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pollfly
2023-12-03 14:27:46 +02:00
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docs/integrations/pytorch_lightning.md
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@@ -18,7 +18,7 @@ from clearml import Task
task = Task.init(task_name="<task_name>", project_name="<project_name>")
```
And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* PyTorch Models
@@ -43,8 +43,7 @@ To control a task's framework logging, use the `auto_connect_frameworks` paramet
Completely disable all automatic logging by setting the parameter to `False`. For finer grained control of logged
frameworks, input a dictionary, with framework-boolean pairs.
For example, the following code will log PyTorch models, but will not log any information reported to TensorBoard.
:
For example, the following code will log PyTorch models, but will not log any information reported to TensorBoard:
```python
auto_connect_frameworks={
@@ -143,7 +142,7 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.