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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. 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 class method. Then a [ClearML Agent](../clearml_agent.md) assigned to the queue will pull the task from the queue and execute
it. 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. 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. 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. stops the current manual execution, and then re-runs it on a remote machine.
For example: 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 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). 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. the `offline_mode` argument to `True`. The method returns the Task ID and a path to the session folder.
:::caution :::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 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 ```python
from clearml import Task from clearml import Task
Task.import_offline_session(session_folder_zip="path/to/session/.clearml/cache/offline/b786845decb14eecadf2be24affc7418.zip") 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 ### Updating Models Manually
The snapshots of manually uploaded models aren't automatically captured. To update a task's model, use the 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 ```python
task.update_output_model(model_path='path/to/model') task.update_output_model(model_path='path/to/model')
@ -728,7 +728,7 @@ config_file_yaml = task.connect_configuration(
### User Properties ### User Properties
A task’s user properties do not impact task execution, so you can add / modify the properties at any stage. Add user 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 ```python
task.set_user_properties( 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> **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: method to store label enumeration:
```python ```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> **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: method:
```python ```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> **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) 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 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) [`OutputModel.update_weights`](references/sdk/model_outputmodel.md#update_weights)
method to manually connect a model weights file. method to manually connect a model weights file.
```python ```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/), 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). [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`, 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: `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> **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: you can provide an experiment type. ClearML supports [multiple experiment types](fundamentals/task.md#task-types). For example:
```python ```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 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. 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 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 session folder. In order to upload to the ClearML Server the execution data that the Task captured offline, do one of the
following: following:
* Use the `import-offline-session <session_path>` option of the [clearml-task](apps/clearml_task.md) CLI * 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). 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> **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. 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`: 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> **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. 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`: 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 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). method to specify the host, port, access key and secret key (see step 1).
```python ```python
# Set your credentials using the clearml apiserver URI and port, access_key, and secret_key. # 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 ## 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`. `offline_mode` argument to `True`.
:::caution :::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. 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 ```python
from clearml import Task from clearml import Task