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A distributed machine learning framework.

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Parameter Server

Parameter server is distributed machine learning framework. It targets cloud-computing situations where machines are possibly unreliable, jobs may get preempted, data may be lost, and where network latency and temporary workloads lead to a much more diverse performance profile. In other words, we target real cloud computing scenarios applicable to Google, Baidu, Amazon, Microsoft, etc. rather than low utilization-rate, exclusive use, high performance supercomputer clusters.

Features

  • Ease of use. The globally shared parameters are represented as (potentially sparse) vectors and matrices, which are more convenient data structures for machine learning applications than the widely used (key,value) store or tables. High-performance and convenient multi-threaded linear algebra operations, such as vector-matrix multiplication between parameters and local training data, are provided to facilitate developing applications.
  • Efficiency. Communication between nodes is asynchronous. Importantly, synchronization does not block computation. This framework allows the algorithm designer to balance algorithmic convergence rate and system efficiency, where the best trade-off depends on data, algorithm, and hardware.
  • Elastic Scalability. New nodes can be added without restarting the running framework. This property is desirable, e.g. for streaming sketches or when deploying a parameter server as an online service that must remain available for a long time.
  • Fault Tolerance and Durability. Conversely, node failure is inevitable, particularly at large scale using commodity servers. We use an optimized data replication architecture that efficiently stores data on multiple server nodes to enable fast (in less than 1 second) recovery from node failure.

Architecture

The parameter server architecture has two classes of nodes: Each server node maintains a partition of the globally shared parameters. They communicate with each other to replicate and/or to migrate parameters for reliability and scaling. The client nodes perform the bulk of the computation. Each client typically stores locally a portion of the training data, computing local statistics such as gradients. Clients communicate only with the server nodes, updating and retrieving the shared parameters. Clients may be added or removed.

./doc/img/arch.png

APIs

Parameter server provides several globally shared data structures, for example, (sparse) vectors and matrices. The class Container is the base class of all these structures. It provides two simple functions to do communication. Push sends out local modifications to other nodes, while Pull retrieves modifications from the others. These two functions are non-blocking, if the user specified consistency model is satisfied. All control information is set in Header.

Status Push(const Header& h);
Status Pull(const Header& h);

A particular data structure may provides more convenient functions to avoid explicitly construct Header. For example, Vectors could store several sparse vectors (namely a thin sparse matrix).

Status PushPull(KeyRange key_range     = KeyRange::All(),
                VecList  push_vec_list = {0},
                bool     push_delta    = kDelta,
                VecList  pull_vec_list = {0},
                bool     pull_delta    = kValue,
                uid_t    dest          = kServer);

A typical usage is that, we first construct 2 vectors, the first one stores the gradient, while the second one has the weight. Then each client write local computed gradient into the first one, push these values into all server nodes, and then retrieve the modification of weight from the server. Then we can write it as

Vectors<double> W("my_vec", p, 2);
// write local gradient into W.vec(0)
W.PushPull(KeyRange::All(), {0}, kValue, {1}, kDelta, kServer);

Sample Codes

TODO

Current Progress

We are now on developing a total new version, which is quite different and contains much more features than the previous versions the authors developed at Yahoo, Google, Baidu and CMU. However, the current codes are not be easily deployed yet, we hope to release a beta version after the winter break. If you would like to run it for your projects, please wait for our beta release.

Input data

Plain text format

TODO

Binary format

Binary format data is often more efficient than plain text data, because of more compact representation, no tokenization is required, easy to seek to a particular row/instance.

Sparse Matrices

A row-majored sparse matrix stores the nonzero entries in each row sequentially. For example, consider the following the matrix

[ 1 2 0 0 ]
[ 0 3 9 0 ]
[ 0 1 4 0 ]

Then it is stored by three binary files

name.rowcnt = [ 0 2 4 6 ]      // array of offsets of first nonzero element of a row
name.colidx = [ 0 1 1 2 1 2 ]  // array of column index of each element
name.value  = [ 1 2 3 9 1 4 ]  // array of non-zero element value

Google Protobuf

TODO

Build

pre-build step:

  • zeromq A socket library.
  • gflags Google’s flag processing library, used for parsing commandline options
  • glogs Google’s log library
  • gtest Google’s code test library
  • protobuf Google’s serialization library, used for serializing push/pull flags

All these library could be installed by make third_party_all

build

compiler required gcc >= 4.7 or LLVM 3.3, tested under Ubuntu 12.04, 12.10

Reference

Mu Li, Li Zhou, Zichao Yang, Aaron Li, Fei Xia, David Andersen and Alexander Smola. Parameter Server for Distributed Machine Learning, Big Learning Workshop, NIPS 2013

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