What is Merlin Inference Container in NVIDIA GPU Cloud?

April 8, 2022

NVIDIA has launched several products and technologies to fit the market needs. Currently, the company is releasing products in gaming, AI, data centers, Merlin Inference, and others. Merlin Inference Container is a library that provides a way to inject custom logic into the CUDA kernel launch process. It can be used to insert, log, profile, or debug codes. 

The library is implemented as a set of hooks that are called at various points in the launch process. Merlin provides data researchers, supervised learning computer vision engineers, and academicians with the tools to create high-performance recommenders on a large scale. 

Merlin comprises libraries, techniques, and features that make deep learning recommender development more accessible. It solves the typical pre-processes, algorithm development, mentoring, and inference difficulties. Each component of the Merlin pipeline is designed to handle thousands of petabytes, along with APIs that are simple to use. Merlin can make better forecasts than standard approaches and boost click-through levels.

Merlin inference container is a new technology introduced by NVIDIA, which helps in improving the performance of virtual machines running on the GPU cloud. The GPU Direct RDMA feature provides low latency communication between VMs and GPUs. However, this feature can cause inference when multiple VMs are running on the same host. 

Merlin inference container helps in resolving this issue by creating a separate physical network for each VM. This results in improved performance and reduced inference. Merlin Interface consists of various components such as Merlin NVTabular, HugeCTR, cuDNN, RAPIDS, TensorRT, Triton, and others. Merlin NVTabular is a library that pre-processes the data and performs feature engineering. It reduces the data preparation time and also helps the researchers in modifying the recommender system. 

Merlin interface consists of HugeCTR which is a neural network framework used for training the system on the existing data. It enhances the prediction task. It is embedded on multiple GPUs and provides high-end predictions. Merlin enhances the throughput by combining latency and utilizing the power of GPUs.

Why Merlin?

Data processing

Corporate organizations train the recommendation systems on huge datasets. Training and processing huge data sets requires a long period. Merlin Interface consists of the feature engineering library that enhances data processing. With its help, terabytes of data can be manipulated in a short period and with higher accuracy. 

Large-scale data training

Organizations face difficulties due to the bottleneck created while loading vast data on the recommendation systems. HugeCTR provides high-end training to the system on large-scale datasets. Terabytes of data can be trained easily to make the recommender system more efficient. Deep learning algorithms are implemented through HugeCTR to make the system accurate. 

Deep learning inference pipelines 

Merlin has embedded inference pipelines through Triton. Merlin Triton comprises Vertex AI Prediction. HugeCTR and NVTabular provide Merlin-accelerated pipelines through the GPE inference. The deployment process is simple—users can access the power of the Merlin interface in a few easy steps. 


Merlin allows researchers to accelerate the entire cloud pipeline. It performs tasks like ingesting, training, and launching GPU-related recommendation systems. Its components are open source, which enables the users to easily build and deploy high-quality production. 

NVIDIA Merlin is used to building large-scale recommender systems that require huge datasets to train, especially for deep learning solutions.

Leaders in media, entertainment, and on-demand distribution use an open-source recommendation framework for accelerated deep learning on GPUs. NVIDIA Merlin is a comprehensive recommender system that accelerates every step of recommender development, from data pre-processing to training and inference.

The NVIDIA Merlin team is developing open-source software (OSS) libraries such as NVTabular and HugeCTR. It aims to improve the feasibility and efficiency of GPU-based recommendations for feature engineering, data loading, training, and inference. NVIDIA works on next-generation recommender tools by pushing the boundaries of ETL acceleration, training, and GPU inference. 

NVIDIA Merlin includes tools that democratize deep learning advice by solving common ETL, learning, and inference problems. The Merlin Collection of Models, Methods, and Libraries includes tools for building deep learning systems capable of processing terabytes of data that can provide more accurate predictions and increase clicks.

The purpose of Jupyter Notebooks in introductory examples/movies is to show how NVIDIA Merlin uses NVIDIA NVTabular to perform ETL, then train TensorFlow, PyTorch, or HugeCTR models, and then infer with Triton. 

Merlin is a comprehensive GPU platform that offers fast-feature engineering and high learning rates. Merlin Training is a collection of DL recommender templates and training tools. Recommendation systems are one of the most practical categories of machine learning. They are used to provide millions of recommendations to company users every day. 

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This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

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.

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This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

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

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This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

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.

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