What is a LAMMPS Container in the NVIDIA GPU Cloud?

March 15, 2022

LAMMPS is a molecular dynamics simulation software tool. It is a parallel computing system that you can create and use on a laptop or computer. It can run in serial and parallel mode on any machine that supports the MPI message-passing library. Shared-memory boxes, distributed-memory clusters, and supercomputers all fall within this category. LAMMPS supports OpenMP multithreading, vectorization, and GPU acceleration. It is built to be flexible enough to add additional features like force fields, atom kinds, boundary conditions, and diagnostics.

LAMMPS integrates Newton's equations of motion for the collection of interacting particles. An atom, molecule, electron, a coarse-grained cluster of atoms, a mesoscopic particle, or microscopic clump of material can all be considered single particles. The interaction models contained in LAMMPS are generally short-range in nature, with a few long-range models thrown in for good measure. LAMMPS uses message-passing techniques and spatial decomposition of the simulation domain to run on single processors or in parallel. LAMMPS also allows for rapid coupling of spin and molecular dynamics. LAMMPS is also linked to a variety of analysis tools and engines.

LAMMPS Container Features

  • LAMMPS is a GPLv2 licensed open-source distribution that runs on a single processor or in parallel distributed memory message-passing parallelism (MPI).
  • Shared memory multithreading parallelism (OpenMP) 
  • Spatial decomposition of simulation domain for MPI parallelism 
  • Particle decomposition inside of spatial decomposition for OpenMP and GPU parallelism 
  • C++-11 modular code with the most functionality in optional packages.
  • Rely on the MPI library for basic parallel capability and the MPI stub for serial compilation. All other libraries are optional and only necessary for specific packages. 
  • Many code features are supported by GPU, Intel Xeon Phi, and OpenMP, making it easy to add new features and capabilities. 
  • Runs based on an input script
  • Variables and formulae: syntax for declaring and using variables and formulas 
  • Looping overruns and breaking out of loops syntax 
  • Invoke LAMMPS through the library interface or provided Python wrapper or SWIG-based wrappers. 
  • Coupled with other codes: Other code calls LAMMPS, and LAMMPS calls other code. Both LAMMPS and umbrella codes are used.

LAMMPS Ensembles, Constraints, and Boundary Conditions

  • 2d or 3d systems 
  • Orthogonal or non-orthogonal simulation domains 
  • Options for atoms' groups and geometric regions to be thermostated 
  • Pressure control in 1 to 3 dimensions through Nose or Berendsen barostatting 
  • Deformation of a simulation box (tensile and shear) 
  • Rigid body constraints are imposed via the harmonic (umbrella) constraint. 
  • Manifold surface motion restrictions 
  • Bond breaking, formation, swapping, template-based reaction modeling
  • Several types of atom/molecule insertion and deletion barriers
  • Static and moving non-equilibrium molecular dynamics (NEMD), and 
  • several other boundary conditions and limitations

Particle and Model Types

  • Atoms 
  • Particles with a coarse granularity (for example, bead-spring polymers) 
  • Organic compounds or polymers with a single atom 
  • Polymers with all atoms, organic compounds, proteins, and DNA metals 
  • Granular materials 
  • Coarse-grained mesoscale models 
  • Stiff collections of n particles 
  • Hybrid combinations of these finite-size spherical and ellipsoidal particles 
  • Finite-size line segment (2d) and triangle (3d) particles 
  • Finite-size rounded polygons (2d) and polyhedra (3d) particles

LAMMPS Open-Source License

GPL Version

LAMMPS is a free open-source program distributed under the terms of the GNU Public License Version 2 (GPLv2). It means you can use or adjust the program however you want for your purposes. But you must follow some rules when redistributing it - especially in binary form - or distributing software derived from it or containing parts of it. LAMMPS containers are sold without any warranty.

LGPL Version

This version removes all the Non-LGPL compatible files and is only available upon request. Users can link non-GPL compatible software to the (otherwise unmodified) LAMMPS library or dynamically load it at runtime as a result of this. Any modifications to the LAMMPS code must be provided under the same open-source terms as LAMMPS itself, even if the LGPL license version is used. 

Final Words

LAMMPS is a molecular dynamics (MD) simulation engine. It only has a limited range of features for creating simulations and analyzing their results. LAMMPS keeps track of adjacent particles using neighbor lists. The lists are designed for systems with particles that repel each other across short distances, ensuring that the particle density in the local area never exceeds a certain threshold. 

On parallel machines, LAMMPS partitions the simulation domain into small sub-domains of similar computing cost, one for each processor, using spatial-decomposition techniques and MPI parallelization. In addition, multi-threading parallelization and GPU acceleration with particle-decomposition are possible.

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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
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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:

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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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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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