mirror of
https://github.com/clearml/clearml-docs
synced 2025-06-26 18:17:44 +00:00
120 lines
5.3 KiB
Markdown
120 lines
5.3 KiB
Markdown
---
|
||
title: Scikit-Learn
|
||
---
|
||
|
||
:::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 [Scikit-Learn](https://scikit-learn.org/stable/), automatically logging models created
|
||
with `joblib`.
|
||
|
||
All you have to do is simply add two lines of code to your scikit-learn 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
|
||
* Joblib model files
|
||
* Console output
|
||
* General details such as machine details, runtime, creation date etc.
|
||
* Hyperparameters created with standard python packages (e.g. argparse, click, Python Fire, etc.)
|
||
* And more
|
||
|
||
You can view all the task details in the [WebApp](../webapp/webapp_exp_track_visual.md).
|
||
|
||
## Automatic Logging Control
|
||
By default, when ClearML is integrated into your scikit-learn script, it captures models, and
|
||
scalars. 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={
|
||
'joblib': False, 'xgboost': True, 'catboost': True, 'tensorflow': True, 'tensorboard': True,
|
||
'pytorch': True, 'scikit': True, 'fastai': True, 'lightgbm': False,
|
||
'hydra': True, 'detect_repository': True, 'tfdefines': True,
|
||
'megengine': True, 'jsonargparse': 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 joblib models only if their paths have the `.pkl` extension. The
|
||
unspecified frameworks' values default to true so all their models are automatically logged.
|
||
|
||
```python
|
||
auto_connect_frameworks={'joblib' : '*.pkl'}
|
||
```
|
||
|
||
## 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).
|
||
|
||
## Examples
|
||
|
||
Take a look at ClearML's scikit-learn examples. The examples use scikit-learn and ClearML in different configurations with
|
||
additional tools, like Matplotlib:
|
||
* [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.
|
||
* [Scikit-Learn with Matplotlib](../guides/frameworks/scikit-learn/sklearn_matplotlib_example.md) - Demonstrates ClearML automatically logging scatter diagrams created with Matplotlib.
|
||
|
||
|
||
## 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)
|
||
```
|