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docs/en/data/external_links.yml
Articles: English: - author: Kurtis Pykes - NVIDIA link: https://developer.nvidia.com/blog/building-a-machine-learning-microservice-with-fastapi/ title: Building a Machine Learning Microservice with FastAPI - author: Ravgeet Dhillon - Twilio link: https://www.twilio.com/en-us/blog/booking-appointments-twilio-notion-fastapi title: Booking Appointments with Twilio, Notion, and FastAPI - author: Abhinav Tripathi - Microsoft Blogs
Others - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Thu Mar 21 20:57:27 GMT 2024 - 21.3K bytes - Viewed (2) -
docs/en/docs/deployment/concepts.md
### Server Memory For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**. And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨
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cmd/net.go
} } return host, port, nil } // isLocalHost - checks if the given parameter // correspond to one of the local IP of the // current machine func isLocalHost(host string, port string, localPort string) (bool, error) { hostIPs, err := getHostIP(host) if err != nil { return false, err } nonInterIPV4s := mustGetLocalIP4().Intersection(hostIPs)
Go - Registered: Sun Apr 21 19:28:08 GMT 2024 - Last Modified: Tue Mar 26 15:00:38 GMT 2024 - 9.3K bytes - Viewed (0) -
apache-maven/src/assembly/maven/conf/settings.xml
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docs/en/docs/advanced/events.md
## Use Case Let's start with an example **use case** and then see how to solve it with this. Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖
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.zenodo.json
{ "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.", "license": "Apache-2.0", "title": "TensorFlow", "upload_type": "software", "creators": [ { "name": "TensorFlow Developers" }
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.github/ISSUE_TEMPLATE/feature_request.md
[ ] Extensions and Telemetry [ ] Security [ ] Test and Release [ ] User Experience [ ] Developer Infrastructure **Affected features (please put an X in all that apply)** [ ] Multi Cluster [ ] Virtual Machine [ ] Multi Control Plane
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docs/en/docs/deployment/index.md
## What Does Deployment Mean To **deploy** an application means to perform the necessary steps to make it **available to the users**. For a **web API**, it normally involves putting it in a **remote machine**, with a **server program** that provides good performance, stability, etc, so that your **users** can **access** the application efficiently and without interruptions or problems.
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docs/es/docs/async.md
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SECURITY.md
## TensorFlow models are programs TensorFlow [**models**](https://developers.google.com/machine-learning/glossary/#model) (to use a term commonly used by machine learning practitioners) are expressed as programs that TensorFlow executes. TensorFlow programs are encoded as computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted
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