What is ISaaC Sim container available in NVIDIA GPU Cloud?

April 14, 2022

Artificial Intelligence is the latest technology where human intelligence is put into the machines and performs tasks that humans do. Some examples are automated cars, digital assistants, text summarizers etc. Machine Learning and Deep learning is part Artificial Intelligence used in this technological advancement.

Some of the applications of Artificial Intelligence are:

  1. Language modelling is a natural language processing (NLP) application that decides the sentence formation with a collection of words in a particular order.
  2. Recommendation Systems filters information to successfully predict a situation or preference for a user against any item.
  3. Image recognition is when machines can locate and identify images as humans can see some objects and make a decision. Computer Vision is the domain of study here.
  4. Language translation is the application of translating text from one language to another so that different types of humans can absorb a considerable amount of information. Machines, to be more precise, machine learning algorithms complete these processes.
  5. Object detection in a video or any image is that process where the object is a human or a car or a plant is detected and accordingly categorized.
  6. Automatic speech recognition (ASR) systems are where human spoken instructions or problems are recognized. Chatbots are good examples. There it needs to convert audio to subtitles in a video and more.
  7. Speech processing or text-to-speech produces human speech from written text or messages.
  8. High-performance computing (HPC) is one of the most important instruments which improves computations in terms of time and accuracy in machines.

NVIDIA provides a wide range of resources that satisfy the needs of data scientists, developers, and researchers in different levels of work, including containers for Deep Learning and Machine Learning applications. NVIDIA GPU Cloud computing provides flexible use in AI applications, mainly graphical and gaming systems.

NVIDIA provides GPU Cloud to execute high-performance workloads. NVIDIA containers are used to run high-performance computing applications.

The Isaac Sim Container in NVIDIA GPU Cloud is one of them. Above all, data-hungry workloads are successfully executed with the help of NVIDIA Cloud GPU Servers.

The NVIDIA Isaac Sim is a container that provides a virtual environment, specifically for the AI-enabled robots to program. The environment is suitable for machine performance testing and program modification. In this environment, robotic experiments are performed, including robot models and simulating sensors like RGB-D, contact, ultrasonic, Lidar, etc.

The NVIDIA Isaac Sim supports Navigation, modification, deep learning applications using Python, ROS, and Isaac SDK. In AI developments, NVIDIA Isaac Sim provides powerful utilities for synthetic data generation such as domain randomization, ground truth labelling, recognition, and bounding boxes. It is built on the NVIDIA Omniverse platform. Advanced NVIDIA GPU Cloud-enabled container is successfully used to run high-performance applications.

Some of the features of the Isaac Sim Container available in NVIDIA GPU Cloud:

  1. Realistic Simulation:

The Isaac Sim container is developed on the Omniverse platform. It uses the strong logic of the platform, including GPU supported simulation system with PhysX 5, photo reproduction with the help of rays and path tracing.

2. Modulor Building Balances Breadth of Application:

Isaac Sim is developed to address most of the typical simulation challenges in robotics to modify, navigate, synthetic data creation for training data. Because of the modular architecture, the utility can be customized easily.

3. Flawless Connectivity and Integration:

NVIDIA Isaac Sim can support cooperative development, sharing, and import robot models. It is easy to make the robots work in the virtual environment and improve robotic brains to work. NVIDIA Omniverse Nucleus and Omniverse Connectors are used here.

4. Creation of Artificial Data to Train Models:

Train models for large amounts of data and diversified data can be expensive, time-taking and risky to manage in some situations.

Isaac Sim container helps developers in training the machines with the use of Omniverse Replicator.

Artificial data can speed up machine learning workflows. While developing machines, real-world data can be augmented with synthetic data to reduce the training time required to train a production model. 

  1. Simulating Manipulation:

It is essential in robotics that they can recognize objects, collect them and keep them in the proper place. This is important in modern factories and warehouse environments, and Isaac Sim has the built-in logic to increase actions in robots.

2. To imitate Navigation:

AI-enabled robotic machines have to be able to move from one position to another position without collision. This capability is programmed in the robots by the navigation stack. NVIDIA Isaac Sim container helps in the programming.

3. Importing Robots or Assets into Isaac Sim Container:

Because of NVIDIA Omniverse, Isaac Sim provides import operations for robot models and other assets into the robotics simulator system. It helps a lot to train and test the models efficiently. Isaac Sim also provides an excellent product design.

The Power of an Accelerated Platform For Robotics And AI

The Isaac Autonomous Mobile Robots (AMR) platform widens NVIDIA Isaac's strengths for programmers to develop and deploy robotic machines, to provide mapping, site analytics, and fleet and route escalation onto NVIDIA GPU online servers.

The Isaac Sim container platform helps to enhance and accelerate AMR

developments for the logistics industry from warehouses to retail.

Isaac Sim now facilitates developers with a data cockpit to artificially create datasets for machine learning (ML) models from easy-to-understand frameworks. Powered by Omniverse Replicator, NVIDIA Isaac Sim generates synthetic data that can be used to train DNNs running on an AMR. This helps developers build and deploy AI-enabled robots that work with safety and avoid common mistakes, like the collision of robots with forklift tines.

Some of the benefits revisited here:

  1. Creation of Artificial Data
  2. Simulation of AI machines
  3. To Train the robots in the NVIDIA GPU cloud computing environment.
  4. High-performance Robotic application development, management and deployment.

E2E GPU cloud provider

E2E provides NVIDIA GPU cloud computing. Accelerated cloud computing improves the development of Autonomous Mobile Robots development, and E2E cloud GPU servers can be used here. E2E cloud supported by NVIDIA, in Machine Learning and Deep Learning works improves productivity.

E2E GPU Cloud is powered entirely with NVIDIA T4, Tesla V100, RTX 8000, NVIDIA A30, and NVIDIA A100 GPUs. It provides cloud GPU for startups with the power of NVIDIA. E2E is a cheap GPU Cloud provider.

Latest Blogs
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.

Reference Links




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

Reference Links



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.

Reference Links



This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

Reference Links




Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure