aim
manifests
aim | manifests | |
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70 | 6 | |
4,797 | 746 | |
1.8% | 1.3% | |
8.0 | 8.4 | |
3 days ago | about 17 hours ago | |
Python | YAML | |
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.
aim
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aim VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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End-to-end observability for LlamaIndex environment
LlamaIndex Observer is one of the logging apps built in AimOS (aimstack.io).
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Data Registry suggestions for ML projects
I've been working with Aim for a while, and it's been solid. What stands out for me is its open-source nature. https://aimstack.io/
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Building and debugging LLMs with Aim: self-hosted and open-source AI metadata tracking tool
If you haven't yet, drop a star to support open-source project! βοΈ https://github.com/aimhubio/aim
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Any tools that offer In-depth tracking of model runtime performance?
Here is the GitHub repository: https://github.com/aimhubio/aim
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Using MLflow(Machine Learning experimentation tracking tool) in Kaggle notebooks with the help of DagsHub
You can also check out Aim, which has an integration with MLflow, called aimlflow.
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Visualize metadata with Aim on Hugging Face Spaces and seamlessly share training results with anyone
Hope you enjoyed reading and thanks for your time! Feel free to share your thoughts, would love to read them. Support Aim by dropping a star on GitHub: https://github.com/aimhubio/aim
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Effortless image tracking and analysis for 3D segmentation task with Aim
Aim: An easy-to-use & supercharged open-source AI metadata tracker aimstack.io
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Evaluate Different Vector Databases
Seems useful: https://github.com/aimhubio/aim
- Metadata visualization via Aim Explorers
manifests
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CloudRun for Anthos and Kubeflow conflict
I have a baremetal k8s with Anthos and successfully installed ASM (Cloud Run for Anthos) on it. However, there is some conflict when trying to install Kubeflow following this repo (https://github.com/kubeflow/manifests).
- Kubeflow v1.7.0 installation with M1/M2 Apple Silicon Mac
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Any MLOps platform you use?
That said I personally use Kubeflow hosted on a local baremetal kubernetes cluster (8 nodes, 4 gpus), but a lot of it is a bit of a bear to get installed correctly in a multi-machine environment (specifically this issue is still open and exposing the built-in dashboards outside of the cluster is a problem). Also because it's a Google product it's very clearly intended to run in the cloud with self-hosting being very much an afterthought
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How to run kubeflow locally on Mac os M1 ?
kind create cluster and then use the single installation command from https://github.com/kubeflow/manifests
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Help wanted to deploy Kubeflow using ArgoCD on some local VM's
I have deployed kubelfow v1.6 using using ArgoCD and itβs fairly simple. Every component of kubeflow has kustomze file ready kubeflow kustomize link. You just need to make argocd app for every component and then apply in that order.
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Self-hosting tools for ML ops/experiment management (e.g. wandb or kubeflow)
For context, I run a local baremetal k8s cluster distributed over a number of machines and I've tried both [wandb](https://wandb.ai/site) and [kubeflow](https://www.kubeflow.org/), finding them to be a serious headache to manage in a local deployment. Almost none of the self-hosted builds they provide work out of the box, there's a frequent issues that have caused me significant amounts of data loss, and there are several known issues on both projects that have been open for years that make for my particular use-case difficult (e.g. [access outside of the cluster requiring a bunch of yak shaving](https://github.com/kubeflow/manifests/issues/974)).
What are some alternatives?
tensorboard - TensorFlow's Visualization Toolkit
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
dvc - π¦ ML Experiments and Data Management with Git
argoflow - Argoflow has been superseded by deployKF
guildai - Experiment tracking, ML developer tools
manifests - β οΈ [Unofficial] Modified version for M1/M2 Apple Silicon Mac. β οΈ
wandb - π₯ A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
MLflow - Open source platform for the machine learning lifecycle
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
neptune-client - π The MLOps stack component for experiment tracking
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!