Deep Gradient Compression reduces the communication bandwidth required for distributed deep learning training by 300-600x without impacting accuracy. It achieves this through gradient sparsification, local gradient accumulation, momentum correction, local gradient clipping, momentum factor masking, and warm-up training. These techniques compress gradients to reduce data transfer size while preventing accuracy loss. The paper demonstrates Deep Gradient Compression can scale distributed training to use inexpensive network infrastructure.
Deep Learning in Limited Resource EnvironmentsOguzVuruskaner
The field of deep learning has experienced a remarkable improvement in last decade. In the varying set of problems, deep learning models have achieved and have surpassed human performance. However, success comes up with tradeoffs. In order to achieve “superhuman” performances, deep learning models need powerful hardware and vast amount of memory. That’s why, majority of the deep learning models are mobilized over relatively big computing centers. At the end of 2010s, deep learning architectures have started being mobilized on embedded devices, edge devices and mobile phones. Since then, successful mobile architectures have been proposed. These architectures have increased inference speed or memory footprint significantly compared with state-of-the-art models. In this presentation, we are going to compare subsequent optimization modules with naïve implementations with respect to predefined metrics.
This document provides an overview of deep learning including:
1. Why deep learning performs better than traditional machine learning for tasks like image and speech recognition.
2. Common deep learning applications such as image recognition, speech recognition, and healthcare.
3. Challenges of deep learning like the need for large datasets and lack of interpretability.
Solving Large Scale Optimization Problems using CPLEX Optimization Studiooptimizatiodirectdirect
- Optimization Direct is an IBM business partner that sells CPLEX optimization software and provides training to help customers maximize the benefits of optimization technologies.
- The document discusses how to get the most out of optimization through improved modeling techniques like exploiting sparsity and tight formulations, and tuning the optimizer by choosing algorithms and strategies tailored to specific model classes.
- A large example of scheduling optimization is presented where a heuristic approach solving smaller sub-models sequentially or in parallel can find good quality solutions faster than solving the full model directly when optimality is not required.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
The document summarizes sparse kernel-based ensemble learning (SKEL) and generalized kernel-based ensemble learning (GKEL) techniques for hyperspectral image classification problems. SKEL starts with a large number of initial support vector machines (SVMs) and optimizes them to a smaller subset, while GKEL starts with a single SVM and iteratively adds SVMs optimized for the ensemble. GKEL performs as well as SKEL while using fewer computational resources by starting simply and building up optimally. Both techniques perform better than single SVMs by leveraging ensemble learning.
Generalized Pipeline Parallelism for DNN TrainingDatabricks
DNN training is extremely time-consuming, necessitating efficient multi-accelerator parallelization. Current approaches to parallelizing training primarily use intra-batch parallelization, where a single iteration of training is split over the available workers, but suffer from diminishing returns at higher worker counts. We present PipeDream, a system that adds inter-batch pipelining to intra-batch parallelism to further improve parallel training throughput, helping to better overlap computation with communication and reduce the amount of communication when possible. Unlike traditional pipelining, DNN training is bi-directional, where a forward pass through the computation graph is followed by a backward pass that uses state and intermediate data computed during the forward pass.
Deep Learning in Limited Resource EnvironmentsOguzVuruskaner
The field of deep learning has experienced a remarkable improvement in last decade. In the varying set of problems, deep learning models have achieved and have surpassed human performance. However, success comes up with tradeoffs. In order to achieve “superhuman” performances, deep learning models need powerful hardware and vast amount of memory. That’s why, majority of the deep learning models are mobilized over relatively big computing centers. At the end of 2010s, deep learning architectures have started being mobilized on embedded devices, edge devices and mobile phones. Since then, successful mobile architectures have been proposed. These architectures have increased inference speed or memory footprint significantly compared with state-of-the-art models. In this presentation, we are going to compare subsequent optimization modules with naïve implementations with respect to predefined metrics.
This document provides an overview of deep learning including:
1. Why deep learning performs better than traditional machine learning for tasks like image and speech recognition.
2. Common deep learning applications such as image recognition, speech recognition, and healthcare.
3. Challenges of deep learning like the need for large datasets and lack of interpretability.
Solving Large Scale Optimization Problems using CPLEX Optimization Studiooptimizatiodirectdirect
- Optimization Direct is an IBM business partner that sells CPLEX optimization software and provides training to help customers maximize the benefits of optimization technologies.
- The document discusses how to get the most out of optimization through improved modeling techniques like exploiting sparsity and tight formulations, and tuning the optimizer by choosing algorithms and strategies tailored to specific model classes.
- A large example of scheduling optimization is presented where a heuristic approach solving smaller sub-models sequentially or in parallel can find good quality solutions faster than solving the full model directly when optimality is not required.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
The document summarizes sparse kernel-based ensemble learning (SKEL) and generalized kernel-based ensemble learning (GKEL) techniques for hyperspectral image classification problems. SKEL starts with a large number of initial support vector machines (SVMs) and optimizes them to a smaller subset, while GKEL starts with a single SVM and iteratively adds SVMs optimized for the ensemble. GKEL performs as well as SKEL while using fewer computational resources by starting simply and building up optimally. Both techniques perform better than single SVMs by leveraging ensemble learning.
Generalized Pipeline Parallelism for DNN TrainingDatabricks
DNN training is extremely time-consuming, necessitating efficient multi-accelerator parallelization. Current approaches to parallelizing training primarily use intra-batch parallelization, where a single iteration of training is split over the available workers, but suffer from diminishing returns at higher worker counts. We present PipeDream, a system that adds inter-batch pipelining to intra-batch parallelism to further improve parallel training throughput, helping to better overlap computation with communication and reduce the amount of communication when possible. Unlike traditional pipelining, DNN training is bi-directional, where a forward pass through the computation graph is followed by a backward pass that uses state and intermediate data computed during the forward pass.
This document discusses high performance computing in the cloud. It addresses different types of workloads like I/O bound, CPU bound, and latency bound tasks. It also discusses strategies for handling task streams and structured batch jobs in the cloud. Specific challenges around data delivery and distribution are examined, along with approaches like data grids, caching, and task stealing. The document looks at how MapReduce frameworks could be better optimized for the cloud model. Overall trends and anticipated future features around schedulers as a service and cloud-friendly data processing frameworks are considered.
