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6
docs/clearml_agent.md
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@ -48,7 +48,7 @@ While the agent is running, it continuously reports system metrics to the ClearM
Continue using ClearML Agent once it is running on a target machine. Reproduce experiments and execute Continue using ClearML Agent once it is running on a target machine. Reproduce experiments and execute
automated workflows in one (or both) of the following ways: automated workflows in one (or both) of the following ways:
* Programmatically (using [`Task.enqueue`](references/sdk/task.md#taskenqueue) or [`Task.execute_remotely`](references/sdk/task.md#execute_remotely)) * Programmatically (using [`Task.enqueue()`](references/sdk/task.md#taskenqueue) or [`Task.execute_remotely()`](references/sdk/task.md#execute_remotely))
* Through the ClearML Web UI (without working directly with code), by cloning experiments and enqueuing them to the * Through the ClearML Web UI (without working directly with code), by cloning experiments and enqueuing them to the
queue that a ClearML Agent is servicing. queue that a ClearML Agent is servicing.
@ -57,7 +57,7 @@ code modification. Modifying a task clone’s configuration will have the ClearM
original values: original values:
* Modified package requirements will have the experiment script run with updated packages * Modified package requirements will have the experiment script run with updated packages
* Modified recorded command line arguments will have the ClearML agent inject the new values in their stead * Modified recorded command line arguments will have the ClearML agent inject the new values in their stead
* Code-level configuration instrumented with [`Task.connect`](references/sdk/task.md#connect) will be overridden by modified hyperparameters * Code-level configuration instrumented with [`Task.connect()`](references/sdk/task.md#connect) will be overridden by modified hyperparameters
For more information, see [ClearML Agent Reference](clearml_agent/clearml_agent_ref.md), For more information, see [ClearML Agent Reference](clearml_agent/clearml_agent_ref.md),
and [configuration options](configs/clearml_conf.md#agent-section). and [configuration options](configs/clearml_conf.md#agent-section).
@ -109,7 +109,7 @@ it can't do that when running from a virtual environment.
Detected credentials key="********************" secret="*******" Detected credentials key="********************" secret="*******"
``` ```
1. **Enter** to accept default server URL, which is detected from the credentials or enter a ClearML web server URL. 1. **Enter** to accept the default server URL, which is detected from the credentials or enter a ClearML web server URL.
A secure protocol, https, must be used. **Do not use http.** A secure protocol, https, must be used. **Do not use http.**

2
docs/clearml_agent/clearml_agent_env_var.md
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@ -28,7 +28,7 @@ but can be overridden by command-line arguments.
|**CLEARML_AGENT_EXEC_USER** | User for Agent executing tasks (root by default) | |**CLEARML_AGENT_EXEC_USER** | User for Agent executing tasks (root by default) |
|**CLEARML_AGENT_EXTRA_DOCKER_ARGS** | Overrides extra docker args configuration | |**CLEARML_AGENT_EXTRA_DOCKER_ARGS** | Overrides extra docker args configuration |
|**CLEARML_AGENT_EXTRA_DOCKER_LABELS** | List of labels to add to docker container. See [Docker documentation](https://docs.docker.com/config/labels-custom-metadata/). | |**CLEARML_AGENT_EXTRA_DOCKER_LABELS** | List of labels to add to docker container. See [Docker documentation](https://docs.docker.com/config/labels-custom-metadata/). |
|**CLEARML_EXTRA_PIP_INSTALL_FLAGS**| List of additional flags to use when the agent install packages. For example: `["--use-deprecated=legacy-resolver", ]`| |**CLEARML_EXTRA_PIP_INSTALL_FLAGS**| List of additional flags to use when the agent installs packages. For example: `["--use-deprecated=legacy-resolver", ]`|
|**CLEARML_AGENT_EXTRA_PYTHON_PATH** | Sets extra python path | |**CLEARML_AGENT_EXTRA_PYTHON_PATH** | Sets extra python path |
|**CLEARML_AGENT_INITIAL_CONNECT_RETRY_OVERRIDE** | Overrides initial server connection behavior (true by default), allows explicit number to specify number of connect retries) | |**CLEARML_AGENT_INITIAL_CONNECT_RETRY_OVERRIDE** | Overrides initial server connection behavior (true by default), allows explicit number to specify number of connect retries) |
|**CLEARML_AGENT_NO_UPDATE** | Boolean. Set to `true` to skip agent update in the k8s pod container before the agent executes the task | |**CLEARML_AGENT_NO_UPDATE** | Boolean. Set to `true` to skip agent update in the k8s pod container before the agent executes the task |

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docs/clearml_data/best_practices.md
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@ -29,19 +29,19 @@ Like any ClearML tasks, datasets can be organized into [projects (and subproject
Additionally, when creating a dataset, tags can be applied to the dataset, which will make searching for the dataset easier. Additionally, when creating a dataset, tags can be applied to the dataset, which will make searching for the dataset easier.
Organizing your datasets into projects by use-case makes it easier to access the most recent dataset version for that use-case. Organizing your datasets into projects by use-case makes it easier to access the most recent dataset version for that use-case.
If only a project is specified when using [`Dataset.get`](../references/sdk/dataset.md#datasetget), the method returns the If only a project is specified when using [`Dataset.get()`](../references/sdk/dataset.md#datasetget), the method returns the
most recent dataset in a project. The same is true with tags; if a tag is specified, the method will return the most recent dataset that is labeled with that tag. most recent dataset in a project. The same is true with tags; if a tag is specified, the method will return the most recent dataset that is labeled with that tag.
