# Defining MLJ pipelines within a function
**URL:** https://discourse.julialang.org/t/defining-mlj-pipelines-within-a-function/60552
**Category:** Machine Learning
**Created:** [May 4, 2021, 11:26pm UTC](https://discourse.julialang.org/t/defining-mlj-pipelines-within-a-function/60552 "2021-05-04T23:26:42Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@ablaom](https://discourse.julialang.org/u/ablaom)
#### Post date: [May 4, 2021, 11:26pm UTC](https://discourse.julialang.org/t/defining-mlj-pipelines-within-a-function/60552/1 "2021-05-04T23:26:42Z")
Question asked on slack:
> Hello, I’m new to Julia and MLJ. Is there a recommended way to compose models within functions? I tried placing the code from the “ _Lightning Tour_ ” within a function of my own package, however when compiling that package I’m getting `iterated_booster not defined` when I can see it _ **is** _ defined (or at least I think it is…).
Here’s the relevant code posted by the commenter:
```julia
module Slotter
using MLJ
using MLJIteration
using EvoTrees
function main()
Booster = @load EvoTreeRegressor # loads code defining a model type
booster = Booster(max_depth = 2) # specify hyper-parameter at construction
booster.nrounds = 50 # or mutate post facto
iterated_booster = IteratedModel(
model = booster,
resampling = Holdout(fraction_train = 0.8),
controls = [Step(2), NumberSinceBest(3), NumberLimit(300)],
measure = l1,
retrain = true,
)
pipe = @pipeline ContinuousEncoder iterated_booster
max_depth_range =
range(pipe, :(deterministic_iterated_model.model.max_depth), lower = 1, upper = 10)
self_tuning_pipe = TunedModel(
model = pipe,
tuning = RandomSearch(),
ranges = max_depth_range,
resampling = CV(nfolds = 3, rng = 456),
measure = l1,
acceleration = CPUThreads(),
n = 50,
)
X, y = @load_reduced_ames
mach = machine(self_tuning_pipe, X, y)
evaluate!(
mach,
measures = [l1, l2],
resampling = CV(nfolds = 5, rng = 123),
acceleration = CPUThreads(),
verbosity = 2,
)
end
```
---
### Author:  [@ablaom](https://discourse.julialang.org/u/ablaom)
#### Post date: [May 4, 2021, 11:29pm UTC](https://discourse.julialang.org/t/defining-mlj-pipelines-within-a-function/60552/2 "2021-05-04T23:29:50Z")
The problem is that macros evaluate their arguments in the global scope. In your code `@pipeline ContinuousEncoder iterated_model` throws an error, because `iterated_model` is defined in the function and not in global scope. There is an open issue (which I couldn’t find just now) to re-implement pipelines without macros. In the meantime, there are various work-arounds. The most robust would be to use the more general learning network syntax described in [this manual section](https://alan-turing-institute.github.io/MLJ.jl/dev/composing_models/#Learning-Networks), “exporting” your learning network using Method II (no macro). There is admittedly a wee bit of a learning curve here. Simpler workarounds might exist but will depend on what exactly it is you want to do.
---
### Author:  [@CameronBieganek](https://discourse.julialang.org/u/CameronBieganek)
#### Post date: [May 5, 2021, 2:57am UTC](https://discourse.julialang.org/t/defining-mlj-pipelines-within-a-function/60552/3 "2021-05-05T02:57:58Z")
Here’s the relevant github issue, for anyone interested:
[https://github.com/alan-turing-institute/MLJ.jl/issues/594](https://github.com/alan-turing-institute/MLJ.jl/issues/594)
---
### Author:  [@mwlp](https://discourse.julialang.org/u/mwlp)
#### Post date: [May 5, 2021, 5:06pm UTC](https://discourse.julialang.org/t/defining-mlj-pipelines-within-a-function/60552/4 "2021-05-05T17:06:22Z")
Awesome, thanks to everyone 🙂