Demystifying NVIDIA A100 GPU: When, Where, and How to Use

October 8, 2020

Breaking into the most compelling GPU announcements this year, the NVIDIA A100 GPU sets a new standard for data centres across the globe. Find out how.

Unveiling the All-New NVIDIA A100 Data Center GPU

In the wake of proliferating AI networks, NVIDIA introduced the new A100 Tensor Core GPU in May this year. Designed especially for HPC, AI, and data analytics, the A100 GPU promises significantly speedy performance beyond the prior NVIDIA Tesla V100 GPU. The A100 GPU is packed with exciting new features that are poised to take computing and AI applications by storm.

Built for modern data centers, NVIDIA A100 GPUs can amplify the scaling of GPU compute and deep learning applications running in-

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

Touted as the ‘8th Generation Data Center GPU’, NVIDIA A100 claims to become a powerhouse for elastic, versatile, and high throughput data centers. Tensor Core GPU’s architecture is based on NVIDIA Ampere GPU coupled with new CUDA software advances. 

Let’s take a closer look at the tech specifications of this brand new A100 GPU. 

Technologies Powering NVIDIA A100 GPU

The announcement of the NVIDIA A100 Tensor Core GPU has rewritten history by delivering over 1.5 terabytes for frame buffer bandwidth. Below are some other unique features of this Ampere GPU-

1. 54 Billion Xtors

NVIDIA affirms that the A100 GPUs crams 54 billion transistors onto an 826 mm^2 size of a die. It is considerably larger than the earlier models, namely NVIDIA’s flagship gaming card, RTX 2080 Ti, and the V100. This incredible transistor density (7-nanometer processor) along with a huge die size translates to almost three times the speed of RTX 2080 Ti. 

2. 3rd Gen Tensor Cores

Not only transistors but the A100 is also aiming high with 6,912 FP32 CUDA cores, 3,456 FP64 CUDA cores, and 422 Tensor cores. Together with the range of FP32 and the precision of FP16 results in no code change during model training. 

Here’s the difference between the operating speed of the V100 FP32 matrix on the left and the A100 accelerated TF32 on the right.

3. Fine-grained Structured Sparsity

Another mega introduction in the A100 GPU is the fine-grained structured sparsity that doubles the compute throughput for deep neural networks. Considering the heavy sparsity of most neural networks, the approach zeros out smaller weights to retrain the network with 2X data processing.

Such an amplified processing speed is extremely beneficial for applications involving precision and physics simulations. 

4. Multi-instance GPU

Talking about hardware, the new multi-instance GPU (MIG) feature enables the A100 to be easily partitioned into seven separate GPU instances. NVIDIA offers these 7 GPU instances as part of their DGX A100 system to be adjusted like a pod into a server. This hardware utilization makes DGX ideal for 56 different users with each one of them experiencing an equivalent performance of a Volta. 

The pack becomes much more with 9 Mellanox ConnectX-6 200Gb/s network interface and 15TB Gen4NVME SSD.

5. 3rd Gen NVLink

Last but not the least, A100’s third-generation NVLink interconnect is a power booster for the GPU’s scalability and reliability. To support successful data transmission, NVLink provides link-level error detection and packet replay mechanism combined with its low-latency shared memory interconnect architecture. 

When, Where, and How to Use NVIDIA A100 GPUs

Hardly four months after its release, the A100 has already become a star performer for tech giants. 

Businesses worldwide are capitalizing on this remarkable collaboration of various cloud providers with NVIDIA A100 GPUs to build dynamic applications. Below are some effective use cases-

1. Data Analytics

The ongoing digital transformation of businesses has exploded data ingestion from disparate sources. Thanks to big data analytics, data scientists can now make sense of enormous data, both structured and unstructured. 

The NVIDIA A100 propels data analytics by providing ready-to-run, optimized AI software. It eliminates time-consuming set-up with a simple plug-in start that encourages businesses to build high-performance AI models. 

2. AI Model Training and Inference

Earlier, GPUs were confined to perform domain-specific tasks with either training or inference. With NVIDIA A100, companies get the best of both worlds with an accelerator for training as well as inference. This means that A100 GPUs can not only support AI model training but also helps to analyze new models and make predictions. Applications like predictive analytics for healthcare, demand forecasting, business sales, and a lot more are now seemingly possible with A100. 

3. Deep Video Analytics

Another AI application emerging as an essential breakthrough overriding manual workload is deep video analytics. From media publishers to surveillance systems, deep video analytics is the new vogue for extracting actionable insights from streaming video clips. NVIDIA A100’s capacity to transmit 1.5 terabytes of data makes it perfect for image recognition, contactless attendance, and other deep learning applications. 

Ultimately, the power-pack of NVIDIA A100 GPU awaits the tech industry to leverage it for innovating futuristic technology solutions.

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A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

This is a decorative image for: Constructing 3D objects through Deep Learning
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Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

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