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PyPI · #3381 most downloaded on PyPI
The Rerun Logging SDK
Last release 18 days ago
16 Sep 2026
Ships on a steady schedule
a new release about every 2 weeks
Nearly every release is documented
notes for 60 of the last 60 stable releases
Nothing withdrawn
no release was ever pulled
4 years old
153 releases · first in 2023
One column per quarter.
Bump versions to 0.38.1
Bump versions to 0.38.1
Bump versions to 0.38.0
Bump versions to 0.38.0
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer .
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data.
Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedRerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer .
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data.
Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedVideoFrameReference works with VideoStream📖 Release notes: https://rerun.io/docs/changelog/changeset-0-37#videoframereference-works-with-videostream
All SDKs : datatypes is renamed to encodings . The old spelling still works, deprecated.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data.
Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --locked🧳 Migration guide: https://rerun.io/docs/changelog/changeset-0-37#breaking-changes
rerun rrd stats tells you whether a recording should be optimized📖 Release notes: https://rerun.io/docs/changelog/changeset-0-37#highlights
datatypes is renamed to encodings. The old spelling still works, deprecated.send_properties(false).Loggable is replaced by ArrowDataType, ToArrow, ToArrowOpt, FromArrow and FromArrowOpt.rerun-sdk[datafusion] and rerun-sdk[dataplatform] extras are removed, use rerun-sdk[catalog].Mp4Reader emits VideoStream:is_keyframe as a single sparse marker chunk.🧳 Migration guide: https://rerun.io/docs/changelog/changeset-0-37#breaking-changes
datatypes to encodings 582f273datatypes to encodings 582f273rerun-sdk[datafusion] and rerun-sdk[dataplatform] in favor of rerun-sdk[catalog] b51a09ais_keyframe for video fields c7feeedNone 19dd869segment_store() 52a16b8datatypes to encodings 582f273Loggable with four (de)serialization traits de85d2frrd::optimize_file API to the rerun crate 7f4ee31Clear c358596TransformAxes3D blanking the whole 3D view when one frame is unresolvable 7c76d0d (thanks @lsy3, @luke-alloy!)Struct proto into JSON and guard against recursion 1202ee7/dataset/<entry_id> for URI's leading to datasets 5025e20save_recording & save_blueprint javascript APIs a2660e5Bump versions to 0.37.0-rc.2
Bump versions to 0.37.0-rc.2
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer .
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data.
Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedrrd::optimize📖 Release notes: https://rerun.io/docs/changelog/changeset-0-36#optimize-a-recording-from-rust-with-rrdoptimize
rrd::optimize_file API to the rerun crate 389767fBump versions to 0.36.3-rc.1
Bump versions to 0.36.3-rc.1
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer .
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data.
Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedRerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer .
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data.
Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedTransformAxes3D blanking the whole 3D view when one frame is unresolvable d3d8a99 (thanks @lsy3!)Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer .
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data.
Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --locked🧳 Migration guide: https://rerun.io/docs/changelog/changeset-0-36#breaking-changes
GrpcServerSink📖 Release notes: https://rerun.io/docs/changelog/changeset-0-36#highlights
rerun mcap info now prints detailed file information; the diagnostic checks moved to rerun mcap check.ParquetReader's loading options moved from the constructor to stream().🧳 Migration guide: https://rerun.io/docs/changelog/changeset-0-36#breaking-changes
ViewDir is now a regular (codegen'ed) enum instead of a set of constants 0f6d842Costmap colormap for GridMap 033d6f9ApplicationId to always be EntryName compatible ba6ff46GrpcServerSink multisink compatible and plumb it into C++ & Python 514a49brerun_c is now a public dependency of rerun_cpp 5fcb022clang toolchains ef56603root_group to Hdf5Reader.stream() 3d4f394Chunk.from_property() to Chunk API 9557a7aHdf5Reader using windowed dataset reads 55b95fdGrpcServerSink multisink compatible and plumb it into C++ & Python 514a49bParquetReader: move data conversion knobs from the constructor to .stream() 43ff07aMcapInfo in Python binding via McapReader.info() 3850ed1GrpcServerSink multisink compatible and plumb it into C++ & Python 514a49bBlueprintActivationCommand in right order for newest_first 3fbef01--detach-process for standalone CLI #12873 (thanks @Travor278!)Asset3D and Mesh3D 6986e8bMcapFile abstraction with cached derived McapInfo b613eb5mcap info and move checks to mcap check 147668dre_lenses 5f1ba60File loading paths across web and native 8399e93Nothing published for this version
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer .
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data.
Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --locked🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-35
The Viewer's command palette (Cmd+k/Ctrl+k) now lets you find & select entities & components!
It also adds context dependent commands like, like refreshing the currently selected
catalog or dataset.
The Viewer now includes an experimental built-in catalog for working with local recordings without starting a separate catalog server.
For now, it has to be activated through the settings menu since there's still some rough edges.
The main advantage of this is that it allows you to stream arbitrary large rrd files from disk with ease!
The internal Viewer catalog implements the entire functionality of the OSS redap server protocol and can be connected to via the Python SDK.
For security reasons, we limit this to connections from the same machine.
It also is a first step in a series of changes that make the Viewer more streamlined & explicit
about consuming live versus server data.
Rerun now detects links known url formats (links to rrds, hub datasets, etc.) and shows them as a compact link button:
Previously these all would all be shown as a plain link.
The Rerun Hub dataset open button has been reworked as well:
This release introduces Hdf5Reader, which reads an HDF5 file into a lazy stream of chunks — each group becomes an entity, each dataset a component:
from rerun.experimental import Hdf5Reader, IndexColumn
reader = Hdf5Reader("episode.h5")
store = reader.stream(index_column=IndexColumn.timestamp("/time", input_unit="s")).collect()Mp4Reader (Rust & Python) can now plumb data through FFmpeg to remove unsupported B-frames, transcode to different output formats, adjust gop size, and take advantage of some GPU accelerated codecs.
