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14
docs/fundamentals/hpo.md
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@ -7,7 +7,7 @@ Hyperparameters are variables that directly control the behaviors of training al
the performance of the resulting machine learning models. Finding the hyperparameter values that yield the best
performing models can be complicated. Manually adjusting hyperparameters over the course of many training trials can be
slow and tedious. Luckily, hyperparameter optimization can be automated and boosted using ClearML's
**`HyperParameterOptimizer`** class.
[**`HyperParameterOptimizer`**](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class.
## ClearML's HyperParameter Optimization
@ -50,17 +50,17 @@ The `HyperParameterOptimizer` class contains **ClearML**’s hyperparameter opti
using different optimizers, including existing software frameworks, enabling simple, accurate, and fast hyperparameter
optimization.
* **Optuna** - `automation.optuna.optuna.OptimizerOptuna`. Optuna is the default optimizer in ClearML. It makes use of
* **Optuna** - [`automation.optuna.OptimizerOptuna`](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md). Optuna is the default optimizer in ClearML. It makes use of
different samplers such as grid search, random, bayesian, and evolutionary algorithms.
For more information, see the [Optuna](https://optuna.readthedocs.io/en/latest/)
documentation.
* **BOHB** - `automation.hpbandster.bandster.OptimizerBOHB`. BOHB performs robust and efficient hyperparameter optimization
* **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.
For more information about HpBandSter BOHB, see the [HpBandSter](https://automl.github.io/HpBandSter/build/html/index.html)
documentation and a [code example](../guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md).
* **Random** uniform sampling of hyperparameters - `automation.optimization.RandomSearch`.
* **Full grid** sampling strategy of every hyperparameter combination - `Grid search automation.optimization.GridSearch`.
* **Custom** - `automation.optimization.SearchStrategy` - Use a custom class and inherit from the ClearML automation base strategy class
* **Random** uniform sampling of hyperparameters - [`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** - [`automation.optimization.SearchStrategy`](https://github.com/allegroai/clearml/blob/master/clearml/automation/optimization.py#L268) - Use a custom class and inherit from the ClearML automation base strategy class
## Defining a Hyperparameter Optimization Search Example
@ -114,7 +114,7 @@ optimization.
max_iteration_per_job=150000,
)
```
<br/><br/>
<br/>
For more information about `HyperParameterOptimizer` and supported optimization modules, see the [HyperParameterOptimizer class reference](../references/sdk/hpo_optimization_hyperparameteroptimizer.md).

26
docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md
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@ -5,17 +5,15 @@ title: Hyperparameter Optimization
The [hyper_parameter_optimizer.py](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py)
example script demonstrates hyperparameter optimization, which is automated by using **ClearML**
<a class="tr_top_negative" name="strategy"></a>
## Set the Search Strategy for Optimization
A search strategy is required for the optimization, as well as a search strategy optimizer class to implement that strategy.
The following search strategies can be used:
* Optuna hyperparameter optimization - [automation.optuna.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.bandster.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.
@ -24,11 +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.optimization.RandomSearch](../../../references/sdk/hpo_optimization_randomsearch.md)
* Full grid sampling strategy of every hyperparameter combination - Grid search [automation.optimization.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md).
* Random uniform sampling of hyperparameter strategy - [automation.RandomSearch](../../../references/sdk/hpo_optimization_randomsearch.md)
* Full grid sampling strategy of every hyperparameter combination - Grid search [automation.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md).
* Custom - Use a custom class and inherit from the **ClearML** automation base strategy class, automation.optimization.SearchStrategy.
The search strategy class that is chosen will be passed to the [automation.optimization.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
@ -40,14 +38,14 @@ the `RandomSearch` for the search strategy.
if not aSearchStrategy:
try:
from clearml.automation.optuna import OptimizerOptuna
from clearml.optuna import OptimizerOptuna
aSearchStrategy = OptimizerOptuna
except ImportError as ex:
pass
if not aSearchStrategy:
try:
from clearml.automation.hpbandster import OptimizerBOHB
from clearml.automation.hpbandster import OptimizerBOHB
aSearchStrategy = OptimizerBOHB
except ImportError as ex:
pass
@ -104,7 +102,7 @@ In this example, an experiment named **Keras HP optimization base** is being opt
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, see [tuning experiments](../../../webapp/webapp_exp_tuning.md).
to optimize a different experiment.
```python
# experiment template to optimize in the hyper-parameter optimization
@ -122,7 +120,7 @@ to optimize a different experiment, see [tuning experiments](../../../webapp/web
## Creating the Optimizer Object
Initialize an [automation.optimization.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
@ -131,8 +129,8 @@ object, setting the optimization parameters, beginning with the ID of the experi
base_task_id=args['template_task_id'],
```
Set the hyperparameter ranges to sample, instantiating them as **ClearML** automation objects using [automation.parameters.UniformIntegerParameterRange](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md)
and [automation.parameters.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=[
@ -186,8 +184,6 @@ Specify the remaining parameters, including the time limit per Task (minutes), p
```
<a class="tr_top_negative" name="service"></a>
## Running as a Service
The optimization can run as a service, if the `run_as_service` argument is set to `true`. For more information about

2
docs/guides/reporting/artifacts.md
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@ -18,7 +18,7 @@ Artifacts can be uploaded and dynamically tracked, or uploaded without tracking.
<a name="configure_artifact_storage" class="tr_top_negative"></a>
Configure **ClearML** for uploading artifacts to any of the supported types of storage, which include local and shared folders,
S3 buckets, Google Cloud Storage, and Azure Storage ([debug sample storage](../../references/sdk/logger.md#set_default_upload_destination)
S3 buckets, Google Cloud Storage, and Azure Storage ([debug sample storage](../../references/sdk/logger.md#set_default_upload_destination)
is different). Configure **ClearML** in any of the following ways:
* In the configuration file, set [default_output_uri](../../configs/clearml_conf.md#sdkdevelopment).