mlem
truckfactor
| mlem | truckfactor | |
|---|---|---|
| 18 | 1 | |
| 704 | 27 | |
| - | - | |
| 8.2 | 2.9 | |
| 8 months ago | 6 months ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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.
mlem
- The open-source tool to simplify your ML model deployments
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Anyone else just holding out hope that Apollo will find a way to live on (even tho it’s probably like .01% chance)?
Mlem for Lemmy has a similar look and feel to Apollo. Still missing a lot of features though. https://github.com/iterative/mlem
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How to log model artifacts with MLFLOW and DVC?
Here are a few things to consider: 1. You're using both DVC and MLflow to store the model artifact, why? 2. How I envision MLflow, DVC and git to work together is like this. DVC to manage the training dataset, git to manage the code, and MLflow will do the rest. About the part about "versioning" the model, MLflow has a model registry feature to "tag" a well-performing experiment. 3. Or just to do everything in DVC. DVC also has a way to do experiment tracking. Then if you need a model registry there's MLEM by the same company.
- MLEM: Open-source tool to package, serve, and deploy ML models on any platform
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Open-source tool to simplify ML model deployment
No, it's a completely separate open source tool, not directly related to DVC - https://github.com/iterative/mlem
- Tool to package, serve, and deploy any ML model on any platform
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Git-based Model Registry
This functionality can be used from open source tool mlem.ai and our released UI - https://studio.iterative.ai/
- Open source tool to package and deploy models
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MLEM - versioning and deploying your machine learning models using GitOps principles and a standard format for ML models
MLEM is a new MLOps tool to bridge the gap between ML engineers and DevOps teams by using the git-based approach that developers are already familiar with. Using MLEM, developers can store and track their ML models throughout their lifecycle: GitHub - iterative/mlem: 🐶 Version and deploy your ML models following GitOps principles
truckfactor
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The Bus factor is the number of people on a project that would have to be hit by a bus (or quit) before the project is in serious trouble. We analyzed the bus factors for the top 1,000 projects on GitHub. [Interactive dashboard] OC
We used a library called truckfactor to compute the bus/truck factor. We also described how we did the computation here.
What are some alternatives?
ZnTrack - Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
revup - Effortlessly create and manage pull requests without changing branches. Powers a stacked diffs workflow with python and git "plumbing" commands.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
gita - Manage many git repos with sanity 从容管理多个git库
streamlit - Streamlit — A faster way to build and share data apps.
dvc - 🦉 ML Experiments and Data Management with Git
torchlambda - Lightweight tool to deploy PyTorch models to AWS Lambda
git-privacy - Redact Git author and committer dates to keep committing behaviour more private.
git-repo-updater - A console script that allows you to easily update multiple git repositories at once
gitless - A simple version control system built on top of Git
fiftyone - The open-source tool for building high-quality datasets and computer vision models