ryanthompson591 commented on code in PR #22250: URL: https://github.com/apache/beam/pull/22250#discussion_r921251940
########## website/www/site/content/en/documentation/sdks/python.md: ########## @@ -46,7 +46,13 @@ new I/O connectors. See the [Developing I/O connectors overview](/documentation/ for information about developing new I/O connectors and links to language-specific implementation guidance. -## Using Beam Python SDK in your ML pipelines +## Making machine learning inferences with Python + +To integrate machine learning models into your pipelines for making inferences, use the RunInference API for PyTorch and Scikit-learn models. If you are using TensorFlow models, you can make use of the +[library from `tfx_bsl`](https://github.com/tensorflow/tfx-bsl/tree/master/tfx_bsl/beam). Further integrations for TensorFlow are planned. For more information, see [Beam issue #21442](https://github.com/apache/beam/issues/21442). Review Comment: You could say --- Further integrations for popular machine learning frameworks like Tensorflow are planned. But I'm wondering if its worth saying anything at all, since this documentation could end up out of date soon. ########## website/www/site/content/en/documentation/sdks/python-machine-learning.md: ########## @@ -0,0 +1,198 @@ +--- +type: languages +title: "Apache Beam Python Machine Learning" +--- +<!-- +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +--> + +# Machine Learning + +You can use Apache Beam with the RunInference API to use machine learning (ML) models to do local and remote inference with batch and streaming pipelines. Starting with Apache Beam 2.40.0, PyTorch and Scikit-learn frameworks are supported. You can create multiple types of transforms using the RunInference API: the API takes multiple types of setup parameters from model handlers, and the parameter type determines the model implementation. + +## Why use the RunInference API? + +RunInference takes advantage of existing Apache Beam concepts, such as the the `BatchElements` transform and the `Shared` class, to enable you to use models in your pipelines to create transforms optimized for machine learning inferences. The ability to create arbitrarily complex workflow graphs also allows you to build multi-model pipelines. + +### BatchElements PTransform + +To take advantage of the optimizations of vectorized inference that many models implement, we added the `BatchElements` transform as an intermediate step before making the prediction for the model. This transform batches elements together. The batched elements are then applied with a transformation for the particular framework of RunInference. For example, for numpy `ndarrays`, we call `numpy.stack()`, and for torch `Tensor` elements, we call `torch.stack()`. + +To customize the settings for `beam.BatchElements`, in `ModelHandler`, override the `batch_elements_kwargs` function. For example, use `min_batch_size` to set the lowest number of elements per batch or `max_batch_size` to set the highest number of elements per batch. + +For more information, see the [`BatchElements` transform documentation](https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.util.html#apache_beam.transforms.util.BatchElements). + +### Shared helper class + +Instead of loading a model for each thread in the process, we use the `Shared` class, which allows us to load one model that is shared across all threads of each worker in a DoFn. For more information, see the +[`Shared` class documentation](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/utils/shared.py#L20). + +### Multi-model pipelines + +The RunInference API can be composed into multi-model pipelines. Multi-model pipelines can be useful for A/B testing or for building out ensembles that are comprised of models that perform tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, language detection, coreference resolution, and more. + +## Modify a pipeline to use an ML model + +To use the RunInference transform, add the following code to your pipeline: + +``` +from apache_beam.ml.inference.base import RunInference + +with pipeline as p: + predictions = ( p | 'Read' >> beam.ReadFromSource('a_source') + | 'RunInference' >> RunInference(<model_handler>) +``` +Where `model_handler` is the model handler setup code. + +To import models, you need to wrap them around a `ModelHandler` object. Which `ModelHandler` you import depends on the framework and type of data structure that contains the inputs. The following examples show some ModelHandlers that you might want to import. + +``` +from apache_beam.ml.inference.sklearn_inference import SklearnModelHandlerNumpy +from apache_beam.ml.inference.sklearn_inference import SklearnModelHandlerPandas +from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerTensor +from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerKeyedTensor +``` +### Use pre-trained models + +The section provides requirements for using pre-trained models with PyTorch and Scikit-learn + +#### PyTorch + +You need to provide a path to a file that contains the model saved weights. This path must be accessible by the pipeline. To use pre-trained models with the RunInference API and the PyTorch framework, complete the following steps: + +1. Download the pre-trained weights and host them in a location that the pipeline can access. +2. Pass the path of the model to the PyTorch `model_handler` by using the following code: `state_dict_path=<path_to_weights>`. + +#### Scikit-learn + +You need to provide a path to a file that contains the pickled Scikit-learn model. This path must be accessible by the pipeline. To use pre-trained models with the RunInference API and the Scikit-learn framework, complete the following