The understanding of life chemistry took a significant leap when three scientists won the 2017 Nobel Prize in Chemistry for developing electron cryo-microscopy (cryo-EM), an imaging technique that would help researchers see biomolecules with atomic precision.
Determining the pockets and crevices in a molecule helps chemists design drugs to fill those gaps. Thus, cryo-EM can assist in understanding and treating diseases.
What is Relion?
Relion stands for Regularized Likelihood Optimization, an open-source computer program for analyzing cryo-electron microscopy. It is an essential tool for studying living cells.
Relion employs an empirical Bayesian approach to refine macromolecular structures by single-particle analysis of the cryo-EM data.
The challenges it solves:
- The earlier electron microscopes and computational power did not reveal much about the molecular structure.
- Images obtained were noisy and lacked clarity, thus making it challenging to visualize small molecular complexes.
- The chances of lack of tuning between specific parameters to achieve the best quality visuals.
How does it work?
The steps in the entire workflow of a single-particle analysis sum up together as Relion. Relion comprises the following steps:
- Beam-induced motion-correction
- Estimating contrast-transfer function (CTF)
- Automated particle picking
- Extracting particle
- 2D Class averaging
- 3D classification
- High-res refinement in 3D
Relion provides exceptional computing prowess to complete projections and back-projection calculations on-the-fly. As a result, it solves storage requirements by not storing 2D images in the computer memory.
Containers in NVIDIA GPU Cloud
Artificial intelligence and its associated technologies, such as machine learning and deep learning, are responsible for achieving large-scale computational ability.
AI, ML, and Deep Learning require running complex processes briskly to achieve the desired results. Thus, you need to set up a robust system with advanced equipment to run those processes. It can be a daunting task.
NVIDIA provides you with NGC containers to save you from building complex environments and unleash accelerated cloud computing. It is a catalog of specialized GPU-optimized containers to help you develop and deploy applications with minimal effort.
These containers have built-in libraries, and they combine all dependencies of an application within a single SDK. Since they are on cloud servers, you can run them anywhere.
Relion container in NVIDIA GPU cloud
The Relion container in the NGC catalog facilitates high-performance computing to run an empirical Bayesian approach and refine 3D reconstruction(s) or 2D Class averages in the cryo-EM data.
Basic system requirements:
You can run the Relion NGC container on your system if it meets the following requirements:
- Any of the container runtimes: NVIDIA-docker or singularity (version 3.1 and above)
- Any of the following NVIDIA GPU(s): Pascal(sm60), Volta (sm70), Ampere (sm80)
- CPU with AVX2 instruction support
- One of the following CUDA driver versions
- >= r460
- r450 (>=.80.02)
- r440 (>=.33.01)
- r418 (>=.40.04)
- ARM 8.1 + ARM NEON CPU
- CUDA driver version >= r450
Please note the following system recommendations that go for using the Relion container:
- If you have multiple GPUs, it will work excellently.
- Ample storage space is essential, preferably SSD or RamFS.
- It is better to focus on a high clock rate than increasing the number of cores, but having both will be helpful.
- Use NVIDIA Multi-process Service (MPS) to utilize GPU better by launching multiple ranks per GPU.
How to deploy the Relion container?
The best way possible in the modern tech world is availing of the cloud GPUs. Cloud GPUs can execute complex processes requiring advanced computational techniques at a faster rate. With the help of cloud GPUs, you can take the next step in your research work.
E2E cloud supports all the leading cloud GPUs for deep learning so that you can deploy your next application hassle-free.
The range of cloud GPUs gives you a choice to pick the right one based on the computing requirements.
Here are the options to choose from:
- NVIDIA A100 Tensor Core GPU. Check NVIDIA A100 40 GB Pricing, NVIDIA A100 80 GB Pricing.
- NVIDIA A30 GPU. Check NVIDIA A30 Pricing.
- NVIDIA Quadro RTX 8000
- NVIDIA® Tesla® V100 Tensor Core
- NVIDIA Tesla T4 GPU
These GPUs support NGC containers you may require for deep learning, machine learning, high-performance computing.
Relion container and possibilities
A human’s inquisitive nature is responsible for overcoming hurdles and achieving greater heights in every field. The Relion computer program is an excellent example of how much the healthcare sector can benefit from it.
The technology speaks volumes with its results. For example, medical research labs will witness an influx of newcomers who want to break the barrier and identify new solutions to existing disorders.
E2E cloud, with its products and solutions, is one of the cheapest cloud GPU providers in India. We provide cloud GPU for startups to handle data-hungry workloads without any fuss.
Our cloud GPU products are better priced than existing GPU cloud providers.