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inference using URL #576

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inference using URL #576

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inference using URL
Pratichhya Jun 17, 2024
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Pratichhya Jun 21, 2024
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Pratichhya Jun 24, 2024
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26 changes: 16 additions & 10 deletions docs/machine_learning.rst
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Expand Up @@ -89,30 +89,36 @@ Finally execute this whole training flow as a batch job::
training_job = model.create_job()
training_job.start_and_wait()


Inference
----------

When the batch job finishes successfully, the trained model can then be used
with the ``predict_random_forest`` process on the raster data cube
(or another cube with the same band structure) to classify all the pixels.

Thus, the training job results is inspected to fetch the trained models url::
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from openeo.rest.job import JobResults
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results : JobResults = training_job.get_results()
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links = results.get_metadata()['links']
ml_model_metadata_url = [link for link in links if 'ml_model_metadata.json' in link['href']][0]['href']
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Why getting the model URL from the links? Isn't this an asset in the batch job results?

Also, this "ml_model_metadata.json" looks like a VITO-specific implementation detail. Can this be generalized. Or at least documented why you are looking for that string?

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This is within the metadata indeed but different than the Assets's link. The link in the assets is as tar.gz . It throws a JSON error when trying to load it using that specific url and same is with the canonical link.
ref: Open-EO/openeo-geopyspark-driver#562

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If I understand save_ml_model correctly (https://github.com/Open-EO/openeo-processes/pull/441/files#diff-0aae18f348c05c0f26c5caee2dcb0ca4af218a55287c9cbf8a286fc35b8faae5) there should be a asset that is a STAC Item that is to be used with load_ml_model.

that 'ml_model_metadata.json' reference in the current docs is highly implementation specific and should be avoided

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I guess we are missing some specification to allow proper extraction of the model's URL, so I started the discussion at https://github.com/Open-EO/openeo-processes/pull/441/files/b162040dea04acd12fad73166274bbff370ed29b#r1650820880

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We need a better solution for

[link for link in links if 'ml_model_metadata.json' in link['href']][0]['href']

this construct is quite brittle and implementation specific.
Also note that https://github.com/Open-EO/openeo-processes/pull/441/files/b162040dea04acd12fad73166274bbff370ed29b#r1650872242 explicitly says that this the ML Model extension is outdated and going to be replaced, so it is very likely this is going to break

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This is apparently the next-gen ML extension

I think we better make sure we make this snippet future-proof

print(ml_model_metadata_url)


Next, load the model from the URL::

from openeo.rest.mlmodel import MlModel
saved_model = MlModel.load_ml_model(connection=connection, id=ml_model_metadata_url)
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Technically, the openEO ``predict_random_forest`` process has to be used as a reducer function
inside a ``reduce_dimension`` call, but the openEO Python client library makes it
a bit easier by providing a :py:meth:`~openeo.rest.datacube.DataCube.predict_random_forest` method
directly on the :py:class:`~openeo.rest.datacube.DataCube` class, so that you can just do::

predicted = cube.predict_random_forest(
model=training_job.job_id,
model = saved_model,
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dimension="bands"
)

predicted.download("predicted.GTiff")


We specified the model here by batch job id (string),
but it can also be specified in other ways:
as :py:class:`~openeo.rest.job.BatchJob` instance,
as URL to the corresponding STAC Item that implements the `ml-model` extension,
or as :py:class:`~openeo.rest.mlmodel.MlModel` instance (e.g. loaded through
:py:meth:`~openeo.rest.connection.Connection.load_ml_model`).
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