--- 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 a 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](../fundamentals/task.md), so the models are traceable to experiments. ## Output Models ### Manually Logging Models To manually log a model, create an instance of OutputModel class. ```python 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`](../references/sdk/model_outputmodel.md#set_upload_destination) and [`OutputModel.update_labels`](../references/sdk/model_outputmodel.md#update_labels) methods respectively. ```python # 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](../references/sdk/model_outputmodel.md#update_weights) method. ```python # 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`](../references/sdk/task.md#update_output_model) method. ## Input Models ### Using Registered Models To use a ClearML model as an input model, create an InputModel object and [connect](../references/sdk/task.md#connect) it to a task. ```python # 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`](../references/sdk/model_outputmodel.md#inputmodelimport_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](../references/sdk/task.md#connect) the model to a task. ```python # 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) ``` ## Accessing Models ### Querying Models Retrieve a list of model objects by querying the system by model names, projects, tags, and more, using the [`Model.query_models`](../references/sdk/model_model.md#modelquery_models) and/or the [`InputModel.query_models`](../references/sdk/model_inputmodel.md#inputmodelquery_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. ```python 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 or models 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"} ) ``` #### Tag Filters The `tags` field supports advanced queries through combining tag names and operators into a list. The supported operators are: * `not` * `and` * `or` Input the operators in the following format: `"__$"`. To exclude a tag, you can also use the `-` prefix before the tag name, unless the tag name begins with the dash character (`-`), in which case you can use `"__$not"`. The `or`, and `and` operators apply to all tags that follow them until another operator is specified. The `not` operator applies only to the immediately following tag. The default operator for a query is `or`, unless `and` is placed at the beginning of the query. #### Examples * The following query will return models that have at least one of the provided tags, since the default operator is `or` (`"a" OR "b" OR "c"`) ```python model_list = Model.query_models(tags=["a", "b", "c"]) ``` * The following query will return models that have all three provided tags, since the `and` operator was placed in the beginning of the list, making it the default operator (`"a" AND "b" AND "c"`). ```python model_list = Model.query_models(tags=["__$and", "a", "b", "c"]) ``` * The following query will return models that have neither tag `a` nor tag `c`, but do have tag `b` (`NOT "a" AND "b" AND NOT "c"`). ```python model_list = Model.query_models(tags=["__$not", "a", "b", "__$not" "c"]) ``` * The following query will return models with either tag `a` or tag `b` or both `c` and `d` tags (`"a" OR "b" OR ("c" AND "d")`). ```python model_list = Model.query_models(tags=["a", "b", "__$and", "c", "d"]) ``` * The following query will return models that have either tag `a` or tag `b` and both tag `c` and tag `d` (`("a" OR "b") AND "c" AND "d"`). ```python model_list = Model.query_models( tags=["__$and", "__$or", "a", "b", "__$and", "c", "d"] ) ``` ### Retrieving Models Retrieve a local copy of a ClearML model through a `Model`/`InputModel` object's [`get_local_copy()`](../references/sdk/model_model.md#get_local_copy). The method returns a path to a cached local copy of the model. In the case that the model is already cached, you can set `force_download` to `True` to download a fresh version. ## Logging Metrics and Plots Use the following methods to explicitly log additional information to your models. These methods can be used on `Model`, `InputModel`, and/or `OutputModel` objects: * Scalars * Scalar series plots - [`report_scalar`](../references/sdk/model_outputmodel.md#report_scalar) * Single metric values - [`report_single_value`](../references/sdk/model_outputmodel.md#report_single_value) * Plots * 2d plots * Histogram - [`report_histogram`](../references/sdk/model_outputmodel.md#report_histogram) * Vector as histogram plot - [`report_vector`](../references/sdk/model_outputmodel.md#report_vector) * Table - [`report_table`](../references/sdk/model_outputmodel.md#report_table) * Line plot - [`report_line_plot`](../references/sdk/model_outputmodel.md#report_line_plot) * Scatter plot - [`report_scatter2d`](../references/sdk/model_outputmodel.md#report_scatter2d) * Confusion matrix (heat map) - [`report_confusion_matrix`](../references/sdk/model_outputmodel.md#report_confusion_matrix) * 3d plots * Scatter plot - [`report_scatter3d`](../references/sdk/model_outputmodel.md#report_scatter3d) * Surface plot - [`report_surface`](../references/sdk/model_outputmodel.md#report_surface) ## SDK Reference For information about all model methods, see the following SDK reference pages: * [Model](../references/sdk/model_model.md) * [InputModel](../references/sdk/model_inputmodel.md) * [OutputModel](../references/sdk/model_outputmodel.md)