = cholesky(x)
+        L, D, U, P = SparseInverseSubset.get_ldup(F)
+        Ω = logabsdet(D)
+        function logabsdet_pullback(ΔΩ)
+            (Δy, Δsigny) = ΔΩ
+            (_, signy) = Ω
+            f = signy' * Δsigny
+            imagf = f - real(f)
+            g = real(Δy) + imagf
+            Z, P = sparseinv(F, depermute=true)
+            ∂x = g * Z'
+            return (NoTangent(), ∂x)
+        end
+        return Ω, logabsdet_pullback
     end
-    return Ω, logabsdet_pullback
-end
-
-function rrule(::typeof(logdet), x::SparseMatrixCSC)
-    Ω = logdet(x)
-    function logdet_pullback(ΔΩ)
-        Z, p = sparseinv(x, depermute=true)
-        ∂x = ΔΩ * Z'
-        return (NoTangent(), ∂x)
+    
+    function rrule(::typeof(logdet), x::SparseMatrixCSC)
+        Ω = logdet(x)
+        function logdet_pullback(ΔΩ)
+            Z, p = sparseinv(x, depermute=true)
+            ∂x = ΔΩ * Z'
+            return (NoTangent(), ∂x)
+        end
+        return Ω, logdet_pullback
     end
-    return Ω, logdet_pullback
-end
-
-function rrule(::typeof(det), x::SparseMatrixCSC)
-    Ω = det(x)
-    function det_pullback(ΔΩ)
-        Z, _ = sparseinv(x, depermute=true)
-        ∂x = Z' * dot(Ω, ΔΩ)
-        return (NoTangent(), ∂x)
+    
+    function rrule(::typeof(det), x::SparseMatrixCSC)
+        Ω = det(x)
+        function det_pullback(ΔΩ)
+            Z, _ = sparseinv(x, depermute=true)
+            ∂x = Z' * dot(Ω, ΔΩ)
+            return (NoTangent(), ∂x)
+        end
+        return Ω, det_pullback
     end
-    return Ω, det_pullback
-end
-
+    
+end # rrules that depend on CHOLMOD
 
 function rrule(::typeof(spdiagm), m::Integer, n::Integer, kv::Pair{<:Integer,<:AbstractVector}...)
 
diff --git a/test/rulesets/SparseArrays/sparsematrix.jl b/test/rulesets/SparseArrays/sparsematrix.jl
index 283452a8a..ea0cf5199 100644
--- a/test/rulesets/SparseArrays/sparsematrix.jl
+++ b/test/rulesets/SparseArrays/sparsematrix.jl
@@ -79,12 +79,14 @@ end
     test_rrule(findnz, v ⊢ dv, output_tangent=(zeros(length(I)), V̄))
 end
 
-@testset "[log[abs[det]]] SparseMatrixCSC" begin
-    ii = [1:5; 2; 4]
-    jj = [1:5; 4; 2]
-    x = [ones(5); 0.1; 0.1]
-    A = sparse(ii, jj, x)
-    test_rrule(logabsdet, A)
-    test_rrule(logdet, A)
-    test_rrule(det, A)
+if Base.USE_GPL_LIBS # these rrules don't work without CHOLMOD from SuiteSparse.jl
+    @testset "[log[abs[det]]] SparseMatrixCSC" begin
+        ii = [1:5; 2; 4]
+        jj = [1:5; 4; 2]
+        x = [ones(5); 0.1; 0.1]
+        A = sparse(ii, jj, x)
+        test_rrule(logabsdet, A)
+        test_rrule(logdet, A)
+        test_rrule(det, A)
+    end
 end