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توضیحات

Reverb is an efficient and easy-to-use data storage and transport system designed for machine learning research.

ویژگی	مقدار
سیستم عامل	-
نام فایل	dm-reverb-nightly-0.9.0.dev20221207
نام	dm-reverb-nightly
نسخه کتابخانه	0.9.0.dev20221207
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	DeepMind
ایمیل نویسنده	DeepMind <no-reply@google.com>
آدرس صفحه اصلی	https://github.com/deepmind/reverb
آدرس اینترنتی	https://pypi.org/project/dm-reverb-nightly/
مجوز	Apache 2.0

# Reverb ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dm-reverb) [![PyPI version](https://badge.fury.io/py/dm-reverb.svg)](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 [![PyPI version](https://badge.fury.io/py/dm-reverb-nightly.svg)](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 ``` ### Debug builds Starting with version 0.6.0, debug builds of Reverb are uploaded to Google Cloud Storage. The builds can be downloaded or installed directly via `pip` following the patterns below. `gsutils` can be used to navigate the directory structure to ensure the files are there, e.g. `gsutil ls gs://rl-infra-builds/dm_reverb/builds/dbg`. To build your own debug binary, see the [build instructions](https://github.com/deepmind/reverb/tree/master/reverb/pip_package#create-a-stable-reverb-release). For python 3.8 and 3.9 follow this pattern: ```shell $ export reverb_version=0.8.0 # Python 3.9 $ export python_version=39 $ pip install https://storage.googleapis.com/rl-infra-builds/dm_reverb/builds/dbg/$reverb_version/dm_reverb-$reverb_version-cp$python_version-cp$python_version-manylinux2010_x86_64.whl ``` ### 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: * 0.11.0 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 0.11.0 | [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': 1.0}) ``` An item can also reference multiple data elements: ```python # Appends three data elements and inserts a single item which references all # of them as {'a': [2, 3, 4], 'b': [12, 13, 14]}. with client.trajectory_writer(num_keep_alive_refs=3) as writer: writer.append({'a': 2, 'b': 12}) writer.append({'a': 3, 'b': 13}) writer.append({'a': 4, 'b': 14}) # Create an item referencing all the data. writer.create_item( table='my_table', priority=1.0, trajectory={ 'a': writer.history['a'][:], 'b': writer.history['b'][:], }) # Block until the item has been inserted and confirmed by the server. writer.flush() ``` The items we have added to Reverb can be read by sampling them: ```python # client.sample() returns a generator. print(list(client.sample('my_table', num_samples=2))) ``` Continue with the [Reverb Tutorial](https://github.com/deepmind/reverb/tree/master/examples/demo.ipynb) for an interactive tutorial. ## Detailed overview Experience replay has become an important tool for training off-policy reinforcement learning policies. It is used by algorithms such as [Deep Q-Networks (DQN)][DQN], [Soft Actor-Critic (SAC)][SAC], [Deep Deterministic Policy Gradients (DDPG)][DDPG], and [Hindsight Experience Replay][HER], ... However building an efficient, easy to use, and scalable replay system can be challenging. For good performance Reverb is implemented in C++ and to enable distributed usage it provides a gRPC service for adding, sampling, and updating the contents of the tables. Python clients expose the full functionality of the service in an easy to use fashion. Furthermore native TensorFlow ops are available for performant integration with TensorFlow and `tf.data`. Although originally designed for off-policy reinforcement learning, Reverb's flexibility makes it just as useful for on-policy reinforcement -- or even (un)supervised learning. Creative users have even used Reverb to store and distribute frequently updated data (such as model weights), acting as an in-memory lightweight alternative to a distributed file system where each table represents a file. ### Tables A Reverb `Server` consists of one or more tables. A table holds items, and each item references one or more data elements. Tables also define sample and removal [selection strategies](#item-selection-strategies), a maximum item capacity, and a [rate limiter](#rate-limiting). Multiple items can reference the same data element, even