cloud-run-faq
Flask
cloud-run-faq | Flask | |
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8 | 135 | |
2,288 | 66,417 | |
- | 0.4% | |
0.0 | 8.7 | |
about 2 years ago | 1 day ago | |
Shell | Python | |
Creative Commons Attribution 4.0 | BSD 3-clause "New" or "Revised" License |
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cloud-run-faq
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Fly Kubernetes
I kind of miss the point of this. So if I'm reading this right, fly.io practically only exposes the Pods API, but Kubernetes is really much more than that. I'm not very familiar with any serious company that directly uses Pods API to launch containers, so if their reimplementation of Pods API is just a shim, and they're not going to be able to implement ever-growing set of features in Kubernetes Pod lifecycle/configuration (starting from /logs, /exec, /proxy...) why even bother branding it Kubernetes? Instead they could do what Google does with Cloud Run (https://cloud.run/) which Fly.io is already doing?
I don't know why would anyone would be like "here's a container execution platform, let me go ahead and use their fake Pods API instead of their official API".
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Make predictions on a hosted pretrained model without it running 24/7
I agree with this sentiment. If your model is available as a file, yes you can use GCS and have your Cloud Function fetch it from its bucket upon start-up, but if performance matters, you should consider bundling your function into a container and running with Cloud Run instead, because you have filesystem access there (no need to make an API call to GCS if you can read it as a file).
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Easy Google Cloud Logging from your Golang project in Google Cloud Run
I am building a HTTP Service running in Google Cloud Run in Go and wanted an easy way to log stuff to Google Cloud Logging.
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Help! Difference between native and datastore
Datastore mode had its start in App Engine's early days (launched in 2008), where its Datastore was the original scalable NoSQL database provided for all App Engine apps. In 2013, Datastore was made available all developers outside of App Engine, and "re-launched" as Cloud Datastore. In 2014, Google acquired Firebase for its RTDB (real-time database). Both teams worked together for the next 4 years, and in 2017, the next generation of Cloud Datastore was released, having merged in some of the Firebase RTDB features, and was re-branded as Cloud Firestore (in Datastore mode). This mode targets cloud compute as its users, whether serverless (App Engine, Cloud Functions, Cloud Run) or "serverful" (Compute Engine VMs, GKE/Kubernetes/Knative-compliant systems). If you provide a service via compute, use this mode.
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Can I use google cloud for free for non commercial purpose?
You don't need to learn about containers unless that's something you wish to explicitly use to put together your app in a consistent, reproducible manner. Cloud Run is the service that can host your containerized app. If you are in this camp and have learned Docker, you can use that if you wish, but it's optional. Cloud Run (well, Cloud Build, the tool that builds your container for Cloud Run) can build your app by detecting what's in your app so a Dockerfile isn't needed. So like App Engine, Cloud Run can host your full-on web apps if desired. You don't even need to build the container image yourself. Both App Engine and Cloud Run deploy source code directly from the command-line, and along w/Cloud Functions, your app is generally deployed and available globally in less than 60 seconds.
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Host a machine learning model on GCP
Next to the docs, this page also is quite useful: https://github.com/ahmetb/cloud-run-faq
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Cloud Run: Setting up test environment.
Actually meant to link this "unofficial" one.. Hope it helps a bit: https://github.com/ahmetb/cloud-run-faq
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Strapi Docker: Development is not working, but Production works (Google Cloud Run)
https://github.com/ahmetb/cloud-run-faq#can-i-mount-storage-volumes-or-disks-on-cloud-run
Flask
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Ask HN: High quality Python scripts or small libraries to learn from
I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:
https://github.com/pallets/flask
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Rapid Prototyping with Flask, Bootstrap and Secutio
#!/usr/bin/python # # https://flask.palletsprojects.com/en/3.0.x/installation/ # from flask import Flask, jsonify, request contacts = [ { "id": "1", "firstname": "Lorem", "lastname": "Ipsum", "email": "[email protected]", }, { "id": "2", "firstname": "Mauris", "lastname": "Quis", "email": "[email protected]", }, { "id": "3", "firstname": "Donec Purus", "lastname": "Purus", "email": "[email protected]", } ] app = Flask(__name__, static_url_path='', static_folder='public',) @app.route("/contact//save", methods=["PUT"]) def save_contact(id): data = request.json contacts[id - 1] = data return jsonify(contacts[id - 1]) @app.route("/contact/", methods=["GET"]) @app.route("/contact//edit", methods=["GET"]) def get_contact(id): return jsonify(contacts[id - 1]) @app.route('/') def root(): return app.send_static_file('index.html') if __name__ == '__main__': app.run(debug=True)
- Microdot "The impossibly small web framework for Python and MicroPython"
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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10 Github repositories to achieve Python mastery
Explore here.
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Ask HN: What would you use to build a mostly CRUD back end today?
I may use Flask-Admin initially to offload the "CRUD" operations to have an initial prototype fast but then drop it ASAP because I don't want to write a "flask-admin application" to fight against later on. If the application is mainly "CRUD", then Flask-Admin is suitable.
Now...
Would you do a breakdown/list of all the jobs you've done by sector/vertical and by function/role and by application functionality?
- [0]: https://flask.palletsprojects.com
- [1]: https://flask-admin.readthedocs.io/en/latest
- [2]: https://flask.palletsprojects.com/en/2.3.x/patterns/celery
- [3]: https://sentry.io
- [4]: https://posthog.com
- [5]: https://www.docker.com
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Implementing continuous delivery pipelines with GitHub Actions
In the lab to follow, we will be setting up an end-to-end DevOps workflow for a Flask microservice with GitHub Actions, using a self-managed custom runner for maximal control over the pipeline execution environment and automating deployments to a local Kubernetes cluster. Furthermore, we will construct separate pipelines for our "development" and "production" environments to further elaborate on the concepts of continuous deployment and delivery.
- How do you iterate on a library built locally?
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Flask Application Load Balancing using Docker Compose and Nginx
Flask Micro web Framework: You will use Flask to build a Flask web application.
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Open Source Flask-based web applications
In an earlier post I mentioned a bunch of Open Source web applications. Let's now focus on the ones written in Python using Flask the light-weight web framework.
What are some alternatives?
firebase-gcp-examples - 🔥 Firebase app architectures, languages, tools & some GCP things! React w Next.js, Svelte w Sapper, Cloud Functions, Cloud Run.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
gcr-cleaner - Delete untagged image refs in Google Container Registry or Artifact Registry
Django - The Web framework for perfectionists with deadlines.
Pyramid - Pyramid - A Python web framework
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
golang-docker - Docker Official Image packaging for golang
starlette - The little ASGI framework that shines. 🌟
quart - An async Python micro framework for building web applications.
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.
uvicorn - An ASGI web server, for Python. 🦄
CherryPy - CherryPy is a pythonic, object-oriented HTTP framework. https://cherrypy.dev