mlem
Ray
mlem | Ray | |
---|---|---|
18 | 42 | |
704 | 31,101 | |
- | 1.6% | |
8.2 | 10.0 | |
8 months ago | 6 days ago | |
Python | 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.
mlem
- The open-source tool to simplify your ML model deployments
-
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
-
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
-
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
-
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
-
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
Ray
-
Open Source Advent Fun Wraps Up!
22. Ray | Github | tutorial
-
Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Custom Models
Training times for GSM8k are mentioned here: https://github.com/ray-project/ray/tree/master/doc/source/te...
- Ray – an open source project for scaling AI workloads
-
Methods to keep agents inside grid world.
Here's a reference from RLlib that points to docs and an example, and here's one from one of my projects that includes all my own implementations
-
TransformerXL + PPO Baseline + MemoryGym
RLlib
- Is dynamic action masking possible in Rllib?
-
AWS re:Invent 2022 Recap | Data & Analytics services
⦿ AWS Glue Data Quality - Automatic data quality rule recommendations based on your data AWS Glue for Ray - Data integration with Ray (ray.io), a popular new open-source compute framework that helps you scale Python workloads
-
Think about it for a second
https://ray.io (just dropping the link)
-
Elixir Livebook now as a desktop app
I've wondered whether it's easier to add data analyst stuff to Elixir that Python seems to have, or add features to Python that Erlang (and by extension Elixir) provides out of the box.
By what I can see, if you want multiprocessing on Python in an easier way (let's say running async), you have to use something like ray core[0], then if you want multiple machines you need redis(?). Elixir/Erlang supports this out of the box.
Explorer[1] is an interesting approach, where it uses Rust via Rustler (Elixir library to call Rust code) and uses Polars as its dataframe library. I think Rustler needs to be reworked for this usecase, as it can be slow to return data. I made initial improvements which drastically improves encoding (https://github.com/elixir-nx/explorer/pull/282 and https://github.com/elixir-nx/explorer/pull/286, tldr 20+ seconds down to 3).
[0] https://github.com/ray-project/ray
-
Learn various techniques to reduce data processing time by using multiprocessing, joblib, and tqdm concurrent
Adding these for anyone who had a similar question about Ray vs dask 1, 2, 3
What are some alternatives?
ZnTrack - Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
optuna - A hyperparameter optimization framework
truckfactor - Tool to compute the truck factor of a Git repository
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
streamlit - Streamlit — A faster way to build and share data apps.
Faust - Python Stream Processing
torchlambda - Lightweight tool to deploy PyTorch models to AWS Lambda
gevent - Coroutine-based concurrency library for Python
git-repo-updater - A console script that allows you to easily update multiple git repositories at once
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
dvc - 🦉 ML Experiments and Data Management with Git
SCOOP (Scalable COncurrent Operations in Python) - SCOOP (Scalable COncurrent Operations in Python)