📝 Add deployment to Deta, the first gold sponsor 🎉 (fastapi#2303)

* 📝 Add docs for sponsors

* 📝 Add docs for deploying on Deta

* 🍱 Add Deta docs screenshots

* ♻️ Refactor and separate deployment docs

* 📝 Update internal docs links to Deployment

* 📝 Update links in Deta tutorial

* 🍱 Update sponsors images

* 🔧 Use sponsors data from YAML

* 📝 Update sponsors in languages docs

* ✨ Update docs generation scripts

* ✅ Update README.md testing

* 📝 Update main README

* 🔧 Update fastapi-people action handling individual sponsors

* ✏️ Fix typos in Deta tutorial

.github/actions/people/app/main.py

@@ -375,7 +375,7 @@ def get_contributors(settings: Settings):
     return contributors, commentors, reviewers, authors
 
 
-def get_sponsors(settings: Settings):
+def get_individual_sponsors(settings: Settings, max_individual_sponsor: int = 5):
     nodes: List[SponsorshipAsMaintainerNode] = []
     edges = get_graphql_sponsor_edges(settings=settings)
 
@@ -387,6 +387,8 @@ def get_sponsors(settings: Settings):
 
     entities: Dict[str, SponsorEntity] = {}
     for node in nodes:
+        if node.tier.monthlyPriceInDollars > max_individual_sponsor:
+            continue
         entities[node.sponsorEntity.login] = node.sponsorEntity
     return entities
 
@@ -473,7 +475,7 @@ def get_top_users(
         skip_users=skip_users,
     )
 
-    sponsors_by_login = get_sponsors(settings=settings)
+    sponsors_by_login = get_individual_sponsors(settings=settings)
     sponsors = []
     for login, sponsor in sponsors_by_login.items():
         sponsors.append(

README.md

@@ -43,6 +43,16 @@ The key features are:
 
 <small>* estimation based on tests on an internal development team, building production applications.</small>
 
+## Gold Sponsors
+
+<!-- sponsors -->
+
+<a href="https://www.deta.sh/?ref=fastapi" target="_blank" title="The launchpad for all your (team's) ideas"><img src="/img/sponsors/deta.svg"></a>
+
+<!-- /sponsors -->
+
+<a href="https://fastapi.tiangolo.com/fastapi-people/#sponsors" class="external-link" target="_blank">Other sponsors</a>
+
 ## Opinions
 
 "_[...] I'm using **FastAPI** a ton these days. [...] I'm actually planning to use it for all of my team's **ML services at Microsoft**. Some of them are getting integrated into the core **Windows** product and some **Office** products._"

docs/en/data/sponsors.yml

@@ -0,0 +1,8 @@
+gold:
+  - url: https://www.deta.sh/?ref=fastapi
+    title: The launchpad for all your (team's) ideas
+    img: /img/sponsors/deta.svg
+silver:
+  - url: https://testdriven.io/
+    title: Learn to build high-quality web apps with best practices
+    img: /img/sponsors/testdriven.svg

docs/en/docs/alternatives.md

@@ -410,7 +410,7 @@ It is the recommended server for Starlette and **FastAPI**.
 
     You can combine it with Gunicorn, to have an asynchronous multi-process server.
 
-    Check more details in the [Deployment](deployment.md){.internal-link target=_blank} section.
+    Check more details in the [Deployment](deployment/index.md){.internal-link target=_blank} section.
 
 ## Benchmarks and speed
 

docs/en/docs/async.md

@@ -261,7 +261,7 @@ But you can also exploit the benefits of parallelism and multiprocessing (having
 
 That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).
 
-To see how to achieve this parallelism in production see the section about [Deployment](deployment.md){.internal-link target=_blank}.
+To see how to achieve this parallelism in production see the section about [Deployment](deployment/index.md){.internal-link target=_blank}.
 
 ## `async` and `await`