Score based Generative Modeling through Stochastic Differential EquationsSungchul Kim
This document discusses score-based generative modeling using stochastic differential equations (SDEs). It introduces modeling data diffusion as an SDE from the data distribution to a simple prior and generating samples by reversing this diffusion process. It also describes estimating the score (gradient of the log probability density) needed for the reverse process using score matching. Finally, it notes that noise perturbation models like NCSN and DDPM can be viewed as discretizations of specific SDEs called variance exploding and variance preserving SDEs.
This document discusses various optimization techniques for training neural networks, including gradient descent, stochastic gradient descent, momentum, Nesterov momentum, RMSProp, and Adam. The key challenges in neural network optimization are long training times, hyperparameter tuning such as learning rate, and getting stuck in local minima. Momentum helps accelerate learning by amplifying consistent gradients while canceling noise. Adaptive learning rate algorithms like RMSProp, Adagrad, and Adam automatically tune the learning rate over time to improve performance and reduce sensitivity to hyperparameters.
C-Cube: Elastic Continuous Clustering in the CloudQian Lin
C-Cube is an elastic framework for continuous clustering of streaming data in cloud environments. It dynamically adjusts computational resources based on workload by increasing or decreasing the number of processing units. It uses a verification module to determine whether previous clustering results still fit the current data distribution and only re-runs the clustering algorithm when needed. The document discusses C-Cube's architecture and implementation, as well as its scaling strategy and evaluation of system performance under different conditions.
Regularization helps prevent overfitting in machine learning models. It can be viewed as adding extra constraints or terms to the training objective function. Common regularization methods include l2 regularization, which adds a penalty that is proportional to the square of the weights, and l1 regularization, which uses an absolute value penalty. These help prevent overfitting by constraining the complexity of the model. l2 regularization scales down the weights, while l1 regularization induces sparsity by driving small weights to exactly zero.
[PR12] PR-050: Convolutional LSTM Network: A Machine Learning Approach for Pr...Taegyun Jeon
PR-050: Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Original Slide from http://home.cse.ust.hk/~xshiab/data/valse-20160323.pptx
Youtube: https://youtu.be/3cFfCM4CXws
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-sidhu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Sammy Sidhu, Senior Engineer at DeepScale, presents the "A Shallow Dive into Training Deep Neural Networks" tutorial at the May 2017 Embedded Vision Summit.
In this talk, Sidhu introduces the basics of training deep neural network models for vision tasks. He begins by explaining fundamental training concepts and terms, including loss functions and gradients. He then provides an accessible explanation of how the training process works. Next, he highlights common challenges in training deep neural networks, such as overfitting, and explores proven techniques for addressing these challenges, including regularization and data augmentation. Throughout, he illustrates training techniques and challenges using examples taken from real-world applications.
From Hours to Minutes: The Journey of Optimizing Mask-RCNN and BERT Using MXNetEric Haibin Lin
Training large deep learning models like Mask R-CNN and BERT takes lots of time and compute resources. Using MXNet, the Amazon Web Services deep learning framework team has been working with NVIDIA to optimize many different areas to cut the training time from hours to minutes.
Deferred rendering decouples lighting from scene complexity by calculating lighting in a post-processing step. The Leadwerks Engine previously used forward rendering but had issues with shader variations and light limits. Version 2.1 introduced deferred rendering to address these problems. Deferred rendering provided easier control of per-light settings, smaller shaders, no light limits, and a 50% performance improvement per light. While alpha blending and anti-aliasing were difficult, the benefits of deferred rendering outweighed the few disadvantages.
Probabilistic consolidation of virtual machines in self organizing cloud data...WMLab,NCU
The document describes a probabilistic approach called ecoCloud for consolidating virtual machines (VMs) across physical servers in a cloud data center. EcoCloud uses two probabilistic procedures - assignment and migration - to autonomously distribute VMs among servers based on local resource utilization information, with the goal of improving utilization levels, reducing power consumption, and avoiding SLA violations. The assignment procedure determines whether an idle server should accept a new VM or not, while the migration procedure determines whether an underutilized VM should migrate to another server for better consolidation. Both procedures are based on simple Bernoulli trials using resource utilization-dependent probability functions.
Power Consumption in cloud centers is increasing
rapidly due to the popularity of Cloud Computing. High power
consumption not only leads to high operational cost, it also leads
to high carbon emissions which is not environment friendly.
Thousands of Physical Machines/Servers inside Cloud Centers
are becoming a commonplace. In many instances, some of the
Physical Machines might have very few active Virtual Machines,
migration of these Virtual Machines, so that, less loaded Physical
Machines can be shutdown, which in-turn aids in reduction of
consumed power has been extensively studied in the literature.
However, recent studies have demonstrated that, migration of
Virtual Machines is usually associated with excessive cost and
delay. Hence, recently, a new technique in which the load
balancing in cloud centers by migrating the extra tasks of
overloaded Virtual Machines was proposed. This task migration
technique has not been properly studied for its effectiveness
w.r.t. Server Consolidation in the literature. In this work, the
Virtual Machine task migration technique is extended to address
the Server Consolidation issue. Empirical results reveal excellent
effectiveness of the proposed technique in reducing the power
consumed in Cloud Centers.
Exploiting latency bounds for energy efficient load balancingMichael May
These slides are taken from a research paper the PSL group wrote while under the direction of Dr. Vijay Garg at the Universtiy of Texas at Austin. The abstract is provided below.
In this paper we explore exploitation of latency bounds in order to gain energy efficiency in load balancing applications. We are proposing an energy aware job scheduler that uses vary-on, vary-off features in order to maximize time spent at peak utilization, while maintaining bounded latency. Computing resources will either be at load saturation(highest work per joule) or off. The premise being that servers are most efficient at peak utilization, measured in terms of energy per calculation. We explore the efficiency gains achieved through this approach and compare our results to other methods.
The document discusses using temporal shift modules (TSM) for efficient video recognition, where TSM enables temporal modeling in 2D CNNs with no additional computation cost; TSM models achieve better performance than 3D CNNs and previous methods while using less computation, and can be used for applications like online video understanding, low-latency deployment on edge devices, and large-scale distributed training on supercomputers.
