Many Openlayer tests are based on your model outputs. Therefore, if you plan to evaluate your model, when you set up pushes to Openlayer, you must either:
  • provide a way for Openlayer to run your model on your datasets, or
  • before pushing, generate the model outputs yourself and push them alongside your artifacts.
The most conventional option is to provide a way for Openlayer to run your model on your datasets. The setup is simple by leveraging Openlayer’s SDKs and a few commands in the openlayer.json. This guide explains how model output generation works with Openlayer. We also explain how to generate the outputs yourself, if that’s your preferred path.

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Providing a way for Openlayer to run your model on your datasets

Openlayer uses the information provided in the openlayer.json to run your model on your datasets. To do so, it goes through the following steps:
1

Runtime setup

Set up the runtime environment specified in the runtime field from your openlayer.json. Then, it runs the installCommand from your openlayer.json, to install your dependencies.
2

Run the model

Run the batchCommand from your openlayer.json.
The expectation is that the batchCommand iterates through your datasets, runs your models in each of them, and creates the directory specified in outputDirectory that has the following structure: Output directory structure where {dataset[i].name} is the name of the i-th dataset specified in the datasets array in the openlayer.json, dataset.json is the corresponding dataset with an extra column with the model outputs, and config.json is a config file for the dataset. If you are leveraging one of Openlayer’s SDKs, you don’t need to worry about the output directory structure or the configs.
You can browse a template from our Template gallery that feels closest to your use case and see what the openlayer.json and the run script look like using Openlayer’s SDKs.
With Openlayer’s SDKs, your batchCommand should call a script you wrote and append it with 
--dataset-path {{ path }} --output-dir {{ outputDirectory }}/{{ name }}
Our SDKs abstract away the code that:
  1. parses command line arguments --dataset-path and --output-dir so it knows which dataset to generate batch outputs on, and where to write the generated outputs.
  2. loads the dataset specified in --dataset-path into memory and calls your code that generates outputs for a single row.
  3. writes the generated outputs along with additional fields and the input data to a dataset.json (or CSV) file to a directory that adheres to the output directory structure presented above.
This allows you to just focus on writing a method that can generate outputs for a single row.

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How Openlayer checks if it should compute outputs

Regardless of the method you choose, right after you push artifacts to the Openlayer platform, it checks if the directory specified as the outputDirectory in the model section of your openlayer.json exists and if it contains the output files Openlayer expects. If both conditions are satisfied, Openlayer interprets this as signaling that you already ran your model on your datasets before pushing. Therefore, Openlayer will not try to compute the model predictions again. However, if one of the conditions above is not satisfied, Openlayer will try to compute your model outputs for your datasets.