if these items exist in different tables. This is because items only contain references to data elements (as opposed to a copy of the data itself). This also means that a data element is only removed when there exists no item that contains a reference to it. For example, it is possible to set up one Table as a Prioritized Experience Replay (PER) for transitions (sequences of length 2), and another Table as a (FIFO) queue of sequences of length 3. In this case the PER data could be used to train DQN, and the FIFO data to train a transition model for the environment. ![Using multiple tables](docs/images/multiple_tables_example.png) Items are automatically removed from the Table when one of two conditions are met: 1. Inserting a new item would cause the number of items in the Table to exceed its maximum capacity. Table's removal strategy is used to determine which item to remove. 1. An item has been sampled more than the maximum number of times permitted by the Table's rate limiter. Such item is deleted. Data elements not referenced anymore by any item are also deleted. Users have full control over how data is sampled and removed from Reverb tables. The behavior is primarily controlled by the [item selection strategies](#item-selection-strategies) provided to the `Table` as the `sampler` and `remover`. In combination with the [`rate_limiter`](#rate-limiting) and `max_times_sampled`, a wide range of behaviors can be achieved. Some commonly used configurations include: **Uniform Experience Replay** A set of `N=1000` most recently inserted items are maintained. By setting `sampler=reverb.selectors.Uniform()`, the probability to select an item is the same for all items. Due to `reverb.rate_limiters.MinSize(100)`, sampling requests will block until 100 items have been inserted. By setting `remover=reverb.selectors.Fifo()` when an item needs to be removed the oldest item is removed first. ```python reverb.Table( name='my_uniform_experience_replay_buffer', sampler=reverb.selectors.Uniform(), remover=reverb.selectors.Fifo(), max_size=1000, rate_limiter=reverb.rate_limiters.MinSize(100), ) ``` Examples of algorithms that make use of uniform experience replay include [SAC] and [DDPG]. **Prioritized Experience Replay** A set of `N=1000` most recently inserted items. By setting `sampler=reverb.selectors.Prioritized(priority_exponent=0.8)`, the probability to select an item is proportional to the item's priority. Note: See [Schaul, Tom, et al.][PER] for the algorithm used in this implementation of Prioritized Experience Replay. ```python reverb.Table( name='my_prioritized_experience_replay_buffer', sampler=reverb.selectors.Prioritized(0.8), remover=reverb.selectors.Fifo(), max_size=1000, rate_limiter=reverb.rate_limiters.MinSize(100), ) ``` Examples of algorithms that make use of Prioritized Experience Replay are DQN (and its variants), and [Distributed Distributional Deterministic Policy Gradients][D4PG]. **Queue** Collection of up to `N=1000` items where the oldest item is selected and removed in the same operation. If the collection contains 1000 items then insert calls are blocked until it is no longer full, if the collection is empty then sample calls are blocked until there is at least one item. ```python reverb.Table( name='my_queue', sampler=reverb.selectors.Fifo(), remover=reverb.selectors.Fifo(), max_size=1000, max_times_sampled=1, rate_limiter=reverb.rate_limiters.Queue(size=1000), ) # Or use the helper classmethod `.queue`. reverb.Table.queue(name='my_queue', max_size=1000) ``` Examples of algorithms that make use of Queues are [IMPALA](https://arxiv.org/abs/1802.01561) and asynchronous implementations of [Proximal Policy Optimization](https://arxiv.org/abs/1707.06347). ### Item selection strategies Reverb defines several selectors that can be used for item sampling or removal: - **Uniform:** Sample uniformly among all items. - **Prioritized:** Samples proportional to stored priorities. - **FIFO:** Selects the oldest data. - **LIFO:** Selects the newest data. - **MinHeap:** Selects data with the lowest priority. - **MaxHeap:** Selects data with the highest priority. Any of these strategies can be used for sampling or removing items from a Table. This gives users the flexibility to create customized Tables that