sconce.monitors package

sconce.monitors.base module

class sconce.monitors.base.CompositeMonitor(monitors)

Bases: sconce.monitors.base.Monitor

A monitor composed of two or more monitors. Using this allows you to pass a single monitor object to a trainer method and have it use all of the composed monitors. Composed monitors can be accessed using their name like so:

>>> from sconce import monitors
>>> metric_names = {'training_loss': 'loss', 'validation_loss': 'val_loss'}
>>> stdout_monitor = monitors.StdoutMonitor(metric_names=metric_names)
>>> dataframe_monitor = monitors.DataframeMonitor()
>>> monitor = dataframe_monitor + stdout_monitor
>>> monitor.dataframe_monitor
<sconce.monitors.dataframe_monitor.DataframeMonitor at 0x7fb1fbd498d0>
>>> dataframe_monitor is monitor.dataframe_monitor
True

Parameters:monitors (iterable of Monitor) – the monitors you want to compose together.

add_monitor(other)

end_session(**kwargs)

start_session(num_steps, **kwargs)

write(data, step, **kwargs)

class sconce.monitors.base.Monitor(name)

Bases: abc.ABC

Base class for monitors in sconce. A monitor is an object that a Trainer uses to record metrics during training or other tasks. This base class defines the interface that trainers use. Monitors can be composed together (using addition operator) to produce a CompositeMonitor object.

Parameters:name (str) – used to gain access to a monitor when it has been composed together into a CompositeMonitor.

end_session(**kwargs)

Called by a Trainer when a training/evaluation session has ended.

Parameters:**kwargs – must be accepted to allow for future use cases.

start_session(num_steps, **kwargs)

Called by a Trainer when starting a training/evaluation session.

Parameters:

• num_steps (int) – [1, inf) the number of update/evaluation steps to expect.
• **kwargs – must be accepted to allow for future use cases.

write(data, step, **kwargs)

Called by a Trainer during a training/evaluation session just after the training/evaluation step.

Parameters:

• data (dict) – the output of the training/evaluation step. The keys may include, but are not limited to: {‘training_loss’, ‘validation_loss’, ‘learning_rate’}.
• step (float) – (0.0, inf) the step that was just completed. Fractional steps are possible (see batch_multiplier option on sconce.trainer.Trainer.train()).
• **kwargs – must be accepted to allow for future use cases.

sconce.monitors.dataframe_monitor module

class sconce.monitors.dataframe_monitor.DataframeMonitor(df=None, metadata=None, blacklist=['._inputs', '._outputs', '._targets'], name='dataframe_monitor')

Bases: sconce.monitors.base.Monitor

df

classmethod from_file(filename, key)

is_blacklisted(key)

plot(title='Training History', figsize=(15, 5), skip_first=100, smooth_window=50, metrics=['loss'], hyperparameters=['learning_rate'], validation_color='tomato', training_color='mediumseagreen', fig=None)

plot_learning_rate_survey(ax=None, figure_kwargs={}, **plot_kwargs)

save(filename, key)

start_session(num_steps, **kwargs)

write(data, step, **kwargs)

sconce.monitors.losswise_monitor module

class sconce.monitors.losswise_monitor.LosswiseMonitor(api_key, tag, params={}, min_graphs={'loss': {'training_loss': 'Training Loss', 'validation_loss': 'Validation Loss'}, 'lr': {'learning_rate': 'Learning Rate'}}, max_graphs={}, name='losswise_monitor')

Bases: sconce.monitors.base.Monitor

start_session(num_steps, **kwargs)

write(data, step, **kwargs)

sconce.monitors.ringbuffer_monitor module

class sconce.monitors.ringbuffer_monitor.RingbufferMonitor(capacity=100, key='training_loss', name='ringbuffer_monitor')

Bases: sconce.monitors.base.Monitor

mean(*args, **kwargs)

movement_index

start_session(num_steps, **kwargs)

std(*args, **kwargs)

write(data, step, **kwargs)

sconce.monitors.stdout_monitor module

class sconce.monitors.stdout_monitor.StdoutMonitor(metric_names, progbar_kwargs={}, name='stdout_monitor')

Bases: sconce.monitors.base.Monitor

start_session(num_steps, **kwargs)

write(data, step, **kwargs)