Python Logger#
You can use NeptuneHandler
to track logs from the Python Logger
.
NeptuneHandler
#
A handler that sends the records created by the logger to Neptune.
Logs will be stored as a StringSeries
field.
Parameters
Name | Type | Default | Description |
---|---|---|---|
run |
Run , optional |
None |
An existing run reference, as returned by neptune.init_run() . |
level |
int , optional |
logging.NOTSET |
Log level of the logs to be captured by the handler. Defaults to logging.NOTSET , which captures all logs that match the logger level. |
path |
str , optional |
None |
Path to the StringSeries field used for captured logs. If None , tracked metadata will be stored under the monitoring namespace. |
Examples
Create a logger:
Create a Neptune run:
If Neptune can't find your project name or API token
As a best practice, you should save your Neptune API token and project name as environment variables:
Alternatively, you can pass the information when using a function that takes api_token
and project
as arguments:
run = neptune.init_run(
api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8", # (1)!
project="ml-team/classification", # (2)!
)
- In the bottom-left corner, expand the user menu and select Get my API token.
- You can copy the path from the project details ( → Details & privacy).
If you haven't registered, you can log anonymously to a public project:
Make sure not to publish sensitive data through your code!
Create a NeptuneHandler object to capture the logs:
from neptune.integrations.python_logger import NeptuneHandler
npt_handler = NeptuneHandler(run=run)
logger.addHandler(npt_handler)
# Log something to the logger
logger.debug("Starting data preparation")
logger.debug("Data preparation done")
See also
Logger
reference in the Python documentation