flyte
microservices-demo
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flyte | microservices-demo | |
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31 | 31 | |
4,761 | 15,777 | |
3.3% | 2.1% | |
9.8 | 9.7 | |
about 6 hours ago | 1 day ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
flyte
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First 15 Open Source Advent projects
9. Flyte by Union AI | Github | tutorial
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Flyte 1.10: Self-hosted solution to build production-grade data and ML pipelines; now ships with monorepo, new agents and sensors, eager workflows and more 🚀 (4.1k stars on GitHub)
GitHub: https://github.com/flyteorg/flyte
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Flyte: Open-source orchestrator for building production-grade ML pipelines
This is actually but a link to Flyte, this is a link to the documentation for the Flyte integration in LangChain, a separate product.
Flyte's homepage is https://flyte.org/
- Flyte: Advanced workflow orchestration alternative to Apache Airflow
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Orchestration: Thoughts on Dagster, Airflow and Prefect?
Anyone tried Flyte?
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Flyte 1.6.0: Self-hosted solution to build production-grade data and ML pipelines; now ships with PyTorch elastic training, image specification without dockerfile, enhanced task execution insights and more 🚀 (3.4k stars on GitHub)
Website: https://flyte.org/
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Flyte(v1.5.0) - Self-hosted solution to build production-grade data and ML pipelines; now ships with streaming support, pod templates, partial tasks and more 🚀 (3.2k stars on GitHub)
Flyte is an open source orchestration tool for managing the workflow of machine learning and AI projects. It runs on top of Kubernetes.
- Flyte: Open-Source Kubernetes-Native ML Orchestrator Implemented in Go
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What is MLOps and how to get started? | MLOps series | Deploying ML in production
I have a question though, what is your opinion on https://flyte.org. My pipeline uses this and it’ll be interesting to get your perspectives on it’s capabilities.
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Github alternative for ML?
Have you looked at flyte.org. It aims to bring "versioning", "compute" and "reproducibility" together in one package.
microservices-demo
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Small non complicated apps for k8s demo
You can check https://github.com/GoogleCloudPlatform/microservices-demo for Kubernetes show-casing
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Jump into Microservices Testing with Docker Compose and Skyramp
Skyramp provides a sample project, sample-microservices, which serves as an excellent starting point for demonstrating testing and mocking with a full-featured distributed application. The application is based on Google's Online Boutique repo, which is an e-commerce store consisting of 11 different microservices. The docker-compose-demo branch referenced above showcases how Skyramp can be seamlessly integrated with Docker Compose for testing microservices with no local setup required. You can also clone the repository and explore the structure of the microservices setup for your own purposes.
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Turbocharge Your Debugging with Skyramp's Hot Code Reload
Our starting point is the hot-code-reload-demo branch in Skyramp's letsramp/sample-microservices GitHub repo. You can use your browser to navigate to the correct branch in the repo here. The sample-microservices repo contains a demo project based on GCP's Online Boutique with added support for REST and Thrift APIs. This sample e-commerce application is perfect for demonstrating cloud-native development and testing, including debugging with Hot Code Reload with Skyramp.
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Testing Microservices with Skyramp in IntelliJ IDEA
This blog features the Skyramp fork of Google’s popular cloud-native microservices demo app, Online Boutique. Online Boutique is a web-based e-commerce app containing microservices that mimic real-world services, such as a product catalog, shopping cart, ad service, recommendation service, payment service, and others. The services use gRPC APIs by default, but Skyramp has also added support for REST and Thrift APIs.
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I'm looking for a homelab partner!
I'm planning to host this application: Google Microservices Demo (It's an online shop)
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[P] Machine Learning Threat Detection in k8s
Well, what is considered "real" data here? Why couldn't you simply set up a managed k8s cluster with some prometheus monitoring and run the microservices-demo on it. There is even a synthetic load generator. You could purposefully add in specific kinds of faults into the working system, ones that are supported in metasploit so you can automate intrusions. Consider some goals for gaining access like: exfiltration, denial of service, ransomware. Then consider how you might detect such attacks purely from what you can read out of the prometheus time series data (eg. high egress traffic plus high req/s to redis might mean an exfiltration).
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Keep Calm! Kubernetes Cluster!! with helm file
The microservices source code repository for this project is from this link; google-microservices-demo, containing 11 services we will deploy with this demo. Also, from the same repo, it was illustrated and visualized how these services are connected to each other including a 3rd party service for database - redis. Among the services, Frontend serves as an entrypoint for all the services receiving external requests from the browser. Meanwhile, the load generator deployment is optional, so in this demo we wouldn't bother deploying it.
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How to organize monorepo for microservices?
This demo repo might be a good starting point: https://github.com/GoogleCloudPlatform/microservices-demo
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Microservice Communication
OpenAPI and possibly developing reusable, versioned client libraries could help, but it's a major undertaking that gRPC makes redundant. I'd be tempted to use grpc-gateway even if I had to implement a REST API. Try looking into buf and monorepo structures for proto management, e.g. something like GoogleCloudPlatform/microservices-demo. For more thorough proto and grpc-gateway definition examples, see googleapis/googleapis.
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Is it worth instrumenting with open-telemetry?
I also just discovered Google Cloud's microservices-demo repository, which has some samples of how to set up otel observability and GCP-specific Go profiling on GCP. I wish I'd found it before setting up otel myself.
What are some alternatives?
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
argocd-example-apps - Example Apps to Demonstrate Argo CD
argo - Workflow Engine for Kubernetes
bank-of-anthos - Retail banking sample application showcasing Kubernetes and Google Cloud
temporal - Temporal service
devtron - Tool integration platform for Kubernetes
kubeflow - Machine Learning Toolkit for Kubernetes
truenas-csp - TrueNAS Container Storage Provider for HPE CSI Driver for Kubernetes
Celery-Kubernetes-Operator - An operator to manage celery clusters on Kubernetes (Work in Progress)
example-helm-go-microservice - Example Go microservice with Helm chart
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
container-service-extension - Container Service for VMware vCloud Director