README.md

MEDYANSimRunner.jl

Manage long running restartable MEDYAN.jl simulations.

Simulations run using julia code in a main.jl script and write outputs to an output directory.

Inspired by how build scripts work in https://github.com/JuliaPackaging/BinaryBuilder.jl

Installation

First install and run Julia https://julialang.org/downloads/

Then in Julia install this repo as a regular Julia package.

import Pkg
Pkg.add("MEDYANSimRunner")

Warning: MEDYANSimRunner may be incompatible with a future release of Julia.

Specifically MEDYANSimRunner uses ccall :jl_fs_rename and expects copy(Random.default_rng()) to be Xoshiro. These are Julia internals.

Example

Run the following in the root of this repo.

julia --project=test -e 'using Pkg; pkg"dev ."; pkg"instantiate";'
JULIA_LOAD_PATH="@" julia --project=test --startup-file=no test/example/main.jl --out=test/output --batch=1 --continue

This will run the 1st batch of the example simulation in test/example/main.jl with the test/ environment and store the output in test/output/.

The output directory will be created if it doesn't already exist.

If the "--batch=<job index>" option is not included, all jobs specified in main.jl will be run.

main.jl script

This file contains the julia functions used when running the simulation. These functions can modify the input state variable, but in general should return the state. These functions can also use the default random number generator, this will automatically saved and loaded.

At the end of main.jl there should be the lines:

if abspath(PROGRAM_FILE) == @__FILE__
    MEDYANSimRunner.run(ARGS; jobs, setup, loop, load, save, done)
end

To run the simulation if main.jl is called as a julia script.

Standard input parameters.

• step::Int: starts out at 0 after setup and is auto incremented right after every loop.

jobs::Vector{String}

A vector of jobs to run. Each job represents one variant of the simulation that can be run. This is useful if many simulations need to be run in parallel. The "--batch=<job index>" argument can be used to pick just one job to run.

The selected job string gets passed to the setup function in main.jl. The job string is also used to seed the default RNG right before setup is called.

setup(job::String; kwargs...) -> header_dict, state

Return the header dictionary to be written as the header.json file in output trajectory. Also return the state that gets passed on to loop and the state that gets passed to save and load.

job::String: The job. This is used for multi job simulations.

save(step::Int, state; kwargs...)-> group::SmallZarrGroups.ZGroup

Return the state of the system as a SmallZarrGroups.ZGroup This function should not mutate state

load(step::Int, group::SmallZarrGroups.ZGroup, state; kwargs...) -> state

Load the state saved by save This function can mutate state. state may be the state returned from setup or the state returned by loop. This function should return the same output if state is the state returned by loop or the state returned by setup.

done(step::Int, state; kwargs...) -> done::Bool, expected_final_step::Int

Return true if the simulation is done, or false if loop should be called again.

Also return the expected value of step when done will first be true, used for displaying the simulation progress.

This function should not mutate state

loop(step::Int, state; output::SmallZarrGroups.ZGroup, kwargs...) -> state

Return the state that gets passed to save and load

Optionally, mutate the output keyword argument. When saving the snapshot, this group will get saved as "out"

Main loop pseudo code

activate and instantiate the environment
include("main.jl")
create output directory based on job if it doesn't exist
Random.seed!(collect(reinterpret(UInt64, sha256(job))))
job_header, state =  setup(job)
save job_header
step = 0
group = ZGroup(childern=Dict("snap" => save(step, state))
SmallZarrGroups.save_zip(snapshot_zip_file, group)
state = load(step, SmallZarrGroups.load_zip(snapshot_zip_file)["snap"], state)
while true
    output = ZGroup()
    state = loop(step, state; output)
    step = step + 1
    group = ZGroup(childern=Dict("snap"=>save(step, state), "out"=>output)
    SmallZarrGroups.save_zip(snapshot_zip_file, group)
    state = load(step, SmallZarrGroups.load_zip(snapshot_zip_file)["snap"], state)
    if done(step::Int, state)[1]
        break
    end
end

output directory

The output directory has a subdirectory for each job's output. The job string is the name of the subdirectory.

Each job's output subdirectory has the following files.

logs/<timestamp_randomstring>/{info|warn|error}.log

Any logs, warnings, and errors generated by the simulation are saved in these files.

traj/header.json

A description of the system.

traj/snap<step>.zarr.zip

Contains snap$i.zarr.zip files where i is the step of the simulation. The state returned by setup is stored in snap0.zarr.zip The user data is stored in the "snap" sub group. The root group contains some metadata used by MEDYANSimRunner.

traj/footer.json

This is created to show a trajectory is complete. It contains some metadata about the trajectory.