Neptune app overview#
In the Neptune web application (UI), you can:
- View all logged metadata in a folder-like structure.
- Preview various data types, such as tables, images, and video.
- Monitor your training metrics live.
- Organize, group, and filter runs.
- View run diffs side by side.
- Contrast artifact metadata between runs.
- Compare run metrics individually or as group averages.
- Save customized views and dashboards project-wide.
- Share persistent links with anyone.
- Download data directly from the app.
- Edit run descriptions and add tags.
- Stop or abort runs directly from the app.
- Manage your workspace and monitor usage.
How does it work?#
import neptune
from sklearn.datasets import load_wine
...
run = neptune.init_run()
data = load_wine()
X_train, X_test, y_train, y_test = train_test_split(...)
PARAMS = {"n_estimators": 10, "max_depth": 3, ...}
run["parameters"] = PARAMS
clf = RandomForestClassifier(**PARAMS)
...
test_f1_score = f1_score(y_test, y_test_pred.argmax(axis=1), average="macro")
run["test_f1"] = test_f1_score
run["model"].upload("model.pkl")
Browse examples#
Click the link to see an example in Neptune.
Component | Neptune example | Description & docs |
---|---|---|
Runs table | View in Neptune ≫ | The metadata of the runs organized in a table view. You can customize and save table views for later. |
Comparison of runs | View in Neptune ≫ | Comparison view of selected runs. Contrast the metadata in different ways by switching between the dashboards. |
Logged metrics | View in Neptune ≫ | Series of values are auto-displayed as charts. |
Logged hardware consumption | View in Neptune ≫ | Neptune logs hardware consumption metrics by default. |
Logged interactive charts | View in Neptune ≫ | You can display charts generated with plotting libraries in an interactive way. |
Logged images | View in Neptune ≫ | Preview any uploaded images. |
Custom dashboard | View in Neptune ≫ | You can combine different metadata types and widgets in a single view by creating custom dashboards. |