Predicting Drug Target Interaction Using Deep Belief NetworkRashim Dhaubanjar
With the advancement in AI field, machine learning methods are being used to train the classifier for separating intractable drug-target pair as it is difficult to classify dockable and non-dockable ligands due to non-linear nature of big-biological data. As deep learning has been shown to produce state-of-the-art results on various tasks, we propose a new approach to predict the interaction between drug and targets efficiently. The DBN is used to extract the high level features from 2D chemical substructure represented in fingerprint format. DBN is trained in a greedy layer-wise unsupervised fashion and the result from this pre-training phase is used to initialize the parameters prior to BP used for fine tuning. Similarly, logistic regression layer is staked as output layer. Then it is fine-tuned using BP of error derivative to build classification model that directly predict whether a drug interacts with a target of interest or not. In addition to this we too propose an approach to reduce the time complexity of training the learning method with the use of GPU which is highly parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart.
This document provides an outline and overview of training convolutional neural networks. It discusses update rules like stochastic gradient descent, momentum, and Adam. It also covers techniques like data augmentation, transfer learning, and monitoring the training process. The goal of training a CNN is to optimize its weights and parameters to correctly classify images from the training set by minimizing output error through backpropagation and updating weights.
This document discusses mixed precision training techniques for deep neural networks. It introduces three techniques to train models with half-precision floating point without losing accuracy: 1) Maintaining a FP32 master copy of weights, 2) Scaling the loss to prevent small gradients, and 3) Performing certain arithmetic like dot products in FP32. Experimental results show these techniques allow a variety of networks to match the accuracy of FP32 training while reducing memory and bandwidth. The document also discusses related work and PyTorch's new Automatic Mixed Precision features.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
improve deep learning training and inference performances.rohit
factors affecting gpu performance for machine learning training and inference.
1. Deep Learning Performance Benchmarks
2. Gpu hardware basics
3. Internal data Transfer
4. Models, Datasets and Parallelism
5. Data training pipeline
6. Performance Tuning
7. Deep Learning Load Distribution Strategies.
8. Misc algorithms like Automatic Differentiation etc.
This document discusses techniques for deploying deep learning models on low-power devices with limited compute resources. It describes methods such as parameter quantization, pruning parameters and connections, convolutional filter compression, matrix factorization, network architecture search, and knowledge distillation that can reduce model size and computational requirements while maintaining accuracy. Parameter quantization decreases model size by reducing precision. Pruning removes redundant connections and filters. Filter compression replaces large filters with smaller ones. Matrix factorization and architecture search optimize models for efficiency. Knowledge distillation transfers knowledge from large models to smaller, more efficient ones.
This document discusses high performance computing in the cloud. It addresses different types of workloads like I/O bound, CPU bound, and latency bound tasks. It also discusses strategies for handling task streams and structured batch jobs in the cloud. Specific challenges around data delivery and distribution are examined, along with approaches like data grids, caching, and task stealing. The document looks at how MapReduce frameworks could be better optimized for the cloud model. Overall trends and anticipated future features around schedulers as a service and cloud-friendly data processing frameworks are considered.
Score based Generative Modeling through Stochastic Differential EquationsSungchul Kim
This document discusses score-based generative modeling using stochastic differential equations (SDEs). It introduces modeling data diffusion as an SDE from the data distribution to a simple prior and generating samples by reversing this diffusion process. It also describes estimating the score (gradient of the log probability density) needed for the reverse process using score matching. Finally, it notes that noise perturbation models like NCSN and DDPM can be viewed as discretizations of specific SDEs called variance exploding and variance preserving SDEs.
This document discusses various optimization techniques for training neural networks, including gradient descent, stochastic gradient descent, momentum, Nesterov momentum, RMSProp, and Adam. The key challenges in neural network optimization are long training times, hyperparameter tuning such as learning rate, and getting stuck in local minima. Momentum helps accelerate learning by amplifying consistent gradients while canceling noise. Adaptive learning rate algorithms like RMSProp, Adagrad, and Adam automatically tune the learning rate over time to improve performance and reduce sensitivity to hyperparameters.
C-Cube: Elastic Continuous Clustering in the CloudQian Lin
C-Cube is an elastic framework for continuous clustering of streaming data in cloud environments. It dynamically adjusts computational resources based on workload by increasing or decreasing the number of processing units. It uses a verification module to determine whether previous clustering results still fit the current data distribution and only re-runs the clustering algorithm when needed. The document discusses C-Cube's architecture and implementation, as well as its scaling strategy and evaluation of system performance under different conditions.
Regularization helps prevent overfitting in machine learning models. It can be viewed as adding extra constraints or terms to the training objective function. Common regularization methods include l2 regularization, which adds a penalty that is proportional to the square of the weights, and l1 regularization, which uses an absolute value penalty. These help prevent overfitting by constraining the complexity of the model. l2 regularization scales down the weights, while l1 regularization induces sparsity by driving small weights to exactly zero.
[PR12] PR-050: Convolutional LSTM Network: A Machine Learning Approach for Pr...Taegyun Jeon
PR-050: Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Original Slide from http://home.cse.ust.hk/~xshiab/data/valse-20160323.pptx
Youtube: https://youtu.be/3cFfCM4CXws
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-sidhu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Sammy Sidhu, Senior Engineer at DeepScale, presents the "A Shallow Dive into Training Deep Neural Networks" tutorial at the May 2017 Embedded Vision Summit.
In this talk, Sidhu introduces the basics of training deep neural network models for vision tasks. He begins by explaining fundamental training concepts and terms, including loss functions and gradients. He then provides an accessible explanation of how the training process works. Next, he highlights common challenges in training deep neural networks, such as overfitting, and explores proven techniques for addressing these challenges, including regularization and data augmentation. Throughout, he illustrates training techniques and challenges using examples taken from real-world applications.
From Hours to Minutes: The Journey of Optimizing Mask-RCNN and BERT Using MXNetEric Haibin Lin
Training large deep learning models like Mask R-CNN and BERT takes lots of time and compute resources. Using MXNet, the Amazon Web Services deep learning framework team has been working with NVIDIA to optimize many different areas to cut the training time from hours to minutes.
Deferred rendering decouples lighting from scene complexity by calculating lighting in a post-processing step. The Leadwerks Engine previously used forward rendering but had issues with shader variations and light limits. Version 2.1 introduced deferred rendering to address these problems. Deferred rendering provided easier control of per-light settings, smaller shaders, no light limits, and a 50% performance improvement per light. While alpha blending and anti-aliasing were difficult, the benefits of deferred rendering outweighed the few disadvantages.
