<commit_before># Parameter Server

Parameter server is distributed machine learning framework. It scales machine
learning applications to industry level problems.

## Features

- **Efficient communication:** The asynchronous communication model does not
  block computation (unless requested). It is optimized for machine learning
  tasks to reduce network traffic and overhead.

- **Flexible consistency models:** Relaxed consistency further hides
  synchronization cost and latency. We allow the algorithm designer to balance
  algorithmic convergence rate and system efficiency. The best trade-off depends
  on data, algorithm, and hardware.

- **Elastic Scalability:** New nodes can be added without restarting the running
  framework.

- **Fault Tolerance and Durability:** Recovery from and repair of
  non-catastraphic machine failures within several seconds, without interrupting
  computation.  Vector clocks ensure well-defined behavior after network
  partition and failure.

- **Ease of Use:** The globally shared parameters are represented as
  (potentially sparse) vectors and matrices to facilitate development of machine
  learning applications. The linear algebra data types come with
  high-performance multi-threaded linear algebra libraries.

## Build<commit_msg>edit readme<commit_after># Parameter Server

Parameter server is a distributed machine learning framework. It scales machine
learning applications to industry-level problems, namely billions of samples and
features.

## Features

- **Efficient communication:** The asynchronous communication model does not
  block computation (unless requested). It is optimized for machine learning
  tasks to reduce network traffic and overhead.
- **Flexible consistency models:** Relaxed consistency further hides
  synchronization cost and latency. We allow the algorithm designer to balance
  algorithmic convergence rate and system efficiency. The best trade-off depends
  on data, algorithm, and hardware.
- **Elastic Scalability:** New nodes can be added without restarting the running
  framework.
- **Fault Tolerance and Durability:** Recovery from and repair of
  non-catastraphic machine failures within several seconds, without interrupting
  computation.  Vector clocks ensure well-defined behavior after network
  partition and failure.
- **Ease of Use:** The globally shared parameters are represented as
  (potentially sparse) vectors and matrices to facilitate development of machine
  learning applications. The linear algebra data types come with
  high-performance multi-threaded linear algebra libraries.

## Build
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