cortex
zenml
| cortex | zenml | |
|---|---|---|
| 33 | 1 | |
| 7,990 | 1,402 | |
| 0.1% | - | |
| 2.2 | 10.0 | |
| 11 months ago | over 2 years ago | |
| Go | Python | |
| 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.
cortex
- Ask HN: Are there any reliable benchmarks for Machine Learning Model Serving?
-
Local text generation (InferKit alternative)
I found GPT-2 with PyTorch, Cortex, and Transformers.
-
Why ML should be written as pipelines from the get-go
Technologies: Flask/FastAPI, Kubernetes, Docker, Cortex, Seldon
-
[P] I made tinymodels.io to deploy models quickly, feel free to use for your prototypes and web demos
oh I see - that's neat. I think cortex.dev had some support for that sort of thing. It is? was? a wrapper to manage an EKS cluster for you and it could do things like hot swap what's in ram, autoscale, or use spot instances to save costs.
- Containers as a service on AWS
- Containers as a Service on AWS
-
How would you go about deploying transformer based model?
I use cortex.dev for deploying my models. I have successfully deployed finetuned T5s, which seems similar to your usecase.
-
Microservice on ECS having burst load issue
Arghs, my bad, yeah, doesn't seem to be available yet. But maybe Cortex might help: https://github.com/cortexlabs/cortex
- Self-hosted AWS Lambda alternative
zenml
-
Why ML should be written as pipelines from the get-go
ZenML is an exercise in finding the right layer of abstraction for ML. Here, we treat pipelines as first-class citizens. This means that data scientists are exposed to pipelines directly in the framework, but not in the same manner as the data pipelines from the ETL space (Prefect, Airflow et al.). Pipelines are treated as experiments — meaning they can be compared and analyzed directly. Only when it is time to flip over to productionalization, can they be converted to classical data pipelines.
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
golang-design-pattern - 设计模式 Golang实现－《研磨设计模式》读书笔记
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
oauth2 - Go OAuth2
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
prysm - Go implementation of Ethereum proof of stake
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
terraform-provider-azurerm - Terraform provider for Azure Resource Manager
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
OpenFaaS - OpenFaaS - Serverless Functions Made Simple
sarama - Sarama is a Go library for Apache Kafka. [Moved to: https://github.com/IBM/sarama]