Also added improvements around reporting unsupported codecs more clearly, and handles large MP4 offsets without crashing.
This is experimental so we are still iterating on how to make it as seamless as possible to go from mp4 to RRD.
Feedback is welcome!
You can now read a selected time range from a source MCAP file.
The corresponding option is available both for the Python McapReader and the CLI (see rerun mcap convert --help).
Besides simple time filtering, this also enables large recordings to be converted and optimized in bounded windows instead of loading the entire recording at once.
In a 20 GB test recording, processing 32 windows reduced peak memory use from about 26 GB to 1.4 GB and reduced wall-clock time from 14.3 seconds to 5.8 seconds.
Some user code is required to loop over windows in the source MCAP.
The converter can also read corrupted MCAP files directly without a separate recovery pass.
When the recover option is enabled in McapReader or CLI, the converter will attempt to recover the missing summary and index on-the-fly during processing.
All ROS 2 MCAP messages that have a top-level std_msgs/msg/Header "header" or a builtin_interfaces/Time "stamp" field now appear also on the ros2_timestamp timeline in addition to the standard MCAP log and publish timelines.
Previously, the ros2_timestamp timeline was only populated for ROS messages that were converted to Rerun archetypes.
Now this is supported for any ROS message that goes through schema reflection (e.g. custom ROS message types), making it easier to see all data in header timestamp order if desired.
StateChange::with_state now takes an iterator of values.StateChange::single("open") for one state, or pass an array such as with_state(["open"]).--follow mode for tailing .rrd files has been removed..rrd file with multiple sinks instead.🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-35
Mp4Reader using ffmpeg sidecar 43f4e1fBarChart 05c087cre_hdf5 378ecb1Hdf5Reader fe43874re_video utilities to Python 494d5c1IndexColumn dataclass in ParquetReader a7bac45ctrl+m and ctrl+shift+m shortcuts when a view is focused 128b5a7Mp4Reader 6b45b0d:<port> suffix to remote servers (except for HTTPS on 443) 823af63--follow) mode 14f4d32SpatialInformation blueprint controls to 2D view 5797b85.rrds (eagerly) into the internal catalog on Wasm 33cbf85tf#/<entity> for "" frame IDs in transform retrieval 507732fRrdFingerprint as a way to uniquely identify recordings ab5f279convert_mcap_protobuf doc snippets 0f1484e.rrds into re_server via Origin Private Filesystem APIs 11482fbChunkProvider and RrdChunkProvider async 8d0e46csensor_msgs/CameraInfo to lens and remove superfluous CoordinateFrame 745647bquery_metrics() accuracy d66340dtonic-web-wasm-client with cherry-picked trailer changes 961bf9bRerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer .
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data.
Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedtonic-web-wasm-client with cherry-picked trailer changes 7436cdaBump versions to 0.34.1-rc.1
Bump versions to 0.34.1-rc.1
Python: The deprecated python module rerun.recording has been removed; Use rerun.experimental.RrdReader instead.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --locked🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-34
We've added a MCP that allows an llm agent to see and interact with the Viewer! You could ask your agent to
The agent has full control over the Viewer, meaning it can see and click any widget.
Here's an example where Claude Sonnet was asked to create a fancy particle animation of the Rerun logo and verify its work using the mcp in the open Viewer (sped up by a lot, except when showing the end result):
https://github.com/user-attachments/assets/14ffe7ed-6000-4193-900c-627784682125
Once it wrote the script, it logged the recording to the Viewer, and then iterated until the result looked as requested. It adjusted the camera position, improved the particle rendering by looking at different frames in the animation, and then debugged why the fade out animation was still showing particles on the last frame.
<details> <summary>Full prompt</summary>
/goal Create a new rerun python example in this folder that uses reruns 2D shapes to recreate the rerun logo (rerun-wordmark-black.svg). There should be a nice fade-in animation in the beginning, 10 frames duration. Then pause a bit with the full rerun logo visible and then the shapes should explosively fade away with a 20 frame animation before the recording ends.
You may only stop once the recreated logo in the viewer looks close to the provided svg (black text, white background). Use the mcp to verify in the open viewer, don't ever kill it. Once done, launch an opus agent and ask it to judge how closely it looks to the original image. Keep going until it's convinced that it looks close. </details>
See our mcp docs to get started.
https://rerun.io/learn is a great way to learn how the Rerun data model covers the full physical AI experiment loop. It is a short, hands-on course for robotics ML engineers who want the full robot learning data loop in one place:
raw data -> RRD -> derived layers -> dataset queries -> training -> evaluation
We added new skills to the Rerun repo to make it easier to investigate existing robotics data with Rerun. You can install the skills in your project via:
npx skills add rerun-io/rerun
The new learning course also shows how these agent skills can be used to collect, refine and train with robotics data.
VoxelGridMap archetypeRerun now supports sparse voxel grids through a new VoxelGridMap archetype (thanks to @makeecat for the contribution!).
The archetype supports sparse indexing, anisotropic voxel sizes, pose offsets, and optional explicit colors or values & colormap per voxel.
Rerun's MCAP importer now also converts the dense ROS nav2_msgs/VoxelGrid and Foxglove VoxelGrid formats to Rerun VoxelGridMap.
And if you wonder how the smooth 3D navigation through the voxel scene in this video was done, see below!
<!-- https://static.rerun.io/7724132292eb25c643530304c6699270aeaa68e1_voxel_grid_teaser.mp4 --> https://github.com/user-attachments/assets/87fb80da-66dd-4fcd-8b35-ab553696f536
You can now use a gamepad to navigate 3D views in the native viewer. This makes it easier to do fine-grained, complex maneuvers with varying speed - e.g. for navigating large scenes or for screen videos. Analog sticks control the eye position and look target, shoulder triggers move the eye up and down, and shoulder buttons accelerate/decelerate.
