ANL-CEEESA/MIPLearn.jl
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Update README.md

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Alinson S. Xavier
2021-05-25 11:34:21 -05:00
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README.md
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@@ -178,10 +178,12 @@ end
In many situations, instances can be solved in parallel to accelerate the training process. MIPLearn.jl provides the method `parallel_solve!(solver, instances)` to easily achieve this. In many situations, instances can be solved in parallel to accelerate the training process. MIPLearn.jl provides the method `parallel_solve!(solver, instances)` to easily achieve this.
First, launch Julia in multi-process mode: First, launch Julia in multi-process mode:
``` ```
julia --procs 4 julia --procs 4
``` ```
Then run the following script:
Then call `parallel_solve!` as follows:
```julia ```julia
@everywhere using MIPLearn @everywhere using MIPLearn
@@ -195,7 +197,7 @@ test_instances = [...]
solver = LearningSolver(Cbc.Optimizer) solver = LearningSolver(Cbc.Optimizer)
# Solve training instances in parallel. The number of instances solved # Solve training instances in parallel. The number of instances solved
# simultaneously is the same as the `--procs` specified when running Julia. # simultaneously is the same as the `--procs` argument provided to Julia.
parallel_solve!(solver, training_instances) parallel_solve!(solver, training_instances)
# Train machine learning models # Train machine learning models
@@ -205,6 +207,7 @@ fit!(solver, training_instances)
parallel_solve!(solver, test_instances) parallel_solve!(solver, test_instances)
``` ```
**NOTE:** Only `FileInstance` instances are currently supported.
## 2. Customization ## 2. Customization