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43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
# TRAINS - Example of manual model configuration and uploading
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#
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import os
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import tempfile
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import tensorflow as tf
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from trains import Task
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task = Task.init(project_name='examples', task_name='Model configuration and upload')
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model = tf.Module()
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# Connect a local configuration file
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config_file = os.path.join('..', '..', 'reporting', 'data_samples', 'sample.json')
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config_file = task.connect_configuration(config_file)
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# then read configuration as usual, the backend will contain a copy of it.
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# later when executing remotely, the returned `config_file` will be a temporary file
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# containing a new copy of the configuration retrieved form the backend
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# # model_config_dict = json.load(open(config_file, 'rt'))
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# Or Store dictionary of definition for a specific network design
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model_config_dict = {
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'value': 13.37,
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'dict': {'sub_value': 'string', 'sub_integer': 11},
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'list_of_ints': [1, 2, 3, 4],
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}
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model_config_dict = task.connect_configuration(model_config_dict)
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# We now update the dictionary after connecting it, and the changes will be tracked as well.
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model_config_dict['new value'] = 10
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model_config_dict['value'] *= model_config_dict['new value']
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# store the label enumeration of the training model
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labels = {'background': 0, 'cat': 1, 'dog': 2}
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task.connect_label_enumeration(labels)
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# storing the model, it will have the task network configuration and label enumeration
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print('Any model stored from this point onwards, will contain both model_config and label_enumeration')
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tempdir = tempfile.mkdtemp()
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tf.saved_model.save(model, os.path.join(tempdir, "model"))
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print('Model saved')
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