Manual Model Upload

The manual_model_upload.py example demonstrates Trains tracking of a manually configured model created with TensorFlow, including model checkpoints (snapshots), hyperparameters, and output to the console. When the script runs, it creates an experiment named Model configuration and upload, which is associated with the examples project.

Configure Trains for model checkpoints (model snapshot) storage in any of the following ways (debug sample storage is different):

Configuration

This example shows two ways to connect a configuration, using the same method, Task.connect_configuration.

Connect a configuration file by providing the file's path. Trains Server stores a copy of the file.

# Connect a local configuration file
config_file = os.path.join('..', '..', 'reporting', 'data_samples', 'sample.json')
config_file = task.connect_configuration(config_file)

Or, create a configuration dictionary and provide the dictionary.

model_config_dict = {
    'value': 13.37,
    'dict': {'sub_value': 'string', 'sub_integer': 11},
    'list_of_ints': [1, 2, 3, 4],
}
model_config_dict = task.connect_configuration(model_config_dict)

If the configuration changes, Trains track it.

model_config_dict['new value'] = 10
model_config_dict['value'] *= model_config_dict['new value']

The configuration appears in CONFIGURATIONS > CONFIGURATON OBJECTS.

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In the Trains Web (UI), the output model's configuration appears in ARTIFACTS > Output Model.

Artifacts

Model artifacts associated with the experiment appear in the experiment info panel (in the EXPERIMENTS tab), and in the model info panel (in the MODELS tab).

The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).

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The model info panel contains the model details, including the model design (which is also in the experiment info panel), the class label enumeration, model URL, framework, and snapshot locations.

General model information

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Class label enumeration

Connect a class label enumeration dictionary by calling the Task.connect_label_enumeration method.

# store the label enumeration of the training model
labels = {'background': 0, 'cat': 1, 'dog': 2}
task.connect_label_enumeration(labels)

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