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2
docs/apps/clearml_session.md
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@ -169,7 +169,7 @@ The Task must be connected to a git repository, since currently single script de
| `--project`| Set the project name to the interactive session task| `DevOps` | | `--project`| Set the project name to the interactive session task| `DevOps` |
| `--shutdowm`, `-S`| Shut down an active session | Previous session| | `--shutdowm`, `-S`| Shut down an active session | Previous session|
| `--disable-session-cleanup` | If `True`, previous interactive sessions are not deleted | `false`| | `--disable-session-cleanup` | If `True`, previous interactive sessions are not deleted | `false`|
| `--requirements`| Specify requirements.txt file to install when setting the interactive session. | `none` or previously used requirements (can be overridden by calling `--packages`)| | `--requirements`| Specify `requirements.txt` file to install when setting the interactive session. | `none` or previously used requirements (can be overridden by calling `--packages`)|
| `--packages`| Additional packages to add. Supports version numbers. Example: `--packages torch==1.7 tqdm` | Previously added packages.| | `--packages`| Additional packages to add. Supports version numbers. Example: `--packages torch==1.7 tqdm` | Previously added packages.|
| `--upload-files`| Specify local files/folders to upload to the remote session|`None`| | `--upload-files`| Specify local files/folders to upload to the remote session|`None`|
| `--git-credentials` | If `True`, local `.git-credentials` file is sent to the interactive session.| `false`| | `--git-credentials` | If `True`, local `.git-credentials` file is sent to the interactive session.| `false`|

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docs/apps/clearml_task.md
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@ -31,7 +31,7 @@ Specify a Docker container to run the code in with the `--docker <docker_image>`
The ClearML Agent pulls it from Docker Hub or a Docker artifactory automatically. The ClearML Agent pulls it from Docker Hub or a Docker artifactory automatically.
### Package Dependencies ### Package Dependencies
`clearml-task` automatically finds the requirements.txt file in remote repositories. `clearml-task` automatically finds the `requirements.txt` file in remote repositories.
If a local script requires certain packages, or the remote repository doesn't have a `requirements.txt` file, If a local script requires certain packages, or the remote repository doesn't have a `requirements.txt` file,
manually specify the required Python packages using `--packages "<package_name>"`, for example `--packages "keras" "tensorflow>2.2"`. manually specify the required Python packages using `--packages "<package_name>"`, for example `--packages "keras" "tensorflow>2.2"`.

4
docs/clearml_sdk/clearml_sdk.md
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@ -53,7 +53,7 @@ See the [Logger SDK reference page](../references/sdk/logger.md).
### Hyperparameter Optimization ### Hyperparameter Optimization
ClearML's `optimization` module includes classes that support hyperparameter optimization (HPO): ClearML's `optimization` module includes classes that support hyperparameter optimization (HPO):
* [HyperParameterOptimizer](../references/sdk/automation_controller_pipelinecontroller.md) - Hyperparameter search * [HyperParameterOptimizer](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) - Hyperparameter search
controller class controller class
* Optimization search strategy classes including [Optuna](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md), [HpBandSter](../references/sdk/hpo_hpbandster_bandster_optimizerbohb.md), * Optimization search strategy classes including [Optuna](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md), [HpBandSter](../references/sdk/hpo_hpbandster_bandster_optimizerbohb.md),
[GridSearch](../references/sdk/hpo_optimization_gridsearch.md), [RandomSearch](../references/sdk/hpo_optimization_randomsearch.md), [GridSearch](../references/sdk/hpo_optimization_gridsearch.md), [RandomSearch](../references/sdk/hpo_optimization_randomsearch.md),
@ -115,4 +115,4 @@ The `clearml` GitHub repository includes an [examples folder](https://github.com
with example scripts demonstrating how to use the various functionalities of the ClearML SDK. with example scripts demonstrating how to use the various functionalities of the ClearML SDK.
These examples are preloaded in the [ClearML Hosted Service](https://app.clear.ml), and can be viewed, cloned, These examples are preloaded in the [ClearML Hosted Service](https://app.clear.ml), and can be viewed, cloned,
and edited in the ClearML Web UI's `ClearML Examples` project. The examples are each explained in the [examples section](../guides/main.md). and edited in the ClearML Web UI's `ClearML Examples` project. Each example is explained in the [examples section](../guides/main.md).

