mirror of
https://github.com/clearml/clearml
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47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
# ClearML - Example of manual graphs and statistics reporting
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#
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from clearml import Task, Logger
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def report_scalars(logger):
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# type: (Logger) -> ()
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"""
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reporting scalars to scalars section
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:param logger: The task.logger to use for sending the scalars
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"""
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# report two scalar series on the same graph
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for i in range(100):
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logger.report_scalar("unified graph", "series A", iteration=i, value=1./(i+1))
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logger.report_scalar("unified graph", "series B", iteration=i, value=10./(i+1))
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# report two scalar series on two different graphs
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for i in range(100):
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logger.report_scalar("graph A", "series A", iteration=i, value=1./(i+1))
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logger.report_scalar("graph B", "series B", iteration=i, value=10./(i+1))
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def main():
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# Connecting ClearML with the current process,
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# from here on everything is logged automatically
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task = Task.init(project_name="examples", task_name="Scalar reporting")
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print('reporting scalar graphs')
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# Get the task logger,
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# You can also call Task.current_task().get_logger() from anywhere in your code.
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logger = task.get_logger()
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# report scalars
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report_scalars(logger)
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# force flush reports
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# If flush is not called, reports are flushed in the background every couple of seconds,
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# and at the end of the process execution
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logger.flush()
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print('We are done reporting, have a great day :)')
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if __name__ == "__main__":
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main()
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