E2E Offering Nvidia A100 Tensor Core GPU Services

November 20, 2020

Introduction

E2E networks offer a very high-performance cloud infrastructure and are a world-class cloud in India.

Their GPU Cloud is apt for a wide variety of applications comprising Computer Vision, AI, Scientific Research, Computational Finance, and Big Data. Taking the extensive benefit of GPU cloud computing, E2E networks announced its GPU power-driven cloud provision in association with NVIDIA.

In a world where cloud computing is the future and deep neural networks are being used extensively in the business, data scientists, scholars, and engineers can stop worrying about their precious time, money and memory, and concentrate on the next big AI innovation. E2E networks always intend on evolving and providing profitable cloud solutions for businesses. This article is about how E2E is offering powerful Nvidia A100 Tensor Core GPU Services and how important it is for all types of consumers extending from startups to big industries, from ML experts to CXOs of tech companies, and even tech enthusiasts.

Why do we need GPU Services?

When any data scientist wants to achieve high performance while training enormous datasets or deep learning (DL) models, most of the time, they take hours or even weeks without a powerful GPU.

While DL models require a lot of computational power to run, Machine Learning (ML) and recognition algorithms require great accuracy. Even in our digital world, high-res images and videos consume a lot of computational cost and storage. GPU driven clouds solve all such problems.

NVIDIA GPU Cloud (NGC)

NGC can achieve optimal solutions for all existing problems while executing scientific computing and deep learning models and is very technologically advanced. NGC involves an extensive directory of GPU – powered packages essential for ML, DL, and High-Performance Computing (HPC).

Source: HPC in NVIDIA

NGC containers deliver an easy and powerful stage for deploying software systems efficiently to achieve faster and more efficient solutions. NGC allows operators to emphasize on developing slender models, congregating quicker intuitions, and creating optimum resolutions.

The key factors on which they rely are: -

  • Staying up to date
  • Faster innovation
  • Running anywhere

The benefits of using NGC are: -

  1. It provides models that are already trained and help deliver streamlined SDKs, thus enabling endwise AI explications.
  2. It has dedicated Augmented DL Frameworks, which make deep learning gear easily accessible. 
  3. It has renowned DL Stack Containers such as TensorFlow (Powerful library for data flow and mathematical computations), MXNET (framework for deep neural networks), and framework customizations.

Nvidia A100 Tensor Core GPUs in E2E

Source: E2E Networks

The NVIDIA A100 is capable of carrying out unparalleled acceleration. It can boost performance in big data, data analytics, artificial intelligence, and high-performance computing (HPC), and confront the most difficult computations easily. The A100 is capable of scaling up effectively to thousands of GPUs or being segregated into 7 GPU instances to boost jobs of every dimension using NVIDIA Multi-Instance GPU (MIG) technology.

The A100 is targeting to achieve a big milestone with 3,456 FP64 CUDA cores, 6,912 FP32 CUDA cores, and 422 3

rd

– Gen Tensor cores. There is no alteration in code due to the combination of the exactness of FP16 and the range of FP32 during model training.

The NVIDIA A100 GPU is powered by the latest technologies like: -

  • Fine-grained Designed Thinness: 2X the computational output for deep neural networks.
  • 54 Billion Transistors (Xtors) on top of an 826 mm^2 size of a die: A 7 – nm processor, almost 3 times the speed of RTX 2080 Ti.  
  • 3rd Gen NVLink: Delivers connection-level fault finding and packet rerun mechanism.
  • 3rd - Gen Tensor Cores
  • Multi-instance GPU: It allows the partition of the A100 into seven distinct GPU occurrences.

Wrapping Up

The A100 GPU familiarizes revolutionary and innovative features designed to enhance implication loads. It offers extraordinary adaptability, stability, and performance. It can train huge Artificial Intelligence programs like BERT (Language Model) on a cluster of 210 A100s in only 37 minutes. It can also fast-track an entire range of accuracies, ranging from (floating point) FP32 to FP16 to INT8 and way down to INT4.

Looking at the HPC Performance Curve, we can see how it has delivered 9X more performance since 2016: -

Source: Nvidia

Speaking of Cloud Gaming, latency, or the delay in time between input and output, is a crucial part. E2E GPU cloud improves the gaming experience by delivering an ultra-low latency system to “Cloud Hunt” Users.  

