Initialization functions

rerun

def init(application_id, *, recording_id=None, spawn=False, init_logging=True, default_enabled=True, strict=False, exp_init_blueprint=False, exp_add_to_app_default_blueprint=True)

Initialize the Rerun SDK with a user-chosen application id (name).

You must call this function first in order to initialize a global recording. Without an active recording, all methods of the SDK will turn into no-ops.

For more advanced use cases, e.g. multiple recordings setups, see rerun.new_recording.

Warning

If you don't specify a recording_id, it will default to a random value that is generated once at the start of the process. That value will be kept around for the whole lifetime of the process, and even inherited by all its subprocesses, if any.

This makes it trivial to log data to the same recording in a multiprocess setup, but it also means that the following code will not create two distinct recordings:

rr.init("my_app")
rr.init("my_app")

To create distinct recordings from the same process, specify distinct recording IDs:

from uuid import uuid4
rr.init("my_app", recording_id=uuid4())
rr.init("my_app", recording_id=uuid4())

PARAMETER	DESCRIPTION
application_id	Your Rerun recordings will be categorized by this application id, so try to pick a unique one for each application that uses the Rerun SDK. For example, if you have one application doing object detection and another doing camera calibration, you could have rerun.init("object_detector") and rerun.init("calibrator"). Application ids starting with rerun_example_ are reserved for Rerun examples, and will be treated specially by the Rerun Viewer. In particular, it will opt-in to more analytics, and will also seed the global random number generator deterministically. TYPE: str
recording_id	Set the recording ID that this process is logging to, as a UUIDv4. The default recording_id is based on multiprocessing.current_process().authkey which means that all processes spawned with multiprocessing will have the same default recording_id. If you are not using multiprocessing and still want several different Python processes to log to the same Rerun instance (and be part of the same recording), you will need to manually assign them all the same recording_id. Any random UUIDv4 will work, or copy the recording id for the parent process. TYPE: Optional[str] DEFAULT: None
spawn	Spawn a Rerun Viewer and stream logging data to it. Short for calling spawn separately. If you don't call this, log events will be buffered indefinitely until you call either connect, show, or save TYPE: bool DEFAULT: False
default_enabled	Should Rerun logging be on by default? Can be overridden with the RERUN env-var, e.g. RERUN=on or RERUN=off. TYPE: bool DEFAULT: True
init_logging	Should we initialize the logging for this application? TYPE: bool DEFAULT: True
strict	If True, an exceptions is raised on use error (wrong parameter types, etc.). If False, errors are logged as warnings instead. TYPE: bool DEFAULT: False
exp_init_blueprint	(Experimental) Should we initialize the blueprint for this application? TYPE: bool DEFAULT: False
exp_add_to_app_default_blueprint	(Experimental) Should the blueprint append to the existing app-default blueprint instead of creating a new one. TYPE: bool DEFAULT: True

def connect(addr=None, *, flush_timeout_sec=2.0, recording=None)

Connect to a remote Rerun Viewer on the given ip:port.

Requires that you first start a Rerun Viewer by typing 'rerun' in a terminal.

This function returns immediately.

PARAMETER	DESCRIPTION
addr	The ip:port to connect to TYPE: str | None DEFAULT: None
flush_timeout_sec	The minimum time the SDK will wait during a flush before potentially dropping data if progress is not being made. Passing None indicates no timeout, and can cause a call to flush to block indefinitely. TYPE: float | None DEFAULT: 2.0
recording	Specifies the rerun.RecordingStream to use. If left unspecified, defaults to the current active data recording, if there is one. See also: rerun.init, rerun.set_global_data_recording. TYPE: RecordingStream | None DEFAULT: None

def disconnect(recording=None)

Closes all TCP connections, servers, and files.

Closes all TCP connections, servers, and files that have been opened with [rerun.connect], [rerun.serve], [rerun.save] or [rerun.spawn].

PARAMETER	DESCRIPTION
recording	Specifies the rerun.RecordingStream to use. If left unspecified, defaults to the current active data recording, if there is one. See also: rerun.init, rerun.set_global_data_recording. TYPE: RecordingStream | None DEFAULT: None

def save(path, recording=None)

Stream all log-data to a file.

Call this before you log any data!

PARAMETER	DESCRIPTION
path	The path to save the data to. TYPE: str
recording	Specifies the rerun.RecordingStream to use. If left unspecified, defaults to the current active data recording, if there is one. See also: rerun.init, rerun.set_global_data_recording. TYPE: RecordingStream | None DEFAULT: None

def serve(*, open_browser=True, web_port=None, ws_port=None, recording=None, server_memory_limit='25%')

Serve log-data over WebSockets and serve a Rerun web viewer over HTTP.

You can also connect to this server with the native viewer using rerun localhost:9090.

The WebSocket server will buffer all log data in memory so that late connecting viewers will get all the data. You can limit the amount of data buffered by the WebSocket server with the server_memory_limit argument. Once reached, the earliest logged data will be dropped. Note that this means that timeless data may be dropped if logged early.

This function returns immediately.

PARAMETER	DESCRIPTION
open_browser	Open the default browser to the viewer. TYPE: bool DEFAULT: True
web_port	The port to serve the web viewer on (defaults to 9090). TYPE: int | None DEFAULT: None
ws_port	The port to serve the WebSocket server on (defaults to 9877) TYPE: int | None DEFAULT: None
recording	Specifies the rerun.RecordingStream to use. If left unspecified, defaults to the current active data recording, if there is one. See also: rerun.init, rerun.set_global_data_recording. TYPE: RecordingStream | None DEFAULT: None
server_memory_limit	Maximum amount of memory to use for buffering log data for clients that connect late. This can be a percentage of the total ram (e.g. "50%") or an absolute value (e.g. "4GB"). TYPE: str DEFAULT: '25%'

def spawn(*, port=9876, connect=True, memory_limit='75%', recording=None)

Spawn a Rerun Viewer, listening on the given port.

This is often the easiest and best way to use Rerun. Just call this once at the start of your program.

You can also call rerun.init with a spawn=True argument.

PARAMETER	DESCRIPTION
port	The port to listen on. TYPE: int DEFAULT: 9876
connect	also connect to the viewer and stream logging data to it. TYPE: bool DEFAULT: True
memory_limit	An upper limit on how much memory the Rerun Viewer should use. When this limit is reached, Rerun will drop the oldest data. Example: 16GB or 50% (of system total). TYPE: str DEFAULT: '75%'
recording	Specifies the rerun.RecordingStream to use if connect = True. If left unspecified, defaults to the current active data recording, if there is one. See also: rerun.init, rerun.set_global_data_recording. TYPE: RecordingStream | None DEFAULT: None

def memory_recording(recording=None)

Streams all log-data to a memory buffer.

This can be used to display the RRD to alternative formats such as html. See: rerun.MemoryRecording.as_html.

PARAMETER	DESCRIPTION
recording	Specifies the rerun.RecordingStream to use. If left unspecified, defaults to the current active data recording, if there is one. See also: rerun.init, rerun.set_global_data_recording. TYPE: RecordingStream | None DEFAULT: None

RETURNS	DESCRIPTION
MemoryRecording	A memory recording object that can be used to read the data.