Keras Hosting

Keras GPU: Using Keras On Single GPU or Multi-GPU

 

Keras improves the development and training of deep learning models with GPUs. GPUMart offers a variety of Keras GPUs designed for deep learning with Keras.

 

Keras GPU Plans & Pricing

While Keras does not offer its own GPU plans, you can our cloud services to run Keras models on GPUs. Here are some options:

Basic GPU Dedicated Server - RTX 4060

$159/mo
  • 64GB RAM
  • GPU: Nvidia GeForce RTX 4060
  • Eight-Core E5-2690
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Basic GPU Dedicated Server - RTX 5060

$199/mo
  • 64GB RAM
  • GPU: Nvidia GeForce RTX 5060
  • 24-Core Platinum 8160
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - RTX 3060 Ti

$349/mo
  • 128GB RAM
  • GPU: GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - A4000

$289/mo
  • 128GB RAM
  • GPU: Nvidia Quadro RTX A4000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - A5000

$279/mo
  • 128GB RAM
  • GPU: Nvidia Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - V100

$309/mo
  • 128GB RAM
  • GPU:Nvidia V100
  • Dual 12-Core E5-2690v3î… 
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX 3060 Ti

$379/mo
  • 256GB RAM
  • GPU:3 x GeForce RTX 3060 Ti
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - RTX A6000

$559/mo
  • 256GB RAM
  • GPU: Nvidia Quadro RTX A6000
  • Dual 18-Core E5-2697v4î… 
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xV100

$609/mo
  • 256GB RAM
  • GPU: 3 x Nvidia V100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A100

$809/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX A6000

$909/mo
  • 256GB RAM
  • GPU: 3 x Quadro RTX A6000
  • Dual 18-Core E5-2697v4î… 
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 4xRTX A6000

$1209/mo
  • 512GB RAM
  • GPU: 4 x Quadro RTX A6000
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

How to Install Keras with GPU

To install Keras with GPU support, you need to ensure you have the necessary software and drivers installed.
Here are requirements and a step-by-step guide:

 

Requirement for Keras Installation

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 of Keras

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 GPU Support
# Sample:
pip install --upgrade pip
pip install tensorflow
4. Verify GPU Availability
# If a list of GPU devices is returned, you've installed TensorFlow successfully.
import tensorflow as tf;
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

from tensorflow import keras

6 Reasons to Choose our Keras GPU Servers

Utilizing Keras with GPU support provides significant benefits in terms of speed, efficiency, scalability, and overall performance, making it a powerful choice for deep learning applications.

Cost-effective

Renting GPU servers may be a more cost-effective solution than purchasing your own hardware, especially if you only need to use computing resources in a limited time.

Dedicated GPU Cards

When you purchase a GPU server from GPU Mart, you benefit from dedicated GPU resources. This means you have exclusive access to the entire GPU card’s computing power, including all GPU memory, cores, and other resources.

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.

Customization

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

NVIDIA CUDA

NVIDIA CUDA is a parallel computing platform and API model created by NVIDIA. It provides a range of advantages that significantly enhance the performance and capabilities of various computational tasks.

Advantages of Deep Learning with Keras GPU

Using Keras with GPU support offers several advantages for deep learning

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.

 

FeaturesKerasTensorFlowPyTorchMXNet
API LevelHighHigh and lowLowHign and low
ArchitectureSimple, concise, readableNot easy to useComplex, less readableComplex, less readable
DatasetsSmaller datasetsLarge datasets, high performanceLarge datasets, high performanceLarge datasets, high performance
DebuggingSimple network, so debugging is not often neededDifficult to conduct debuggingGood debugging capabilitiesHard to debug pure symbol codes
Trained ModelsYesYesYesYes
PopularityMost popularSecond most popularThird most popularFourth most popular
SpeedSlow, low performanceFastest on VGG-16, high performanceFastest on Faster-RCNN, high performanceFastest on ResNet-50, high performance
Written InPythonC++, CUDA, PythonLua, LuaJIT, C, CUDA, and C++C++, Python

Quickstart Video – Keras Tutorial For Beginners

Learn to implement neural networks faster and easier on Keras!

FAQs of Keras 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.

Does Keras automatically use GPU?

Keras models will transparently run on a single GPU with no code changes required. Note: Use tf. config. list_physical_devices(‘GPU’) to confirm that TensorFlow is using the GPU.

What is Keras GPU?

Keras is a Python-based, deep learning API that runs on top of the TensorFlow machine learning platform, and fully supports GPUs. Keras was historically a high-level API sitting on top of a lower-level neural network API. It served as a wrapper for lower-level TensorFlow libraries.

Do I need to install Keras if I have TensorFlow?

Thanks to a new update in TensorFlow 2.0+, if you installed TensorFlow as instructed, you don’t need to install Keras anymore because it is installed with TensorFlow. For those using TensorFlow versions before 2.0, here are the instructions for installing Keras using Pip.

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.