Thus, E2E offering NVIDIA A100 Tensor Core GPU services are capable of giving a boost in: -

  1. Cloud data centres
  2. Supercomputers
  3. Single and Multi-GPU Workstations
  4. Servers and clusters, and
  5. Edge computing systems.                                

We are building a strong appreciation from the end-users because of our trustworthiness, scalability, affordability, and improved privacy features.

For free trial please click here :- http://bit.ly/3hhaiJm

Latest Blogs
This is a decorative image for Clustering in deep learning- A acknowledged tool.
June 27, 2022

Clustering in deep learning- A acknowledged tool

When learning something new about anything, such as music, one strategy may be to seek relevant groupings or collections. You may organize your music by genre, but your friend may organize it by the singer. The way you combine items allows you to learn more about them as distinct pieces of music, somewhat similar to what clustering algorithms do. 

Let’s discuss a detailed brief on Clustering algorithms, their applications, and how GPUs can be used to accelerate the potential of clustering models. 

Table of content-

  1. What is Clustering?
  2. How to do Clustering?
  3. Methods of Clustering. 
  4. DNN in Clustering. 
  5. Accelerating analysis with clustering and GPU. 
  6. Applications of Clustering.
  7. Conclusion. 

What is Clustering?

In machine learning, we typically group instances as a first step in interpreting a data set in a machine learning system. The technique of grouping unlabeled occurrences is known as clustering. Clustering is based on unsupervised machine learning since the samples are unlabeled. When the instances are tagged, clustering transforms into classification.

Clustering divides a set of data points or populations into groups so that data points in the same group are more similar to one another and dissimilar from data points in other groups. It is simply a collection of elements classified according to their similarity and dissimilarity.

How to do Clustering?

Clustering is critical because it determines the intrinsic grouping of the unlabeled data provided. There are no requirements for good clustering. It is up to the user to decide which criteria will be used to satisfy their demands. For example, we might be interested in locating representatives for homogenous groups (data reduction), locating "natural clusters" and defining their unknown qualities ("natural" data types), locating useful and appropriate groupings ("useful" data classes), or locating odd data items (outlier detection). This method must make assumptions about point resemblance, and each assumption results in a unique but equally acceptable cluster.

Methods of Clustering: 

  1. Density-Based Approaches: These methods assume clusters to be the dense region of the space, with some similarities and differences to the lower dense region. These algorithms have high accuracy and can combine two clusters. DBSCAN (Density-Based Spatial Clustering of Applications with Noise), OPTICS (Ordering Points to Identify Clustering Structure), and other algorithms are examples. 

  1. Methods Based on Hierarchy: The clusters created in this approach form a tree-like structure based on the hierarchy. The previously established cluster is used to generate new clusters. It is classified into two types. aggregative (bottom-up approach) dividing (top-down approach)

  1. Partitioning Methods: These methods divide the items into “k” clusters, with each split becoming a separate cluster. This approach is used to optimize an objective criterion similarity function, such as K-means, CLARANS (Clustering Large Applications Based on Randomized Search), and so on. 

  1. Grid-based Methods: The data space is divided into a finite number of cells that create a grid-like structure in this approach. STING (Statistical Information Grid), wave cluster, CLIQUE (Clustering In Quest), and other clustering processes performed on these grids are rapid and independent of the number of data items.

DNN in clustering-

In Deep Learning, DNNs serve as mappings to better representations for clustering. The properties of these representations might be drawn from different layers of the network, or even from many. This choice may be divided into two categories:

  • One layer: Refers to the general scenario in which just the output of the network's last layer is used. This method makes use of the representation's low dimensionality. 

  • Several layers: This representation is a composite of the outputs of several layers. As a result, the representation is more detailed and allows for embedded space to convey more sophisticated semantic representations, potentially improving the separation process and aiding in the computation of similarity.

Accelerating analysis with clustering and GPU-

Clustering is essential in a wide range of applications and analyses, but it is now facing a computational problem as data volumes continue to grow. One of the most promising options for tackling the computational barrier is parallel computing using GPUs. Because of their huge parallelism and memory access-bandwidth benefits, GPUs are an excellent approach to speed data-intensive analytics, particularly graph analytics. The massively parallel architecture of a GPU, which consists of thousands of tiny cores built to handle numerous tasks concurrently, is ideally suited for the computing job. This may be used for groups of vertices or edges in a big graph.

