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