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100 lines
3.5 KiB
Markdown
100 lines
3.5 KiB
Markdown
---
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title: Model Updating
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---
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The [model_update_pytorch.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/model_update_pytorch.py)
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example demonstrates training a model and logging it using the [OutputModel](../../references/sdk/model_outputmodel.md)
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class.
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The example does the following:
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* Creates a task named `Model update pytorch` in the `examples` project.
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* Trains a neural network on the CIFAR10 dataset for image classification.
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* Uses an OutputModel object to log the model, its label enumeration and configuration dictionary.
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:::note Disabling automatic framework logging
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This example disables the default automatic capturing of PyTorch outputs, to demonstrate how to manually control what is
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logged from PyTorch. See [this FAQ](../../faq.md#controlling_logging) for more information.
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:::
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## Initialization
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An OutputModel object is instantiated for the task.
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```python
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from clearml import Task, OutputModel
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task = Task.init(
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project_name="examples",
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task_name="Model update pytorch",
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auto_connect_frameworks={"pytorch": False}
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)
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output_model = OutputModel(task=task)
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```
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## Label Enumeration
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The label enumeration dictionary is logged using the [`Task.connect_label_enumeration`](../../references/sdk/task.md#connect_label_enumeration)
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method which will update the task’s resulting model information. The current running task is accessed using the
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[`Task.current_task`](../../references/sdk/task.md#taskcurrent_task) class method.
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```python
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# store the label enumeration of the training model
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classes = ("plane", "car", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck",)
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enumeration = {k: v for v, k in enumerate(classes, 1)}
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Task.current_task().connect_label_enumeration(enumeration)
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```
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:::note Directly Setting Model Enumeration
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You can set a model’s label enumeration directly using the [`OutputModel.update_labels`](../../references/sdk/model_outputmodel.md#update_labels)
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method
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:::
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## Model Configuration
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Add a configuration dictionary to the model using the [`OutputModel.update_design`](../../references/sdk/model_outputmodel.md#update_design)
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method.
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```python
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model_config_dict = {
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"list_of_ints": [1, 2, 3, 4],
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"dict": {
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"sub_value": "string",
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"sub_integer": 11
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},
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"value": 13.37
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}
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model.update_design(config_dict=model_config_dict)
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```
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## Updating Models
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To update a model, use the [OutputModel.update_weights](../../references/sdk/model_outputmodel.md#update_weights) method.
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This uploads the model to the set storage destination (see [Setting Upload Destination](../../fundamentals/artifacts.md#setting-upload-destination)),
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and registers that location to the task as the output model.
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```python
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# CONDITION depicts a custom condition for when to save the model. The model is saved and then updated in ClearML
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CONDITION = True
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if CONDITION:
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torch.save(net.state_dict(), PATH)
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model.update_weights(weights_filename=PATH)
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```
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## WebApp
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The model appears in the task’s **ARTIFACTS** tab.
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Clicking on the model name takes you to the [model’s page](../../webapp/webapp_model_viewing.md), where you can view the
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model’s details and access the model.
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The model’s **NETWORK** tab displays its configuration.
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The model’s **LABELS** tab displays its label enumeration.
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