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pollfly
2022-05-19 09:59:10 +03:00
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parent 1b010a79b0
commit f377b140e9
25 changed files with 102 additions and 99 deletions
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2
docs/guides/frameworks/autokeras/integration_autokeras.md
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@@ -33,7 +33,7 @@ from clearml import Task
task = Task.init(project_name="myProject", task_name="myExperiment")
```
When the code runs, it initializes a Task in **ClearML Server**. A hyperlink to the experiment's log is output to the console.
When the code runs, it initializes a Task in ClearML Server. A hyperlink to the experiment's log is output to the console.
CLEARML Task: created new task id=c1f1dc6cf2ee4ec88cd1f6184344ca4e
CLEARML results page: https://app.clear.ml/projects/1c7a45633c554b8294fa6dcc3b1f2d4d/experiments/c1f1dc6cf2ee4ec88cd1f6184344ca4e/output/log

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docs/guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline.md
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@@ -269,7 +269,7 @@ By hovering over a step or path between nodes, you can view information about it
1. Run the pipeline controller one of the following two ways:
* Run the notebook [tabular_ml_pipeline.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/tabular_ml_pipeline.ipynb).
* Remotely execute the Task - If the Task `tabular training pipeline` which is associated with the project `Tabular Example` already exists in **ClearML Server**, clone it and enqueue it to execute.
* Remotely execute the Task - If the Task `tabular training pipeline` which is associated with the project `Tabular Example` already exists in ClearML Server, clone it and enqueue it to execute.
:::note

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docs/guides/frameworks/pytorch/pytorch_distributed_example.md
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@@ -35,7 +35,9 @@ All of these artifacts appear in the main Task, **ARTIFACTS** **>** **OTHER**.
## Scalars
We report loss to the main Task by calling the [Logger.report_scalar](../../../references/sdk/logger.md#report_scalar) method on `Task.current_task().get_logger`, which is the logger for the main Task. Since we call `Logger.report_scalar` with the same title (`loss`), but a different series name (containing the subprocess' `rank`), all loss scalar series are logged together.
Report loss to the main Task by calling the [Logger.report_scalar](../../../references/sdk/logger.md#report_scalar) method
on `Task.current_task().get_logger`, which is the logger for the main Task. Since `Logger.report_scalar` is called with the
same title (`loss`), but a different series name (containing the subprocess' `rank`), all loss scalar series are logged together.
Task.current_task().get_logger().report_scalar(
'loss', 'worker {:02d}'.format(dist.get_rank()), value=loss.item(), iteration=i)