Evaluation pipelines

An Evaluation Pipeline is used to evaluate a trained machine learning model. To use this pipeline, the package must contain code to evaluate a model (the evaluate() function in the train.py file). This code, together with a dataset or sub-folder within a dataset, produce a score (the return of the evaluate() function) and any arbitrary outputs the user would like to persist in addition to the score.

Creating evaluation pipelines

Create a new evaluation pipeline and make sure to provide the following evaluation pipeline specific information:

In the Pipeline type field, select Evaluation run.
In the Choose evaluation dataset field, select a dataset or folder from which you want to import data for evaluation. All files in this dataset/folder should be available locally during the runtime of the pipeline, being passed to the argument to your evaluate() function.
In the Enter parameters section, enter the environment variables defined and used by your pipeline, if any. The environment variables are:
- artifacts_directory, with default value artifacts: This defines the path to a directory that will be persisted as ancillary data related to this pipeline. Most, if not all users, will never have the need to override this through the UI. Anything can be saved during pipeline execution including images, pdfs, and subfolders. Concretely, any data your code writes in the directory specified by the path os.environ['artifacts_directory'] will be uploaded at the end of the pipeline run and will be viewable from the Pipeline details page.
- save_test_data, with default value false: If set to true, data_directory folder will be uploaded at the end of the pipeline run as an output of the pipeline under directory data_directory.
  Note: The pipeline execution might take some time. Check back to it after a while to see its status.
  
  After the pipeline was executed, in the Pipelines page, the pipeline's status changed to Successful. The Pipeline Details page displays the arbitrary files and folders related to the pipeline run. In our example, the run created a file called my-evaluate-artifact.txt.

Conceptual analogy for building your own evaluation pipeline

This example is a conceptually analogous execution of an evaluation pipeline on some package, for example version 1.1, the output of a training pipeline on version 1.0.

Note: This is a simplified example. Its purpose is to illustrate how datasets and packages interact in an evaluation pipeline. The steps are merely conceptual and do not represent how the platform works.

Copy package version 1.1 into ~/mlpackage.
Copy the evaluation dataset or the dataset subfolder selected from the UI to ~/mlpackage/evaluation_data.

Execute the following Python code:

 from train import Main 
m = Main() 
score = m.evaluate('./evaluation_data')from train import Main 
m = Main() 
score = m.evaluate('./evaluation_data')

The returned score is surfaced in the grid showing pipelines and the results.json file.

Persist artifacts if written, snapshot data if save_test_data is set to true.

Pipeline outputs

The _results.json file contains a summary of the pipeline run execution, exposing all inputs/outputs and execution times for an evaluation pipeline.

 {
    "parameters": {
        "pipeline": "< Pipeline_name >",
        "inputs": {
            "package": "<Package_name>",
            "version": "<version_number>",
            "evaluation_data": "<storage_directory>",
            "gpu": "True/False"
        },
        "env": {
            "key": "value",
            ...
        }
    },
    "run_summary": {
     "execution_time": <time>, #in seconds 
     "start_at": <timestamp>, #in seconds 
     "end_at": <timestamp>, #in seconds 
     "outputs": {
        "score": <score>, #float
        "train_data": "<test_storage_directory>", 
        "evaluation_data": "<test_storage_directory>/None", 
        "artifacts_data": "<artifacts_storage_directory>",
         }
    }
}{
    "parameters": {
        "pipeline": "< Pipeline_name >",
        "inputs": {
            "package": "<Package_name>",
            "version": "<version_number>",
            "evaluation_data": "<storage_directory>",
            "gpu": "True/False"
        },
        "env": {
            "key": "value",
            ...
        }
    },
    "run_summary": {
     "execution_time": <time>, #in seconds 
     "start_at": <timestamp>, #in seconds 
     "end_at": <timestamp>, #in seconds 
     "outputs": {
        "score": <score>, #float
        "train_data": "<test_storage_directory>", 
        "evaluation_data": "<test_storage_directory>/None", 
        "artifacts_data": "<artifacts_storage_directory>",
         }
    }
}

Artifacts folder, visible only if not empty, is a folder regrouping all the artifacts generated by the pipeline and saved under the artifacts_directory folder.

Dataset folder, existing only if save_data was set to the default true value, is a copy of the evaluation dataset folder.

Model governance

As in training pipelines, a user can set the parameter save_test_data = true to snapshot data passed in for evaluation.

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