mirror of
https://github.com/clearml/clearml-docs
synced 2025-02-19 02:29:07 +00:00
35 lines
1.5 KiB
Markdown
35 lines
1.5 KiB
Markdown
---
|
||
title: XGBoost
|
||
displayed_sidebar: mainSidebar
|
||
---
|
||
|
||
The [xgboost_metrics.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/xgboost/xgboost_metrics.py)
|
||
example demonstrates the integration of ClearML into code that uses XGBoost to train a network on the scikit-learn [iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris)
|
||
classification dataset. ClearML automatically captures models and scalars logged with XGBoost.
|
||
|
||
When the script runs, it creates a ClearML experiment named `xgboost metric auto reporting`, which is associated with
|
||
the `examples` project.
|
||
|
||
## Scalars
|
||
ClearML automatically captures scalars logged with XGBoost, which can be visualized in plots in the
|
||
ClearML WebApp, in the experiment's **SCALARS** tab.
|
||
|
||
|
||
|
||
## Models
|
||
|
||
ClearML automatically captures the model logged using the `xgboost.save` method, and saves it as an artifact.
|
||
|
||
View saved snapshots in the experiment's **ARTIFACTS** tab.
|
||
|
||
|
||
|
||
To view the model details, click the model name in the **ARTIFACTS** page, which will open the model's info tab. Alternatively, download the model.
|
||
|
||
|
||
|
||
## Console
|
||
|
||
All console output during the script’s execution appears in the experiment’s **CONSOLE** page.
|
||
|
||
 |