clearml-docs/docs/clearml_sdk/model_sdk.md

title
Model

The following page provides an overview of the basic Pythonic interface to ClearML Models.

ClearML provides the following classes to work with models:

• Model - Represents a ClearML model, regardless of any task connection. Use this class to programmatically access and manage the ClearML model store.
• InputModel - Represents an existing ClearML model to be used in an experiment. Use this class to load a model from ClearML's model store or to import a pre-trained model from an external resource to use as an experiment's initial starting point.
• OutputModel - Represents an experiment's output model (training results). An OutputModel is always connected to a task, so the models are traceable to experiments.

Output Models

Manually Logging Models

To manually log a model, create an instance of OutputModel class.

from clearml import OutputModel, Task

# Instantiate a Task 
task = Task.init(project_name="myProject", task_name="myTask")

# Create an output model for the PyTorch framework
output_model = OutputModel(task=task, framework="PyTorch")

You can set the destination the model will be uploaded to and its label enumeration using the OutputModel.set_upload_destination and OutputModel.update_labels methods respectively.

# Set the URI of the storage destination for uploaded model weight files
output_model.set_upload_destination(uri=models_upload_destination)

# Set the label numeration
output_model.update_labels({'background': 0, 'label': 255})

Updating Models

ClearML doesn’t automatically log the snapshots of manually logged models. To update an experiment’s model use the OutputModel.update_weights method.

# If validation shows this network is the best so far, update the output model
if val_log['iou'] > best_iou:
    output_model.update_weights(weights_filename='models/model.pth')

• Specify either the path of a local weights file to upload (weights_filename), or the network location of a remote weights file (registered_uri).
• Use the upload_uri argument to explicitly specify an upload destination for the weights file.
• Model metadata
  • update_comment - update the model's description
  • iteration - input the iteration number

Alternatively, update a model through its associated task, using the Task.update_output_model method.

Input Models

Using Registered Models

To use a ClearML model as an input model, create an InputModel object and connect it to a task.

# Create an input model using the ClearML ID of a model already registered in the ClearML platform
input_model = InputModel(model_id="fd8b402e874549d6944eebd49e37eb7b")

# Connect the input model to the task
task.connect(input_model)

Importing Models

To import an existing model, use the InputModel.import_model class method and specify the weights_url - the URL for the imported model. If the URL already exists in the ClearML server, it is reused. Otherwise, a new model is registered.

Then connect the model to a task.

# Instantiate a Task 
task = Task.init(project_name="examples", task_name="example task")

input_model = InputModel.import_model(
    # Name for model in ClearML
    name='Input Model with Network Design',
    # Import the model using a URL
    weights_url='https://s3/models/model.pth',
    # Set label enumeration values
    label_enumeration={'person' : 1, 'car' : 2, 'truck' : 3, 'bus' : 4,
                       'motorcycle' : 5, 'bicycle' : 6, 'ignore': -1},
    framework='PyTorch'
)

# Connect the input model to the task
task.connect(input_model)

Querying Models

Retrieve a list of model objects by querying the system by model names, projects, tags, and more, using the Model.query_models and / or the InputModel.query_models class methods. These methods return a list of model objects that match the queries. The list is ordered according to the models’ last update time.

When you query models by tags, use the - prefix in order to filter out models with that tag.

model_list = Model.query_models(
    # Only models from `examples` project
    project_name='examples', 
    # Only models with input name
    model_name=None,
    # Only models with `demo` tag but without `TF` tag
    tags=['demo', '-TF'],
    # If `True`, only published models
    only_published=False,
    # If `True`, include archived models
    include_archived=True,
    # Maximum number of models returned
    max_results=5,
    # Only models with matching metadata
    metadata={"key":"value"}
)

SDK Reference

For information about all model methods, see the following SDK reference pages:

• Model
• InputModel
• OutputModel