Manage training results, weights and data flow of your Tensorflow models
Manage 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.
>> pip install mlpipe-trainer
Setup - install MongoDB
MongoDB 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".
Follow the instructions on the MongoDB website for installation e.g. for Linux: https://docs.mongodb.com/manual/administration/install-on-linux/
# The config is used to specify the localhost connections # for saving trained models to the mongoDB as well as fetching training data from mlpipe.utils import Config Config.add_config('./path_to/config.ini')
Each Connection config consists of these fields in the .ini file
[example_mongo_db_connection] db_type=MongoDB url=localhost port=27017 user=read_write pwd=rw
from mlpipe.processors.i_processor import IPreProcessor from mlpipe.data_reader.mongodb import MongoDBGenerator class PreProcessData(IPreProcessor): def process(self, raw_data, input_data, ground_truth, piped_params=None): # Process raw_data to output input_data and ground_truth # which will be the input for the model ... return raw_data, input_data, ground_truth, piped_params train_data = [...] # consists of MongoDB ObjectIds that are used for training processors = [PreProcessData()] # Chain of Processors (in our case its just one) # Generator that can be used e.g. with keras' fit_generator() train_gen = MongoDBGenerator( ("connection_name", "cifar10", "train"), # specify data source from a MongoDB train_data, batch_size=128, processors=processors )
Data generators inherit from
tf.keras.utils.Sequence. Check out this tensorflow docu to find out how you can write your custom generators (e.g. for other data sources than MongoDB).
As long as there is a keras (tensorflow.keras) model in the end, there are no restrictions on this step
model = Sequential() model.add(Conv2D(32, (3, 3), padding='same', input_shape=(32, 32, 3))) ... model.add(Dense(10, activation='softmax')) opt = optimizers.RMSprop(lr=0.0001, decay=1e-6) model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=["accuracy"])
Training and Callbacks
from mlpipe.callbacks import SaveToMongoDB save_to_mongodb_cb = SaveToMongoDB(("localhost_mongo_db", "models"), "test", model) model.fit_generator( generator=train_gen, validation_data=val_gen, epochs=10, verbose=1, callbacks=[save_to_mongodb_cb], initial_epoch=0, )
SaveToMongoDB is a custom keras callback class as described in the tensorflow docu. Again, feel free to create custom callbacks for any specific needs.
If, 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.
A full Cifar10 example can be found in the example folder here
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