GPU Dedicated Server for Keras and Deep Learning

Keras is the high-level API of TensorFlow 2: an approachable, highly-productive interface for solving machine learning problems, with a focus on modern deep learning. We provide bare metal servers with GPU that are specifically designed for deep learning with Keras.

Windows or Linux OS
Full Root/Admin Access
Support RDP/SSH Access
Free 24/7/365 Expert Online Support
Servers Delivered within 4 to 48 Hours

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Plans & Prices of GPU Servers for Keras

We offer cost-effective and optimized NVIDIA GPU servers for Keras.

Basic Keras GPU

Nvidia Tesla K40

For high-performance computing and large data workloads, such as deep learning and AI reasoning.

Starting at

$109.00

/month

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64 GB RAM
Eight-Core Xeon E5-2670
120GB SSD + 960GB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Windows & Linux

GPU: Nvidia Tesla K40
Microarchitecture: Kepler
Max GPU: 2
CUDA Cores: 2880
GPU Memory: 12GB
Performance: 4.29 TFLOPS

Professional Keras GPU

Nvidia Tesla K80

For high-performance computing and large data workloads, such as deep learning and AI reasoning.

Starting at

$159.00

/month

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128 GB RAM
Dual 10-Core E5-2660v2
120GB SSD + 960GB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Linux & Windows

GPU: Nvidia Tesla K80
Microarchitecture: Kepler
Max GPU: 2
CUDA Cores: 4992
GPU Memory: 24GB
Performance: 8.73 TFLOPS

Spring Sale! Save 30%

Advanced Keras GPU

Nvidia RTX A4000

RTX A4000 delivers real-time ray tracing, AI accelerated computing, and high-performance graphics to desktops.

30% off

209.00/m

$ 146.30/m

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128 GB RAM
Dual 12-Core E5-2697v2
240GB SSD + 2TB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Linux & Windows

GPU: Nvidia RTX A4000
Microarchitecture: Ampere
Max GPU: 2
CUDA Cores: 6144
Tensor Cores: 192
GPU Memory: 16GB GDDR6
Performance: 19.2 TFLOPS

Advanced Keras GPU

Nvidia RTX A5000

RTX A5000 achieves an excellent balance between function, performance, and reliability. Assist designers, engineers, and artists to realize their visions.

Starting at

$269.00

/month

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128GB RAM
Dual 12-Core E5-2697v2
240GB SSD + 2TB SSD
100Mbps-1Gbps Bandwidth
Supported OS: Linux & Windows

GPU: Nvidia RTX A5000
Microarchitecture: Ampere
Max GPU: 2
CUDA Cores: 8192
GPU Memory: 24GB GDDR6
Performance: 27.8 TFLOPS

Enterprise Keras GPU

Nvidia A40

Accelerate data science and computation-based workloads. A40 is very suitable for AI and deep learning projects.

Starting at

$369.00

/month

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256 GB RAM
Dual E5-2697v4
240GB SSD + 2TB SSD + 2TB NVMe
100Mbps-1Gbps Bandwidth
Supported OS: Linux & Windows 10

GPU: Nvidia A40
Microarchitecture: Ampere
Max GPU: 1
CUDA Cores: 10,752
Tensor Cores: 336
GPU Memory: 48GB
Performance: 37.4 TFLOPS

Enterprise Keras GPU

Nvidia V100

V100 server is a cloud product that can accelerate for more than 600 HPC applications and various deep learning frameworks.

Starting at

$369.00

/month

Order Now

256 GB RAM
Dual E5-2697v4
240GB SSD + 2TB SSD + 2TB NVMe
100Mbps-1Gbps Bandwidth
Supported OS: Linux & Windows 10

GPU: Nvidia V100
Microarchitecture: Volta
Max GPU: 1
CUDA Cores: 5,120
Tensor Cores: 640
GPU Memory: 16GB
Performance: 14 TFLOPS

More GPU Hosting Plans

Keras With CUDA Install - Quick And Easy

Getting started with Keras is very easy. The recommended option is to use the Anaconda Python package manager. Keras comes packaged with TensorFlow 2 as tensorflow.keras. To start using Keras, simply install TensorFlow 2.

Prerequisites

1. Choose a plan and place an order
2. Ubuntu 16.04 or higher (64-bit), Windows 10 or higher (64-bit) + WSL2
3. Install NVIDIA® CUDA® Toolkit & cuDNN
4. Python 3.7 - 3.10 recommended

Step-by-Step Instructions

Go to TensorFlow's site , read the pip install guide.

1. Install Miniconda or Anaconda
2. Create a Conda Environment
- Sample:
- conda create --name tf python=3.9
3. Install TensorFlow with pip
- Sample:
- pip install --upgrade pip
- pip install tensorflow
4. Verify the Installation
- # If a list of GPU devices is returned, you've installed TensorFlow successfully.
- import tensorflow as tf;
- print(tf.config.list_physical_devices('GPU'))
- from tensorflow import keras

6 Reasons to Choose our GPU Servers for Keras

DBM enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.

Intel Xeon CPU

Intel Xeon has extraordinary processing power and speed, which is very suitable for running deep learning frameworks. So you can totally use our Intel-Xeon-powered GPU Servers for Keras.

SSD-Based Drives

You can never go wrong with our own top-notch dedicated GPU servers for Keras, loaded with the latest Intel Xeon processors, terabytes of SSD disk space, and 128 GB of RAM per server.

