Web Hosting With Tensorflow Support

Tensorflow Hosting

A reliable Tensorflow host should enable you to set up and modify a Python environment optimized for supporting Tensorflow framework, including installing needed dependencies, updating server configurations, and setting environment variables. They should also offer secure methods of file transfers from and to their hosted machine.

GPU Support

One of the hallmarks of Tensorflow’s immense popularity lies in its use of graphics processing units (GPUs) to accelerate computations during training, making use of this feature essential. A good host will ensure you have access to sufficient resources and hardware in order to take full advantage of it.

Note that GPU functionality may not work with all versions of Tensorflow Python API. To experience GPU functionality, you’ll require a version that supports CUDA, such as Python 2.6 or later. To install it using pip (please refer to our TensorFlow docs for details), or download one of Google Colaboratory’s preconfigured images using Jupyter notebook and server environment Colaboratory as described herein.

If you intend to deploy your machine learning model onto a production server, Kubernetes should be your container orchestration software of choice. Created by Google and offering automated horizontal scaling, service discovery, load balancing and more features – Kubernetes provides significant savings of both time and effort over the long haul – not to mention being useful when building and testing Tensorflow apps without fear of crashes or data loss.