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Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com> Co-authored-by: architkulkarni <architkulkarni@users.noreply.github.com>
74 lines
2.4 KiB
ReStructuredText
74 lines
2.4 KiB
ReStructuredText
===============================
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Serving Machine Learning Models
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===============================
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.. contents::
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.. _serve-ml-batching:
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Request Batching
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================
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You can also have Ray Serve batch requests for performance, which is especially important for some ML models that run on GPUs.
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In order to do use this feature, you need to:
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1. Set the ``max_batch_size`` in the ``config`` dictionary.
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2. Modify your backend implementation to accept a list of requests and return a list of responses instead of handling a single request.
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.. code-block:: python
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class BatchingExample:
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def __init__(self):
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self.count = 0
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@serve.accept_batch
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def __call__(self, requests):
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responses = []
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for request in requests:
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responses.append(request.json())
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return responses
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config = {"max_batch_size": 5}
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client.create_backend("counter1", BatchingExample, config=config)
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client.create_endpoint("counter1", backend="counter1", route="/increment")
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Please take a look at :ref:`Batching Tutorial<serve-batch-tutorial>` for a deep
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dive.
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.. _serve-model-composition:
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Model Composition
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=================
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Ray Serve supports composing individually scalable models into a single model
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out of the box. For instance, you can combine multiple models to perform
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stacking or ensembles.
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To define a higher-level composed model you need to do three things:
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1. Define your underlying models (the ones that you will compose together) as
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Ray Serve backends
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2. Define your composed model, using the handles of the underlying models
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(see the example below).
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3. Define an endpoint representing this composed model and query it!
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In order to avoid synchronous execution in the composed model (e.g., it's very
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slow to make calls to the composed model), you'll need to make the function
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asynchronous by using an ``async def``. You'll see this in the example below.
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That's it. Let's take a look at an example:
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.. literalinclude:: ../../../python/ray/serve/examples/doc/snippet_model_composition.py
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Framework-Specific Tutorials
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============================
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Ray Serve seamlessly integrates with popular Python ML libraries.
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Below are tutorials with some of these frameworks to help get you started.
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- :ref:`PyTorch Tutorial<serve-pytorch-tutorial>`
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- :ref:`Scikit-Learn Tutorial<serve-sklearn-tutorial>`
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- :ref:`Keras and Tensorflow Tutorial<serve-tensorflow-tutorial>`
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- :ref:`RLlib Tutorial<serve-rllib-tutorial>`
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