In an analysis using data, clustering is a task with high parallelism that can be expedited using GPUs. In the future, GPUs will include spectral and hierarchical clustering/partitioning approaches based on the minimal balanced cut metric. 

Applications of Clustering-

The clustering approach may be applied to a wide range of areas. The following are some of the most popular applications of this technique: 

  • Segmentation of the Market: Cluster analysis, in the context of market segmentation, is the application of a mathematical model to uncover groups of similar consumers based on the smallest variances among customers within each group. In market segmentation, the purpose of cluster analysis is to precisely categorize customers in order to create more successful customer marketing through personalization. 

  • Recommendation engine: Clustering may be used to solve a number of well-known difficulties in recommendation systems, such as boosting the variety, consistency, and reliability of suggestions; the data sparsity of user-preference matrices; and changes in user preferences over time.

  • Analysis of social networks: Clustering in social network analysis is not the same as traditional clustering. It necessitates classifying items based on their relationships as well as their properties. Traditional clustering algorithms group items only on their similarity and cannot be used for social network research. A social network clustering analysis technique, unlike typical clustering algorithms, can classify items in a social network based on their linkages and detect relationships between classes. 

  • Segmentation of images: Segmentation of images using clustering algorithms is a method for doing pixel-wise image segmentation. The clustering algorithm here aims to cluster the pixels that are close together in this form of segmentation. There are two ways to conduct segmentation via clustering - Merging Clustering and Divisive Clustering

  • Detecting Anomaly: Clustering may be used to train the normalcy model by grouping comparable data points together into clusters using a distance function. Clustering is appropriate for anomaly detection since no knowledge of the attack classes is required during training. Outliers in a dataset can be found using clustering and related approaches.

Conclusion-

Clustering is an excellent method for learning new things from old data. Sometimes the resultant clusters will surprise you, and it may help you make sense of an issue. One of the most interesting aspects of employing clustering for unsupervised learning is that the findings may be used in a supervised learning issue. 

Clusters might be the new features that you employ on a different data set! Clustering may be used on almost every unsupervised machine learning issue, but make sure you understand how to examine the results for accuracy.

Clustering is also simple to apply; however, several essential considerations must be made, such as dealing with outliers in your data and ensuring that each cluster has a sufficient population.

This is a decorative image for How GPUs are affecting Deep Learning inference?
June 27, 2022

How GPUs are affecting Deep Learning inference?

The training step of most deep learning systems is the most time-consuming and resource-intensive. This phase may be completed in a fair period of time for models with fewer parameters, but as the number of parameters rises, so does the training time. This has a two-fold cost: your resources will be engaged for longer, and your staff will be left waiting, squandering time. 

We'll go through how GPUs manage such issues and increase the performance of deep learning inferences like multiclass classification and other inferences. 

Table of Content:

  1. Graphical Processing Unit (GPU)
  2. Why GPUs?
  3. How GPUs improved the performance of Deep Learning Inferences?
  4. Critical Decision Criteria for Inference 
  5. Which hardware should you use for DL inferences? 
  6. Conclusion

Graphical Processing Units (GPU)

A graphics processing unit (GPU) is a specialized hardware component capable of performing many fundamental tasks at once. GPUs were created to accelerate graphics rendering for real-time computer graphics, especially gaming applications. The general structure of the GPU is similar to that of the CPU; both are spatial architectures. Unlike CPUs, which have a few ALUs optimized for sequential serial processing, the GPU contains thousands of ALUs that can do a huge number of fundamental operations at the same time. Because of this exceptional feature, GPUs are a strong competitor for deep learning execution.

Why GPUs?

Graphics processing units (GPUs) can help you save time on model training by allowing you to execute models with a large number of parameters rapidly and efficiently. This is because GPUs allow you to parallelize your training activities, divide them across many processor clusters, and perform multiple computing operations at the same time.

GPUs are also tuned to execute certain jobs, allowing them to complete calculations quicker than non-specialized technology. These processors allow you to complete jobs faster while freeing up your CPUs for other duties. As a result, bottlenecks caused by computational restrictions are no longer an issue.

