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doc: update metrics and artifacts docs
Update documentation for metrics and artifacts documentation to show how these may be added by a plugin.
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@ -172,6 +172,21 @@ This is what WA uses to store a single metric collected from executing a workloa
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metric beyond current iteration (e.g. this can be used
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to identify sub-tests).
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Metrics can be added to WA output via the context:
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.. code-block:: python
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context.add_metric("score", 9001)
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context.add_metric("time", 2.35, "seconds", lower_is_better=True)
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You only need to specify the name and the value for the metric. Units and
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classifiers are optional, and, if not specified otherwise, it will be assumed
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that higher values are better.
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The metric will be added to the result for the current job, if there is one;
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otherwise, it will be added to the overall run result.
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.. _artifact:
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Artifacts
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@ -219,6 +234,32 @@ also helps WA decide how it should be handled. Currently supported kinds are:
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processing -- this is left entirely up to the output
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processors.
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As with :ref:`metrics`, artifacts are added via the context:
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.. code-block:: python
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context.add_artifact("benchmark-output", "bech-out.txt", kind="raw",
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description="stdout from running the benchmark")
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.. note:: The file *must* exist on the host by the point at which the artifact
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is added, otherwise an error will be raised.
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The artifact will be added to the result of the current job, if there is one;
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otherwise, it will be added to the overall run result. In some situations, you
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may wish to add an artifact to the overall run while being inside a job context,
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this can be done with ``add_run_artifiact``:
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.. code-block:: python
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context.add_run_artifact("score-summary", "scores.txt", kind="export",
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description="""
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Summary of the scores so far. Updated after
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every job.
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""")
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In this case, you also need to make sure that the file represented by the
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artifact is written to the output directory for the run and not the current job.
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.. _resource-resolution:
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Dynamic Resource Resolution
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