Note: The gamepad feature is currently experimental and can be activated through the settings menu.
Switch the 3D view's eye controls to FirstPerson for optimal experience.
Under the hood, we use the gilrs crate that supports a wide range of devices.
You can now drag & drop a component right from the streams panel to visualize it in a Time series view or Status timeline.
<!-- https://static.rerun.io/95f484cd8a2e937acd2eafa424bc778fe3ef5d7b_615146790-591024b9-57e7-4864-98f6-0b15ffb7ca2b-1782828747052.mp4 -->
https://github.com/user-attachments/assets/d70587a9-2020-4ae8-9cf3-0fef54dcf896
We added a new debugging UI for visual introspection of the 3D transform cache. This allows to view the tree structure of the transform hierarchy, including potentially disconnected trees, and inspect the latest stored values of each frame node or transform edge. The UI supports horizontal and vertical tree layout and you can filter by transform type (e.g. static or temporal).
Note: this UI is currently a tab in the dev panel (accessible via "Toggle dev panel" in the menu or ctrl/cmd+shift+m). But we are open to making this a dedicated view in the future - let us know if you have any feedback!
<!-- https://static.rerun.io/cc6c41138eeeabb31fb2ec988eefdcd8da446c86_transform_dev_panel_teaser.mp4 --> https://github.com/user-attachments/assets/b4b1ea6e-bce9-4e88-9ede-262f545e3b47
log_tick timeline being automatically created, you'll now have to call set_log_tick_enabled(true).rr.send_dataframe is now stricter for more correctness. See the migration guide for more details.rerun.recording has been removed; Use rerun.experimental.RrdReader instead.DatasetEntry methods have been removed.ParquetReader column rules have been removed in favor of lenses.🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-34
log_tick timeline OPT-IN f734978Points3D cd7fa3eMp4Reader LazyChunkStream binding e87dd89Recording and related APIs 2156155ChunkStore querying with .reader() d14018eViewerClient.close not closing subprocesses on Windows 2bc53b6DatasetEntry.manifest() fd24de9Chunk.from_record_batch more flexible d1771d4column_rules from ParquetReader API d18da8epack built-in function to lenses a5ea965DeriveLens helpers for common components a93eb62FixedRateSampler 45bddb7ChunkStore f5747cfApp::logic callbacks to examples #12810 (thanks @adsick!)null values in table UI 8a0f7c5face_rendering on arkit_scenes example 3d04f6frerun rrd optimize: continue on error 63e0882source_component and selector when assigning colors to plots 71e2cf7GridMap colormap e8386c5App::current_query() for external viewer #12811 (thanks @adsick!)rerun viewer-mcp aa56c88cdr-encoding with re_cdr d2bc3b8WatchEvents in re_server 8e07bdbnav2_msgs/VoxelGrid 651c140foxglove.VoxelGrid c8580c1Bump versions to 0.34.0-rc.4
Bump versions to 0.34.0-rc.4
Bump versions to 0.34.0-rc.3
Bump versions to 0.34.0-rc.3
Bump versions to 0.34.0-rc.2
Bump versions to 0.34.0-rc.2
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedRerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --locked🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-33
After our large 0.32.0 release, this one is more focused but still has some great new things in store for you!
This release comes with a new headless mode for the viewer! Together with smaller improvements to the screenshot API, this can be an invaluable tool for automation and LLM usage.
import rerun.blueprint as rrb
from rerun.experimental import ViewerClient
# Spawn a headless viewer; the client owns its lifetime.
# ⚠️ you need a graphics driver to run this (software rasterizers like lavapipe are fine too!).
with ViewerClient(spawn=True, headless=True) as viewer:
rec = rr.RecordingStream("rerun_example_screenshot")
rec.connect_grpc(url=viewer.url)
view = rrb.Spatial3DView(name="my blue 3D", background=[100, 149, 237])
rec.send_blueprint(view)
# Screenshot only the view we created earlier.
viewer.save_screenshot("my_view.png", view_id=view.id)
# Disconnect the RecordingStream before the headless viewer shuts down.
rec.disconnect()
We're planning more features for ViewerClient Python object, including an MCP server allowing agents to fully instrument the Viewer.
Stay tuned!
This release brings a significant optimization to pipelines in the shape of:
from rerun.experimental import RrdReader
lazy_store = RrdReader(...).store()
stream = lazy_store.stream().filter(...)
# more stream operations
The filter is now pushed down to RrdReader, which will selectively load the matching chunks only.
This massively accelerates targeted data extraction from large RRDs (e.g. extract a joint data from a RRD that also contains multiple video streams).
We're continue to perfect the state timeline view, and this release brings this lot of improvements:
https://github.com/user-attachments/assets/b7549593-363f-4e13-ab9b-184d6434fc19
Clear message: the timeline shows a gap until the next state value.Amongst other improvements, we made the play behavior much nicer for our experimental dataset review and table blueprint feature:
<!-- https://static.rerun.io/3cac17c13eb9fe8297161065c939f4001f24cf0a_preview_time_control.mp4 -->
https://github.com/user-attachments/assets/4543af53-52ca-4488-90b0-8c365f9fb89b
On MacOS we used to have a compact title bar for a very long time. Now the same feature comes finally to Windows and some Linux desktops.
