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.
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.
Plans & Prices of GPU Servers for Keras
We offer cost-effective and optimized NVIDIA GPU rental servers for Keras.
New Arrival
Basic GPU-K80
$ 109.00/m
1M3M12M24M
Order Now- 64GB RAM
- Eight-Core Xeon E5-2690report
- 120GB + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Tesla K80
- Microarchitecture: Turing
- Max GPUs: 2report
- CUDA Cores: 4992
- GPU Memory: 24GB GDDR5
- FP32 Performance: 8.73 TFLOPSreport
Basic GPU-RTX 4060
$ 149.00/m
1M3M12M24M
Order Now- 64GB RAM
- Eight-Core E5-2690report
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia GeForece RTX 4060
- Microarchitecture: Ada Lovelace
- Max GPUs: 2report
- CUDA Cores: 3072
- Tensor Cores: 96
- GPU Memory: 8GB GDDR6
- FP32 Performance: 15.11 TFLOPSreport
Advanced GPU-A4000
$ 209.00/m
1M3M12M24M
Order Now- 128GB RAM
- Dual 12-Core E5-2697v2report
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A4000
- Microarchitecture: Ampere
- Max GPUs: 2report
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPSreport
Advanced GPU-V100
$ 229.00/m
1M3M12M24M
Order Now- 128GB RAM
- Dual 14-Core E5-2697v3report
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia V100
- Microarchitecture: Volta
- Max GPUs: 1
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPSreport
Advanced GPU-A5000
$ 269.00/m
1M3M12M24M
Order Now- 128GB RAM
- Dual 12-Core E5-2697v2report
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A5000
- Microarchitecture: Ampere
- Max GPUs: 2report
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPSreport
Enterprise GPU-A40
$ 369.00/m
1M3M12M24M
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4report
- 240GB SSD + 2TB SSD + 2TB NVMe
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia A40
- Microarchitecture: Ampere
- Max GPUs: 1
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 37.48 TFLOPSreport
Enterprise GPU-RTX A6000
$ 409.00/m
1M3M12M24M
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4report
- 240GB SSD + 2TB SSD + 2TB NVMe
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A6000
- Microarchitecture: Ampere
- Max GPUs: 1
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPSreport
Enterprise GPU-RTX 4090
$ 409.00/m
1M3M12M24M
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4report
- 240GB SSD + 2TB SSD + 2TB NVMe
- 100Mbps-1Gbpsreport
- OS: Windows / Linux
- GPU: GeForce RTX 4090
- Microarchitecture: Ada Lovelace
- Max GPUs: 1
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPSreport
More GPU Hosting Plansarrow_circle_right
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 keras6 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.
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 |
Quickstart Video - Keras Tutorial For Beginners
Learn to implement neural networks faster and easier on Keras!
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.
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.
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.
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.
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.
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.
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.
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