encode-attend-navigate-pytorch
mosec
| encode-attend-navigate-pytorch | mosec | |
|---|---|---|
| 4 | 11 | |
| 7 | 707 | |
| - | 1.4% | |
| 0.0 | 8.5 | |
| 10 months ago | 3 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
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encode-attend-navigate-pytorch
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[Pytorch reimplementation] Encode-Attend-Navigate, a RL-based TSP solver;
https://github.com/astariul/encode-attend-navigate-pytorch I recently re-implemented encode-attend-navigate, a TSP solver based on RL. The official repo was using tensorflow 1.x, so I decided tore-implement it at Pytorch. I wanted to share it here to get some opinion :) You can train the model using a free GPU from Google Colab, a Colab notebook is provided in the README !
- [P] Pytorch reimplementation of Encode-Attend-Navigate, a RL-based TSP solver
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[P] Reinforcement Learning with multiple simultaneous actions?
Pytorch : encode-attend-navigate-pytorch
- [Pytorch reimplementation] Encode-Attend-Navigate, a RL-based TSP solver
mosec
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20x Faster as the Beginning: Introducing pgvecto.rs extension written in Rust
Mosec - A high-performance serving framework for ML models, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine. Simple and faster alternative to NVIDIA Triton.
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[D] Handling Concurrent Request for ML Model API
- Yes C++ would be better, but you can try mosec. It has a Python interface and helps you handle all the difficult things about Python multiprocessing. The web service part is implemented in Rust thus it's fast enough for machine learning services.
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Launching ModelZ Beta!
Contribute to open source projects: Modelz is built on top of envd, mosec, modelz-llm and many other open source projects. If you're interested in contributing to these projects, you can check out their GitHub repositories and start contributing.
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Deploying a model with an API in docker
You could first create the image with the framework you like (e.g. bentoml or https://github.com/mosecorg/mosec for light weight).
- PostgresML is 8-40x faster than Python HTTP microservices
- Python Machine Learning Service Can Run Way More Faster
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[D] Open Source ML Organisations to contribute to?
If you're interested in machine learning model serving, can check mosec: https://github.com/mosecorg/mosec
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Why not multiprocessing
During the development of a machine learning serving project Mosec, I used a lot of multiprocessing to make it more efficient. I want to share some experiences and some researches related to Python multiprocessing.
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[P] Mosec: deploy your machine learning model in an easy and efficient way
That's a good example. I have met the same situation before. I have created a discussion in GitHub to track the DAG progress.
- Mosec: deploy your machine learning model in an easy and efficient way
What are some alternatives?
encode-attend-navigate - Learning Heuristics for the TSP by Policy Gradient
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
HybridTSPSolver - A hybrid TSP solver that I made for my master's degree thesis in computer science.
GPflow - Gaussian processes in TensorFlow
VeRyPy - A python library with implementations of 15 classical heuristics for the capacitated vehicle routing problem.
mlrun - MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
text-generation-inference - Large Language Model Text Generation Inference
som-tsp - Solving the Traveling Salesman Problem using Self-Organizing Maps
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.
inference-benchmark - Benchmark for machine learning model online serving (LLM, embedding, Stable-Diffusion, Whisper)