mlem
torchlambda
| mlem | torchlambda | |
|---|---|---|
| 18 | 2 | |
| 704 | 123 | |
| - | - | |
| 8.2 | 0.0 | |
| 8 months ago | over 2 years ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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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
torchlambda
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AWS lambda inference taking 3s even after warmup
Ok I see. Have you maximized the ram in the lambdas? Since performance scale with ram. I have been using torchlambda.
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[D] Anyone deploy DL models with AWS Lambda? Trying to estimate costs
I don't think aws lambda has gpu support. We use torchlambda to static build the deployment. You end up with a small binary
What are some alternatives?
ZnTrack - Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
python-paho-mqtt-for-aws-iot - Use Python and paho client with AWS IoT for MQTT messaging
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.
faiss-server - faiss serving :)
truckfactor - Tool to compute the truck factor of a Git repository
python-lambdarest - Flask like web framework for AWS Lambda
streamlit - Streamlit — A faster way to build and share data apps.
random-dose-of-knowledge - Using the latest Software Engineering practices to create a modern and simple app.
git-repo-updater - A console script that allows you to easily update multiple git repositories at once
cmake_min_version - Determine the minimal requirement CMake version of a project
dvc - 🦉 ML Experiments and Data Management with Git
jina - ☁️ Build multimodal AI applications with cloud-native stack