# Use pmap(f,x) when f takes multiple arguments?
**URL:** https://discourse.julialang.org/t/use-pmap-f-x-when-f-takes-multiple-arguments/65761
**Category:** General Usage
**Tags:** question, distributed, pmap
**Created:** [August 3, 2021, 10:46pm UTC](https://discourse.julialang.org/t/use-pmap-f-x-when-f-takes-multiple-arguments/65761 "2021-08-03T22:46:26Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@mkarikom](https://discourse.julialang.org/u/mkarikom)
#### Post date: [August 3, 2021, 10:46pm UTC](https://discourse.julialang.org/t/use-pmap-f-x-when-f-takes-multiple-arguments/65761/1 "2021-08-03T22:46:27Z")
How can I call `pmap(f,x)` where `f` is a function that takes additional parameters?
I have a function `f(x,y)` where `y` is always known and I want to run `pmap` on elements of `x`.
Here is an example that works (without distributed):
```julia
infil> map(mean,collect(eachrow(SharedArray(rand(1:10,10,10)))))
10-element Vector{Float64}:
4.2
4.9
6.9
6.5
5.0
5.3
5.9
6.1
5.4
4.4
```
But the distributed version has errors:
```julia
infil> pmap(x->x.>1,collect(eachrow(SharedArray(rand(1:10,10,10)))))
ERROR: On worker 34:
UndefVarError: #340#341 not defined
Stacktrace:
[1] deserialize_datatype
@ /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Serialization/src/Serialization.jl:1280
[2] handle_deserialize
@ /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Serialization/src/Serialization.jl:827
[3] deserialize
@ /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Serialization/src/Serialization.jl:774
[4] handle_deserialize
@ /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Serialization/src/Serialization.jl:834
[5] deserialize
@ /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Serialization/src/Serialization.jl:774 [inlined]
[6] deserialize_msg
@ /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Distributed/src/messages.jl:87
[7] #invokelatest#2
@ ./essentials.jl:708 [inlined]
[8] invokelatest
@ ./essentials.jl:706 [inlined]
[9] message_handler_loop
@ /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Distributed/src/process_messages.jl:169
[10] process_tcp_streams
@ /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Distributed/src/process_messages.jl:126
[11] #99
@ ./task.jl:406
Stacktrace:
[1] (::Base.var"#837#839")(x::Task)
@ Base ./asyncmap.jl:177
[2] foreach(f::Base.var"#837#839", itr::Vector{Any})
@ Base ./abstractarray.jl:2141
[3] maptwice(wrapped_f::Function, chnl::Channel{Any}, worker_tasks::Vector{Any}, c::Vector{SubArray{Int64, 1, SharedArrays.SharedMatrix{Int64}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}})
@ Base ./asyncmap.jl:177
[4] wrap_n_exec_twice
@ ./asyncmap.jl:153 [inlined]
[5] #async_usemap#822
@ ./asyncmap.jl:103 [inlined]
[6] #asyncmap#821
@ ./asyncmap.jl:81 [inlined]
[7] pmap(f::Function, p::WorkerPool, c::Vector{SubArray{Int64, 1, SharedArrays.SharedMatrix{Int64}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}; distributed::Bool, batch_size::Int64, on_error::Nothing, retry_delays::Vector{Any}, retry_check::Nothing)
@ Distributed /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Distributed/src/pmap.jl:126
[8] pmap(f::Function, p::WorkerPool, c::Vector{SubArray{Int64, 1, SharedArrays.SharedMatrix{Int64}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}})
@ Distributed /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Distributed/src/pmap.jl:101
[9] pmap(f::Function, c::Vector{SubArray{Int64, 1, SharedArrays.SharedMatrix{Int64}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}}; kwargs::Base.Iterators.Pairs{Union{}, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ Distributed /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Distributed/src/pmap.jl:156
[10] pmap(f::Function, c::Vector{SubArray{Int64, 1, SharedArrays.SharedMatrix{Int64}, Tuple{Int64, Base.Slice{Base.OneTo{Int64}}}, true}})
@ Distributed /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Distributed/src/pmap.jl:156
[11] top-level scope
@ none:1
```
Note that when `f` takes a single argument, this problem does not arise:
```julia
infil> pmap(mean,collect(eachrow(SharedArray(rand(1:10,10,10)))))
10-element Vector{Float64}:
5.7
5.2
5.1
4.7
6.2
4.4
6.8
5.7
4.3
5.3
```
---
### Author:  [@sidpatil](https://discourse.julialang.org/u/sidpatil)
#### Post date: [August 3, 2021, 11:19pm UTC](https://discourse.julialang.org/t/use-pmap-f-x-when-f-takes-multiple-arguments/65761/2 "2021-08-03T23:19:42Z")
Try declaring the anonymous function as a named function instead, and make sure its definition is made available to all worker processes. See [Multi-processing and Distributed Computing ยท The Julia Language](https://docs.julialang.org/en/v1/manual/distributed-computing/#code-availability).
---
### Author:  [@mkarikom](https://discourse.julialang.org/u/mkarikom)
#### Post date: [August 4, 2021, 3:16am UTC](https://discourse.julialang.org/t/use-pmap-f-x-when-f-takes-multiple-arguments/65761/3 "2021-08-04T03:16:37Z")
Hi @sidpatil, the issue with the approach of putting a named function inside a module, then exporting the module to workers ([as described in the docs you linked](https://docs.julialang.org/en/v1/manual/distributed-computing/#code-availability)), is that the something like:
```julia
module foo
export f
f(x) = x .> 1 # f(x,y) where y=1
end
```
would need to be loaded outside of any existing functions in my code. but the value of `y` in my application is local to one particular function.
thanks in advance for any workaround that can accommodate this.
---
### Author:  [@lawless-m](https://discourse.julialang.org/u/lawless-m)
#### Post date: [November 17, 2021, 9:44am UTC](https://discourse.julialang.org/t/use-pmap-f-x-when-f-takes-multiple-arguments/65761/4 "2021-11-17T09:44:22Z")
You need a currying function to build a partial application of f(x, y)
```julia
julia> @everywhere curry(f, y) = x->f(x, y)
julia> @everywhere addxy(x, y) = x + y
julia> pmap(curry(addxy, 5), 1:5)
5-element Vector{Int64}:
6
7
8
9
10
julia> pmap(curry(addxy, 15), 1:5)
5-element Vector{Int64}:
16
17
18
19
20
```