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pollfly
2022-05-19 09:59:10 +03:00
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2
docs/guides/automation/task_piping.md
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@@ -10,7 +10,7 @@ example demonstrates:
This example accomplishes a task pipe by doing the following:
1. Creating the template Task which is named `Toy Base Task`. It must be stored in **ClearML Server** before instances of
1. Creating the template Task which is named `Toy Base Task`. It must be stored in ClearML Server before instances of
it can be created. To create it, run another ClearML example script, [toy_base_task.py](https://github.com/allegroai/clearml/blob/master/examples/automation/toy_base_task.py).
The template Task has a parameter dictionary, which is connected to the Task: `{'Example_Param': 1}`.
1. Back in `programmatic_orchestration.py`, creating a parameter dictionary, which is connected to the Task by calling [Task.connect](../../references/sdk/task.md#connect)

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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)

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docs/guides/ide/google_colab.md
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@@ -7,7 +7,7 @@ compute provided by google.
Users can transform a Google Colab instance into an available resource in ClearML using [ClearML Agent](../../clearml_agent.md).
In this tutorial, we will go over how to create a ClearML worker node in a Google Colab notebook. Once the worker is up
This tutorial goes over how to create a ClearML worker node in a Google Colab notebook. Once the worker is up
and running, users can send Tasks to be executed on the Google Colab's HW.
## Prerequisites

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docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md
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@@ -68,7 +68,7 @@ def job_complete_callback(
## Initialize the Optimization Task
Initialize the Task, which will be stored in **ClearML Server** when the code runs. After the code runs at least once, it
Initialize the Task, which will be stored in ClearML Server when the code runs. After the code runs at least once, it
can be [reproduced](../../../webapp/webapp_exp_reproducing.md) and [tuned](../../../webapp/webapp_exp_tuning.md).
We set the Task type to optimizer, and create a new experiment (and Task object) each time the optimizer runs (`reuse_last_task_id=False`).
@@ -92,7 +92,7 @@ Create an arguments dictionary that contains the ID of the Task to optimize, and
optimizer will run as a service, see [Running as a service](#running-as-a-service).
In this example, an experiment named **Keras HP optimization base** is being optimized. The experiment must have run at
least once so that it is stored in **ClearML Server**, and, therefore, can be cloned.
least once so that it is stored in ClearML Server, and, therefore, can be cloned.
Since the arguments dictionary is connected to the Task, after the code runs once, the `template_task_id` can be changed
to optimize a different experiment.

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docs/guides/reporting/explicit_reporting.md
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@@ -9,7 +9,7 @@ example script from ClearML's GitHub repo:
* Setting an output destination for model checkpoints (snapshots).
* Explicitly logging a scalar, other (non-scalar) data, and logging text.
* Registering an artifact, which is uploaded to **ClearML Server**, and ClearML logs changes to it.
* Registering an artifact, which is uploaded to [ClearML Server](../../deploying_clearml/clearml_server.md), and ClearML logs changes to it.
* Uploading an artifact, which is uploaded, but changes to it are not logged.
## Prerequisites
@@ -202,7 +202,7 @@ logger.report_text(
## Step 3: Registering Artifacts
Registering an artifact uploads it to **ClearML Server**, and if it changes, the change is logged in **ClearML Server**.
Registering an artifact uploads it to ClearML Server, and if it changes, the change is logged in ClearML Server.
Currently, ClearML supports Pandas DataFrames as registered artifacts.
### Register the Artifact
@@ -249,7 +249,7 @@ sample = Task.current_task().get_registered_artifacts()['Test_Loss_Correct'].sam
## Step 4: Uploading Artifacts
Artifact can be uploaded to the **ClearML Server**, but changes are not logged.
Artifact can be uploaded to the ClearML Server, but changes are not logged.
Supported artifacts include:
* Pandas DataFrames