Before:
<picture> <img src="https://static.rerun.io/windows-titlebar-old2/41c8b596e27595e00e758bf4b0c07735ede164a9/full.png" alt="bulky title bar before"> <source media="(max-width: 480px)" srcset="https://static.rerun.io/windows-titlebar-old2/41c8b596e27595e00e758bf4b0c07735ede164a9/480w.png"> <source media="(max-width: 768px)" srcset="https://static.rerun.io/windows-titlebar-old2/41c8b596e27595e00e758bf4b0c07735ede164a9/768w.png"> </picture>
✨ After ✨:
<picture> <img src="https://static.rerun.io/windows-improved-window/2ac04bfd99492e4aafe7b892359bfe5894384cbd/full.png" alt="compact title bar after"> <source media="(max-width: 480px)" srcset="https://static.rerun.io/windows-improved-window/2ac04bfd99492e4aafe7b892359bfe5894384cbd/480w.png"> <source media="(max-width: 768px)" srcset="https://static.rerun.io/windows-improved-window/2ac04bfd99492e4aafe7b892359bfe5894384cbd/768w.png"> </picture>
<!-- Bit too bulky!
On Gnome desktop:
<picture> <img src="https://static.rerun.io/rerun-gnome/2fc36bae4a37631d210d52a62de164a974c81b84/full.png" alt="Rerun on Gnome"> <source media="(max-width: 480px)" srcset="https://static.rerun.io/rerun-gnome/2fc36bae4a37631d210d52a62de164a974c81b84/480w.png"> <source media="(max-width: 768px)" srcset="https://static.rerun.io/rerun-gnome/2fc36bae4a37631d210d52a62de164a974c81b84/768w.png"> <source media="(max-width: 1024px)" srcset="https://static.rerun.io/rerun-gnome/2fc36bae4a37631d210d52a62de164a974c81b84/1024w.png"> <source media="(max-width: 1200px)" srcset="https://static.rerun.io/rerun-gnome/2fc36bae4a37631d210d52a62de164a974c81b84/1200w.png"> </picture> -->
If you experience any issues with this you can turn it off in the settings menu.
The Python optional-dependency extra for catalog/query API tools has been renamed to catalog.
| Before | After |
|---|---|
pip install rerun-sdk[dataplatform] |
pip install rerun-sdk[catalog] |
pip install rerun-sdk[datafusion] |
pip install rerun-sdk[catalog] |
🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-33
trim_metadata_keys argument to Chunk.format 2d6cd8d#when anchors without #time_selection 1c242c8dataset.reader(..., using_index_value=...) 7846d38LazyChunkStore.filter() to LazyStore 99a2149SetTime and fix #when anchors d33a6absensor_msgs/PointCloud2 offsets for extra fields 20fe293Clear log support for state timeline 639c5e6VideoStream::is_keyframe in rrd optimize ab74f37Nothing published for this version
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedNothing published for this version
Nothing published for this version
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedNothing published for this version
_Note_: this API is experimental and subject to breaking changes as we continue to improve it.
Rerun is the data layer for physical AI. Log, query, visualize, and stream to training on shared columnar storage built for multimodal data.
Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --locked🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-32
This release introduces a chunk processing API designed for systematic and efficient wrangling of robotics data. It includes:
Chunk object for inspecting, creating, and manipulating chunks.LazyChunkStream class to define memory-bounded chunk-based filtering and transformation pipelines.In addition to enabling powerful data wrangling pipelines, the chunk processing API is significant for offering read/write chunk-level control of RRD files down to the raw Arrow data.
Note: this API is experimental and subject to breaking changes as we continue to improve it.
You can now build tables of recording previews configured with arbitrary blueprints!
<!-- https://static.rerun.io/59636c2a3c79f0f4f543e353a87315ec416bdad8_datasetpreview%20new.mp4 -->
https://github.com/user-attachments/assets/7acf9671-c46a-4355-a50f-2670cc80c4d9
Clickable flags let you curate data directly from the table: toggles update a boolean flag column and are written back to the server.
To try it out, enable the experimental options in the Viewer's settings and try the two Python examples:
table_grid_with_flags for basic grids with clickable flags,
and table_blueprints for the full preview experience.
Limitations, or why this is still experimental:
A new experimental view for visualizing discrete state transitions over time as horizontal colored lanes, useful for state machines, mode transitions, and similar discrete signals. Log state changes with the new StateChange archetype; configure their display on the UI or using StateConfiguration in the blueprint API.
<img width="1728" height="810" alt="image" src="https://github.com/user-attachments/assets/9f301b90-6d7b-487a-9bd9-1ef767f90e05" />
Read our guide to get started. Feedback is appreciated!
GridMap archetype and MCAP support for ROS occupancy gridsRerun now supports 2D grid maps, as used e.g. in robot mapping & navigation applications, through a new GridMap archetype.
GridMap is an image buffer with defined cell size per pixel, which can be embedded as a textured rectangle in a 3D scene.GridMap has a regular ImageBuffer component, so you can also send color images (e.g. to do custom color-mapping in your code).CoordinateFrame("map")). Additionally, an optional translation & rotation offset can be specified.For ROS 2 users:
nav_msgs/OccupancyGrid messages as GridMaps.GridMap from your custom ROS nodes.Here's a demo video showing a typical ROS 2 MCAP recording with multiple map and costmap layers in Rerun:
<!-- https://static.rerun.io/9e9a2cce0b76c8bde35edc0b85dfbaa0dd1db6ec_grid_map_release_0.32.mp4 -->
https://github.com/user-attachments/assets/f31b712d-2dd7-4e45-bb6a-0e103e7016b3
The OSS server (rerun server and rr.server.Server) no longer eagerly loads RRDs in memory when registering datasets.
It instead uses the manifest embedded in the RRDs to load chunks on demand when serving requests.
This greatly extends the amount of data that can be registered and queried for a given memory budget, and makes registration orders of magnitude faster.
Note: This requires the RRDs to have a manifest, which most modern RRDs have.
Legacy RRDs are still eagerly loaded.