best fit their needs. ### Rate Limiting Rate limiters allow users to enforce conditions on when items can be inserted and/or sampled from a Table. Here is a list of the rate limiters that are currently available in Reverb: - **MinSize:** Sets a minimum number of items that must be in the Table before anything can be sampled. - **SampleToInsertRatio:** Sets that the average ratio of inserts to samples by blocking insert and/or sample requests. This is useful for controlling the number of times each item is sampled before being removed. - **Queue:** Items are sampled exactly once before being removed. - **Stack:** Items are sampled exactly once before being removed. ### Sharding Reverb servers are unaware of each other and when scaling up a system to a multi server setup data is not replicated across more than one node. This makes Reverb unsuitable as a traditional database but has the benefit of making it trivial to scale up systems where some level of data loss is acceptable. Distributed systems can be horizontally scaled by simply increasing the number of Reverb servers. When used in combination with a gRPC compatible load balancer, the address of the load balanced target can simply be provided to a Reverb client and operations will automatically be distributed across the different nodes. You'll find details about the specific behaviors in the documentation of the relevant methods and classes. If a load balancer is not available in your setup or if more control is required then systems can still be scaled in almost the same way. Simply increase the number of Reverb servers and create separate clients for each server. ### Checkpointing Reverb supports checkpointing; the state and content of Reverb servers can be stored to permanent storage. While pointing, the `Server` serializes all of its data and metadata needed to reconstruct it. During this process the `Server` blocks all incoming insert, sample, update, and delete requests. Checkpointing is done with a call from the Reverb `Client`: ```python # client.checkpoint() returns the path the checkpoint was written to. checkpoint_path = client.checkpoint() ``` To restore the `reverb.Server` from a checkpoint: ```python checkpointer = reverb.checkpointers.DefaultCheckpointer(path=checkpoint_path) # The arguments passed to `tables=` must be the same as those used by the # `Server` that wrote the checkpoint. server = reverb.Server(tables=[...], checkpointer=checkpointer) ``` Refer to [tfrecord_checkpointer.h](https://github.com/deepmind/reverb/tree/master/reverb/cc/platform/tfrecord_checkpointer.h) for details on the implementation of checkpointing in Reverb. ## Starting Reverb using `reverb_server` (beta) Installing `dm-reverb` using `pip` will install a `reverb_server` script, which accepts its config as a textproto. For example: ```bash $ reverb_server --config=" port: 8000 tables: { table_name: \"my_table\" sampler: { fifo: true } remover: { fifo: true } max_size: 200 max_times_sampled: 5 rate_limiter: { min_size_to_sample: 1 samples_per_insert: 1 min_diff: $(python3 -c "import sys; print(-sys.float_info.max)") max_diff: $(python3 -c "import sys; print(sys.float_info.max)") } }" ``` The `rate_limiter` config is equivalent to the Python expression `MinSize(1)`, see `rate_limiters.py`. ## Citation If you use this code, please cite the [Reverb paper](https://arxiv.org/abs/2102.04736) as ``` @misc{cassirer2021reverb, title={Reverb: A Framework For Experience Replay}, author={Albin Cassirer and Gabriel Barth-Maron and Eugene Brevdo and Sabela Ramos and Toby Boyd and Thibault Sottiaux and Manuel Kroiss}, year={2021}, eprint={2102.04736}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```  [D4PG]: https://arxiv.org/abs/1804.08617 [DDPG]: https://arxiv.org/abs/1509.02971 [DQN]: https://www.nature.com/articles/nature14236 [HER]: https://arxiv.org/abs/1707.01495 [PER]: https://arxiv.org/abs/1511.05952 [SAC]: https://arxiv.org/abs/1801.01290

نیازمندی

مقدار	نام
-	dm-tree
-	portpicker
-	dataclasses
-	tf-nightly

زبان مورد نیاز

مقدار	نام
>=3	Python

نحوه نصب

نصب پکیج whl dm-reverb-nightly-0.9.0.dev20221207:

pip install dm-reverb-nightly-0.9.0.dev20221207.whl

نصب پکیج tar.gz dm-reverb-nightly-0.9.0.dev20221207:

pip install dm-reverb-nightly-0.9.0.dev20221207.tar.gz