Probabilistic consolidation of virtual machines in self organizing cloud data...WMLab,NCU
The document describes a probabilistic approach called ecoCloud for consolidating virtual machines (VMs) across physical servers in a cloud data center. EcoCloud uses two probabilistic procedures - assignment and migration - to autonomously distribute VMs among servers based on local resource utilization information, with the goal of improving utilization levels, reducing power consumption, and avoiding SLA violations. The assignment procedure determines whether an idle server should accept a new VM or not, while the migration procedure determines whether an underutilized VM should migrate to another server for better consolidation. Both procedures are based on simple Bernoulli trials using resource utilization-dependent probability functions.
Power Consumption in cloud centers is increasing
rapidly due to the popularity of Cloud Computing. High power
consumption not only leads to high operational cost, it also leads
to high carbon emissions which is not environment friendly.
Thousands of Physical Machines/Servers inside Cloud Centers
are becoming a commonplace. In many instances, some of the
Physical Machines might have very few active Virtual Machines,
migration of these Virtual Machines, so that, less loaded Physical
Machines can be shutdown, which in-turn aids in reduction of
consumed power has been extensively studied in the literature.
However, recent studies have demonstrated that, migration of
Virtual Machines is usually associated with excessive cost and
delay. Hence, recently, a new technique in which the load
balancing in cloud centers by migrating the extra tasks of
overloaded Virtual Machines was proposed. This task migration
technique has not been properly studied for its effectiveness
w.r.t. Server Consolidation in the literature. In this work, the
Virtual Machine task migration technique is extended to address
the Server Consolidation issue. Empirical results reveal excellent
effectiveness of the proposed technique in reducing the power
consumed in Cloud Centers.
Exploiting latency bounds for energy efficient load balancingMichael May
These slides are taken from a research paper the PSL group wrote while under the direction of Dr. Vijay Garg at the Universtiy of Texas at Austin. The abstract is provided below.
In this paper we explore exploitation of latency bounds in order to gain energy efficiency in load balancing applications. We are proposing an energy aware job scheduler that uses vary-on, vary-off features in order to maximize time spent at peak utilization, while maintaining bounded latency. Computing resources will either be at load saturation(highest work per joule) or off. The premise being that servers are most efficient at peak utilization, measured in terms of energy per calculation. We explore the efficiency gains achieved through this approach and compare our results to other methods.
The document discusses using temporal shift modules (TSM) for efficient video recognition, where TSM enables temporal modeling in 2D CNNs with no additional computation cost; TSM models achieve better performance than 3D CNNs and previous methods while using less computation, and can be used for applications like online video understanding, low-latency deployment on edge devices, and large-scale distributed training on supercomputers.
Predicting Drug Target Interaction Using Deep Belief NetworkRashim Dhaubanjar
With the advancement in AI field, machine learning methods are being used to train the classifier for separating intractable drug-target pair as it is difficult to classify dockable and non-dockable ligands due to non-linear nature of big-biological data. As deep learning has been shown to produce state-of-the-art results on various tasks, we propose a new approach to predict the interaction between drug and targets efficiently. The DBN is used to extract the high level features from 2D chemical substructure represented in fingerprint format. DBN is trained in a greedy layer-wise unsupervised fashion and the result from this pre-training phase is used to initialize the parameters prior to BP used for fine tuning. Similarly, logistic regression layer is staked as output layer. Then it is fine-tuned using BP of error derivative to build classification model that directly predict whether a drug interacts with a target of interest or not. In addition to this we too propose an approach to reduce the time complexity of training the learning method with the use of GPU which is highly parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart.
This document provides an outline and overview of training convolutional neural networks. It discusses update rules like stochastic gradient descent, momentum, and Adam. It also covers techniques like data augmentation, transfer learning, and monitoring the training process. The goal of training a CNN is to optimize its weights and parameters to correctly classify images from the training set by minimizing output error through backpropagation and updating weights.
This document discusses mixed precision training techniques for deep neural networks. It introduces three techniques to train models with half-precision floating point without losing accuracy: 1) Maintaining a FP32 master copy of weights, 2) Scaling the loss to prevent small gradients, and 3) Performing certain arithmetic like dot products in FP32. Experimental results show these techniques allow a variety of networks to match the accuracy of FP32 training while reducing memory and bandwidth. The document also discusses related work and PyTorch's new Automatic Mixed Precision features.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
improve deep learning training and inference performances.rohit
factors affecting gpu performance for machine learning training and inference.
1. Deep Learning Performance Benchmarks
2. Gpu hardware basics
3. Internal data Transfer
4. Models, Datasets and Parallelism
5. Data training pipeline
6. Performance Tuning
7. Deep Learning Load Distribution Strategies.
8. Misc algorithms like Automatic Differentiation etc.
This document discusses techniques for deploying deep learning models on low-power devices with limited compute resources. It describes methods such as parameter quantization, pruning parameters and connections, convolutional filter compression, matrix factorization, network architecture search, and knowledge distillation that can reduce model size and computational requirements while maintaining accuracy. Parameter quantization decreases model size by reducing precision. Pruning removes redundant connections and filters. Filter compression replaces large filters with smaller ones. Matrix factorization and architecture search optimize models for efficiency. Knowledge distillation transfers knowledge from large models to smaller, more efficient ones.
DEEP NEURAL NETWORKS APPLIED TO LOW POWER ONBOARD IMAGE COMPRESSION
Over the past decade, rapid developments in digital technologies and access to space have enabled unprecedented capabilities of monitoring our planet and, more generally, our Universe.
This new space race is pushing for a paradigm shift in order to respond to the ever-increasing challenge of delivering the useful information to the end users. With huge number of satellites, greater spatial and spectral resolutions, higher temporal cadence and shrinking spectrum resources, on-board data reduction becomes not only a cost saving solution but, in many cases also, a key enabling technology to achieve viable missions.
https://atpi.eventsair.com/obpdc2022/
The document summarizes the Batch Normalization technique for accelerating deep network training. It addresses the problem of internal covariate shift where the distribution of layer inputs changes during training. Batch Normalization normalizes layer inputs by calculating mini-batch statistics. This allows using higher learning rates and improves training speed while preserving representation ability. Experiments show Batch Normalization leads to 14x faster training and improves accuracy on ImageNet classification.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2016-member-meeting-mit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vivienne Sze, Assistant Professor at MIT, delivers the presentation "Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks" at the September 2016 Embedded Vision Alliance Member Meeting. Sze describes the results of her team's recent research on optimized hardware for deep learning.
