Manual Model Upload¶

The manual_model_upload.py example demonstrates ClearML tracking of a manually configured model created with Keras, 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 ClearML for model checkpoint (snapshot) storage in any of the following ways (debug sample storage is different):

In the configuration file, set default_output_uri.
In code, when initializing a Task, use the output_uri parameter.
In the ClearML Web UI, when modifying an experiment.

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. ClearML 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, ClearML track it.

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

The configuration appears in CONFIGURATIONS > CONFIGURATION OBJECTS.

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:

The model info panel contains the model details, including the model URL, framework, and snapshot locations.