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    "info": {
        "author": "Johannes Dobler",
        "author_email": "jdobler@protonmail.com",
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        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6"
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        "description": "<img src=\"docs/assets/mlpipe_icon_full.png\" width=\"500\">\n\n# MLPipe-Trainer\n\nManage your Data Pipline and Tensorflow & Keras models with MLPipe. It is NOT another \"wrapper\" around Tensorflow, but rather adds utilities to setup an environment to control data flow and managed trained models (weights & results) with the help of MongoDB.</br>\n\n```bash\n>> pip install mlpipe-trainer\n```\n\n## Setup - install MongoDB\nMongoDB database is used to store trained Models including their weights and results. Additionally there is also a data reader for MongoDB implemented (basically just a generator as you know and love from using keras). Currenlty that is the only implemented data reader working \"out of the box\".</br>\nFollow the instructions on the MongoDB website for installation e.g. for Linux: https://docs.mongodb.com/manual/administration/install-on-linux/\n\n## Code Examples\n\n#### Config\n```python\n# The config is used to specify the localhost connections\n# for saving trained models to the mongoDB as well as fetching training data\nfrom mlpipe.utils import Config\nConfig.add_config('./path_to/config.ini')\n```\nEach Connection config consists of these fields in the .ini file\n```ini\n[example_mongo_db_connection]\ndb_type=MongoDB\nurl=localhost\nport=27017\nuser=read_write\npwd=rw\n```\n\n#### Data Pipline\n```python\nfrom mlpipe.processors.i_processor import IPreProcessor\nfrom mlpipe.data_reader.mongodb import MongoDBGenerator\n\nclass PreProcessData(IPreProcessor):\n    def process(self, raw_data, input_data, ground_truth, piped_params=None):\n        # Process raw_data to output input_data and ground_truth\n        # which will be the input for the model\n        ...\n        return raw_data, input_data, ground_truth, piped_params\n\ntrain_data = [...]  # consists of MongoDB ObjectIds that are used for training\nprocessors = [PreProcessData()]  # Chain of Processors (in our case its just one)\n# Generator that can be used e.g. with keras' fit_generator()\ntrain_gen = MongoDBGenerator(\n    (\"connection_name\", \"cifar10\", \"train\"),  # specify data source from a MongoDB\n    train_data,\n    batch_size=128,\n    processors=processors\n)\n```\nData generators inherit from `tf.keras.utils.Sequence`. Check out this [tensorflow docu](https://www.tensorflow.org/api_docs/python/tf/keras/utils/Sequence) to find out how you can write your custom generators (e.g. for other data sources than MongoDB).\n\n#### Model\nAs long as there is a keras (tensorflow.keras) model in the end, there are no restrictions on this step\n```python\nmodel = Sequential()\nmodel.add(Conv2D(32, (3, 3), padding='same', input_shape=(32, 32, 3)))\n...\nmodel.add(Dense(10, activation='softmax'))\n\nopt = optimizers.RMSprop(lr=0.0001, decay=1e-6)\nmodel.compile(optimizer=opt, loss='categorical_crossentropy', metrics=[\"accuracy\"])\n```\n\n#### Training and Callbacks\n```python\nfrom mlpipe.callbacks import SaveToMongoDB\n\nsave_to_mongodb_cb = SaveToMongoDB((\"localhost_mongo_db\", \"models\"), \"test\", model)\n\nmodel.fit_generator(\n    generator=train_gen,\n    validation_data=val_gen,\n    epochs=10,\n    verbose=1,\n    callbacks=[save_to_mongodb_cb],\n    initial_epoch=0,\n)\n```\n`SaveToMongoDB` is a custom keras callback class as described in the [tensorflow docu](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/Callback). Again, feel free to create custom callbacks for any specific needs.</br>\nIf, instead of `fit_generator()`, each batch is trained one-by-one e.g. with a native tensorflow model, you can still loop over the generator. Just remember to call the callback methods at the specific steps e.g. `on_batch_end()`.\n\nA full Cifar10 example can be found in the example folder [here](https://github.com/j-o-d-o/MLPipe-Trainer/tree/master/examples/cifar10)\n\n## Road Map\n- Create and generat MkDocs documentation & host documentation\n- Add tests\n- Set Up CI\n\n\n",
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        "name": "mlpipe-trainer",
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