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https://github.com/clearml/clearml
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43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
from time import sleep
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import pandas as pd
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import numpy as np
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from PIL import Image
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from trains import Task
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task = Task.init('examples', 'artifacts toy')
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df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
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'num_wings': [2, 0, 0, 0],
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'num_specimen_seen': [10, 2, 1, 8]},
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index=['falcon', 'dog', 'spider', 'fish'])
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# Register Pandas object as artifact to watch
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# (it will be monitored in the background and automatically synced and uploaded)
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task.register_artifact('train', df, metadata={'counting': 'legs', 'max legs': 69})
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# change the artifact object
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df.sample(frac=0.5, replace=True, random_state=1)
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# or access it from anywhere using the Task's get_registered_artifacts()
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Task.current_task().get_registered_artifacts()['train'].sample(frac=0.5, replace=True, random_state=1)
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# add and upload pandas.DataFrame (onetime snapshot of the object)
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task.upload_artifact('Pandas', artifact_object=df)
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# add and upload local file artifact
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task.upload_artifact('local file', artifact_object='samples/dancing.jpg')
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# add and upload dictionary stored as JSON)
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task.upload_artifact('dictionary', df.to_dict())
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# add and upload Numpy Object (stored as .npz file)
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task.upload_artifact('Numpy Eye', np.eye(100, 100))
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# add and upload Image (stored as .png file)
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im = Image.open('samples/dancing.jpg')
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task.upload_artifact('pillow_image', im)
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# add and upload a folder, artifact_object should be the folder path
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task.upload_artifact('local folder', artifact_object='samples/')
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# do something
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sleep(1.)
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print(df)
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# we are done
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print('Done')
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