GPUs are capable of doing several calculations at the same time. This allows training procedures to be distributed and can considerably speed up deep learning operations. You can have a lot of cores with GPUs and consume fewer resources without compromising efficiency or power. The decision to integrate GPUs in your deep learning architecture is based on various factors: Memory bandwidth—GPUs, for example, can offer the necessary bandwidth to support big datasets. This is due to the fact that GPUs have specialized video RAM (VRAM), which allows you to save CPU memory for other operations. Dataset size—GPUs can scale more readily than CPUs, allowing you to analyze large datasets more quickly. The more data you have, the more advantage you may get from GPUs. Optimization—one disadvantage of GPUs is that it might be more difficult to optimize long-running individual activities than it is with CPUs.

How GPUs improved the performance of Deep Learning Inferences?

Multiple matrix multiplications make up the computational costly element of the neural network. So, what can we do to make things go faster? We may easily do this by performing all of the processes at the same time rather than one after the other. In a nutshell, this is why, when training a neural network, we utilize GPUs (graphics processing units) rather than CPUs (central processing units). 

Critical Decision Criteria for Inference-

 

The speed, efficiency, and accuracy of these projections are some of the most important decision factors in this phase of development. If a model can't analyze data quickly enough, it becomes a theoretical exercise that can't be used in practice. It becomes too expensive to run in manufacturing if it consumes too much energy. Finally, if the model's accuracy is inadequate, a data science team will be unable to justify its continuous usage. Inference speed, in particular, can be a bottleneck in some scenarios and instances, such as Image Classification, which is utilized in a variety of applications such as social media and image search engines. Even though the tasks are basic, timeliness is crucial, especially when it comes to public safety or platform infractions. 

Self-driving vehicles, commerce site suggestions, and real-time internet traffic routing are all instances of edge computing or real-time computing. Object recognition inside 24x7 video feeds, as well as large volumes of images and videos. Pathology and medical imaging are examples of complex images or tasks. These are some of the most difficult photos to decipher. To achieve incremental speed or accuracy benefits from a GPU, data scientists must now partition pictures into smaller tiles. These cases necessitate a decrease in inference speed while also increasing accuracy. Because inference is often not as resource-intensive as training, many data scientists working in these contexts may start with CPUs. Some may resort to leveraging GPUs or other special hardware to obtain the performance or accuracy enhancements they seek as inference speed becomes a bottleneck.

Which hardware should you use for DL inferences? 

There are several online recommendations on how to select DL hardware for training, however, there are fewer on which gear to select for inference. In terms of hardware, inference and training may be very distinct jobs. When faced with the decision of which hardware to use for inference, you should consider the following factors: How critical is it that my inference performance (latency/throughput) be good? Is it more important for me to maximize latency or throughput? Is the typical batch size for my company modest or large? How much of a financial sacrifice am I ready to make in exchange for better results? Which network am I connected to? 

You know how we choose inference hardware? We start by assessing throughput performance. The V100 clearly outperforms the competition in terms of throughput, especially when employing a big batch size (8 images in this case). Furthermore, because the YOLO model has a significant parallelization potential, the CPU outperforms the GPU in this metric.

Conclusion-

We looked at the various hardware and software techniques that have been utilized to speed up deep learning inference. We began by explaining what GPUs are, why they are needed, how GPUs increased the performance of Deep Learning Inferences, the essential choice criteria for the deep learning model and the hardware that should be employed. 

There is little question that the area of deep learning hardware will grow in the future years, particularly when it comes to specialized AI processors or GPUs. 

How do you feel about it? 

This is a decorative image for Understanding PyTorch
June 27, 2022

Understanding PyTorch

Every now and then, a library or framework emerges that completely changes the way we think about deep learning and aids in the advancement of deep learning studies by making them computationally quicker and less costly. Here we will be discussing one such library: PyTorch.

Overview-

PyTorch is the library or framework for Python scripts that make deep learning projects easier to create. PyTorch's approachability and ease of use drew a large number of early adopters from the academic, research, and development communities. And it has developed into one of the most popular deep learning tools across a wide range of applications in the years after its first release.

PyTorch has two primary characteristics or features at its core: An n-dimensional Tensor that works similarly to NumPy but on GPUs and the other is the construction and training of neural networks using automatic differentiation. Apart from these primary features, PyTorch includes a number of other features, which are detailed below in this blog.

PyTorch Tensor-

Numpy is a fantastic framework, however, it is unable to use GPUs to speed up numerical operations. GPUs can frequently deliver speedups of 50x or more for contemporary deep neural networks and today's parallel computing methods may take advantage of GPUs much more. 

