In India, E2E networks are a world-class cloud. Their cloud infrastructure services are very high performing.
E2E networks offer GPU clouds which are extensively used in many domains and are apt for applications which deal in:
- Big Data
- Computational Finance
- Computer Vision
- Scientific Research
E2E’s cloud achieves such feats with the help of NVIDIA’s powerful GPUs. This article is about running machine learning using NVIDIA GPUs on E2E Cloud and how it is beneficial for all types of clients, be it Cloud Engineers in startups to big industries, from ML experts to CTO’s of tech companies, and even the Tech Enthusiasts.
Machine Learning on GPUs
Machine Learning uses algorithms for training and testing data. It is an AI method that runs its algorithms on the previous datasets and makes patterns and predictions. Thus, the machine makes complex decisions easily, even without the interface of humans.
For every control unit (Stream Processor, or SM), GPUs can secure small register packets. In this way, it collects ample small, fast, and efficient register memories. This increases the memory size of GPU registers 30 times more than that compared to Central Processing Units. Although the size increases, GPUs are 2x faster than CPUs and function at an enormous 80TB/s speed with 14 MB register memory.
GPU vs. CPU for Machine Learning
With the developments and technical advancements in ML, CPU computing based on the numerical value of cores is changing to GPU computing. This is because of the inefficient way CPU computing works. While CPUs perform the jobs in sequential order, GPUs, on the other hand, execute them parallelly. This makes GPUs too resourceful.
Certain other major points, why a GPU is preferred to CPUs are:
· Advanced higher memory
· Efficient distribution of Work
· More number of cores
The following image remarkably shows the latest era of computing – “GPU- Accelerated Computing”:
Speaking of this GPU market, AMD and NVIDIA are the big bulls of the game. With 76% of the GPU market share, NVIDIA is the leader in the business. AMD occupies the rest 14%.
NVIDIA GPUs are mostly preferred due to:
1. Availability of a larger variety of Graphics Cards
2. Availability of a huge pool of high-end Graphics Cards
3. Faster bandwidth of memories.
These factors turn NVIDIA GPUs to be very powerful, competent, and lucrative. The heavy ML algorithms are carried out easily with the power of NVIDIA GPUs. In the world of ML, NVIDIA Tesla T4, with its 2560 cores is widely regarded as the unrivalled and most preferred GPU for training ML models.
NVIDIA GPUs on E2E Cloud
To stay up-to-date with the newest technology, GPUs are the sole important feature in which industries are holding onto that improve the abilities of the system. The Graphical Processors offered by E2E Cloud provide the ultimate computing results.
Source: GPUs in E2E
NVIDIA GPUs are now reasonably priced in E2E, thus accelerating ML usage. The latest features of current-generation GPUs as NVIDIA Tensor Core A100, Tesla T4, RTX, and V100 processors provide power-packed performance.
Some features packed in the GPUs include:
- Tensor - Cores: Dedicated Graphical Processing Units that deliver the required acceleration by performing tasks like complex matrix multiplication very fast.
- 16-bit (Half – precision) arithmetic.
- A mixed Precision mode in TensorFlow.
On E2E GPU instances, all the famous learning frameworks such as Keras, MxNet, TensorFlow, PyTorch can be executed easily.
Here is a link to an article that gives an insight on “ Boosting up ML Workloads. ”
Latest trends in Machine Learning
The International Data Corporation (IDC) predicts that the expenditure on Artificial Intelligence and ML is going to increase from $12 Billion in 2017 to $57.6 billion by the end of 2021. From the reports of CB and PwC, a lump sum of $9.3B is funded by the Venture Capitalists in the year 2018 itself into big AI Firms.
Advancement in ML by NVIDIA GPUs
NVIDIA provides keys to fast-track your business’ ML processes, whether you’re constructing an original system from starting or refining the effectiveness of serious business-enabling progressions. NVIDIA delivers results that blend software and hardware and enhances it for high-performance ML. This makes it easier for industries to produce informative intuitions out of their information.
Through NVIDIA CUDA and RAPIDS, ML pipelines can be enhanced on NVIDIA GPUs, thus decreasing the time taken to complete ML processes such as:
- loading, and,
- training of data
Machine Learning is based on data. With the rising availability of more and more data, ML organizations are becoming consistent and trustworthy. The ML models improve themselves by adjusting bias and variances. As these datasets become larger, Cloud Computing with powerful GPUs is more widely adopted. E2E networks are superior when it comes to performance and are a lot cheaper when it comes to cost. They are building a strong appreciation from the end-users because of their trustworthiness, scalability, affordability, and improved privacy features.