Training Distributed Deep Recurrent Neural Networks with Mixed Precision on G...Databricks
In this talk, we evaluate training of deep recurrent neural networks with half-precision floats on Pascal and Volta GPUs. We implement a distributed, data-parallel, synchronous training algorithm by integrating TensorFlow and CUDA-aware MPI to enable execution across multiple GPU nodes and making use of high-speed interconnects. We introduce a learning rate schedule facilitating neural network convergence at up to O(100) workers.
Strong scaling tests performed on GPU clusters show linear runtime scaling and logarithmic communication time scaling for both single and mixed precision training modes. Performance is evaluated on a scientific dataset taken from the Joint European Torus (JET) tokamak, containing multi-modal time series of sensory measurements leading up to deleterious events called plasma disruptions. Half-precision significantly reduces memory and network bandwidth, allowing training of state-of-the-art models with over 70 million trainable parameters while achieving a comparable test set performance as single precision.
EfficientDet is a single-shot object detector that achieves state-of-the-art accuracy with high efficiency. It introduces a Bi-directional Feature Pyramid Network (BiFPN) that fuses multi-scale features with weighted connections. EfficientDet also uses a compound scaling method to jointly scale the backbone network, BiFPN, prediction network, and input resolution. Experiments on COCO show that EfficientDet outperforms prior models across different resource constraints in terms of accuracy and efficiency for object detection and semantic segmentation tasks.
PR-330: How To Train Your ViT? Data, Augmentation, and Regularization in Visi...Jinwon Lee
The document summarizes a study on training Vision Transformers (ViTs) by exploring different combinations of data augmentation, regularization techniques, model sizes, and training dataset sizes. Some key findings include: 1) Models trained with extensive data augmentation on ImageNet-1k performed comparably to those trained on the larger ImageNet-21k dataset without augmentation. 2) Transfer learning from pre-trained models was more efficient and achieved better results than training models from scratch, even with extensive compute. 3) Models pre-trained on more data showed better transfer ability, indicating more data yields more generic representations.
An overview of gradient descent optimization algorithms.pdfvudinhphuong96
This document provides an overview of gradient descent optimization algorithms. It discusses various gradient descent variants including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. It describes the trade-offs between these methods in terms of accuracy, time, and memory usage. The document also covers challenges with mini-batch gradient descent like choosing a proper learning rate. It then discusses commonly used optimization algorithms to address these challenges, including momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. It provides visualizations to explain how momentum and Nesterov accelerated gradient work to help accelerate SGD.
M.Tech project on Haar wavelet based approach for image compressionVeerendra B R Revanna
This document presents an image compression technique using Haar wavelet transforms. It begins with background on the need for image compression due to increasing image sizes. It then outlines the encoder and decoder block diagrams and classifications of compression techniques. The key aspects of the proposed technique are presented: using wavelet transforms to exploit spatial and frequency correlations, applying averaging and differencing operations on rows and columns, thresholding the transformed matrix, and reconstructing the original image. Advantages include conceptual simplicity, speed, memory efficiency and reversibility. Results and future work are discussed before concluding on the benefits of wavelet-based compression.
Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...The Third Creative Media
"Navigating Invideo: A Comprehensive Guide" is an essential resource for anyone looking to master Invideo, an AI-powered video creation tool. This guide provides step-by-step instructions, helpful tips, and comparisons with other AI video creators. Whether you're a beginner or an experienced video editor, you'll find valuable insights to enhance your video projects and bring your creative ideas to life.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
The Key to Digital Success_ A Comprehensive Guide to Continuous Testing Integ...kalichargn70th171
In today's business landscape, digital integration is ubiquitous, demanding swift innovation as a necessity rather than a luxury. In a fiercely competitive market with heightened customer expectations, the timely launch of flawless digital products is crucial for both acquisition and retention—any delay risks ceding market share to competitors.
Malibou Pitch Deck For Its €3M Seed Roundsjcobrien
French start-up Malibou raised a €3 million Seed Round to develop its payroll and human resources
management platform for VSEs and SMEs. The financing round was led by investors Breega, Y Combinator, and FCVC.
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Preparing Non - Technical Founders for Engaging a Tech AgencyISH Technologies
Preparing non-technical founders before engaging a tech agency is crucial for the success of their projects. It starts with clearly defining their vision and goals, conducting thorough market research, and gaining a basic understanding of relevant technologies. Setting realistic expectations and preparing a detailed project brief are essential steps. Founders should select a tech agency with a proven track record and establish clear communication channels. Additionally, addressing legal and contractual considerations and planning for post-launch support are vital to ensure a smooth and successful collaboration. This preparation empowers non-technical founders to effectively communicate their needs and work seamlessly with their chosen tech agency.Visit our site to get more details about this. Contact us today www.ishtechnologies.com.au
INTRODUCTION TO AI CLASSICAL THEORY TARGETED EXAMPLESanfaltahir1010
Image: Include an image that represents the concept of precision, such as a AI helix or a futuristic healthcare
setting.
Objective: Provide a foundational understanding of precision medicine and its departure from traditional
approaches
Role of theory: Discuss how genomics, the study of an organism's complete set of AI ,
plays a crucial role in precision medicine.
Customizing treatment plans: Highlight how genetic information is used to customize
treatment plans based on an individual's genetic makeup.
Examples: Provide real-world examples of successful application of AI such as genetic
therapies or targeted treatments.
Importance of molecular diagnostics: Explain the role of molecular diagnostics in identifying
molecular and genetic markers associated with diseases.
Biomarker testing: Showcase how biomarker testing aids in creating personalized treatment plans.
Content:
• Ethical issues: Examine ethical concerns related to precision medicine, such as privacy, consent, and
potential misuse of genetic information.
• Regulations and guidelines: Present examples of ethical guidelines and regulations in place to safeguard
patient rights.
• Visuals: Include images or icons representing ethical considerations.
Content:
• Ethical issues: Examine ethical concerns related to precision medicine, such as privacy, consent, and
potential misuse of genetic information.
• Regulations and guidelines: Present examples of ethical guidelines and regulations in place to safeguard
patient rights.
• Visuals: Include images or icons representing ethical considerations.
Content:
• Ethical issues: Examine ethical concerns related to precision medicine, such as privacy, consent, and
potential misuse of genetic information.
