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pollfly
2024-02-20 11:51:54 +02:00
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docs/faq.md
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@@ -295,7 +295,7 @@ Yes! ClearML provides multiple ways to configure your task and track your parame
In addition to argparse, ClearML also automatically captures and tracks command line parameters created using [click](integrations/click.md),
[Python Fire](integrations/python_fire.md), [Hydra](integrations/hydra.md), and/or [LightningCLI](https://lightning.ai/docs/pytorch/stable/cli/lightning_cli.html#lightning-cli).
ClearML also supports tracking code-level configuration dictionaries using the [`Task.connect`](references/sdk/task.md#connect) method.
ClearML also supports tracking code-level configuration dictionaries using [`Task.connect()`](references/sdk/task.md#connect).
For example, the code below connects hyperparameters (`learning_rate`, `batch_size`, `display_step`,
`model_path`, `n_hidden_1`, and `n_hidden_2`) to a task:
@@ -309,7 +309,7 @@ parameters_dict = { 'learning_rate': 0.001, 'batch_size': 100, 'display_step': 1
parameters_dict = Task.current_task().connect(parameters_dict)
```
See more task configuration options [here](fundamentals/hyperparameters.md).
For more task configuration options, see [Hyperparameters](fundamentals/hyperparameters.md).
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