If you need more processing resources to ramp up your dense computations, then you might want to consider using cloud GPUs.
If you're not sure which platforms to use or are comparing different cloud GPU options to find the best fit for you and your company, this guide can be very useful.
This post compares and contrasts some popular options so that you can choose the ideal platform to meet your needs.
The Best Cloud GPUs
What are GPUs?
Over the years, huge improvements in graphics rendering, artificial intelligence, deep learning, and other areas that require a lot of computing power have led to much higher expectations for how fast, accurate, and clear an application should be.
The availability of powerful computer resources that can run the processes behind these applications in large numbers and for long periods has made these improvements possible.
For example, because there are more graphics in modern games, they need to have more storage space.
To improve the gaming experience, faster processing rates are required to keep up with the ever-increasing high-definition images and background processes.
We just need more computer power to do all the things that need to be done to keep up with today's complicated programs.
Central processing units (CPUs) and improvements in processor architecture that have led to even faster CPUs have given computers the computing power they need to do most of their jobs.
However, far quicker processing of denser processes was required, necessitating the development of technology that would open up new avenues for efficient and rapid dense computing. As a result, graphics processing units were created.
GPUs, or graphics processing units, are a type of microprocessor designed to expedite graphical rendering and other multitasking processes by making use of parallel processing and increased memory bandwidth.
Games, 3D imaging, crypto mining, video editing, and machine learning are just some of the applications that have made them indispensable. Incredibly dense calculations are a bottleneck for CPUs, but GPUs make short work of them.
Since deep learning's training phase is so demanding on system resources, GPUs excel where CPUs fall short. There are a lot of convolutional and dense operations, therefore this type of operation requires a lot of data points to analyze.
These include several matrix operations using tensors, weights, and layers and are typical of the massive amounts of data and deep networks required for deep learning applications.
For deep learning processes, GPUs are far superior to CPUs due to their capacity to conduct several tensor operations at once because of their many cores and to store a larger amount of data according to their high memory bandwidth.
In contrast, a low-end GPU may do a task that takes a powerful CPU 50 minutes to complete in under a minute.
Why Use Cloud GPUs?
While there are still many that prefer to keep their GPUs in-house, the data science industry as a whole has been increasingly using cloud-based GPU solutions. Installing, managing, maintaining, and upgrading a GPU locally may be time-consuming and costly.
n contrast, consumers may take advantage of the GPU instances offered by cloud platforms without worrying about the aforementioned technological tasks, all while paying reasonable service fees.
These systems manage the GPU infrastructure as a whole and supply all the services programmers need to use GPUs for computation.
When the technical tasks associated with managing local GPUs are removed, users are free to concentrate on their core competencies. This will help streamline company procedures and boost efficiency.
Using cloud-based GPUs has many advantages over deploying and maintaining hardware locally, including a reduction in administrative burden.
Using cloud GPU services can help smaller firms decrease the barrier to entry when it comes to constructing deep learning infrastructures by transforming the capital expenditures necessary to mount and operate such computing resources into an operating cost.
Additionally, cloud platforms provide benefits including data transfer, accessibility, integration, collaboration, control, storage, security, update, scalability, and support for efficient and stress-free computing.
It is perfectly reasonable that someone else supplies the ingredients, similar to a chef and his helpers so that you can concentrate on cooking the dish.
How do I get started with cloud GPUs?
As cloud platforms strive to make their services more accessible to a wider audience, they create user-friendly interfaces for cloud GPUs.
Selecting a cloud service is the first step in utilizing cloud GPUs. Identifying a platform that best fits your requirements requires doing some research into the features and capabilities of the many options available.
In this post, I will recommend the finest cloud GPU platforms as well as instances for deep learning workloads; however, you are encouraged to research alternative possibilities to discover the one that best suits your needs.
Once a platform is selected, the following step is to learn how to navigate its user interface and internal systems.
Here, repetition is the key to success. Almost all cloud services include extensive online resources for studying their ins and outs, such as blogs, training videos, and written documentation. The information here can help users out.
For a more comprehensive and efficient education and use of their services, some major platforms (including Amazon, IBM, Google, and Azure) provide formalized training and certification.
If you are just getting started with cloud computing and data science, I highly recommend getting started with Gradient Notebooks because of its free, limitless GPU access.
That will provide you with practical knowledge before you go on to more complex enterprise-level systems.
How do I choose a suitable platform and plan?
Choosing the best cloud GPU platform for your unique computing needs, whether personal or professional, can be a bit of a conundrum.
Considering the plethora of cloud services from which to choose, making a decision might seem like a Herculean task.
You should evaluate the cloud GPU platform's GPU instance specs, infrastructure, design, price, availability, and customer support before committing to using it for your deep learning operations.
Each situation calls for a different plan, one that takes into account factors like data volume, cost, and volume of labor.
Linode
Linode's Cloud GPU offering provides a powerful and scalable solution for businesses and individuals that require additional processing resources to run computationally intensive applications.
With Linode, users can easily provision cloud GPUs on demand and take advantage of advanced features such as flexible GPU configurations, optimized drivers, and scalable storage.
