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12
docs/clearml_sdk/task_sdk.md
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@ -337,7 +337,7 @@ cloned = Task.clone(
A newly cloned task has a [draft](../fundamentals/task.md#task-states) status, so it's modifiable.
Once a task is modified, launch it by pushing it into an execution queue with the [Task.enqueue](../references/sdk/task.md#taskenqueue)
Once a task is modified, launch it by pushing it into an execution queue with the [`Task.enqueue`](../references/sdk/task.md#taskenqueue)
class method. Then a [ClearML Agent](../clearml_agent.md) assigned to the queue will pull the task from the queue and execute
it.
@ -359,7 +359,7 @@ A compelling workflow is:
1. Run code on a development machine for a few iterations, or just set up the environment.
1. Move the execution to a beefier remote machine for the actual training.
Use the [Task.execute_remotely](../references/sdk/task.md#execute_remotely) method to implement this workflow. This method
Use the [`Task.execute_remotely`](../references/sdk/task.md#execute_remotely) method to implement this workflow. This method
stops the current manual execution, and then re-runs it on a remote machine.
For example:
@ -406,7 +406,7 @@ Function tasks must be created from within a regular task, created by calling `T
You can work with tasks in Offline Mode, in which all the data and logs that the Task captures are stored in a local
folder, which can later be uploaded to the [ClearML Server](../deploying_clearml/clearml_server.md).
Before initializing a Task, use the [Task.set_offline](../references/sdk/task.md#taskset_offline) class method and set
Before initializing a Task, use the [`Task.set_offline`](../references/sdk/task.md#taskset_offline) class method and set
the `offline_mode` argument to `True`. The method returns the Task ID and a path to the session folder.
:::caution
@ -433,7 +433,7 @@ Upload the execution data that the Task captured offline to the ClearML Server u
```
Pass the path to the zip folder containing the session with the `--import-offline-session` parameter
* [Task.import_offline_session](../references/sdk/task.md#taskimport_offline_session) class method
* [`Task.import_offline_session`](../references/sdk/task.md#taskimport_offline_session) class method
```python
from clearml import Task
Task.import_offline_session(session_folder_zip="path/to/session/.clearml/cache/offline/b786845decb14eecadf2be24affc7418.zip")
@ -560,7 +560,7 @@ output_model = OutputModel(task=task, framework="PyTorch")
### Updating Models Manually
The snapshots of manually uploaded models aren't automatically captured. To update a task's model, use the
[Task.update_output_model](../references/sdk/task.md#update_output_model) method:
[`Task.update_output_model`](../references/sdk/task.md#update_output_model) method:
```python
task.update_output_model(model_path='path/to/model')
@ -728,7 +728,7 @@ config_file_yaml = task.connect_configuration(
### User Properties
A task’s user properties do not impact task execution, so you can add / modify the properties at any stage. Add user
properties to a task with the [Task.set_user_properties](../references/sdk/task.md#set_user_properties) method.
properties to a task with the [`Task.set_user_properties`](../references/sdk/task.md#set_user_properties) method.
```python
task.set_user_properties(

24
docs/faq.md
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@ -162,7 +162,7 @@ that metric column.
**Can I store more information on the models?** <a id="store-more-model-info"></a>
Yes! For example, you can use the [Task.set_model_label_enumeration](references/sdk/task.md#set_model_label_enumeration)
Yes! For example, you can use the [`Task.set_model_label_enumeration`](references/sdk/task.md#set_model_label_enumeration)
method to store label enumeration:
```python
@ -176,7 +176,7 @@ For more information about `Task` class methods, see the [Task Class](fundamenta
**Can I store the model configuration file as well?** <a id="store-model-configuration"></a>
Yes! Use the [Task.set_model_config](references/sdk/task.md#set_model_config)
Yes! Use the [`Task.set_model_config`](references/sdk/task.md#set_model_config)
method:
```python
@ -196,9 +196,9 @@ This will be improved in a future version.
**Can I log input and output models manually?** <a id="manually-log-models"></a>
Yes! Use the [InputModel.import_model](references/sdk/model_inputmodel.md#inputmodelimport_model)
and [Task.connect](references/sdk/task.md#connect) methods to manually connect an input model. Use the
[OutputModel.update_weights](references/sdk/model_outputmodel.md#update_weights)
Yes! Use the [`InputModel.import_model`](references/sdk/model_inputmodel.md#inputmodelimport_model)
and [`Task.connect`](references/sdk/task.md#connect) methods to manually connect an input model. Use the
[`OutputModel.update_weights`](references/sdk/model_outputmodel.md#update_weights)
method to manually connect a model weights file.