steps: + +1. Download the pickled model class and host it in a location that the pipeline can access. +2. Pass the path of the model to the Sklearn `model_handler` by using the following code: + `model_uri=<path_to_pickled_file>` and `model_file_type: <ModelFileType>`, where you can specify + `ModelFileType.PICKLE` or `ModelFileType.JOBLIB`, depending on how the model was serialized. + +### Use multiple models + +You can also use the RunInference transform to add multiple inference models to your pipeline. + +#### A/B Pattern + +``` +with pipeline as p: + data = p | 'Read' >> beam.ReadFromSource('a_source') + model_a_predictions = data | RunInference(<model_handler_A>) + model_b_predictions = data | RunInference(<model_handler_B>) +``` + +Where `model_handler_A` and `model_handler_B` are the model handler setup code. + +#### Ensemble Pattern + +``` +with pipeline as p: + data = p | 'Read' >> beam.ReadFromSource('a_source') + model_a_predictions = data | RunInference(<model_handler_A>) + model_b_predictions = model_a_predictions | beam.Map(some_post_processing) | RunInference(<model_handler_B>) +``` + +Where `model_handler_A` and `model_handler_B` are the model handler setup code. + +### Use a keyed ModelHandler + +If a key is attached to the examples, wrap the `KeyedModelHandler` around the `ModelHandler` object: + +``` +from apache_beam.ml.inference.base import KeyedModelHandler + +keyed_model_handler = KeyedModelHandler(PytorchModelHandlerTensor(...)) + +with pipeline as p: + data = p | beam.Create([ + ('img1', torch.tensor([[1,2,3],[4,5,6],...])), + ('img2', torch.tensor([[1,2,3],[4,5,6],...])), + ('img3', torch.tensor([[1,2,3],[4,5,6],...])), + ]) + predictions = data | RunInference(KeyedModelHandler) +``` + +### Use the PredictionResults object + +When doing a prediction in Apache Beam, the output `PCollection` includes both the keys of the input examples and the inferences. Including both these items in the output allows you to find the input that determined the predictions. + +The `PredictionResult` is a `NamedTuple` object that contains both the input and the inferences, named `example` and `inference`, respectively. When keys are passed with the input data to the RunInference transform, the output `PCollection` returns a `Tuple[str, PredictionResult]`, which is the key and the `PredictionResult` object. Your pipeline interacts with a `PredictionResult` object in steps after the RunInference transform. + +``` +class PostProcessor(beam.DoFn): + def process(self, element: Tuple[str, PredictionResult]): + key, prediction_result = element + inputs = prediction_result.example + predictions = prediction_result.inference + + # Post-processing logic + result = ... + + yield (key, result) + +with pipeline as p: + output = ( + p | 'Read' >> beam.ReadFromSource('a_source') + | 'PyTorchRunInference' >> RunInference(<keyed_model_handler>) + | 'ProcessOutput' >> beam.ParDo(PostProcessor())) +``` + +If you need to use this object explicitly, include the following line in your pipeline to import the object: + +``` +from apache_beam.ml.inference.base import PredictionResult +``` + +For more information, see the [`PredictionResult` documentation](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/ml/inference/base.py#L65). + +## Run a machine learning pipeline + +For detailed instructions explaining how to build and run a pipeline that uses ML models, see the +[Example RunInference API pipelines](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference) on GitHub. + +## Troubleshooting + +If you run into problems with your pipeline or job, this section lists issues that you might encounter and provides suggestions for how to fix them. + +### Incorrect inferences in the PredictionResult object + +In some cases, the `PredictionResults` output might not include the correct predictions in the `inferences` field. This issue occurs when you use a model whose inferences return a dictionary that maps keys to predictions and other metadata. An example return type is `Dict[str, Tensor]`. + +The RunInference API currently expects outputs to be an `Iterable[Any]`. Example return types are `Iterable[Tensor]` or `Iterable[Dict[str, Tensor]]`. When RunInference zips the inputs with the predictions, the predictions iterate over the dictionary keys instead of the batch elements. The result is that the key name is preserved but the prediction tensors are discarded. For more information, see the [Pytorch RunInference PredictionResult is a Dict](https://github.com/apache/beam/issues/22240) issue in the Apache Beam GitHub project. + +To work with the current RunInference implementation, you can create a wrapper class that overrides the `model(input)` call. In PyTorch, for example, your wrapper would override the `forward()` function and return an output with the appropriate format of `List[Dict[str, torch.Tensor]]`. For more information, see our [HuggingFace language modeling example](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/inference/pytorch_language_modeling.py#L49). + +### Unable to batch tensor elements + +RunInference uses dynamic batching. However, the RunInference API cannot batch tensor elements of different sizes, because `torch.stack()` expects tensors of the same length. If you provide images of different sizes or word embeddings of different lengths, errors might occur. Review Comment: Yes, that is a good generalization for sklearn as well. Going forward it may not always going to be the case, TF might have some leeway with the way it sets up protos, but it is a good troubleshooting thing to think about in general. -- This is an automated message from the Apache Git Service. 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