diff --git a/docs/apps/clearml_param_search.md b/docs/apps/clearml_param_search.md index 7e833bc3..85b6dde3 100644 --- a/docs/apps/clearml_param_search.md +++ b/docs/apps/clearml_param_search.md @@ -2,7 +2,7 @@ title: ClearML Param Search --- -Use the `clearml-param-search` CLI tool to launch ClearML's automated hyperparameter optimization. This process finds +Use the `clearml-param-search` CLI tool to launch ClearML's automated hyperparameter optimization (HPO). This process finds the optimal values for your experiments' hyperparameters that yield the best performing models. ## How Does `clearml-param-search` Work? diff --git a/docs/clearml_data/clearml_data_cli.md b/docs/clearml_data/clearml_data_cli.md index d37a10ed..e3e4fd29 100644 --- a/docs/clearml_data/clearml_data_cli.md +++ b/docs/clearml_data/clearml_data_cli.md @@ -68,7 +68,7 @@ clearml-data add [-h] [--id ID] [--dataset-folder DATASET_FOLDER] |`--id` | Dataset's ID. Default: previously created / accessed dataset| Yes | |`--files`| Files / folders to add. Items will be uploaded to the dataset’s designated storage. | Yes | |`--wildcard`| Add specific set of files, denoted by these wildcards. For example: `~/data/*.jpg ~/data/json`. Multiple wildcards can be passed. | Yes | -|`--links`| Files / folders link to add. Supports s3, gs, azure links. Example: `s3://bucket/data` `azure://bucket/folder`. Items remain in their original location. | Yes | +|`--links`| Files / folders link to add. Supports S3, GS, Azure links. Example: `s3://bucket/data` `azure://bucket/folder`. Items remain in their original location. | Yes | |`--dataset-folder` | Dataset base folder to add the files to in the dataset. Default: dataset root| Yes | |`--non-recursive` | Disable recursive scan of files | Yes | |`--verbose` | Verbose reporting | Yes| diff --git a/docs/clearml_sdk/clearml_sdk.md b/docs/clearml_sdk/clearml_sdk.md index d67d539d..597a3840 100644 --- a/docs/clearml_sdk/clearml_sdk.md +++ b/docs/clearml_sdk/clearml_sdk.md @@ -52,7 +52,7 @@ See the [Logger SDK reference page](../references/sdk/logger.md). ### Hyperparameter Optimization -ClearML's `optimization` module includes classes that support hyperparameter optimization: +ClearML's `optimization` module includes classes that support hyperparameter optimization (HPO): * [HyperParameterOptimizer](../references/sdk/automation_controller_pipelinecontroller.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), diff --git a/docs/fundamentals/hpo.md b/docs/fundamentals/hpo.md index 579ad0de..9d6f5e67 100644 --- a/docs/fundamentals/hpo.md +++ b/docs/fundamentals/hpo.md @@ -6,7 +6,7 @@ title: Hyperparameter Optimization Hyperparameters are variables that directly control the behaviors of training algorithms, and have a significant effect on 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, you can automate and boost hyperparameter optimization with ClearML's +slow and tedious. Luckily, you can automate and boost hyperparameter optimization (HPO) with ClearML's [**`HyperParameterOptimizer`**](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class. ## ClearML's Hyperparameter Optimization diff --git a/docs/getting_started/ds/ds_second_steps.md b/docs/getting_started/ds/ds_second_steps.md index 7a8cbb98..16a64cd0 100644 --- a/docs/getting_started/ds/ds_second_steps.md +++ b/docs/getting_started/ds/ds_second_steps.md @@ -178,7 +178,7 @@ or check these pages out: - Scale you work and deploy [ClearML Agents](../../clearml_agent.md) - Develop on remote machines with [ClearML Session](../../apps/clearml_session.md) - Structure your work and put it into [Pipelines](../../pipelines/pipelines.md) -- Improve your experiments with [HyperParameter Optimization](../../fundamentals/hpo.md) +- Improve your experiments with [Hyperparameter Optimization](../../fundamentals/hpo.md) - Check out ClearML's integrations to [external libraries](../../integrations/libraries.md). ## YouTube Playlist diff --git a/docs/getting_started/mlops/mlops_second_steps.md b/docs/getting_started/mlops/mlops_second_steps.md index 0b607e5c..7cfd6c37 100644 --- a/docs/getting_started/mlops/mlops_second_steps.md +++ b/docs/getting_started/mlops/mlops_second_steps.md @@ -16,7 +16,7 @@ The sections below describe