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        "author": "Plumerai",
        "author_email": "lukas@plumerai.co.uk",
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            "Development Status :: 2 - Pre-Alpha",
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            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering",
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        "description": "# Zookeeper\n\n[![Azure DevOps builds](https://img.shields.io/azure-devops/build/plumerai/larq/15.svg?logo=azure-devops)](https://plumerai.visualstudio.com/larq/_build/latest?definitionId=15&branchName=master) [![Azure DevOps coverage](https://img.shields.io/azure-devops/coverage/plumerai/larq/15.svg?logo=azure-devops)](https://plumerai.visualstudio.com/larq/_build/latest?definitionId=15&branchName=master) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/zookeeper.svg)](https://pypi.org/project/zookeeper/) [![PyPI](https://img.shields.io/pypi/v/zookeeper.svg)](https://pypi.org/project/zookeeper/) [![PyPI - License](https://img.shields.io/pypi/l/zookeeper.svg)](https://github.com/plumerai/zookeeper/blob/master/LICENSE) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) [![Join the community on Spectrum](https://withspectrum.github.io/badge/badge.svg)](https://spectrum.chat/larq)\n\nA small library for managing deep learning models, hyper parameters and datasets designed to make training deep learning models easy and reproducible.\n\n## Getting Started\n\nZookeeper allows you to build command line interfaces for training deep learning models with very little boiler plate using [click](https://click.palletsprojects.com/) and [TensorFlow Datasets](https://www.tensorflow.org/datasets/). It helps you structure your machine learning projects in a framework agnostic and effective way.\nZookeeper is heavily inspired by [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor) and [Fairseq](https://github.com/pytorch/fairseq/) but is designed to be used as a library making it lightweight and very flexible.\n\n### Installation\n\n```console\npip install zookeeper\npip install colorama  # optional for colored console output\n```\n\n### Registry\n\nZookeeper keeps track of data preprocessing, models and hyperparameters to allow you to reference them by name from the commandline.\n\n#### Datasets and Preprocessing\n\nTensorFlow Datasets provides [many popular datasets](https://www.tensorflow.org/datasets/datasets) that can be downloaded automatically.\nIn the following we will use [MNIST](http://yann.lecun.com/exdb/mnist) and define a `default` preprocessing for the images that scales the image to `[0, 1]` and uses one-hot encoding for the class labels:\n\n```python\nimport tensorflow as tf\nfrom zookeeper import cli, build_train, HParams, registry, Preprocessing\n\nclass ImageClassification(Preprocessing):\n    @property\n    def kwargs(self):\n        return {\n            \"input_shape\": self.features[\"image\"].shape,\n            \"num_classes\": self.features[\"label\"].num_classes,\n        }\n\n    def inputs(self, data):\n        return tf.cast(data[\"image\"], tf.float32)\n\n    def outputs(self, data):\n        return tf.one_hot(data[\"label\"], self.features[\"label\"].num_classes)\n\n\n@registry.register_preprocess(\"mnist\")\nclass default(ImageClassification):\n    def inputs(self, data):\n        return super().inputs(data) / 255\n```\n\n#### Models\n\nNext we will register a model called `cnn`. We will use the [Keras API](https://keras.io) for this:\n\n```python\n@registry.register_model\ndef cnn(hp, input_shape, num_classes):\n    return tf.keras.models.Sequential(\n        [\n            tf.keras.layers.Conv2D(\n                hp.filters[0], (3, 3), activation=hp.activation, input_shape=input_shape\n            ),\n            tf.keras.layers.MaxPooling2D((2, 2)),\n            tf.keras.layers.Conv2D(hp.filters[1], (3, 3), activation=hp.activation),\n            tf.keras.layers.MaxPooling2D((2, 2)),\n            tf.keras.layers.Conv2D(hp.filters[2], (3, 3), activation=hp.activation),\n            tf.keras.layers.Flatten(),\n            tf.keras.layers.Dense(hp.filters[3], activation=hp.activation),\n            tf.keras.layers.Dense(num_classes, activation=\"softmax\"),\n        ]\n    )\n```\n\n#### Hyperparameters\n\nFor each model we can register one or more hyperparameters sets that will be passed to the model function when called:\n\n```python\n@registry.register_hparams(cnn)\nclass basic(HParams):\n    activation = \"relu\"\n    batch_size = 32\n    filters = [64, 64, 64, 64]\n    learning_rate = 1e-3\n\n    @property\n    def optimizer(self):\n        return tf.keras.optimizers.Adam(self.learning_rate)\n```\n\n### Training loop\n\nTo train the models registered above we will need to write a custom training loop. Zookeeper will then tie everything together:\n\n```python\n@cli.command()\n@build_train()\ndef train(build_model, dataset, hparams, output_dir):\n    \"\"\"Start model training.\"\"\"\n    model = build_model(hparams, **dataset.preprocessing.kwargs)\n    model.compile(\n        optimizer=hparams.optimizer,\n        loss=\"categorical_crossentropy\",\n        metrics=[\"categorical_accuracy\", \"top_k_categorical_accuracy\"],\n    )\n\n    model.fit(\n        dataset.train_data(hparams.batch_size),\n        steps_per_epoch=dataset.train_examples // hparams.batch_size,\n        validation_data=dataset.validation_data(hparams.batch_size),\n        validation_steps=dataset.validation_examples // hparams.batch_size,\n    )\n```\n\nThis will register Click command called `train` which can be executed from the command line.\n\n### Command Line Interface\n\nTo make the file we just created executable we will add the following lines at the bottom:\n\n```python\nif __name__ == \"__main__\":\n    cli()\n```\n\nIf you want to register your models in separate files, make sure to import them before calling `cli` to allow zookeeper to properly register them. 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