One of the standout features of Linode's Cloud GPU is its simplicity and ease of use. Setting up a GPU instance is straightforward, and users can choose from a variety of GPU models to suit their needs. The platform also offers a user-friendly dashboard that makes it easy to monitor GPU usage and adjust configurations as needed.
Another advantage of Linode's Cloud GPU is its competitive pricing. Compared to other cloud GPU providers, Linode's pricing is very reasonable, making it an attractive option for businesses and individuals on a budget.
Overall, Linode's Cloud GPU is a solid choice for anyone looking to take advantage of cloud-based GPU computing. Its ease of use, advanced features, and competitive pricing make it a compelling option for a wide range of use cases.
Lindoe
Tencent Cloud
Fast, robust, and flexible cloud GPU computing is available from Tencent Cloud via a variety of rendering instances that make use of GPUs such as the NVIDIA A10, Tesla P4, Tesla T40, Tesla T4, Tesla V100, & Intel SG1. Their offerings may be found in the Asian cities of Shanghai, Guangzhou, Beijing, and Singapore.
Tencent Cloud's GN6s, GN8, GN10X, GN7, & GN10XP GPU instances may be used for both training and inference in the field of deep learning. There are no additional fees for connecting to other services and using their pay-as-you-go instances in their vPC cloud.
The maximum amount of RAM that can be used on the platform is 256GB, and the hourly rate for GPU-enabled instances ranges from $1.72 to $13.78, depending on the resources needed.
Genesis Cloud
The Genesis cloud employs cutting-edge technology to offer cheap, powerful cloud GPUs for AI as well as other high-performance computing tasks like image processing and machine learning.
Technology like the NVIDIA GeForce RTX 3080, RTX 3090, RTX 3060 Ti, & GTX 1080 Ti is used in its cloud GPU instances to speed up processing.
It has a user-friendly interface for its computing dashboard and lower costs than competing platforms for the same capacity. They have a public API, support the PyTorch and TensorFlow frameworks, and give free credits upon signup and savings for longer-term contracts.
They provide up to 192 GB of RAM and 80 GB of disk space for short and long-term contracts.
Lambda Labs Cloud
If you're looking to train and scale your deep learning models out of a single computer to a large fleet of virtual machines, Lambda Labs has you covered with its cloud GPU instances.
All the necessary software, including Jupyter notebooks, CUDA drivers, and the most popular deep learning frameworks, is already loaded on their virtual machines. Using either the cloud dashboard's web-based terminal or the SSH keys were given to you, you may connect to your instances.
For distributed training & scalability over several GPUs, the instances enable up to 10 gigabits per second of inter-node connectivity, which speeds up the optimization process and saves time. They have both hourly and annual rates, as well as on-demand and reserved rates for up to three years.
NVIDIA RTX 6000s, Quadro RTX 6000s, and Tesla V100s are all examples of GPUs running on the platform.
IBM Cloud GPU
Flexible server-selection operations and smooth connectivity with IBM cloud architecture, applications, and APIs are provided by the IBM Cloud GPU, which is hosted in a globally dispersed network of data centers.
The Intel Xeon 5218, Xeon 4210, & Xeon 6248 GPU instances are part of the bare metal Server GPU offering. Customers may use bare-metal instances to execute the same kinds of latency-sensitive, high-performance, specialized, and legacy applications on the physical servers as they would use with on-premise GPUs.
In addition to the bare-metal server option, they also provide Virtual server alternatives that have instances using NVIDIA V100 as well as P100 models and NVIDIA T4 GPUs & Intel Xeon processors with up to 40 cores.
Bare metal server GPU choices begin at $819 per month, while virtual server options begin at $1.95 per hour.
Oracle Cloud Infrastructure (OCI)
Oracle's GPU instances, both bare metal and virtual, are available for quick, low-cost, high-performance computing. As GPU instances, they provide NVIDIA Tesla P100, V100, and A100 with low-latency networking. Because of this, users may scale up to hosting clusters with 500 GPUs or more whenever they need to.
Oracle's Bare-Metal instances give users the same ability to deploy non-virtualized workloads as are available in IBM's cloud. These instances are on-demand and preemptable, and they work in the US, Germany, and the UK.
Wrap
We discussed the feasibility of doing intensive computations in the cloud, and we argued that deep learning operations are best performed on the most suitable cloud GPU platforms. We demonstrated that graphics processing units (GPUs) are required to enhance the performance & speed of machine-learning tasks and that it is simpler, cheaper, and faster to use a cloud GPU rather than one on-premises, particularly for small enterprises and individual users.
Your demands and money will dictate which cloud GPU platform is best for you. It's important to think about the platform's availability, as well as its infrastructure, cost, performance, design, and support.
NVIDIA's Tesla A100, V100, and P100 are best suited for large-scale deep learning tasks, while a Tesla A4000, A5000, and A6000 can handle anything else. You should prioritize supporting the full range of your workloads on the platforms that provide these GPUs. To run several lengthy iterations at reasonable prices, it is also crucial to think about the location as well as the availability of these platforms to prevent location constraints and expensive costs.