• Regulations and guidelines: Present examples of ethical guidelines and regulations in place to safeguard
patient rights.
• Visuals: Include images or icons representing ethical considerations.
Real-world case study: Present a detailed case study showcasing the success of precision
medicine in a specific medical scenario.
Patient's journey: Discuss the patient's journey, treatment plan, and outcomes.
Impact: Emphasize the transformative effect of precision medicine on the individual's
health.
Objective: Ground the presentation in a real-world example, highlighting the practical
application and success of precision medicine.
Data challenges: Address the challenges associated with managing large sets of patient data in precision
medicine.
Technological solutions: Discuss technological innovations and solutions for handling and analyzing vast
datasets.
Visuals: Include graphics representing data management challenges and technological solutions.
Objective: Acknowledge the data-related challenges in precision medicine and highlight innovative solutions.
Data challenges: Address the challenges associated with managing large sets of patient data in precision
medicine.
Technological solutions: Discuss technological innovations and solutions
UI5con 2024 - Bring Your Own Design SystemPeter Muessig
How do you combine the OpenUI5/SAPUI5 programming model with a design system that makes its controls available as Web Components? Since OpenUI5/SAPUI5 1.120, the framework supports the integration of any Web Components. This makes it possible, for example, to natively embed own Web Components of your design system which are created with Stencil. The integration embeds the Web Components in a way that they can be used naturally in XMLViews, like with standard UI5 controls, and can be bound with data binding. Learn how you can also make use of the Web Components base class in OpenUI5/SAPUI5 to also integrate your Web Components and get inspired by the solution to generate a custom UI5 library providing the Web Components control wrappers for the native ones.
Consistent toolbox talks are critical for maintaining workplace safety, as they provide regular opportunities to address specific hazards and reinforce safe practices.
These brief, focused sessions ensure that safety is a continual conversation rather than a one-time event, which helps keep safety protocols fresh in employees' minds. Studies have shown that shorter, more frequent training sessions are more effective for retention and behavior change compared to longer, infrequent sessions.
Engaging workers regularly, toolbox talks promote a culture of safety, empower employees to voice concerns, and ultimately reduce the likelihood of accidents and injuries on site.
The traditional method of conducting safety talks with paper documents and lengthy meetings is not only time-consuming but also less effective. Manual tracking of attendance and compliance is prone to errors and inconsistencies, leading to gaps in safety communication and potential non-compliance with OSHA regulations. Switching to a digital solution like Safelyio offers significant advantages.
Safelyio automates the delivery and documentation of safety talks, ensuring consistency and accessibility. The microlearning approach breaks down complex safety protocols into manageable, bite-sized pieces, making it easier for employees to absorb and retain information.
This method minimizes disruptions to work schedules, eliminates the hassle of paperwork, and ensures that all safety communications are tracked and recorded accurately. Ultimately, using a digital platform like Safelyio enhances engagement, compliance, and overall safety performance on site. https://safelyio.com/
What to do when you have a perfect model for your software but you are constrained by an imperfect business model?
This talk explores the challenges of bringing modelling rigour to the business and strategy levels, and talking to your non-technical counterparts in the process.
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian CompaniesQuickdice ERP
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
3. Introduction
• Minimize training time by reducing the
bandwidth for gradient exchange in distributed
training
• Preserve model accuracy for faster training
• Focus on reducing data communication on
inexpensive commodity network or training on
mobile devices
4. Introduction
(continue)
• To preserve accuracy during compression:
Momentum correction, Local gradient
clipping, Momentum factor masking and
Warm-up training
• Applied DGC to CNN - Cifar10, ImageNet,
RNN - Penn Treebank (NLP), Speech -
Librispeech Corpus
• No need to modify neural network model
structure
Gradient compression 300x to
600x without losing accuracy
12. The Challenges
● AlexNet 240 MB weights and ResNet has
100 MB weights
● Every node has to exchange 100 MB of
gradient to each other during each
iteration training for ResNet, which make
the bottle neck of the infrastructure
15. Related Distributed Training
Research
• Asynchronous SGD
• Gradient Quantization
• Gradient Dropping ( Aji, 2017)
• Training ImageNet in one hour (FB)
• Training ImageNet in 15 mins (PFN)
16. Related - Gradient Quantization
• Quantizing the gradients to low-precision
values can reduce the communication
bandwidth.
• Seide et al. (2014) proposed 1-bit SGD to
reduce gradients transfer data size and
achieved 10× speedup in traditional speech
applications.
17. Related - Gradient Dropping
• Sparsify the gradients by a single
threshold value.
• To keep the convergence speed, Gradient
Dropping requires adding the layer
normalization
• Gradient Dropping saves 99% of gradient
exchange while incurring 0.3% loss on a
machine translation task.
18. Related - Training ImageNet in 1 hour
• FB Big Basin server
• Large minibatch SGD – 8k
• Caffe2 trains ResNet 50
• 256 GPU, Tesla P100
19. Related - Training ImageNet in 1 hour
• Used Facebook’s Big Basin GPU servers
• Each server has 8 Tesla P100 GPUs and 3.2TB
of SSDs.
• Servers have 50Gbit Ethernet network card
• ResNet-50 has approximately 25 million
parameters. This means the total size of
parameters is 25 · 106 · sizeof(float) = 100MB
$$ Expensive hardware
24. Comparison
• DGC pushes the gradient compression ratio to up to
600× without expensive hardware
• DGC does not require extra layer normalization, and
thus does not need to change the model structure.
• Most importantly, Deep Gradient Compression
results in no loss of accuracy.
27. Deep Gradient Compression
• Gradient Sparsification
• Local Gradient Accumulation
• Momentum Correction
• Local Gradient Clipping
• Momentum Factor Masking
• Warm-up Training
28. 1. GRADIENT
SPARSIFICATION
• Reduce the communication bandwidth by
sending only the important gradients.
• Use the gradient magnitude as a simple
heuristics for importance
• Only gradients larger than a threshold are
transmitted ( top 0.1%)
29. 2. Local Gradient
Accumulation
• To avoid losing information, we
accumulate the rest of the gradients
locally.
• Eventually, these gradients become large
enough to be transmitted.
