You can track data to Neptune using one of the four connection modes:
You can select mode by passing
mode parameter to the
import neptune.new as neptune# A default connection mode is the asynchronous mode# Other possible values are "async", "sync", "offline", "debug"CONNECTION_MODE = "async"run = neptune.init(name="My new run", mode=CONNECTION_MODE)
All tracking calls (like
save()) are non-blocking. The tracked data is temporarily stored on the local disk and synchronized with the Neptune server in the background (by a separate synchronization thread).
We recommend using Neptune with persistent disks to be able to restore tracked data in case your machine got restarted (e.g. spot instances) or in case there were some connectivity issues with Neptune servers.
Neptune triggers disk flushing:
Every 5 seconds (this period is configurable, see
When you invoke
At the end of the run (destruction of the
Tracking calls in asynchronous mode do not throw exceptions related to connectivity or metadata consistency issues (more in the distributed computing section). Issues related to connectivity or metadata consistency issues will be printed to
Tracking calls return only after the Neptune server responds that the data was stored.
import neptune.new as neptunerun = neptune.init(name="My new run", mode="sync")
In this mode, no connection to Neptune servers is established. Instead, all the tracked metadata is stored on the local disk and can be uploaded to Neptune servers manually via
neptune sync command.
import neptune.new as neptunerun = neptune.init(name="My new run", mode="offline")
Debug mode can come in handy when you are debugging your code and would like to not pollute your project. In this mode, no calls are made to Neptune servers, regardless of what happens in the code. In contrast to Offline mode, all data are stored only in memory.
import neptune.new as neptunerun = neptune.init(name="My new run", mode="debug")