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https://github.com/clearml/clearml
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62bc54d7be
Fix sub-process support Fix delete_after_upload option when uploading images Add logugu support Fix subsample plots if they are too big Fix requests for over 15mb
59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
# TRAINS - Example of manual graphs and statistics reporting
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#
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import numpy as np
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import logging
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from trains import Task
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task = Task.init(project_name='examples', task_name='Manual reporting')
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# example python logger
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logging.getLogger().setLevel('DEBUG')
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logging.debug('This is a debug message')
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logging.info('This is an info message')
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logging.warning('This is a warning message')
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logging.error('This is an error message')
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logging.critical('This is a critical message')
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# this is loguru test example
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try:
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from loguru import logger
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logger.debug("That's it, beautiful and simple logging! (using ANSI colors)")
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except ImportError:
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pass
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# get TRAINS logger object for any metrics / reports
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logger = task.get_logger()
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# log text
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logger.console("hello")
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# report scalar values
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logger.report_scalar("example_scalar", "series A", iteration=0, value=100)
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logger.report_scalar("example_scalar", "series A", iteration=1, value=200)
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# report histogram
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histogram = np.random.randint(10, size=10)
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logger.report_vector("example_histogram", "random histogram", iteration=1, values=histogram)
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# report confusion matrix
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confusion = np.random.randint(10, size=(10, 10))
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logger.report_matrix("example_confusion", "ignored", iteration=1, matrix=confusion)
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# report 2d scatter plot
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scatter2d = np.hstack((np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1))))
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logger.report_scatter2d("example_scatter", "series_xy", iteration=1, scatter=scatter2d)
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# report 3d scatter plot
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scatter3d = np.random.randint(10, size=(10, 3))
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logger.report_scatter3d("example_scatter_3d", "series_xyz", iteration=1, scatter=scatter3d)
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# report image
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m = np.eye(256, 256, dtype=np.uint8)*255
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logger.report_image_and_upload("test case", "image uint", iteration=1, matrix=m)
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m = np.eye(256, 256, dtype=np.float)
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logger.report_image_and_upload("test case", "image float", iteration=1, matrix=m)
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# flush reports (otherwise it will be flushed in the background, every couple of seconds)
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logger.flush()
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