Use the rerun rrd optimize CLI to migrate and optimize legacy RRDs.
<picture> <img src="https://static.rerun.io/1b28bdbf505997a039e589a41b6c7cb90971c1dd_tooltip.png" alt="new tooltip for plots" width="600"> </picture>
This release comes with a few significant performance improvements. Among other things:
tf-style named transforms) will now perform vastly betterrrd optimize (former rrd compact) to produce more streaming & object storage friendly data<picture> <img src="https://static.rerun.io/fadf335a4b294030ade19d405811ab41607f898b_brand.png" alt="new rerun app icon" width="400"> </picture> <br>
<picture> <img src="https://static.rerun.io/4aecf4577ab81493fda003882da6faeb886966dd_app-icon.png" alt="new rerun app icon" width="600"> </picture>
You may have noticed a new Rerun logo and app icon! We've also slightly tweaked our color palette. Stay tuned for more exciting news!
As a part of our website update, we've also added a feedback form to all our documentation pages. So you can add your feedback directly to the respective topic.
<picture> <img src="https://static.rerun.io/feedback-form/e96b0889824e4bc0cf42039bead0612953e54a87/full.png" alt="feedback form"> <source media="(max-width: 480px)" srcset="https://static.rerun.io/feedback-form/e96b0889824e4bc0cf42039bead0612953e54a87/480w.png"> <source media="(max-width: 768px)" srcset="https://static.rerun.io/feedback-form/e96b0889824e4bc0cf42039bead0612953e54a87/768w.png"> </picture>
Several improvements in the open-source Rerun SDK are designed specifically to work with Rerun Hub. Here are the key updates that are especially relevant if you're a customer of Rerun Hub:
Rerun Hub customersThe SDK will now fetch chunk data directly from the object store that holds your recordings, without needing to proxy the data through the server. This allows for better performance in highly parallel workloads, as well as lower latency when the client is located close to the data store.
The old proxy path is still supported, and can be opted into using the RERUN_CHUNK_STRATEGY=grpc environment variable.
You can now train PyTorch models directly against the Rerun OSS server, with no intermediate export step!
The new highly experimental rerun.experimental.dataloader module exposes Rerun recordings as iterable or map-style PyTorch datasets, streaming encoded images, scalars, and compressed video (h264/h265/av1) on the fly. Random access, multi-worker prefetching, and DDP support work out of the box.
Each field accepts an optional window=(start_offset, end_offset) parameter, an inclusive range relative to the current index. When set, the field yields the slice of values across that window instead of a single sample. For example, window=(1, CHUNK_SIZE) returns the next CHUNK_SIZE action values after every observation, making action-chunking policies a single query per batch.
See the new LeRobot ACT training example.
Expect breaking changes between releases while we iterate on the design. For large-scale training, Rerun Hub offers a higher-performance backend.
rerun rrd compact renamed to rerun rrd optimize, has profiles and new defaultsDatasetEntry.register requires a sequence of URIs (Python)/tf_static entity by default__mcap_metadata🧳 Full Migration guide: https://rerun.io/docs/reference/migration/migration-0-32
GridMap archetype & visualizer d74cb27/tf_static as default in URDF importer & make configurable in UrdfTree 1f01a57mimic joints from URDF cf4c652GridMap c452a48stream() -> LazyChunkStream to Python UrdfTree dc51f60UrdfTree::compute_joint_transform_batches for lens/chunk pipelines efd045cGridMap at a specific pose 2e99c68LazyChunkStream a0ce421McapLoader to produce LazyChunkStream from MCAP file ef51623Selector in Python SDK ffc088dChunkStore object 9294554ChunkStore 5dd9f23ChunkStream in Python SDK 72ff520RrdLoader produce lazy ChunkStore 2e804c4map and flat_map method to LazyChunkStream 393680cChunk construction methods: from_columns and from_record_batch 547d650exists_ok option to CatalogClient.create_dataset 8d4e1b3Chunk.apply_lenses() API 88fea86rrd compact to rrd optimize c5b027bapply_selector methods to Chunk 5a20bd6Mapping-based LensOutput and improve naming 2fc409e.cancel() on RegistrationHandle 260d119Chunk copy with a new entity path 3d8f97cSelector picklable db20691dataset.segment_store(segment_id) to create a lazy ChunkStore 524b5ccLazyStore from ChunkStore (now returned by dataset.segment_store() and RrdReader.store()) fa63189RrdReader 41ed51asend_chunk to send_chunks and accepts stores and LazyChunkStream c8e0965rerun.recording 8b52512at_entity instead of *_output_columns_at 8e65ff0Lens definition 80ab3a9Chunk-based APIs between Rust and Python a171102apply_selector methods to Chunk 5a20bd6LensOutput to target entity b5709e5Chunk copy with a new entity path 3d8f97cGraphView to rust blueprint api 9327b5frerun download 1c9aa10SpatialView3D e8dc5e0follow not being propagated to http URLs with extensions 09d5f94rerun// and rerun+https at parse time, fixing Viewer bugs for incorrectly distinguishing them 69ff58dSystemCommand::RemoveRedapServer for more thorough cleanup 52bc3eaTexturedRects and use draw order for tie-breaking 76b64c1pose of Foxglove PointCloud (if set) fee2815message_log_time as default timeline for MCAP b687bd6VideoStream streaming 2f73783up_axis in Collada (.dae) mesh importer #12708 (thanks @Abhisheklearn12!). separated dataset in a folder hierarchy a217309VideoStream.is_keyframe component d50eab6rerun rrd compact: split by video GoP boundaries 2485570DatasetView.reader: only fetch schema once b266938CatalogClient: Add RTT and bandwidth probes 87c5e05register now takes a list of URIs 9ec5265is_keyframe marker chunks when running optimize ec6dff0LineStrips3D with VisibleTimeRange 80dd138custom_view example e64abd0CoordinateFrame("") 5bf9c4atf#/ suggestions if applicable 2dbe13aSelector::pipe for calling anonymous functions ea50667LocationFix & LocationFixes 28fe84eRuntime out of Selector 0febb36Selector<DynExpr>-based 75e965anav_msgs/OccupancyGrid c87a9ae__mcap_metadata instead of __properties 3352bb6__mcap_properties instead of __properties 31159e1__mcap_attachments 004539arerun.tracing_session() for support correlation ec9f0480.32.0 - Chunk Processing, Pytorch dataloader, Dataset Review
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Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedon_new_store book-keeping for all messages 029e245follow not being propagated to http URLs with extensions 09d5f94cRerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedCoordinateFrame("") 4b5c2f0Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedSpatialView3D 213957cdocs.rs 6afa84bRerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --lockedFull 0.31.0 changelog
Entry.update(name=…) is deprecated in favor of Entry.set_name(…) #12370
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install -U rerun-sdkcargo add rerun and cargo install rerun-cli --locked🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-31
<!-- Component mappings video --> https://github.com/user-attachments/assets/18954263-1b34-4819-869d-02fa8117d6a3
You can now map components more generally. Want to display your mesh as a point cloud? Just add a point cloud visualizer and select the vertex positions as the source.
