Message Passing Service

Most of the time different components in AIToolbox operate either in isolation or communicate over specified APIs. While this is useful practice for error prevention in some cases less structured form of communication between components might be desired in order to simplify research development. One such example is the communication between different callbacks the user might provide to the TrainLoop. To support the convenient and easy development of callbacks and their communication the TrainLoop provides the message passing service implemented in aitoolbox.torchtrain.train_loop.components.message_passing.

MessageService Details

aitoolbox.torchtrain.train_loop.components.message_passing.MessageService is running as part of the TrainLoop and is exposed inside every provided callback via the self.message_service. When we want to pass some information from one callback to another callback (e.g. path where some intermediary results were saved) the sender callback has to send it into the MessageService by calling aitoolbox.torchtrain.train_loop.components.message_passing.MessageService.write_message() (inside the callback implementation that would be self.message_service.write_message()). Messages can be considered as a key-value pair with added message lifecycle setting.

Depending on the message lifecycle setting, the messages can be kept in the message service until the end of training, end of epoch or until first read. As such the message service allows the asynchronous and independent operation of callbacks enabling the users to add or remove callbacks from the training process as they will without running into interdependency issues. The message lifecycle settings can be imported from the aitoolbox.torchtrain.train_loop.components.message_passing. Currently supported settings are:





In addition to writing messages, the message service of course also supports the reading of the accumulated messages. This can be achieved in any TrainLoop component having access to the MessageService (callbacks included) by calling aitoolbox.torchtrain.train_loop.components.message_passing.MessageService.read_messages(). This method will return all the messages accumulated under the specified key.

In our earlier example of one callback writing a message with the path to the stored intermediary results, the second callbacks tasked with processing the results or maybe saving them to the cloud would read that message with the data path and execute it’s logic on the data originally provided by the first callback.

Example of MessageService in action

An actual example of such message passing between different callbacks can be observed in the implementations of aitoolbox.torchtrain.callbacks.performance_eval.ModelTrainHistoryPlot which sends the message containing the results path and the aitoolbox.torchtrain.callbacks.basic.EmailNotification which reads that message and uses the sent results path.