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docs/faq.md
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@ -166,8 +166,8 @@ in an experiments table and sort by 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 [`Task.set_model_label_enumeration()`](references/sdk/task.md#set_model_label_enumeration)
method to store label enumeration: to store label enumeration:
```python ```python
Task.current_task().set_model_label_enumeration( {"label": int(0), } ) Task.current_task().set_model_label_enumeration( {"label": int(0), } )

18
docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md
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@ -11,9 +11,9 @@ A search strategy is required for the optimization, as well as a search strategy
The following search strategies can be used: The following search strategies can be used:
* Optuna hyperparameter optimization - [automation.optuna.OptimizerOptuna](../../../references/sdk/hpo_optuna_optuna_optimizeroptuna.md). * Optuna hyperparameter optimization - [`automation.optuna.OptimizerOptuna`](../../../references/sdk/hpo_optuna_optuna_optimizeroptuna.md).
For more information about Optuna, see the [Optuna](https://optuna.org/) documentation. For more information about Optuna, see the [Optuna](https://optuna.org/) documentation.
* BOHB - [automation.hpbandster.OptimizerBOHB](../../../references/sdk/hpo_hpbandster_bandster_optimizerbohb.md). * BOHB - [`automation.hpbandster.OptimizerBOHB`](../../../references/sdk/hpo_hpbandster_bandster_optimizerbohb.md).
BOHB performs robust and efficient hyperparameter optimization at scale by combining the speed of Hyperband searches BOHB performs robust and efficient hyperparameter optimization at scale by combining the speed of Hyperband searches
with the guidance and guarantees of convergence of Bayesian Optimization. with the guidance and guarantees of convergence of Bayesian Optimization.
@ -22,11 +22,11 @@ The following search strategies can be used:
For more information about HpBandSter BOHB, see the [HpBandSter](https://automl.github.io/HpBandSter/build/html/index.html) For more information about HpBandSter BOHB, see the [HpBandSter](https://automl.github.io/HpBandSter/build/html/index.html)
documentation. documentation.
* Random uniform sampling of hyperparameter strategy - [automation.RandomSearch](../../../references/sdk/hpo_optimization_randomsearch.md) * Random uniform sampling of hyperparameter strategy - [`automation.RandomSearch`](../../../references/sdk/hpo_optimization_randomsearch.md)
* Full grid sampling strategy of every hyperparameter combination - [automation.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md). * Full grid sampling strategy of every hyperparameter combination - [`automation.GridSearch`](../../../references/sdk/hpo_optimization_gridsearch.md).
* Custom - Use a custom class and inherit from the ClearML automation base strategy class, [SearchStrategy](https://github.com/allegroai/clearml/blob/master/clearml/automation/optimization.py#L310) * Custom - Use a custom class and inherit from the ClearML automation base strategy class, [`SearchStrategy`](https://github.com/allegroai/clearml/blob/master/clearml/automation/optimization.py#L310)
The search strategy class that is chosen will be passed to the [automation.HyperParameterOptimizer](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md) The search strategy class that is chosen will be passed to the [`automation.HyperParameterOptimizer`](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md)
object later. object later.
The example code attempts to import `OptimizerOptuna` for the search strategy. If `clearml.automation.optuna` is not The example code attempts to import `OptimizerOptuna` for the search strategy. If `clearml.automation.optuna` is not
@ -113,7 +113,7 @@ if not args['template_task_id']:
## Creating the Optimizer Object ## Creating the Optimizer Object
Initialize an [automation.HyperParameterOptimizer](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md) Initialize an [`automation.HyperParameterOptimizer`](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md)
object, setting the optimization parameters, beginning with the ID of the experiment to optimize. object, setting the optimization parameters, beginning with the ID of the experiment to optimize.
```python ```python
@ -122,8 +122,8 @@ an_optimizer = HyperParameterOptimizer(
base_task_id=args['template_task_id'], base_task_id=args['template_task_id'],
``` ```
Set the hyperparameter ranges to sample, instantiating them as ClearML automation objects using [automation.UniformIntegerParameterRange](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md) Set the hyperparameter ranges to sample, instantiating them as ClearML automation objects using [`automation.UniformIntegerParameterRange`](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md)
and [automation.DiscreteParameterRange](../../../references/sdk/hpo_parameters_discreteparameterrange.md). and [`automation.DiscreteParameterRange`](../../../references/sdk/hpo_parameters_discreteparameterrange.md).
```python ```python
hyper_parameters=[ hyper_parameters=[

4
docs/pipelines/pipelines.md
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@ -47,8 +47,8 @@ the pipeline via the ClearML Web UI. See [Pipeline Runs](#pipeline-runs).
## Pipeline Features ## Pipeline Features
### Artifacts and Metrics ### Artifacts and Metrics
Each pipeline step can log additional artifacts and metrics on the step task with the usual flows (TB, Matplotlib, or with Each pipeline step can log additional artifacts and metrics on the step task with the usual flows (TB, Matplotlib, or with
[ClearML Logger](../fundamentals/logger.md)). To get the instance of the step's Task during runtime, use the class method [ClearML Logger](../fundamentals/logger.md)). To get the instance of the step's Task during runtime, use the
[Task.current_task](../references/sdk/task.md#taskcurrent_task). [`Task.current_task()`](../references/sdk/task.md#taskcurrent_task) class method.
Additionally, pipeline steps can directly report metrics or upload artifacts / models to the pipeline using these Additionally, pipeline steps can directly report metrics or upload artifacts / models to the pipeline using these
PipelineController and PipelineDecorator class methods: `get_logger`, `upload_model`, `upload_artifact`. PipelineController and PipelineDecorator class methods: `get_logger`, `upload_model`, `upload_artifact`.