TensorFlow with GPU
DBM's TensorFlow with GPU server is a dedicated server with a GPU graphics card designed for high performance computing. Get this GPU-accelerated TensorFlow hosting for deep learning, voice/sound recognition, image recognition, video detection, etc.
Mainly suitable for home games and Android emulators, such as BlueStacks and MEmu Play.
Starting at
$45.00
/month
Mainly suitable for home games and Android emulators, such as BlueStacks and MEmu Play.
Starting at
$49.00
/month
Positioning in professional drawing workstations. Good choice for video editing & rendering, content creation, and gaming.
then $59.00/m
Positioning in professional drawing workstations. Good choice for video editing & rendering, content creation, and gaming.
then $69.00/m
Positioning in professional drawing workstations. Good choice for Video editing & rendering, content creation, and gaming.
then $74.00/m
Enable professionals to tackle multi-app workflows, from 3D modeling to video editing.
Starting at
$99.00
/month
For High-performance computing and large data workloads, such as deep learning and AI reasoning.
then $129.00/m
For High-performance computing and large data workloads, such as deep learning and AI reasoning.
then $199.00/m
Entry-level AI reasoning and edge computing accelerator based on ampere architecture.
Starting at
$199.00
/month
For professionals. It delivers real-time ray tracing, AI accelerated computing, and high-performance graphics to desktops.
then $299.00/m
High performance, low power consumption. The mainstream choice for enterprises in AI reasoning, training, graphics, and computing workloads.
Starting at
$269.00
/month
Achieve an excellent balance between function, performance, and reliability. Assist designers, engineers, and artists to realize their visions.
then $349.00/m
Benefits of TensorFlow
With its capabilities, TensorFlow eases the computations of machine learning and deep learning.
Data visualization
TensorFlow has great computational graph visualizations. It also allows easy debugging of nodes with the help of TensorBoard. This reduces the effort of visiting the whole code and effectively resolves the neural network.
Keras friendly
TensorFlow has compatibility with Keras. Its users can code some high-level functionality sections in it. Keras provides system-specific functionality to TensorFlow, such as pipelining, estimators, and eager execution.
Scalable
With its characteristic of being deployed on every machine and the graphical representation of a model, TensorFlow allows its users to develop any kind of system using TensorFlow.
Compatibility
It is compatible with many languages, including C++, JavaScript, Python, C#, Ruby, and Swift. The language compatibility allows users to work in environments they are comfortable.
Parallelism
Due to the parallelism of work models, TensorFlow find its use as a hardware acceleration library. It uses different distribution strategies in GPU and CPU systems.
Graphical support
Deep learning uses TensorFlow for its development as it allows the building of neural networks with the help of graphs that represent operations as nodes.
Voice/Sound Recognition
Text-Based Applications
Image Recognition
Time Series
Video Detection
Guidance
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.
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