the following scenarios: ## Building Tasks ### Dataset Creation -Let's assume we have some code that extracts data from a production Database into a local folder. +Let's assume we have some code that extracts data from a production database into a local folder. Our goal is to create an immutable copy of the data to be used by further steps: ```bash @@ -24,7 +24,7 @@ clearml-data create --project data --name dataset clearml-data sync --folder ./from_production ``` -We could also add a Tag `latest` to the Dataset, marking it as the latest version. +We could also add a tag `latest` to the Dataset, marking it as the latest version. ### Preprocessing Data The second step is to preprocess the date. First we need to access it, then we want to modify it, diff --git a/docs/getting_started/video_tutorials/hyperparameter_optimization.md b/docs/getting_started/video_tutorials/hyperparameter_optimization.md index 5c8dc2f8..b5f6c787 100644 --- a/docs/getting_started/video_tutorials/hyperparameter_optimization.md +++ b/docs/getting_started/video_tutorials/hyperparameter_optimization.md @@ -20,7 +20,7 @@ keywords: [mlops, components, hyperparameter optimization, hyperparameter]
Read the transcript
-Hello and welcome to ClearML. In this video we’ll take a look at one cool way of using the agent other than rerunning a task remotely: hyperparameter optimization. +Hello and welcome to ClearML. In this video we’ll take a look at one cool way of using the agent other than rerunning a task remotely: hyperparameter optimization (HPO). By now, we know that ClearML can easily capture our hyperparameters and scalars as part of the experiment tracking. We also know we can clone any task and change its hyperparameters, so they’ll be injected into the original code at runtime. In the last video, we learnt how to make a remote machine execute this task automatically by using the agent. diff --git a/docs/guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md b/docs/guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md index 03fbb5e2..4d91c111 100644 --- a/docs/guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md +++ b/docs/guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md @@ -4,7 +4,7 @@ title: Image Hyperparameter Optimization - Jupyter Notebook [hyperparameter_search.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/image/hyperparameter_search.ipynb) demonstrates using ClearML's [HyperParameterOptimizer](../../../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md) -class to perform automated hyperparameter optimization. +class to perform automated hyperparameter optimization (HPO). The code creates a HyperParameterOptimizer object, which is a search controller. The search controller uses the [Optuna](../../../../../references/sdk/hpo_optuna_optuna_optimizeroptuna.md) search strategy optimizer. diff --git a/docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md b/docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md index 7e269201..85856044 100644 --- a/docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md +++ b/docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md @@ -3,7 +3,7 @@ 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 +example script demonstrates hyperparameter optimization (HPO), which is automated by using ClearML. ## Set the Search Strategy for Optimization diff --git a/docs/integrations/storage.md b/docs/integrations/storage.md index 14bb6922..e3e6ee39 100644 --- a/docs/integrations/storage.md +++ b/docs/integrations/storage.md @@ -22,9 +22,9 @@ The ClearML configuration file uses [HOCON](https://github.com/lightbend/config/ ### Configuring AWS S3 -Modify these parts of the clearml.conf file and add the key, secret, and region of the s3 bucket. +Modify these parts of the clearml.conf file and add the key, secret, and region of the S3 bucket. -It's possible to also give access to specific s3 buckets in the `aws.s3.credentials` section. The default configuration +It's possible to also give access to specific S3 buckets in the `aws.s3.credentials` section. The default configuration provided in the `aws.s3` section is applied to any bucket without a bucket-specific configuration. You can also enable using a credentials chain to let Boto3