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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 an experiment's output model (training results). An OutputModel is always connected to a task.
  • InputModel - represents an existing ClearML model to be used in an experiment.
  • OutputModel - represents a ClearML model, regardless of any task connection.

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
# 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:
  • 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

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
# Set label enumeration values
label_enumeration={'person' : 1, 'car' : 2, 'truck' : 3, 'bus' : 4,
'motorcycle' : 5, 'bicycle' : 6, 'ignore': -1},
# Connect the input model to the task

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
# Only models with input name
# Only models with `demo` tag but without `TF` tag
tags=['demo', '-TF'],
# If `True`, only published models
# If `True`, include archived models
# Maximum number of models returned

SDK Reference#

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