Accuracy Image classification: -1.6%
Accuracy speech recognition: -3.3%
30. 3. Momentum Correction
● Momentum SGD – Using part of previous
gradient and current gradient to avoid
noise
● New vector is created as ‘Velocity’
● We should do local accumulation of
velocity than gradient
Accuracy Image classification: -0.3%
Speech recognition: can’t converge
31. 4. Local Gradient Clipping
• Gradient clipping is widely adopted to avoid the
exploding gradient problem
• This step is conventionally executed after gradient
aggregation from all nodes.
• Perform the gradient clipping locally before adding
the current gradient to previous accumulation
Accuracy Image classification: N/A
Speech recognition: -2.0%
32. 5. Momentum Factor Masking
There is a long tail accumulation issue ( ~2k
iterations)
Introduce momentum factor masking, to
alleviate staleness
This mask stops the momentum for delayed
gradients
Preventing the stale momentum from carrying
the weights in the wrong direction.
Accuracy Image classification: -0.1%
Speech recognition: -0.5%
33. 6. Warm-up Training
Use a less aggressive learning rate to slow down the
changing speed of the neural network at the start of
training
Instead of linearly ramping up the learning rate during the
first several epochs, we exponentially increase the gradient
sparsity from a relatively small value to the final value, in
order to help the training adapt to the gradients of larger
sparsity.
Accuracy Image classification: +0.37%
Speech recognition: +0.4%
40. Conclusion
• Limitation on scale up, optimize communication comes next.
• Deep Gradient Compression compresses the gradient by
300-600× for a wide range of CNNs and RNNs.
• To achieve this compression without slowing down the
convergence, DGC employs momentum correction, local
gradient clipping, momentum factor masking and warm-up
training.
• Deep Gradient Compression reduces the required
communication bandwidth and improves the scalability of
distributed training with inexpensive, commodity networking
infrastructure.
This paper is regarding to Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure
Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth and preserve accuracy during this compression with inexpensive commodity hardware
General introduction to this paper, background, author
Some intro to Distributed deep learning training
Related research work/paper regarding to DDLT
Today’s paper - DGC
Experiment they did and detailed result 300-600X
wrap up and discussion
Add more nodes will provide more computing power, but there is another factor: communication which might limit distributed training scalability
99.9% gradient exchange are redundant, especially for recurrent neural networks (RNN) where the computation-to-communication ratio is low. Therefore, the network bandwidth becomes a significant bottleneck for scaling up distributed training.
lots of related work/research can have fast training, like 1 hours or even 15 mins
e.g. Uber framework horoboard requires expensive 40mbits/sec network, same as other big companies like google, amazon, FB…
Enable DT with less expensive network, e.g, AWS 1gbits ethernet to democratize DLT using commodity hardware
Training on mobile – for privacy and better personalization
Cifar10 is an established computer-vision dataset used for object recognition.
The ImageNet project is a large visual database designed for use in visual object recognition software research.
Resnet 50 from 97 MB to 0.35 MB, Deep Speech from 488 MB to .74 MB
Penn Treebank dataset, known as PTB dataset, is widely used in machine learning of NLP (Natural Language Processing) research
LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech,
DGC does not require extra layer normalization, and thus does not need to change the model structure.
especially for recurrent neural networks (RNN) where the computation-to-communication ratio is low. Therefore, the network bandwidth becomes a significant bottleneck for scaling up distributed training
2018 ICLR conference paper
Song Han – PhD from Stanford EECS, now assistant professor at MIT, also mange HAN’s lab @ MIT
His Deep compression paper got 2016 ICLR best paper award
Bill Dally – Professor @ Stanford, Chief scientist @ Nvidia for 10 years
Others: From Tsinghua in China
next is the overview of deep learning training in distributed env
General distributed system, it is similar for distributed db, computing…
vertical vs horizontal scaling
scale up or out
Data parallelism
different chunk of data to different nodes, easier to implement, same model on each node ( CNN or RNN),
node 1 may have batch of training images 1-32, node 2 may have next 32 images, etc.
All the node are sharing the same model but they are fed with different chunk of data and they are calculating local gradients according to their own chunk of data and then they exchange gradients to each other.
Can be implemented in two ways:
a. parameter server - centralized , it receives the gradients from all nodes then sum it up and calculate average and update local weights then broadcast to all the training nodes.
b. All-reduce operation(de-centralized) -
Model parallelism
different chuck of model to different nodes, hard to implement and less people to adopt this approach
For single node training, there is no gradient exchange over network
Every node receives every other's calculated gradients and then calculate the average. still have a master training node ( like a tree structure )
this is one of the basic implementation. more advanced like butterfly structure
where χ is the training dataset,
w are the weights of a network,
f(x, w) is the loss computed from samples x ∈ χ,
𝜂 is the learning rate,
N is the number of training nodes, and Bk,t for 1 ≤ k < N is a sequence of N minibatches sampled from χ at iteration t, each of size b.
After T iterations, we have Equation 2 shows that local gradient accumulation can be considered as increasing the batch size from N b to N bT (the second summation over τ ), where T is the length of the sparse update interval between two iterations at which the gradient of w (i) is sent. Learning rate scaling (Goyal et al., 2017) is a commonly used technique to deal with large minibatch
NVIDIA Collective Communications Library (NCCL) implements multi-GPU and multi-node collective communication primitives that are performance optimized for NVIDIA GPUs. provide all-gather, all-reduce, broadcast...
AlexNet: 2012
ResNet 2015
LARGE SCALE DISTRIBUTED NEURAL NETWORK TRAINING THROUGH ONLINE DISTILLATION. G. E. Hinton
In our first set of experiments, our goal was to approximately determine the maximum number of GPU workers that can be productively employed for SGD in our Common Crawl neural language model setup
Common crawl dataset is an open repository of web crawl data and a largest to-date dataset used for neural language modeling, Common Craw consists of petabytes of data collected since 2011.[3] It completes crawls generally every month
Figure 1a plots the validation error as a function of global steps for the different numbers of workers we tried, using the best learning rate for each number of workers. Increasing the number of workers (and thus the effective batch size) reduced the number of steps required to reach the best validation error until 128 workers, at which point there was no additional improvement. Even with idealized perfect infrastructure, 256 workers would at best result in the same end to end training time on this problem. However, because steps can take so much longer with 256 workers, going from 128 to 256 workers is highly counterproductive in practice. Figure 1b plots validation error against wall time for the same varying numbers of synchronous workers. There is a large degradation in step time, and thus learning progress, at 256 workers. Although it might be possible to improve the step time at 256 workers by using a more sophisticated scheme with backup workers (Chen et al., 2016), the operative limit to scalability on this task is the diminishing return from increasing the effective batch size, not the degradation in step times.
