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Reverb is an efficient and easy-to-use data storage and transport system designed for machine learning research
| Date | Stars |
|---|---|
| 2026-07-31 | 786 |
| 2026-08-06 | 787 |
Today
+1 stars today
This week
— stars this week
This month
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Momentum
4.0
growth rate 0.00%/day
# Reverb
[](https://badge.fury.io/py/dm-reverb)
Reverb is an efficient and easy-to-use data storage and transport system
designed for machine learning research. Reverb is primarily used as an
experience replay system for distributed reinforcement learning algorithms but
the system also supports multiple data structure representations such as FIFO,
LIFO, and priority queues.
## Table of Contents
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Detailed Overview](#detailed-overview)
- [Tables](#tables)
- [Item Selection Strategies](#item-selection-strategies)
- [Rate Limiting](#rate-limiting)
- [Sharding](#sharding)
- [Checkpointing](#checkpointing)
- [Citation](#citation)
## Installation
Please keep in mind that Reverb is not hardened for production use, and while we
do our best to keep things in working order, things may break or segfault.
> :warning: Reverb currently only supports Linux based OSes.
The recommended way to install Reverb is with `pip`. We also provide instructions
to build from source using the same docker images we use for releases.
TensorFlow can be installed separately or as part of the `pip` install.
Installing TensorFlow as part of the install ensures compatibility.
```shell
$ pip install dm-reverb[tensorflow]
# Without Tensorflow install and version dependency check.
$ pip install dm-reverb
```
### Nightly builds
[](https://badge.fury.io/py/dm-reverb-nightly)
```shell
$ pip install dm-reverb-nightly[tensorflow]
# Without Tensorflow install and version dependency check.
$ pip install dm-reverb-nightly
```
### Build from source
[This guide](reverb/pip_package/README.md#how-to-develop-and-build-reverb-with-the-docker-containers)
details how to build Reverb from source.
### Reverb Releases
Due to some underlying libraries such as `protoc` and `absl`, Reverb has to be
paired with a specific version of TensorFlow. If installing Reverb as
`pip install dm-reverb[tensorflow]` the correct version of Tensorflow will be
installed. The table below lists the version of TensorFlow that each release of
Reverb is associated with and some versions of interest:
* 0.13.0 dropped Python 3.8 support.
* 0.11.0 first version to support Python 3.11.
* 0.10.0 last version to support Python 3.7.
Release | Branch / Tag | TensorFlow Version
------- | ---------------------------------------------------------- | ------------------
Nightly | [master](https://github.com/deepmind/reverb) | tf-nightly
0.14.0 | [v0.14.0](https://github.com/deepmind/reverb/tree/v0.14.0) | 2.14.0
0.13.0 | [v0.13.0](https://github.com/deepmind/reverb/tree/v0.13.0) | 2.14.0
0.12.0 | [v0.12.0](https://github.com/deepmind/reverb/tree/v0.12.0) | 2.13.0
0.11.0 | [v0.11.0](https://github.com/deepmind/reverb/tree/v0.11.0) | 2.12.0
0.10.0 | [v0.10.0](https://github.com/deepmind/reverb/tree/v0.10.0) | 2.11.0
0.9.0 | [v0.9.0](https://github.com/deepmind/reverb/tree/v0.9.0) | 2.10.0
0.8.0 | [v0.8.0](https://github.com/deepmind/reverb/tree/v0.8.0) | 2.9.0
0.7.x | [v0.7.0](https://github.com/deepmind/reverb/tree/v0.7.0) | 2.8.0
## Quick Start
Starting a Reverb server is as simple as:
```python
import reverb
server = reverb.Server(tables=[
reverb.Table(
name='my_table',
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
max_size=100,
rate_limiter=reverb.rate_limiters.MinSize(1)),
],
)
```
Create a client to communicate with the server:
```python
client = reverb.Client(f'localhost:{server.port}')
print(client.server_info())
```
Write some data to the table:
```python
# Creates a single item and data element [0, 1].
client.insert([0, 1], priorities={'my_table'Excerpt of 15,313 characters
Read on GitHub
248
129
Gabriel Barth-Maron · Google DeepMind · United Kingdom
78
Toby Boyd · @tensorflow
59
Sabela · @google · Switzerland
33
Eugene Brevdo
26
26
Jean-Baptiste Lespiau
16
Thomas Köppe · Google · United Kingdom
6
4
Copybara Service · @google
4
Rebecca Chen
4
Peter Hawkins · Google
3
Nimrod Gileadi · @Genesis-Embodied-AI
2
Dmitri Gribenko
2
2
2
Iurii Kemaev · United Kingdom
1
Ikko Eltociear Ashimine · Japan
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4e1ef721b2651cfe, llm:Description: 'Reverb is an efficient and easy-to-use data storage and transport system designed for machine learning research' (google-deepmind/reverb). Language: C++.
matched fp:4e1ef721b2651cfe, llm:Description: 'Reverb is an efficient and easy-to-use data storage and transport system designed for machine learning research' (google-deepmind/reverb). Language: C++.