<!-- Primitive video --> https://github.com/user-attachments/assets/4e523454-4b3c-492b-a2a4-463f0f17ec51
Our 3D primitives got a new default look!
rr.Server and rr.CatalogClient: address parameter/method renamed to url; rerun server --address is now --host #12402Entry.update(name=…) is deprecated in favor of Entry.set_name(…) #12370🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-31
Op API to be Selector-based d962bfeMesh3D be5a50fframe_prefix to UrdfTree for multi-robot URDF setups 8e27391DynamicArchetype docs with example on how to use builtin batch types 2052af5using_index_value not accepting pyarrow data of the correct types 62b8ac3save() to Recording 4ab863acompress() and as_pil_image() to DepthImage with PNG compression bde6870Chunk API to the Python SDK 32eb891rr.logout to Python SDK 97af60e?url=rerun+http://… in web viewer 0a47b41lz4_flex to prevent web viewer crashes 9355dd8sensor_msgs::PointCloud2 MCAP parser for empty point clouds #12684 (thanks @Woodii1998!)ListArray path 30a86e1Transform3D updates for URDF joints #12666oneof protobuf fields 975d3baFixedSizeListArray 53d2864! operator to Selector to assert non-null values a53c683magnification_filter component to all image archetypes & add bicubic filtering 2c1cceeChunkStoreDiff::SchemaAddition and use it for heuristics 822ac41DynamicArchetype 64d466dTimeSpecToNanos f7eb4cd--timestamp-offset-ns option to MCAP CLI 787e6cc__properties 0b43178re_arrow_combinators into re_lenses and re_lenses_core a9f4ca3Layer to Decoder cf0a800Map and arbitrary oneof fields 5ba2817/robot_description ROS 2 string topics in MCAP 426fcfcSelector evaluation ArrayRef-based 35dff31jsonwebtoken to 10.3 10a42b6--new flag to always spawn a new viewer even if another one is already using the default port 874d3a8rerun download to download full recording from server ac95098rerun rrd stats reporting identical compressed/uncompressed sizes 5ac9604####🇸🇪 Name
This release has a name — Sodermalm!
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Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install rerun-sdkcargo add rerun and cargo install rerun-cli --lockedAmong many other things, this patch addresses a security advisory (SNYK-RUST-JSONWEBTOKEN-15189005) and adds a new example!
__properties cc8f1c2jsonwebtoken to 10.3 312c3b8Among many other things, this patch addresses a security advisory (SNYK-RUST-JSONWEBTOKEN-15189005) and adds a new example!
__properties cc8f1c2jsonwebtoken to 10.3 312c3b8-l/--layer is now -d/--decoder.Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install rerun-sdkcargo add rerun and cargo install rerun-cli --lockedThis patch on top of 0.30.0 comes not only with a random assortment of bug fixes but also some small improvements to the Viewer.
Most notably it's now possible to inspect values that were logged on the same timestamp! <img width="452" height="467" alt="image" src="https://github.com/user-attachments/assets/ee226e5a-9d75-4b79-87cd-8198fa573dc7" />
using_index_value not accepting pyarrow data of the correct types c59df09oneof protobuf fields 84ee94eDynamicArchetype 32c37e8TimeSpecToNanos 31c9a43Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install rerun-sdkcargo add rerun and cargo install rerun-cli --locked🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-30
<!-- https://static.rerun.io/9985de090ad18e3b169d35e1a485939d761ce60f_anyscalar action 4.mp4 -->
https://github.com/user-attachments/assets/cb10e8fd-7428-44ae-9f22-37fd721a357f
You can now plot any scalar value, even if it lacks Rerun semantics in time series views. For instance, this lets you plot any value in an MCAP file.
In addition to plotting scalars from MCAP files, it is now possible to visualize arbitrary scalar components that were logged using AnyValues or DynamicArchetype. The supported data types are:
Float32 and Float64Int8, Int16, Int32, and Int64UInt8, UInt16, UInt32, and UInt64BooleanThis also makes it possible to log and visualize multiple scalars to the same entity, which can drastically reduce the size of the resulting .rrd files.
Note that by default, and without blueprints, views are still only spawned for entities with Rerun semantics.
Time series views for entities with custom scalar components need to be spawned either:
The components that should be visualized can be selected via a new dropdown menu from the completely revised visualizer section in the selection panel.