Full Root/Admin Access

With full root/admin access, you will be able to take full control of your dedicated GPU servers for Keras very easily and quickly.

99.9% Uptime Guarantee

With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for Keras and networks.

Dedicated IP

One of the premium features is the dedicated IP address. Even the cheapest Keras GPU hosting plan is fully packed with dedicated IPv4 & IPv6 Internet protocols.

DDOS Protection

Resources among different users are fully isolated to ensure your data security. DBM protects against DDoS from the edge fast while ensuring legitimate traffic of your hosted GPUs for Keras is not compromised.

Advantages of Deep Learning with Keras

Here are some of the areas in which Keras compares favorably to existing alternatives.

User-Friendly and Fast Deployment

Keras is a user-friendly API, and it is very easy to create neural network models.

Quality Documentation and Large Community Support

Keras has one of the best documentations ever. It also has great community support.

Easy to Turn Models into Products

Your Keras models can be easily deployed across a greater range of platforms than any other deep learning API.

Multiple GPU Support

Keras allows you to train your model on a single GPU or multiple GPUs. It provides built-in support for data parallelism. It can process a very large amount of data.

Multiple Backend and Modularity

Keras provides multiple backend support, where Tensorflow, Theano, and CNTK being the most common backends.

Pre-Trained models

Keras provides some deep learning models with their pre-trained weights. We can use these models directly for making predictions or feature extraction.

Features Comparison: Keras vs PyTorch vs TensorFlow

Everyone's situation and needs are different, so it boils down to which features matter the most for your AI project.

Features	Keras	TensorFlow	PyTorch	MXNet
API Level	High	High and low	Low	Hign and low
Architecture	Simple, concise, readable	Not easy to use	Complex, less readable	Complex, less readable
Datasets	Smaller datasets	Large datasets, high performance	Large datasets, high performance	Large datasets, high performance
Debugging	Simple network, so debugging is not often needed	Difficult to conduct debugging	Good debugging capabilities	Hard to debug pure symbol codes
Trained Models	Yes	Yes	Yes	Yes
Popularity	Most popular	Second most popular	Third most popular	Fourth most popular
Speed	Slow, low performance	Fastest on VGG-16, high performance	Fastest on Faster-RCNN, high performance	Fastest on ResNet-50, high performance
Written In	Python	C++, CUDA, Python	Lua, LuaJIT, C, CUDA, and C++	C++, Python

FAQs of Cloud GPU Server

A list of frequently asked questions about GPU servers for Keras.

What Keras is used for?

Keras is a high-level, deep-learning API developed by Google for implementing neural networks. It is written in Python and is used to simplify the implementation of the neural network. It also supports multiple backend neural network computations. For these uses, you often need GPUs for Keras.

Why do we need Keras?

Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load:

It offers consistent & simple APIs.

It minimizes the number of user actions required for common use cases.

It provides clear and actionable feedback upon user error.

Is Keras better than PyTorch?

Keras is mostly used for small datasets due to its slow speed. While PyTorch is preferred for large datasets and high performance.

When do I need GPUs for Keras?

If you're training a real-life project or doing some academic or industrial research, then for sure you need a GPU for fast computation.

If you're just learning Keras and want to play around with its different functionalities, then Keras without GPU is fine and your CPU in enough for that.

What are the best GPUs for Keras deep learning?

Today, leading vendor NVIDIA offers the best GPUs for Keras deep learning in 2022. The models are the RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A5000, RTX A4000, Tesla K80, and Tesla K40. We will offer more suitable GPUs for Keras in 2023.

Feel free to choose the best plan that has the right CPU, resources, and GPUs for Keras.

How can I run a Keras model on multiple GPUs?

We recommend doing so using the TensorFlow backend. There are two ways to run a single model on multiple GPUs: data parallelism and device parallelism. In most cases, what you need is most likely data parallelism.

How can I run Keras on GPU?

If you are running on the TensorFlow or CNTK backends, your code will automatically run on GPU if any available GPU is detected.

If you are running on the Theano backend, you can use theano flags or manually set config at the beginning of your code.

What are the advantages of bare metal GPUs for Keras?

Bare metal GPU servers for Keras will provide you with an improved application and data performance while maintaining high-level security. When there is no virtualization, there is no overhead for a hypervisor, so the performance benefits. Most virtual environments and cloud solutions come with security risks.

DBM GPU Servers for Keras use all bare metal servers, so we have best GPU dedicated server for AI.

TensorFlow vs Keras: Key Differences Between Them

1. Keras is a high-level API that can run on top of TensorFlow, CNTK, and Theano, whereas TensorFlow is a framework that offers both high and low-level APIs.

2. Keras is perfect for quick implementations, while Tensorflow is ideal for Deep learning research and complex networks.

3. Keras uses API debug tools, such as TFDBG. On the other hand, in Tensorflow, you can use Tensor board visualization tools for debugging.

4. Keras has a simple architecture that is readable and concise, while Tensorflow is not very easy to use.

5. Keras is usually used for small datasets, but TensorFlow is used for high-performance models and large datasets.

6. In Keras, community support is minimal, while in TensorFlow, it is backed by a large community of tech companies.

7. Keras is mostly used for low-performance models, whereas TensorFlow can be used for high-performance models.

Quickstart Video - Keras Tutorial For Beginners

Learn to implement neural networks faster and easier on Keras!

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