{"data":{"markdownRemark":{"html":"

The RunnerBackend and ModelRunner classes need to be used together to setup and run a simulation that can run multiple times.

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This guide assumes you have a Model created which implements simudyne.core.Model\nThe following imports are assumed

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Imports required for multirun (Java)

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import simudyne.core.runner.ModelRunner;\nimport simudyne.core.runner.RunResult;\nimport simudyne.core.runner.RunnerBackend;
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The RunnerBackend class is needed to set up the ModelRunner. The default RunnerBackend can be created by using the RunnerBackend create method. This will create an instance of the RunnerBackend that can be used to run the model locally.

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Create a model runner (Java)

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RunnerBackend runnerBackend = RunnerBackend.create();
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Create the ModelRunner for a specific model with RunnerBackend#forModel passing the class of the model or RunnerBackend#forConfig passing the ModelConfiguration for the model

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Create a model runner for a specific model (Java)

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ModelRunner modelRunner = runnerBackend.forModel(myModel.class);
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ModelRunner for Batch simulations

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Use the ModelRunner to set the number of ticks(steps) to run each run of the model for, and the number of runs (number of times to run the full simulation).

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withInput can optionally be used to set values of @Input variables in the model.

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Setting inputs (Java)

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modelRunner\n    .forRuns(100)\n    .forTicks(50)\n    .withInput(\"{\\\"myBooleanInput\\\": false}\")\n    .withInput(\"{\\\"myStringInput\\\": \\\"aString\\\"}\");
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To run the model and wait for the results, use ModelRunner#run. Alternatively, the model can be run as an asynchronous process in the background. This means that while its running, the progress can be tracked.

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// To run the model and wait for it to complete\nRunResult runResult = modelRunner.run();\n\n// To run the model a a background process and track the progress\nFuture<RunResult> runResultFuture = modelRunner.runAsync();\nmodelRunner.getProgress();\n// Wait for final result\nRunResult runResult = Await.result(runResultFuture, Duration.Inf());
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Improving multirun performance

\nThe multirun results are stored in memory to be returned to the user. If only the file output matters, this behaviour can be disabled in order to improve performance by setting the config field 'core.return-data' to false.\n

To read more on flags and configuration, see Model Config.

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If this field is set to false, the returned RunResult will be empty.

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ModelRunner for Scenario simulations

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A scenario is a simulation that can be setup to run a series of ticks with different input parameters for each tick.\nEach series can be run multiple times with a different seed.

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The current implementation of scenario does not possess a Java API. The following example will guide you through a simple scenario simulation.\nIf you wish to see all scenario's features, please use the REST API documentation

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We need to register the model and start the server.

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  Server.register(\"Game of Life\", GameOfLife.class);\n  Server.run();
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The model is now available for us to query. In this example, we will update gridSize of the Game of Life Model.\nWe will set two runs with different seeds.

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The parameters sent to the servers are:

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curl -X POST \\\n  http://localhost:8080/api/simulations/scenario \\\n  -H 'Cache-Control: no-cache' \\\n  -H 'Content-Type: application/json' \\\n  -d '{\n  \"modelName\": \"Game of Life\", \n  \"output\": {\"uri\": \"/tmp/scenarios\"},\n  \"scenarios\":[{\n    \"seeds\": [1234, 2314]\n    \"scenarioData\": {\n        \"0\": {\"gridSize\":10},\n        \"10\": {}\n      }\n  }]\n}'
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The server will return the following:

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{\"id\":\"f78057c4-f7f2-477b-bed1-3a690c7be864\",\"name\":\"Game of Life\",\"description\":\"\",\"public\":false,\"session\":\"744d5b5b-ac16-4361-be3e-da5836c234bb\",\"kind\":\"pojo\"}
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Now that this simulation has completed, go in the /tmp/scenarios/ we specified. Open the folder named as the id returned by the server, then runs. Inside are 2 files and 2 folders.

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We only have one scenario, and two runs, this information is present in the folders names:

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In the results.json file in each of the results folder, you can find the seed that we setup earlier as well as all data output.\nTo have more details about the simulation output, refer to the documentation about the output directory structure.

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Running a distributed multirun

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Using Spark

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Running a distributed multirun simulation depends on different packages and imports.

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These elements can be found in the spark requirement tutorial and the spark runner tutorial tutorials.

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To setup a distributed multirun, create a new instance of the SparkRunnerBackend as the RunnerBackend. All other methods are the same as for the local RunnerBackend.

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Multirun with spark (Java)

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RunnerBackend runnerBackend = new SparkRunnerBackend();\nModelRunner modelRunner = runnerBackend.forModel(myModel.class);
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Extracting information from the RunResult is explained in Multirun Output

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