```python
@ -288,7 +288,7 @@ 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](https://click.palletsprojects.com/),
[Python Fire](https://github.com/google/python-fire), 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 the [`Task.connect`](references/sdk/task.md#connect) method.
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 @@ See more task configuration options [here](fundamentals/hyperparameters.md).
**I noticed that all of my experiments appear as "Training" Are there other options?** <a id="other-experiment-types"></a>
Yes! When creating experiments and calling [Task.init](references/sdk/task.md#taskinit),
Yes! When creating experiments and calling [`Task.init`](references/sdk/task.md#taskinit),
you can provide an experiment type. ClearML supports [multiple experiment types](fundamentals/task.md#task-types). For example:
```python
@ -503,12 +503,12 @@ See [`Task.init`](references/sdk/task.md#taskinit).
Yes! You can use ClearML's Offline Mode, in which all the data and logs that a task captures from the code are stored in a
local folder.
Before initializing a task, use the [Task.set_offline](references/sdk/task.md#taskset_offline)
Before initializing a task, use the [`Task.set_offline`](references/sdk/task.md#taskset_offline)
class method and set the `offline_mode` argument to `True`. When executed, this returns the Task ID and a path to the
session folder. In order to upload to the ClearML Server the execution data that the Task captured offline, do one of the
following:
* Use the `import-offline-session <session_path>` option of the [clearml-task](apps/clearml_task.md) CLI
* Use the [Task.import_offline_session](references/sdk/task.md#taskimport_offline_session) method.
* Use the [`Task.import_offline_session`](references/sdk/task.md#taskimport_offline_session) method.
See [Storing Task Data Offline](guides/set_offline.md).
@ -589,7 +589,7 @@ tutorial, which includes a list of methods for explicit reporting.
**How can I report more than one scatter 2D series on the same plot?** <a id="multiple-scatter2D"></a>
The [`Logger.report_scatter2d()`](references/sdk/logger.md#report_scatter2dtitle-series-scatter-iteration-xaxisnone-yaxisnone-labelsnone-modelines-commentnone-extra_layoutnone)
The [`Logger.report_scatter2d`](references/sdk/logger.md#report_scatter2d)
method reports all series with the same `title` and `iteration` parameter values on the same plot.
For example, the following two scatter2D series are reported on the same plot, because both have a `title` of `example_scatter` and an `iteration` of `1`:
@ -628,7 +628,7 @@ experiment info panel > EXECUTION tab.
**I read there is a feature for centralized model storage. How do I use it?** <a id="centralized-model-storage"></a>
When calling [Task.init](references/sdk/task.md#taskinit),
When calling [`Task.init`](references/sdk/task.md#taskinit),
providing the `output_uri` parameter lets you specify the location in which model checkpoints (snapshots) will be stored.
For example, to store model checkpoints (snapshots) in `/mnt/shared/folder`:
@ -744,7 +744,7 @@ Yes! You can run ClearML in Jupyter Notebooks using either of the following:
pip install clearml
1. Use the [Task.set_credentials](references/sdk/task.md#taskset_credentials)
1. Use the [`Task.set_credentials`](references/sdk/task.md#taskset_credentials)
method to specify the host, port, access key and secret key (see step 1).
```python
# Set your credentials using the clearml apiserver URI and port, access_key, and secret_key.

4
docs/guides/set_offline.md
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@ -8,7 +8,7 @@ local folder, which can be later uploaded to the [ClearML Server](../deploying_c
## Setting Task to Offline Mode
Before initializing a Task, use the [Task.set_offline](../references/sdk/task.md#taskset_offline) class method and set the
Before initializing a Task, use the [`Task.set_offline`](../references/sdk/task.md#taskset_offline) class method and set the
`offline_mode` argument to `True`.
:::caution
@ -52,7 +52,7 @@ Upload the session's execution data that the Task captured offline to the ClearM
Pass the path to the zip folder containing the session with the `--import-offline-session` parameter.
* [Task.import_offline_session](../references/sdk/task.md#taskimport_offline_session) method.
* [`Task.import_offline_session`](../references/sdk/task.md#taskimport_offline_session) method.
```python
from clearml import Task