In cases where you use a dataset in a task (e.g. consuming a dataset), you can easily track which dataset the task is In cases where you use a dataset in a task (e.g. consuming a dataset), you can easily track which dataset the task is
using by using `Dataset.get`'s `alias` parameter. Pass `alias=<dataset_alias_string>`, and the task using the dataset using by using `Dataset.get()`'s `alias` parameter. Pass `alias=<dataset_alias_string>`, and the task using the dataset
will store the dataset's ID in the `dataset_alias_string` parameter under the task's **CONFIGURATION > HYPERPARAMETERS > will store the dataset's ID in the `dataset_alias_string` parameter under the task's **CONFIGURATION > HYPERPARAMETERS >
Datasets** section. Datasets** section.
## Document your Datasets ## Document your Datasets
Attach informative metrics or debug samples to the Dataset itself. Use the [`get_logger`](../references/sdk/dataset.md#get_logger) Attach informative metrics or debug samples to the Dataset itself. Use [`Dataset.get_logger()`](../references/sdk/dataset.md#get_logger)
method to access the dataset's logger object, then add any additional information to the dataset, using the methods to access the dataset's logger object, then add any additional information to the dataset, using the methods
available with a [logger](../references/sdk/logger.md) object. available with a [logger](../references/sdk/logger.md) object.
You can add some dataset summaries (like [table reporting](../references/sdk/logger.md#report_table)) to create a preview You can add some dataset summaries (like [table reporting](../references/sdk/logger.md#report_table)) to create a preview

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docs/fundamentals/artifacts.md
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@ -13,14 +13,15 @@ interface.
Once integrated into code, ClearML automatically logs and tracks models and any snapshots created by the following Once integrated into code, ClearML automatically logs and tracks models and any snapshots created by the following
frameworks: frameworks:
- TensorFlow (see [code example](../guides/frameworks/tensorflow/tensorflow_mnist.md)) * [TensorFlow](../integrations/tensorflow.md)
- Keras (see [code example](../guides/frameworks/keras/keras_tensorboard.md)) * [Keras](../integrations/keras.md)
- PyTorch (see [code example](../guides/frameworks/pytorch/pytorch_mnist.md)) * [PyTorch](../integrations/pytorch.md)
- scikit-learn (only using joblib) (see [code example](../guides/frameworks/scikit-learn/sklearn_joblib_example.md)) * [scikit-learn](../integrations/scikit_learn.md) (only using joblib)
- XGBoost (only using joblib) (see [code example](../guides/frameworks/xgboost/xgboost_sample.md)) * [XGBoost](../integrations/xgboost.md) (only using joblib)
- FastAI (see [code example](../guides/frameworks/fastai/fastai_with_tensorboard.md)) * [Fast.ai](../integrations/fastai.md)
- MegEngine (see [code example](../guides/frameworks/megengine/megengine_mnist.md)) * [MegEngine](../integrations/megengine.md)
- CatBoost (see [code example](../guides/frameworks/catboost/catboost.md)) * [CatBoost](../integrations/catboost.md)
* [MONAI](../integrations/monai.md))
When a supported framework loads a weights file, the running task will be automatically updated, with its input model When a supported framework loads a weights file, the running task will be automatically updated, with its input model
pointing directly to the original training task's model. pointing directly to the original training task's model.

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docs/integrations/optuna.md
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@ -9,11 +9,11 @@ Optuna into ClearML's automated hyperparameter optimization.
The [HyperParameterOptimizer](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class contains ClearML's The [HyperParameterOptimizer](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class contains ClearML's
hyperparameter optimization modules. Its modular design enables using different optimizers, including existing software hyperparameter optimization modules. Its modular design enables using different optimizers, including existing software
frameworks, like Optuna, enabling simple, frameworks, like Optuna, enabling simple,
accurate, and fast hyperparameter optimization. The Optuna ([`automation.optuna.OptimizerOptuna`](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md)), accurate, and fast hyperparameter optimization. The Optuna ([`automation.optuna.OptimizerOptuna`](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md))
optimizer lets you simultaneously optimize many hyperparameters efficiently by relying on early stopping (pruning) optimizer lets you simultaneously optimize many hyperparameters efficiently by relying on early stopping (pruning)
and smart resource allocation. and smart resource allocation.
To use optuna in ClearML's hyperparameter optimization, you must first install it. When you instantiate `HyperParameterOptimizer`, To use Optuna in ClearML's hyperparameter optimization, you must first install it. When you instantiate `HyperParameterOptimizer`,
pass `OptimizerOptuna` as the `optimizer_class` argument: pass `OptimizerOptuna` as the `optimizer_class` argument:
```python ```python

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docs/integrations/transformers.md
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@ -56,6 +56,7 @@ Additionally, you can view all of your Transformers runs tracked by ClearML in t
Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model. Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model.
You can also select multiple experiments and directly [compare](../webapp/webapp_exp_comparing.md) them. You can also select multiple experiments and directly [compare](../webapp/webapp_exp_comparing.md) them.
See an example of Transformers and ClearML in action [here](../guides/frameworks/huggingface/transformers.md).
## Remote Execution ## Remote Execution
ClearML logs all the information required to reproduce an experiment on a different machine (installed packages, ClearML logs all the information required to reproduce an experiment on a different machine (installed packages,