What is TensorFlow?

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TensorFlow is an open-source library developed by Google primarily for deep learning applications. It also supports traditional machine learning. TensorFlow was originally developed for large numerical computations without keeping deep learning in mind.
It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and lets developers easily build and deploy ML-powered applications.

Why TensorFlow?

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TensorFlow is an end-to-end platform that makes it easy for users to build and deploy ML models.
1. Easy model building:
Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging.
2. Robust ML production anywhere:
Easily train and deploy models in the cloud, on-prem, in the browser, or on-device, no matter what language you use.
3. Powerful experimentation for research:
TensorFlow is a simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication fast.

What's ML (Machine learning)?

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Machine learning is the practice of helping software perform a task without explicit programming or rules. With traditional computer programming, a programmer specifies the rules that a computer should use. ML requires a different mindset, though. Real-world ML focuses far more on data analysis than coding. Programmers provide a set of examples, and the computer learns patterns from the data. You can think of machine learning as "programming with data."

What's CUDA Toolkit?

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The CUDA Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools, and the CUDA runtime.

What's NVIDIA cuDNN?

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The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.
Deep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration. It allows them to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PaddlePaddle, PyTorch, and TensorFlow.

Learn How to Install TensorFlow on Our GPU Servers

Whether you're an expert or a beginner, TensorFlow is an end-to-end platform that makes it easy for you to build and deploy ML models. TensorFlow GPU support requires a set of drivers and libraries, including a graphics driver, CUDA toolkit, and cuDNN. This guide will show you how to install these libraries and dependencies for starting a GPU-Accelerated TensorFlow step by step.

Remote Access (RDP/SSH)	done	RDP for Windows server and SSH for Linux Server
Control Panel	Free	Free control panel for management of servers, orders, tickets, invoices, etc.
Administrator Permission	done	You have full control of your dedicated server.
24/7/365 Support	done	We offer 24/7 tech support via Ticket and Livechat
Server Reboot	Free
Hardware Replacement	Free
Operating System Re-Installation	Free	Maximum twice a month and $25.00 each time for additional reloads.

Operating System	Optional	Free CentOS, Ubuntu, Debian, Fedora, OpenSUSE, Almalinux, Proxmox, VMWare, FreeNAS. Microsoft Windows Server 2016/2019/2022 Standard Edition x64:$20/m Microsoft Windows 10/11 Pro Evaluation: 90-day free trial. Please purchase a Win10/11 Pro license by yourself after the trial period.
Free Shared DNS Service	done

Additional Memory	16GB: $10.00/month 32GB: $18.00/month 64GB: $32.00/month 128GB: $56.00/month 256GB: $96.00/month
Additional SATA Drives	2TB SATA: $19.00/month 4TB SATA: $29.00/month 8TB SATA: $39.00/month 16TB SATA (3.5’ Only): $49.00/month
Additional SSD Drives	240GB SSD: $9.00/month 960GB SSD: $19.00/month 2TB SSD: $29.00/month 4TB SSD: $39.00/month
Additional Dedicated IP	$2.00/month/IPv4 or IPv6	IP purpose required. Maximum 16 per package.
Shared Hardware Firewall	$29.00/month	Shared firewall is used by 2-7 users who share a single Cisco ASA 5520 firewall, including shared bandwidth. It does not have superuser privileges.
Dedicated Hardware Firewall	$99.00/month	Dedicated firewall allocates one user to a Cisco ASA 5520/5525 firewall, providing superuser access for independent and personalized configurations, such as firewall rules and VPN settings.
Remote Data Center Backup(Windows Only)	40GB Disk Space: $30.00/month 80GB Disk Space: $60.00/month 120GB Disk Space: $90.00/month 160GB Disk Space: $120.00/month	We will use Backup For Workgroups to backup your server data (C: partition only) to our remote data center servers twice per week. You can restore the backup files in your server at any time by yourself.
Bandwidth Upgrade	Upgrade to 200Mbps(Shared): $10.00/month Upgrade to 1Gbps(Shared): $20.00/month	The bandwidth of your server represents the maximum available bandwidth. Real-time bandwidth usage depends on the current situation in the rack where your server is located and the shared bandwidth with other servers. The speed you experience may also be influenced by your local network and geographical distance from the server.
Additional GPU Cards	Nvidia Tesla K80: $99.00/month Nvidia RTX 2060: $99.00/month Nvidia Tesla P100: $119.00/month Nvidia RTX 3060 Ti: $149.00/month Nvidia RTX 4060: $149.00/month Nvidia RTX A4000: $159.00/month Nvidia RTX A5000: $229.00/month	The GPU cards listed here can be added as a second GPU. For customized servers with different GPU models or more GPUs, please contact us.
HDMI Dummy	$15 setup fee per server	A one-time setup fee is charged for each server and cannot be transferred to other servers.

GPU Dedicated Server for TensorFlow and Deep Learning

Choose Your TensorFlow Hosting Plans

Benefits of TensorFlow

Features of TensorFlow with GPU Servers

TensorFlow Hosting Use Cases

FAQs of TensorFlow with GPU