Overview
The TensorFlow stack provides a framework for building and deploying AI applications that involve tasks such as image and speech recognition, NLP, and predictive analytics. It employs a data flow graph to represent mathematical computations. It is highly extensible - apt for any business project. BAAR, cnvrg.io, Actian Avalanche, and Aporia are popular tools that integrate with TensorFlow.
Highlights
- Deploys models in the browser, on-device, cloud, and on-premise.
- Supported by a thriving community of developers and researchers worldwide.
- Facilitates the execution of ML algorithms on hardware platforms such as CPUs, GPUs, and even mobile devices.
Details
Typical total price
$0.038/hour
Pricing
- ...
Instance type | Product cost/hour | EC2 cost/hour | Total/hour |
|---|---|---|---|
t2.nano | $0.00 | $0.006 | $0.006 |
t2.micro AWS Free Tier | $0.00 | $0.012 | $0.012 |
t2.small | $0.00 | $0.023 | $0.023 |
t2.medium | $0.00 | $0.046 | $0.046 |
t2.large | $0.00 | $0.093 | $0.093 |
t2.xlarge | $0.00 | $0.186 | $0.186 |
t2.2xlarge | $0.00 | $0.371 | $0.371 |
t3.nano | $0.00 | $0.005 | $0.005 |
t3.micro AWS Free Tier | $0.00 | $0.01 | $0.01 |
t3.small | $0.00 | $0.021 | $0.021 |
Additional AWS infrastructure costs
Type | Cost |
|---|---|
EBS General Purpose SSD (gp3) volumes | $0.08/per GB/month of provisioned storage |
Vendor refund policy
Intuz will not refund money in any case.However, you can cancel your subscription any time.
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Latest Stable Release
Additional details
Usage instructions
irst, take SSH of the Instance by typing "ssh -i yourpemkeyname.pem ubuntu@yourinstanceip" Then go to the root user by typing "sudo -i" There is a directory called Tensorflow-Dev, Change the directory by typing "cd /root/tensorflow-dev/bin" then type "source activate", It'll activate Python Virtual Environment Now you can add your code and run it in the following path You can use Tensorflow by importing it into the Python Code To import Tensorflow, add the following line, "import tensorflow as tf" To deactivate the Virtual Environment type "deactivate"
Resources
Vendor resources
Support
Vendor support
Intuz team provides the best technical documentation for installation and setup through this guide: https://www.intuz.com/cloud/stack/tensorflow
We provide best effort technical support for this product. We will do our best to respond to your questions within the next 24 hours in business days. For any technical support or query, you can drop an email here: cloudsupport@intuz.com or fill up this form:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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