To train many models at once PyTorch offers distributed training, allowing academic practitioners and developers to parallelize their work. Using many GPUs to process bigger batches of input data the training of models can be made feasible with distributed training, as a result, the computation time is reduced.

The Tensor, the most fundamental PyTorch concept, is capable to do so. A PyTorch Tensor is basically the same as a NumPy array: a Tensor is an n-dimensional array, and PyTorch has several methods for working with them. Tensors may maintain track of a computational graph and gradients behind the scenes, but they can also be used as a general tool for scientific computing. PyTorch Tensors, unlike NumPy, may use GPUs to speed up their numeric operations. You just need to provide the suitable device to execute a PyTorch Tensor on GPU.

Automatic Differentiation-

Automatic differentiation is a method used by PyTorch to record all of our operations and then compute gradients by replaying them backward. Generally while training neural networks, developers have to manually implement both forward and backward passes. While manually implementing backward pass is easy but doing the same for forward pass might get a bit tricky or exhausting task. This is exactly what the autograd package in PyTorch does. 

When you use autograd, your network's forward pass will construct a computational graph, with nodes being Tensors and edges being functions that produce output Tensors from input Tensors. Because we calculate the gradients on the forward pass, this approach allows us to save time on each epoch.  You may also simply compute gradients by back propagating across this graph.

Flow control and weight sharing-

 

PyTorch implements a weird model as an example of dynamic graphs and weight sharing: a third-fifth order polynomial that selects a random integer between 3 and 5 and utilizes that many orders on each forward pass, recycling the same weights several times to calculate the fourth and fifth order. We can construct the loop in this model using standard Python flow control, and we can achieve weight sharing by simply repeating the same argument many times.

Torchscript-

TorchScript allows you to turn PyTorch code into serializable and optimizable models. Any TorchScript application may be saved from a Python process and loaded into another process that doesn't require or doesn’t have a Python environment.

Pytorch has tools for converting a model from a pure Python program to a TorchScript program that can be executed in any standalone application such as of C++. This allows users to train models in PyTorch using familiar Python tools before exporting the model to a production environment where Python applications may be inefficient due to performance and multi-threading issues.

Dynamic Computation Graphs-

In frameworks like PyTorch, you usually have a set up of the computational network and a distinct execution mechanism than the host language. This unusual design is largely motivated by the need for efficiency and optimization. DL frameworks keep track of a computational graph that specifies the sequence in which calculations must be completed in a model. Researchers have found it difficult to test out more creative ideas because of this inconvenient arrangement.

There are two such types of computational graphs, one is static and the other is dynamic. Variable sizes must be established at the start with a static network i.e. when the graph is Static all the variables are to be created and connected in the beginning, and then later is settled up in a static (non-changing) session which might be inconvenient for some applications, such as NLP as for NLP Dynamic computational graphs are critical since language or input can arrive in a variety of expression lengths.

PyTorch, on the other hand, employs a dynamic graph. That is, the computational graph is constructed dynamically once variables are declared. As a result, after each training cycle, this graph is regenerated. Dynamic graphs are adaptable, allowing us to change and analyze the graph's internals at any moment. 

When all you had before were "goto" commands, introducing dynamic computational graphs is like introducing the idea of a process. We may write our programs in a composable manner thanks to the idea of the procedure. Of course, one may argue that DL designs do not require a stack. Recent research on Stretcher networks and Hyper networks, on the other hand, demonstrates this. Context switching, such as a stack, appears to be beneficial in some networks in studies.

nn Module

Autograd and computational graphs are a powerful paradigm for automatically generating sophisticated operators and computing derivatives; nevertheless, raw autograd may be too low-level for huge neural networks. We often consider stacking the computation when developing neural networks, with some layers containing learnable parameters that will be tweaked throughout the learning process. 

In such cases, we can make use of PyTorch’s nn module. The nn package defines modules, which are fundamentally equivalent to neural network layers. A Module can contain internal data such as Tensors with learnable parameters in addition to taking input Tensors and computing output Tensors.

Conclusion-

In this blog, we understood how PyTorch is different from other libraries like NumPy, What are the special features that it offers including Tensor computing with substantial GPU acceleration and a tape-based autograd system used to build deep neural networks. 

We also studied other features like flow control and weight sharing, torch scripts, computation graphs, and nn module. 

This description was adequate to gain a general notion of what PyTorch is and how academicians, researchers, and developers may utilize it to construct better projects.

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