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78 lines
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78 lines
4.0 KiB
Markdown
---
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title: Models
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---
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ClearML supports tracking, updating, and visualizing models.
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Models are stored in ClearML as experiment artifacts, but unlike other artifacts that are dependent on their creating
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task, models are independent entities with their own unique ID. Models can be accessed directly with a model object or
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indirectly via their creating task. This property makes Models a standalone entry that can be used as an artifactory
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interface.
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## Automatically Logging Models
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Once integrated into code, ClearML automatically logs and tracks models and any snapshots created by the following
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frameworks:
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- TensorFlow (see [code example](../guides/frameworks/tensorflow/tensorflow_mnist.md))
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- Keras (see [code example](../guides/frameworks/keras/keras_tensorboard.md))
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- PyTorch (see [code example](../guides/frameworks/pytorch/pytorch_mnist.md))
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- scikit-learn (only using joblib) (see [code example](../guides/frameworks/scikit-learn/sklearn_joblib_example.md))
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- XGBoost (only using joblib) (see [code example](../guides/frameworks/xgboost/xgboost_sample.md))
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- FastAI (see [code example](../guides/frameworks/fastai/fastai_with_tensorboard.md))
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- MegEngine (see [code example](../guides/frameworks/megengine/megengine_mnist.md))
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- CatBoost (see [code example](../guides/frameworks/catboost/catboost.md))
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When a supported framework loads a weights file, the running task will be automatically updated, with its input model
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pointing directly to the original training task's model.
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## Manually Logging Models
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### Output Models
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ClearML stores training results as output models. The `OutputModel` object is instantiated with a task object as an
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argument (see [`task`](../references/sdk/model_outputmodel.md) parameter), so it's automatically registered as the Task’s
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output model. Since OutputModel objects are connected to tasks, the models are traceable in experiments.
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Output models are read-write so weights can be updated throughout training. Additionally, users can specify a model's
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network design and label enumeration. Once an output model is registered, it can be used as the input model for another
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experiment.
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The snapshots of manually uploaded models aren't automatically captured, but ClearML provides methods to update them
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through a `Task` or `OutputModel` object.
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### Input Models
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ClearML provides flexibility for explicitly connecting input models and experimentation, including:
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* Importing pre-trained models from external sources such as Amazon AWS, GIT repositories, PyTorch, and TensorFlow.
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* Using standalone models already registered in ClearML by previously run experiments.
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* Defining your own input models in scripts
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## Setting Upload Destination
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* ClearML automatically captures the storage path of Models created by supported frameworks. By default, it stores the
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local path they are saved to.
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* Upload destinations can be specified explicitly on a per OutputModel or per experiment basis. Alternatively, the upload
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destination of all OutputModels can be specified in the ClearML [configuration file](../configs/clearml_conf.md).
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## WebApp Interface
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In the ClearML's web UI, model information can be located through a project's Model Table or through the model's creating
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task.
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Models associated with a task appear in the task's **ARTIFACTS** tab. To see further model details, including design,
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label enumeration, lineage, and general information, click the model name, which is a hyperlink to the
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[model's detail page](../webapp/webapp_model_viewing.md).
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Models can also be accessed through their associated project's [Model Table](../webapp/webapp_model_table.md), where all
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the models associated with a project are listed.
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## SDK Interface
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See [the Models SDK interface](../clearml_sdk/model_sdk.md) for an overview for using the most basic Pythonic methods of the model
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classes. See a detailed list of all available methods in the [Model](../references/sdk/model_model.md), [OutputModel](../references/sdk/model_outputmodel.md), and [InputModel](../references/sdk/model_inputmodel.md)
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reference pages.
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