Fastest And Best GPU SERVERS PROVIDER

GPU Dedicated Server for MXNet and Deep Learning

Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It is portable and lightweight, scalable to many GPUs and machines. We provide bare metal servers with GPUs that are specifically designed for deep learning with MXNet.

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Get what you need to get started with MXNet quickly.

Prerequisites

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1. Choose a plan and place an order
 
2. Ubuntu 16.04 or higher, Windows 10 or higher
 
3. Install NVIDIA® CUDA® Toolkit & cuDNN
 
4. Python 3.6 – 3.8 recommended
 

Step-by-Step Instructions

Go to MXNet’s site, read the installation guide.
1. Indicate your preferred configuration to see specific instructions.
Sample:
Instruction
2. Install MXNet with pip
Make sure your installed CUDA version matches the CUDA version in the pip package.
Sample:
pip install --upgrade pip
pip install mxnet-cu112
3. Verify the installation
Make sure your installed CUDA version matches the CUDA version in the pip package.
Sample:
# Python with GPU, use mx.gpu(), to set MXNet context to be GPUs
import mxnet as mx
a = mx.nd.ones((2, 3), mx.gpu())
b = a * 2 + 1
b.asnumpy()
# If you don't get an import error, then MXNet is ready for python.

Feature Comparison: MXNet vs 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 projects.
FeaturesMXNetKerasPyTorchTensorFlow
API LevelHigh and lowHighLowHigh and low
ArchitectureComplex, less readableSimple, concise, readableComplex, less readableNot easy to use
DatasetsLarge datasets, high performanceSmaller datasetsLarge datasets, high performanceLarge datasets, high performance
DebuggingHard to debug pure symbol codesSimple network, so debugging is not often neededGood debugging capabilitiesDifficult to conduct debugging
Trained Models IncludedYesYesYesYes
PopularityFourth most popularMost popularThird most popularSecond most popular
SpeedFastest on ResNet-50, high performanceSlow, low performanceFastest on Faster-RCNN, high performanceFastest on VGG-16, high performance
Written InC++, PythonPythonLua, LuaJIT, C, CUDA, and C++C++, CUDA, Python

6 Reasons to Choose our GPU Servers for MXNet

GPU hosting enables powerful MXNet 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 MXNet.

SSD-Based Drives

You can never go wrong with our own top-notch GPU dedicated servers, 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 GPU dedicated server 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 MXNet and networks.

Dedicated IP

One of the premium features is the dedicated IP address. Even the cheapest GPU dedicated 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 hosted GPUs for MXNet is not compromised.

FAQs of GPU Dedicated Server Hosting

Find answers to the most frequently asked questions about GPU dedicated server hosting.

MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It’s highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. To get started Deep learning with MXNet, you often need GPUs for MXNet.

MXNet is supported by public cloud providers including Amazon Web Services (AWS) and Microsoft Azure. Amazon has chosen MXNet as its deep learning framework of choice at AWS.

Both MXNet and TensorFlow use computation graph abstraction, which was initially used by Theano, then adopted by other packages, such as CGT, Caffe2, and Purine. Currently, TensorFlow adopts an optimized symbolic API. MXNet supports a mixed approach, with a dynamic dependency scheduler to combine symbolic and imperative programming.

Today, leading vendor NVIDIA offers the best GPUs for MXNet 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 MXNet in 2023.
Feel free to choose the best plan that has the right CPU, resources, and GPUs for MXNet.

In Apache MXNet, you don’t need to flatten the 4-D input into 2-D when feeding the data into forward pass. MXNet has the fastest training speed on ResNet-50, and PyTorch is the fastest on Faster-RCNN. Though you need to be cautious with such toy comparisons.

Compared to TensorFlow, MXNet has a smaller open-source community. Improvements, bug fixes, and other features take longer due to a lack of major community support. Despite being widely used by many organizations in the tech industry, MXNet is not as popular as Tensorflow.

What is the advantage of bare metal GPUs for MXNet?

Bare metal GPU servers for MXNet 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 MXNet are all bare metal servers, so we have best GPU dedicated server for AI.