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79 lines
2.8 KiB
Markdown
79 lines
2.8 KiB
Markdown
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---
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title: Manual Model Upload
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---
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The [manual_model_upload.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/manual_model_upload.py)
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example demonstrates **ClearML**'s tracking of a manually configured model created with PyTorch, including model checkpoints
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(snapshots), and output to the console. When the script runs, it creates an experiment named `Model configuration and upload`,
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which is associated with the `examples` project.
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Configure **ClearML** for model checkpoint (snapshot) storage in any of the following ways ([debug sample](../../../references/sdk/logger.md#set_default_upload_destination) storage is different):
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* In the configuration file, set [default_output_uri](../../../configs/clearml_conf.md#sdkdevelopment).
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* In code, when [initializing a Task](../../../references/sdk/task.md#taskinit), use the `output_uri` parameter.
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* In the **ClearML Web UI**, when [modifying an experiment](../../../webapp/webapp_exp_tuning.md#output-destination).
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## Configuration
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This example shows two ways to connect a configuration, using the [Task.connect_configuration](../../../references/sdk/task.md#connect_configuration)
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method.
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* Connect a configuration file by providing the file's path. **ClearML Server** stores a copy of the file.
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```python
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# Connect a local configuration file
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config_file = os.path.join('..', '..', 'reporting', 'data_samples', 'sample.json')
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config_file = task.connect_configuration(config_file)
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```
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* Create a configuration dictionary and plug it into the method.
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```python
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model_config_dict = {
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'value': 13.37,
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'dict': {'sub_value': 'string', 'sub_integer': 11},
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'list_of_ints': [1, 2, 3, 4],
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}
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model_config_dict = task.connect_configuration(model_config_dict)
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```
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If the configuration changes, **ClearML** tracks it.
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```python
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model_config_dict['new value'] = 10
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model_config_dict['value'] *= model_config_dict['new value']
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```
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
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in the **MODELS** tab.
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The model info panel contains model details, including:
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* Model design (which is also in the experiment info panel)
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* Label enumeration
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* Model URL
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* Framework
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* Snapshot locations.
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### General model information
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### Model design
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### Label enumeration
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Connect a label enumeration dictionary by calling the [Task.connect_label_enumeration](../../../references/sdk/task.md#connect_label_enumeration)
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method.
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```python
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# store the label enumeration of the training model
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labels = {'background': 0, 'cat': 1, 'dog': 2}
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task.connect_label_enumeration(labels)
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```
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