netket.logging#
This module contains the loggers that can be used with drivers by passing them to
the out= keyword argument of run().
The base class follows the API protocol declared here. You should reimplement this class to make a logger that works well together with our drivers.
Abstract base class detailing the interface that loggers must implement in order to work with netket drivers. |
While the loggers available for simulations are the following:
This logger accumulates log data in a set of nested dictionaries which are stored in memory. |
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This logger serializes expectation values and other log data to a JSON file and can save the latest model parameters in MessagePack encoding to a separate file. |
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A logger which serializes the variables of the variational state during a run. |
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Creates a tensorboard logger using tensorboardX's summarywriter. |
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HDF5 Logger, that can be passed with keyword argument logger to Monte Carlo drivers in order to serialize the output data of the simulation. |
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Logger that streams metrics and optional model checkpoints to an MLflow tracking server. |
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Callback to save the variational state at fixed intervals. |
In the netket.experimental module there are also some experimental loggers such as the HDF5 logger