<picture style="zoom: 0.5"> <img src="https://static.rerun.io/viscomp-add-custom/ac6e0df27139c7be2f446c17981bed74509c0b31/full.png" alt="New Source dropdown menu"> </picture>
To quickly navigate to the desired visualizer, each time series view now shows an overview of it's current visualizers.
<picture> <img src="https://static.rerun.io/visualizer-list/93a598b8423ffba3d302447f0da519014cb79a10/480w.png" alt="List of visualizer in view selection panel."> </picture>
For more details please refer to our documentation:
Thanks to a contribution from @vfilter, the series lines visualizer now also supports different interpolation modes to render staircase (or step) functions:
<picture> <img src="https://static.rerun.io/interpolation-mode/093d901acd73f84baf838cee37bb579135f15dfa/480w.png" alt="Dropdown of different interpolation modes"> </picture>
The Rerun Viewer now supports on-demand streaming, when connected to either the OSS server or Rerun Cloud.
https://github.com/user-attachments/assets/feb0028f-9492-484b-9b07-02280f111699
With on-demand streaming, whatever you are currently viewing will be downloaded first. This includes time-scrubbing to the end of a very long recording and quickly seeing what is there, or viewing only one camera feed of many.
Of course, your memory limit will be respected, and when you change your view or move the time cursor, the stale data will be evicted and the new data downloaded.
This also means that the web viewer can finally view recordings larger than the 4GiB limit enforced by Wasm32, as long as those recordings are served by a Rerun server.
It also means that Rerun Cloud users can view huge recordings, larger than what fits into RAM. The OSS server, however, still loads everything into RAM before serving it.
Usage:
> rerun server -d folder_with_large_recordings
Then either open the native viewer:
> rerun "rerun+http://127.0.0.1:51234"
Or the web viewer:
> rerun --serve-web "rerun+http://127.0.0.1:51234"
Like in the previous releases, we're continually expanding our support for common robotics data to make it easier for users to load their existing recordings.
This release adds support for Foxglove Protobuf schemas to our built-in MCAP data loader, in addition to the existing set of supported ROS 2 messages.
You can find an overview of all the messages that are currently supported here.
<picture> <img src="https://static.rerun.io/fg-data-demo/8873255bdbbd17f1f53b2fecb3a964099b95e53b/1200w.png" alt="Complex scene imported from MCAP"> </picture>
<!-- <picture> <img src="https://static.rerun.io/fg-data-demo/8873255bdbbd17f1f53b2fecb3a964099b95e53b/full.png" alt=""> <source media="(max-width: 480px)" srcset="https://static.rerun.io/fg-data-demo/8873255bdbbd17f1f53b2fecb3a964099b95e53b/480w.png"> <source media="(max-width: 768px)" srcset="https://static.rerun.io/fg-data-demo/8873255bdbbd17f1f53b2fecb3a964099b95e53b/768w.png"> <source media="(max-width: 1024px)" srcset="https://static.rerun.io/fg-data-demo/8873255bdbbd17f1f53b2fecb3a964099b95e53b/1024w.png"> <source media="(max-width: 1200px)" srcset="https://static.rerun.io/fg-data-demo/8873255bdbbd17f1f53b2fecb3a964099b95e53b/1200w.png"> </picture> -->
Previously, extending the Viewer with custom Rust code required creating an entirely new view type, even if you just wanted to add a single new visualization to the existing 3D view.
Now, you can register custom visualizers that plug directly into existing views, using fully custom archetypes & shaders in the process!
<!-- GH embeds only its own videos. Here's a mirror:ƒ https://static.rerun.io/146c3dfb86db05ee6796850f5fae5272595d3f3f_customvisualizer.mp4 -->
https://github.com/user-attachments/assets/df609f10-5515-49bc-86fd-6940cc25706f
In practice this works currently only well for 2D, 3D, and Map views but we'll keep working towards making the Viewer more and more modular & extensible!
For more details, see the custom visualizer example and the viewer rust extension docs for a general overview.
segment_url_udf and segment_url_with_timeref_udf have been removedsegment_url parameter names have been updated.rrd files are no longer tailed by defaultSeriesVisible component type has been removed🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-30
--server-memory-limit 0 cfc9a4cSeriesVisible component in favor of Visible 1fca08arerun rrd verify now checks for the presence of RRD manifests 2000ba5import rerun_sdk.rerun can be used 196c658on_duplicate in the Python SDK e909e20api. on connection error a2ef1b3filter_content() matches nothing 5997d6fDatasetEntry.manifest() 881cee6AnyValues/DynamicArchetype .columns d2b53daurl argument of CatalogClient.create_table() is now a prefix 1eb6c79entity_paths(), archetypes(), and component_types() methods to Schema 015f1fcrerun-sdk[datafusion] to rerun-sdk[dataplatform] and add pandas dependency b82cd06segment_url b2e7efftarget_frame in 3D views for scenes with pinholes & named frames 3c678ccsensor_msgs::PointCloud2 MCAP parser for small pointclouds 6491b95.dae with multiple triangle groups is not rendered 83e96bfSelectors from (nested) StructArray fields 8319663Selector when resolving component mappings 81879fdListArray support for field extraction into Selectors 6f769d6spawn_heuristics and recommended_visualizers for time series views cebc107RecommendedVisualizer now contains all nested Float*Arrays 27a7fbdInterpolationMode component for step function rendering #12657 (thanks @vfilter!)(U)Int16 in time series plots 6bb58e4rerun mcap convert 1436027Log protobuf message in MCAP loader f7cdf09foxglove.FrameTransform in MCAP data loader a018942foxglove.RawImage in MCAP data loader ff7db99foxglove.PointCloud in MCAP data loader 9e8e34badd to new view section 8871a13rerun auth logout 7b3ae54rerun rrd split 9bde24f--follow option to explicitly follow files and URLs c34a84bThis release has a name — Skeppsholmen!