Next related work
Researchers have proposed many approaches to overcome the communication bottleneck in distributed training
We will quickly take a look of existing research for DDL and compare today’s paper I presented
Bilingual Evaluation Understudy, is a score for comparing a candidate translation of text to one or more reference translations.
FB - large minibatch SGD, caffe2 trains ResNet 50 with minibatch 8192 on 256 GPU, P100
Closer look of FB big basin
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
ImageNet top-1 validation error vs. minibatch size.
large minibatch SGD, caffe2 trains ResNet 50 with minibatch 8192 on 256 GPU
“Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes,” arXiv, 2017
basically it is a supercomputer
Chainer - The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia
NVIDIA Collective Communications Library ( NCCL2) - multi nodes, multi-GPU systems
provide functions like : all-gather, all-reduce, broadcast...
pytorch has integrated NCCL2 to accelerate deep learning training on multi-GPU systems.
large minibatch SGD, caffe2 trains ResNet 50 with minibatch 8192 on 256 GPU
in some case, it improves accuracy
next - today’s paper
Gradient basic
- Optimization problem - single node: like find direction when climbing downhill
- multiple nodes - each node have images, finds their own direction how to merge together? they need to communicate and exchange the gradient via network
- Exchange can be bulky, e.g., alexnet 240 mB weights resnet 100 MB, every iteration, every node has to exchange 100 MB of gradient to each other which make the bottle neck of the infrastructure
In synchronized training, each node need to know every other nodes' computed gradient.
DeepSpeech from 488MB to 0.74MB
Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile
Some of the gradients are very small, not zero but small. so, sort the gradients, only send out the top 0.1% largest gradients. but just send 0,1%.
Only simply doing this using threading doesn’t event converge in CNN or RNN
So, some small gradients still affect accuracy
If we don't send the small gradients, it will hurt the accuracy => locally accumulate the gradients for more iteration until it gets large enough then sent them out, In this way, accuracy can be recovered.
Almost equivalent to increase batch size in N iteration, it mathematica way to interpret it
Let’s say we accumulate the gradients locally for 3 iterations, it almost equivalent to increase the batch 3 times
take into the prev gradient into account
Using part of prev gradients and current gradient to do weighted average. which give a new vector called velocity. We should do local accumulation of velocity rather then local accumulation of gradients.
gradient clipping is for RNN only. change the order between clipping and summation
go through continuous matrix multiplications because of the the chain rule, and as they approach the earlier layers, if they have small values (<1), they shrink exponentially until they vanish and make it impossible for the model to learn , this is the vanishing gradient problem. While on the other hand if they have large values (>1) they get larger and eventually blow up and crash the model, this is the exploding gradient problem
Long tail accumulation ( 2k iteration), it is necessary to cut or mask the gradients.
to mask away the obsoleted velocity
75, 95,.. 99.9%
In the early stages of training, the network is changing rapidly, and the gradients are more diverse and aggressive
The only hyper-parameter introduced by Deep Gradient Compression is the warm-up training strategy. In all experiments related to DGC, we rise the sparsity in the warm-up period as follows: 75%, 93.75%, 98.4375%, 99.6%, 99.9%
The warm-up period for DGC is 4 epochs out of164 epochs for Cifar10 and 4 epochs out of 90 epochs for ImageNet Dataset
Figure 6 shows the speedup of multi-node training compared with single-node training. Conventional training achieves much worse speedup with 1Gbps (Figure 6(a)) than 10Gbps Ethernet (Figure 6(b)). Nonetheless, Deep Gradient Compression enables the training with 1Gbps Ethernet to be competitive with conventional training with 10Gbps Ethernet
We refer to this migration as the momentum correction. It is a tweak to the update equation, it doesn’t incur any hyper parameter
Shorter training time
Equal to better model accuracy ( no degradation)
Programming?
LARGE SCALE DISTRIBUTED NEURAL NETWORK TRAINING THROUGH ONLINE DISTILLATION
As the number of machines increases, there are diminishing improvements to the time needed to train a high quality model, to a point where adding workers does not further improve training time
For the synchronous algorithm, there are rapidly diminishing returns from increasing the effective batch size
For the asynchronous algorithm, gradient interference from inconsistent weights can cause updates to thrash and even, in some cases, result in worse final accuracy or completely stall learning progress
In our experience it can be very difficult to scale effectively much beyond a hundred GPU workers in realistic setups
The encode() function packs the 32-bit nonzero gradient values and 16-bit run lengths of zeros.
where χ is the training dataset,
w are the weights of a network,
f(x, w) is the loss computed from samples x ∈ χ,
𝜂 is the learning rate,
N is the number of training nodes, and Bk,t for 1 ≤ k < N is a sequence of N minibatches sampled from χ at iteration t, each of size b.
After T iterations, we have Equation 2 shows that local gradient accumulation can be considered as increasing the batch size from N b to N bT (the second summation over τ ), where T is the length of the sparse update interval between two iterations at which the gradient of w (i) is sent. Learning rate scaling (Goyal et al., 2017) is a commonly used technique to deal with large minibatch
PFN’s strategies to improve all-reduce network bottleneck
Downpour SGD is an asynchronous variant of SGD in their DistBelief (predecessor to TensorFlow) at Google. It runs multiple replicas of a model in parallel on subsets of the training data. These models send their updates to a parameter server, which is split across many machines. Each machine is responsible for storing and updating a fraction of the model's parameters. However, as replicas don't communicate with each other e.g. by sharing weights or updates, their parameters are continuously at risk of diverging, hindering convergence.
Image net in one hour : FB – large minibatch SGD, caffe2 trains ResNet 50 with minibatch 8192 on 256 GPU
ImageNet in 15 mins : Preferred Network ( Japaness IOT company) – Chainer, 1024 P100 GPUs, BS = 32k
Codistillatin - Google
The idea of distillation is to first train a teacher model, which traditionally is an ensemble or another high-capacity model, and then, once this teacher model is trained, train a student model with an additional term in the loss function which encourages its predictions to be similar to the predictions of the teacher model.
large minibatch SGD, caffe2 trains ResNet 50 with minibatch 8192 on 256 GPU
large minibatch SGD, caffe2 trains ResNet 50 with minibatch 8192 on 256 GPU