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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` |
| `--shutdowm`, `-S`| Shut down an active session | Previous session|
| `--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.|
| `--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`|

2
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.
### 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,
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
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
* 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),
@ -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.
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).

4
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>
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:
Yes! For example, you can use [`Task.set_model_label_enumeration()`](references/sdk/task.md#set_model_label_enumeration)
to store label enumeration:
```python
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:
* 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.
* 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
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)
documentation.
* 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).
* 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)
* 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).
* 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.
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
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.
```python
@ -122,8 +122,8 @@ an_optimizer = HyperParameterOptimizer(
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)
and [automation.DiscreteParameterRange](../../../references/sdk/hpo_parameters_discreteparameterrange.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).
```python
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
### Artifacts and Metrics
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
[Task.current_task](../references/sdk/task.md#taskcurrent_task).
[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) class method.
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`.