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docs/tr/docs/async.md
Production'da nasıl oldugunu görmek için şu bölüme bakın [Deployment](deployment/index.md){.internal-link target=_blank}. ## `async` ve `await`
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CONTRIBUTING.md
For any non-trivial change, we need to be able to answer these questions: * Why is this change done? What's the use case? * For user facing features, what will the API look like? * What test cases should it have? What could go wrong? * How will it roughly be implemented? We'll happily provide code pointers to save you time.
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ci/devinfra/docker_windows/Dockerfile
[Environment]::SetEnvironmentVariable('HOME', 'C:\Users\ContainerAdministrator\', 'Machine'); \ [Environment]::SetEnvironmentVariable('HOMEDRIVE', 'C:', 'Machine'); \ [Environment]::SetEnvironmentVariable('HOMEPATH', '\Users\ContainerAdministrator\', 'Machine'); \ [Environment]::SetEnvironmentVariable('GOROOT', 'C:\Program Files\Go\', 'Machine'); \
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api/go1.11.txt
pkg debug/elf, const EM_C166 = 116 pkg debug/elf, const EM_C166 Machine pkg debug/elf, const EM_CDP = 215 pkg debug/elf, const EM_CDP Machine pkg debug/elf, const EM_CE = 119 pkg debug/elf, const EM_CE Machine pkg debug/elf, const EM_CLOUDSHIELD = 192 pkg debug/elf, const EM_CLOUDSHIELD Machine pkg debug/elf, const EM_COGE = 216 pkg debug/elf, const EM_COGE Machine pkg debug/elf, const EM_COOL = 217 pkg debug/elf, const EM_COOL Machine
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doc/go_mem.html
</p> <p> Reads of memory locations larger than a single machine word are encouraged but not required to meet the same semantics as word-sized memory locations, observing a single allowed write <i>w</i>. For performance reasons, implementations may instead treat larger operations as a set of individual machine-word-sized operations in an unspecified order.
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README.md
MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.
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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
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apache-maven/src/assembly/maven/conf/settings.xml
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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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docs/es/docs/async.md
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