cortex
MLflow
cortex | MLflow | |
---|---|---|
33 | 56 | |
7,990 | 17,284 | |
0.1% | 1.3% | |
2.2 | 9.9 | |
11 months ago | 4 days 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.
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cortex
- Ask HN: Are there any reliable benchmarks for Machine Learning Model Serving?
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Local text generation (InferKit alternative)
I found GPT-2 with PyTorch, Cortex, and Transformers.
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Why ML should be written as pipelines from the get-go
Technologies: Flask/FastAPI, Kubernetes, Docker, Cortex, Seldon
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[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
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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.
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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
MLflow
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Observations on MLOps–A Fragmented Mosaic of Mismatched Expectations
How can this be? The current state of practice in AI/ML work requires adaptivity, which is uncommon in classical computational fields. There are myriad tools that capture the work across the many instances of the AI/ML lifecycle. The idea that any one tool could sufficiently capture the dynamic work is unrealistic. Take, for example, an experiment tracking tool like W&B or MLFlow; some form of experiment tracking is necessary in typical model training lifecycles. Such a tool requires some notion of a dataset. However, a tool focusing on experiment tracking is orthogonal to the needs of analyzing model performance at the data sample level, which is critical to understanding the failure modes of models. The way one does this depends on the type of data and the AI/ML task at hand. In other words, MLOps is inherently an intricate mosaic, as the capabilities and best practices of AI/ML work evolve.
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My Favorite DevTools to Build AI/ML Applications!
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It includes features for experiment tracking, model versioning, and deployment, enabling developers to track and compare experiments, package models into reproducible runs, and manage model deployment across multiple environments.
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
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EL5: Difference between OpenLLM, LangChain, MLFlow
MLFlow - http://mlflow.org
- Explain me how websites like Dall-E, chatgpt, thispersondoesntexit process the user data so quickly
- [D] What licensed software do you use for machine learning experimentation tracking?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
MLflow:
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Options for configuration of python libraries - Stack Overflow
In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
What are some alternatives?
golang-design-pattern - 设计模式 Golang实现-《研磨设计模式》读书笔记
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
oauth2 - Go OAuth2
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
prysm - Go implementation of Ethereum proof of stake
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
terraform-provider-azurerm - Terraform provider for Azure Resource Manager
guildai - Experiment tracking, ML developer tools
OpenFaaS - OpenFaaS - Serverless Functions Made Simple
dvc - 🦉 ML Experiments and Data Management with Git
zenml - ZenML 🙏: MLOps framework to create reproducible ML pipelines for production machine learning. [Moved to: https://github.com/zenml-io/zenml]
tensorflow - An Open Source Machine Learning Framework for Everyone