wehub-resource-sync
108 lines
3.9 KiB
ReStructuredText
108 lines
3.9 KiB
ReStructuredText
How To Train and Deploy Image Classifier with MLflow and Keras
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--------------------------------------------------------------
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In this example we demonstrate how to train and deploy image classification models with MLflow.
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We train a VGG16 deep learning model to classify flower species from photos using a `dataset
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<http://download.tensorflow.org/example_images/flower_photos.tgz>`_ available from `tensorflow.org
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<http://www.tensorflow.org>`_. Note that although we use Keras to train the model in this case,
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a similar approach can be applied to other deep learning frameworks such as ``PyTorch``.
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The MLflow model produced by running this example can be deployed to any MLflow supported endpoints.
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All the necessary image preprocessing is packaged with the model. The model can therefore be applied
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to image data directly. All that is required in order to pass new data to the model is to encode the
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image binary data as base64 encoded string in pandas DataFrame (standard interface for MLflow python
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function models). The included Python scripts demonstrate how the model can be deployed to a REST
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API endpoint for realtime evaluation or to Spark for batch scoring..
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In order to include custom image pre-processing logic with the model, we define the model as a
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custom python function model wrapping around the underlying Keras model. The wrapper provides
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necessary preprocessing to convert input data into multidimensional arrays expected by the
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Keras model. The preprocessing logic is stored with the model as a code dependency. Here is an
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example of the output model directory layout:
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.. code-block:: bash
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tree model
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::
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model
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├── MLmodel
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├── code
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│ └── image_pyfunc.py
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├── data
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│ └── image_model
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│ ├── conf.yaml
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│ └── keras_model
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│ ├── MLmodel
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│ ├── conda.yaml
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│ └── model.h5
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└── mlflow_env.yml
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The example contains the following files:
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* MLproject
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Contains definition of this project. Contains only one entry point to train the model.
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* conda.yaml
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Defines project dependencies. NOTE: You might want to change tensorflow package to tensorflow-gpu
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if you have gpu(s) available.
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* train.py
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Main entry point of the projects. Handles command line arguments and possibly downloads the
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dataset.
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* image_pyfunc.py
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The implementation of the model train and also of the outputed custom python flavor model. Note
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that the same preprocessing code that is used during model training is packaged with the output
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model and is used during scoring.
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* score_images_rest.py
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Score an image or a directory of images using a model deployed to a REST endpoint.
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* score_images_spark.py
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Score an image or a directory of images using model deployed to Spark.
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Running this Example
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^^^^^^^^^^^^^^^^^^^^
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To train the model, run the example as a standard MLflow project:
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.. code-block:: bash
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mlflow run examples/flower_classifier
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This will download the training dataset from ``tensorflow.org``, train a classifier using Keras and
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log results with MLflow.
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To test your model, run the included scoring scripts. For example, say your model was trained with
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run_id ``101``.
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- To test REST api scoring do the following two steps:
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1. Deploy the model as a local REST endpoint by running ``mlflow models serve``:
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.. code-block:: bash
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# deploy the model to local REST api endpoint
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mlflow models serve --model-uri runs:/101/model --port 54321
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1. Apply the model to new data using the provided score_images_rest.py script:
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.. code-block:: bash
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# score the deployed model
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python score_images_rest.py --host http://127.0.0.1 --port 54321 /path/to/images/for/scoring
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- To test batch scoring in Spark, run score_images_spark.py to score the model in Spark like this:
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.. code-block:: bash
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python score_images_spark.py --model-uri runs:/101/model /path/to/images/for/scoring
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