ML_101
  • Introduction
  • ML Fundamentals
    • Basics
    • Optimization
    • How to prevent overfitting
    • Linear Algebra
    • Clustering
    • Calculate Parameters in CNN
    • Normalization
    • Confidence Interval
    • Quantization
  • Classical Machine Learning
    • Basics
    • Unsupervised Learning
  • Neural Networks
    • Basics
    • Activation function
    • Different Types of Convolution
    • Resnet
    • Mobilenet
  • Loss
    • L1 and L2 Loss
    • Hinge Loss
    • Cross-Entropy Loss
    • Binary Cross-Entropy Loss
    • Categorical Cross-Entropy Loss
    • (Optional) Focal Loss
    • (Optional) CORAL Loss
  • Computer Vision
    • Two Stage Object Detection
      • Metrics
      • ROI
      • R-CNN
      • Fast RCNN
      • Faster RCNN
      • Mask RCNN
    • One Stage Object Detection
      • FPN
      • YOLO
      • Single Shot MultiBox Detector(SSD)
    • Segmentation
      • Panoptic Segmentation
      • PSPNet
    • FaceNet
    • GAN
    • Imbalance problem in object detection
  • NLP
    • Embedding
    • RNN
    • LSTM
    • LSTM Ext.
    • RNN for text prediction
    • BLEU
    • Seq2Seq
    • Attention
    • Self Attention
    • Attention without RNN
    • Transformer
    • BERT
  • Parallel Computing
    • Communication
    • MapReduce
    • Parameter Server
    • Decentralized And Ring All Reduce
    • Federated Learning
    • Model Parallelism: GPipe
  • Anomaly Detection
    • DBSCAN
    • Autoencoder
  • Visualization
    • Saliency Maps
    • Fooling images
    • Class Visualization
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  • Parameter Server Architecture
  • The Parameter Server
  • Synchronous Algorithm
  • Asynchronous Algorithm
  • Parallel Asynchronous Gradient Descent
  • Pro and Con of Asynchronous Algorithms
  • Footnote

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  1. Parallel Computing

Parameter Server

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Last updated 3 years ago

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Parameter Server Architecture

The Parameter Server

  • The parameter server was proposed by for scalable machine learning.

  • Characters: client-server architecture, message-passing communication, and asynchronous.

  • (Note that MapReduce is bulk synchronous.)

  • Ray , an open-source software system, supports parameter server.

Synchronous Algorithm

Asynchronous Algorithm

Parallel Asynchronous Gradient Descent

Pro and Con of Asynchronous Algorithms

  • In practice, asynchronous algorithms are faster than the synchronous.

  • In theory, asynchronous algorithms has slower convergence rate.

  • Asynchronous algorithms have restrictions, e.g., a worker cannot be much slower than the others.

Footnote

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