clearml-docs/docs/guides/frameworks/pytorch/manual_model_upload.md

title
Manual Model Upload

The manual_model_upload.py example demonstrates ClearML's tracking of a manually configured model created with PyTorch, including model checkpoints (snapshots), 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 Task.connect_configuration method.

• 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)

• Create a configuration dictionary and plug it into the method.

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 tracks it.

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

Artifacts

Model artifacts associated with the experiment appear in the info panel of the EXPERIMENTS tab and in the info panel in the MODELS tab.

The model info panel contains model details, including:

• Model design (which is also in the experiment info panel)
• Label enumeration
• Model URL
• Framework
• Snapshot locations.

General model information

Model design

Label enumeration

Connect a 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)