mirror of
https://github.com/clearml/clearml-docs
synced 2025-01-31 14:37:18 +00:00
122 lines
5.2 KiB
Markdown
122 lines
5.2 KiB
Markdown
---
|
|
title: LightGBM
|
|
---
|
|
|
|
:::tip
|
|
If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
|
|
instructions.
|
|
:::
|
|
|
|
ClearML integrates seamlessly with [LightGBM](https://github.com/microsoft/LightGBM), automatically logging its models,
|
|
metric plots, and parameters.
|
|
|
|
All you have to do is simply add two lines of code to your LightGBM script:
|
|
|
|
```python
|
|
from clearml import Task
|
|
task = Task.init(task_name="<task_name>", project_name="<project_name>")
|
|
```
|
|
|
|
And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
|
|
* Source code and uncommitted changes
|
|
* Installed packages
|
|
* LightGBM model files
|
|
* Configuration applied to LightGBM (parameters)
|
|
* LightGBM metric plots
|
|
* Console output
|
|
* General details such as machine details, runtime, creation date etc.
|
|
* And more
|
|
|
|
You can view all the task details in the [WebApp](../webapp/webapp_exp_track_visual.md).
|
|
|
|
See an example of LightGBM and ClearML in action [here](../guides/frameworks/lightgbm/lightgbm_example.md).
|
|
|
|
|
|
|
|
## Automatic Logging Control
|
|
By default, when ClearML is integrated into your LightGBM script, it captures models, metric plots, and configuration.
|
|
But, you may want to have more control over what your experiment logs.
|
|
|
|
To control a task's framework logging, use the `auto_connect_frameworks` parameter of [`Task.init()`](../references/sdk/task.md#taskinit).
|
|
Completely disable all automatic logging by setting the parameter to `False`. For finer grained control of logged
|
|
frameworks, input a dictionary, with framework-boolean pairs.
|
|
|
|
For example:
|
|
|
|
```python
|
|
auto_connect_frameworks={
|
|
'lightgbm': False, 'catboost': False, 'tensorflow': False, 'tensorboard': False,
|
|
'xgboost': False, 'scikit': True, 'fastai': True, 'pytorch': True,
|
|
'hydra': True, 'detect_repository': True, 'tfdefines': True, 'joblib': True,
|
|
'megengine': True
|
|
}
|
|
```
|
|
|
|
You can also input wildcards as dictionary values, so ClearML will log a model created by a framework only if its local
|
|
path matches at least one wildcard.
|
|
|
|
For example, in the code below, ClearML will log LightGBM models only if their paths have the `.pt` extension. The
|
|
unspecified frameworks' values default to true so all their models are automatically logged.
|
|
|
|
```python
|
|
auto_connect_frameworks={'lightgbm' : '*.pt'}
|
|
```
|
|
|
|
## Manual Logging
|
|
To augment its automatic logging, ClearML also provides an explicit logging interface.
|
|
|
|
See more information about explicitly logging information to a ClearML Task:
|
|
* [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
|
|
* [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
|
|
* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
|
|
* [Scalars](../clearml_sdk/task_sdk.md#scalars)
|
|
* [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
|
|
|
|
See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).
|
|
|
|
## Remote Execution
|
|
ClearML logs all the information required to reproduce an experiment on a different machine (installed packages,
|
|
uncommitted changes etc.). The [ClearML Agent](../clearml_agent) listens to designated queues and when a task is enqueued,
|
|
the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the
|
|
experiment manager.
|
|
|
|
Deploy a ClearML Agent onto any machine (e.g. a cloud VM, a local GPU machine, your own laptop) by simply running the
|
|
following command on it:
|
|
|
|
```commandline
|
|
clearml-agent daemon --queue <queues_to_listen_to> [--docker]
|
|
```
|
|
|
|
Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to help you manage cloud workloads in the
|
|
cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up
|
|
and shuts down instances as needed, according to a resource budget that you set.
|
|
|
|
### Cloning, Editing, and Enqueuing
|
|
|
|
|
|
|
|
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
|
|
with the new configuration on a remote machine:
|
|
|
|
* Clone the experiment
|
|
* Edit the hyperparameters and/or other details
|
|
* Enqueue the task
|
|
|
|
The ClearML Agent executing the task will use the new values to [override any hard coded values](../clearml_agent).
|
|
|
|
### Executing a Task Remotely
|
|
|
|
You can set a task to be executed remotely programmatically by adding [`Task.execute_remotely()`](../references/sdk/task.md#execute_remotely)
|
|
to your script. This method stops the current local execution of the task, and then enqueues it to a specified queue to
|
|
re-run it on a remote machine.
|
|
|
|
```python
|
|
# If executed locally, process will terminate, and a copy will be executed by an agent instead
|
|
task.execute_remotely(queue_name='default', exit_process=True)
|
|
```
|
|
|
|
## Hyperparameter Optimization
|
|
Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
|
|
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
|
|
for more information.
|