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This patch release contains bug fixes and adds a documentation guide with an example for converting existing data to Rerun.
This patch release contains bug fixes and adds a documentation guide with an example for converting existing data to Rerun.
--server-memory-limit 0 cefbdf6.mcap files on web #12631This is mainly a patch release with some fixes and improvements.
This is mainly a patch release with some fixes and improvements.
We also included a change that makes labels translucent. This makes them less obtrusive and improves visibility of overlapping labels, as shown in the image segmentation example below. <picture> <img src="https://static.rerun.io/translucent_labels/19694d87d846e01cc59837c4f3982eeab6d4a1a5/full.png" alt="Example of translucent labels in Rerun 0.29.1"> </picture>
import rerun_sdk.rerun can be used fd28cd3Nothing published for this version
Python: Entry.update() deprecated in favor of Entry.set_name()
Rerun is an easy-to-use database and visualization toolbox for multimodal and temporal data. Try it live at https://rerun.io/viewer.
pip install rerun-sdkcargo add rerun and cargo install rerun-cli --locked🧳 Migration guide: https://rerun.io/docs/reference/migration/migration-0-29
In the previous 0.28 release, we overhauled Rerun's built-in URDF loader to work with TF-style transforms with parent and child frames.
Now, taking advantage of these entity-path-independent transforms, we simplified the hierarchy in which URDF assets are stored.
Collision and visual meshes are now below separate entity path roots, making it easy to toggle one or the other.
Additionally, the paths are now more compact to make it easier to scroll through them.
https://github.com/user-attachments/assets/7a3f6112-e87f-4249-977b-ed9944e2c356
UrdfTree utility in PythonWe added a UrdfTree Python utility that can be used to simplify operations with URDF models, e.g.:
We also updated our animated_urdf.py demo to use this utility, showing for example how you can dynamically change the color of a gripper link based on its angle.
https://github.com/user-attachments/assets/e0b6882f-b5dd-47d9-9afc-3ea30bc38e28
There's now an experimental screenshot API which allows to take screenshots of the Viewer or individual views:
# Connect to a local viewer.
viewer = ViewerClient()
# Screenshot the entire viewer.
viewer.save_screenshot("entire_viewer.jpg")
# Screenshot only the view we created earlier.
viewer.save_screenshot("my_view.png", view_id=view.id)
For a full snippet check here.
⚠️ There's still a lot of rough edges and this API may change in the future.
The target frame selection UI now shows matching suggestions, making it easier to select a frame name from the transforms in your data.
https://github.com/user-attachments/assets/4ba25a41-de6a-4209-bd06-c31a33d8d993
We refreshed the documentation showing examples of how some ROS concepts and messages can be mapped to Rerun, together with an updated Python node example. The documentation page can be found here.
<picture> <img src="https://static.rerun.io/ros_node_example/ddc3387995cda1b283a5c58ffbc6021d91abde7d/full.png" alt="Rerun viewer showing data streamed from the example ROS node"> <source media="(max-width: 480px)" srcset="https://static.rerun.io/ros_node_example/ddc3387995cda1b283a5c58ffbc6021d91abde7d/480w.png"> <source media="(max-width: 768px)" srcset="https://static.rerun.io/ros_node_example/ddc3387995cda1b283a5c58ffbc6021d91abde7d/768w.png"> <source media="(max-width: 1024px)" srcset="https://static.rerun.io/ros_node_example/ddc3387995cda1b283a5c58ffbc6021d91abde7d/1024w.png"> <source media="(max-width: 1200px)" srcset="https://static.rerun.io/ros_node_example/ddc3387995cda1b283a5c58ffbc6021d91abde7d/1200w.png"> </picture>
You can now get some insight on which parts of your recording use how much memory in the viewer using the improved memory panel:
<img width="2352" height="724" alt="image" src="https://github.com/user-attachments/assets/580a9e8a-641c-4c4c-a71c-bd65e20ef117" />
VisualizerOverrides removed, now pass visualizer objects directly (e.g., rr.SeriesLines())Entry.update() deprecated in favor of Entry.set_name()CatalogClient and Server constructor parameters renamed (addr → url/host)rerun.dataframe module (use rerun.server.Server and rerun.catalog instead)rerun.catalog APIs from 0.28rr.color_conversion → rr._color_conversion)rerun server --addr renamed to rerun server --host.rbl files created in previous versions cannot be loaded in 0.29name and start_time in segment table🧳 Check the migration guide for details: https://rerun.io/docs/reference/migration/migration-0-29
rerun rrd filter #12584Selector usage in re_sdk::lenses 521c796rerun.dataframe) #12320rerun.catalog #12321Entry.update in favor of Entry.set_name #12370child_frame/parent_frame arguments from pinhole constructor #12360address to url or host depending on context #12402RecordingStream so it has a unique recording id when none is provided eb14e16RecordingStream.__del__ 201e7c3rerun-sdk CLI and return exit codes #12496rr.experimental.ViewerClient.send_table more flexible ba733adblueprint support #12307 (thanks @sectore!)RecordingStreamBuilder::with_blueprint() apply to everything, not just spawn() #12347 (thanks @kpreid!)furthest_from GC when we can download chunks again #12363InstancePoses3D for geometry scale #12371InstancePoses3D #12385 (thanks @yujeong1jeong!)oneof fields in protobuf MCAP messages #12462--newest-first #12484RecordingInfo properties not included in the segment table 3781b18/ without set parent_frame #12588sensor_msgs::PointCloud2 MCAP parser for small pointclouds f705229Float64Array in time series views #12342Entity #12275CoordinateFrame instances #12514re_log_channel 3a25a25ros_node example and documentation #11968animated_urdf.py example #12571This release has a name — Riddarholmen!
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