API reference#
-
 
Run
Work with runs individually or in bulk.
-
 
management
Manage your workspace and users.
-
 Field types
A field is a location where metadata is logged. The field type determines the available methods and display options.
-
 Environment variables
Use environment variables to make your setup more smooth and secure.
-
 Connection modes
Set the logging and synchronization mode.
-
 CLI
You can use the Neptune Command Line Interface to manage and sync locally stored metadata.
Browse the complete list of functions, parameters, and constants in the index →
Integrations#
API references for Neptune integrations with other ML frameworks:
- Apache Airflow
- Catalyst
- Detectron2
- fastai
- Transformers
- Kedro
- Keras
- LightGBM
- Lightning
- MLflow
- Optuna
- Prophet
- PyTorch
- PyTorch Ignite
- Sacred
- scikit-learn
- skorch
- TensorBoard
- XGBoost
Errors, warnings, and other messages#
For error help pages, expand the Erros and messages section in the left nav.
Other errors:
Out of range float values are not JSON compliant
→ Downgrade simplejson to3.18
Connection pool is full, discarding connection: app.neptune.ai
→ Non-issue unrelated to Neptune.1
See also#
- NQL (Neptune Query Language) – used to construct complex queries when fetching runs.
- System namespace (
sys
) – contains the name, description, tags, creation time, and other auto-logged metadata. - Change the location of the
.neptune
folder – see how to move the directory or access it from elsewhere in your system. - Set up a Neptune error handling function
- Help ≫ Debugging options
-
The error is shown by urllib3 to indicate that a connection is dropped after a request is completed. It doesn't imply data loss. For details, see StackOverflow . ↩