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Copy file name to clipboardExpand all lines: README-INPUT-OUTPUT.md
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***discardTopBottom** (number between 0.0 and 0.4, by default is 0.2): By default, the state machine will discard the top/bottom 20% of "outliers" (the fastest and slowest), to filter out the effects of cold starts that would bias the overall averages. You can customize this parameter by providing a value between 0 and 0.4, with 0 meaning no results are discarded and 0.4 meaning that 40% of the top/bottom results are discarded (i.e. only 20% of the results are considered).
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***sleepBetweenRunsMs** (integer) If provided, the time in milliseconds that the tuner function will sleep/wait after invoking your function, but before carrying out the Post-Processing step, should that be provided. This could be used if you have aggressive downstream rate limits you need to respect. By default this will be set to 0 and the function won't sleep between invocations. Setting this value will have no effect if running the invocations in parallel.
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***disablePayloadLogs** (boolean) If provided and set to a truthy value, suppresses `payload` from error messages and logs. If `preProcessorARN` is provided, this also suppresses the output payload of the pre-processor.
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***includeOutputResults** (boolean) If provided and set to true, the average cost and average duration for every power value configuration will be included in the state machine output.
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## State machine configuration (at deployment time)
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***results.stateMachine.executionCost**: the AWS Step Functions cost corresponding to this state machine execution (fixed value for "worst" case)
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***results.stateMachine.lambdaCost**: the AWS Lambda cost corresponding to this state machine execution (depending on `num` and average execution time)
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***results.stateMachine.visualization**: if you visit this autogenerated URL, you will be able to visualize and inspect average statistics about cost and performance; important note: average statistics are NOT shared with the server since all the data is encoded in the URL hash ([example](https://lambda-power-tuning.show/#gAAAAQACAAQABsAL;ZooQR4yvkUa/pQRGRC5zRaADHUVjOftE;QdWhOEMkoziDT5Q4xhiIOMYYiDi6RNc4)), which is available only client-side
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***results.stats**: the average duration and cost for every tested power value configuration (only included if `includeOutputResults` is set to a truthy value)
## What results can I expect from Lambda Power Tuning?
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The state machine will generate a visualization of average cost and speed for each power configuration.
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How to interpret the chart above: execution time goes from 2.4s with 128MB to 300ms with 1GB, for the very same average cost.
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## How to deploy the state machine
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## How to deploy the state machine
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There are a few options documented [here](README-DEPLOY.md).
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## How to execute the state machine
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You can execute the state machine manually or programmatically, see the documentation [here](README-EXECUTE.md).
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## State Machine Input and Output
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Here's a typical execution input with basic parameters:
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Full output documentation [here](README-INPUT-OUTPUT.md#user-content-state-machine-output).
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## Data visualization
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You can visually inspect the tuning results to identify the optimal tradeoff between cost and performance.
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[Here](README-ADVANCED.md) you can find out more about some advanced features of this project, its internals, and some considerations about security and execution cost.
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## CHANGELOG (SAR versioning)
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From most recent to oldest, with major releases in bold:
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****1.0.0*** (2019-05-13): AWS SAM refactor (published on SAR)
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