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ahkarami

Deep-Learning-in-Production

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

TutorialsMLE roadmap
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创建于 2018-05-03 · 更新于 2026-10-16 · 今日第 7624 名
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ahkarami/Deep-Learning-in-Production 排名第 7,624,有 4,378 个星标。

同类别比较

ahkarami/Deep-Learning-in-Production 排名第 7,624,此处有 8 个同类条目;dsgiitr/d2l-pytorch 排名第 7,625。

同类别比较
名称排名指标
zotroneneis/machine_learning_basics7,612
语言:
Jupyter Notebook
星标数:
4,420
分叉数:
833
简介:
Plain python implementations of basic machine learning algorithms
JWarmenhoven/ISLR-python7,613
语言:
Jupyter Notebook
星标数:
4,415
分叉数:
2,377
简介:
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
decodingai-magazine/llm-twin-course7,620
语言:
Python
星标数:
4,390
分叉数:
733
简介:
🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴
panaversity/learn-agentic-ai7,621
语言:
Jupyter Notebook
星标数:
4,385
分叉数:
1,009
简介:
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.
dsgiitr/d2l-pytorch7,625
语言:
Jupyter Notebook
星标数:
4,370
分叉数:
1,232
简介:
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
MorvanZhou/Tensorflow-Tutorial7,632
语言:
Python
星标数:
4,335
分叉数:
1,829
简介:
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
collabora/WhisperLive7,636
语言:
Python
星标数:
4,323
分叉数:
602
简介:
A nearly-live implementation of OpenAI's Whisper.
gaussic/text-classification-cnn-rnn7,640
语言:
Python
星标数:
4,296
分叉数:
1,446
简介:
CNN-RNN中文文本分类,基于TensorFlow
关键参数与规格
关键参数与规格
参数值
类别Tutorials
子类别MLE roadmap
星标数4,378
分叉数685
贡献者3
创建日期2018-05-03
更新日期2026-10-16
1 天星标数0
7 天星标数0
排名7,624
上次排名18,334
排名变化10,710
7 天星标排名11,768
上次 7 天星标排名11,828
7 天星标排名变化60
总星标排名3,197
上次总星标排名3,194
总星标排名变化-3
国家Iran
README 摘录

Deep-Learning-in-Production In this repository, I will share some useful notes and references about deploying deep learning-based models in production. Convert PyTorch Models in Production: PyTorch Production Level Tutorials [_Fantastic_] The road to 1. 0 tracing JIT and LibTorch C++ API to integrate PyTorch into NodeJS [_Good Article_] Model Serving in PyTorch PyTorch Summer Hackathon [_Very Important_] Deploying PyTorch and Building a REST API using Flask [_Important_] PyTorch model recognizing hotdogs and not-hotdogs deployed on flask

Serving PyTorch 1. 0 Models as a Web Server in C++ [_Useful Example_] PyTorch Internals [_Interesting & Useful Article_] Flask application to support pytorch model prediction Serving PyTorch Model on Flask Thread-Safety Serving PyTorch Models on AWS Lambda with Caffe2 & ONNX Serving PyTorch Models on AWS Lambda with Caffe2 & ONNX (Another Version) Deep Dive into ONNX Runtime EuclidesDB - _multi-model machine learning feature database with PyTorch_ EuclidesDB - GitHub WebDNN: Fastest DNN Execution Framework on Web Browser FastAI PyTorch Serverless API

(with AWS Lambda) FastAI PyTorch in Production (discussion) OpenMMLab Model Deployment Framework PyTorch Native Architecture Optimization: torchao TorchServe [Great Tool] TorchServe Video Tutorial LitServe: Easily serve AI models Lightning fast Convert PyTorch Models to C++: **Loading a PyTorch Model in C++** [_**Fantastic**_] **PyTorch C++ API** [_Bravo_] An Introduction To Torch (Pytorch) C++ Front-End [_Very Good_] Blogs on using PyTorch C++ API [_Good_] ATen: A TENsor library Important Issue about PyTorch-like C++ interface PyTorch C++ API Test PyTorch via

C++ [_Useful Notes_] AUTOGRADPP PyTorch C++ Library Direct C++ Interface to PyTorch A Python module for compiling PyTorch graphs to C Deploy TensorFlow Models in Production: How to deploy Machine Learning models with TensorFlow - _Part1_ How to deploy Machine Learning models with TensorFlow - _Part2_ How to deploy Machine Learning models with TensorFlow - _Part3_ Neural Structured Learning (NSL) in TensorFlow [_Great_] Building Robust Production-Ready Deep Learning Vision Models Creating REST API for TensorFlow models "How to Deploy a Tensorflow Model

in Production" by _Siraj Raval_ on YouTube Code for the "How to Deploy a Tensorflow Model in Production" by _Siraj Raval_ on YouTube How to deploy an Object Detection Model with TensorFlow serving [_Very Good Tutorial_] Freeze Tensorflow models and serve on web [_Very Good Tutorial_] How to deploy TensorFlow models to production using TF Serving [_Good_] How Zendesk Serves TensorFlow Models in Production TensorFlow Serving Example Projects Serving Models in Production with TensorFlow Serving [_TensorFlow Dev Summit 2017 Video

_] Building TensorFlow as a Standalone Project TensorFlow C++ API Example TensorFlow. js: Machine Learning in Javascript Convert Keras Models in Production: Deep learning in production with Keras, Redis, Flask, and Apache [_Rank: 1st & General Usefult Tutorial_] Deploying a Keras Deep Learning Model as a Web Application in Python [_Very Good_] Deploying a Python Web App on AWS [_Very Good_] Deploying Deep Learning Models Part 1: Preparing the Model Deploying your Keras model Deploying your Keras model using Keras. 0 Some

PyTorch Workflow Changes wandb - A tool for visualizing and tracking your machine learning experiments PyTorch and Caffe2 repos getting closer

常见问题

记录了多少星标和分叉?

ahkarami/Deep-Learning-in-Production 有 4,378 个星标和 685 个分叉。

仓库排名是多少?

ahkarami/Deep-Learning-in-Production 排名第 7,624。

哪个仓库排名最接近?

ahkarami/Deep-Learning-in-Production 与 dsgiitr/d2l-pytorch 比较,后者排名第 7,625,有 4,370 个星标。

README

Deep-Learning-in-Production

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

Convert PyTorch Models in Production:

Convert PyTorch Models to C++:

Deploy TensorFlow Models in Production:

Convert Keras Models in Production:

Deploy MXNet Models in Production:

Deploy Machine Learning Models with Go:

General Deep Learning Deployment Toolkits:

Huawei Deep Learning Framework:

General Deep Learning Compiler Stack:

Model Conversion between Deep Learning Frameworks:

Some Caffe2 Tutorials:

Some Useful Resources for Designing UI (Front-End Development):

Mobile & Embedded Devices Development:

Back-End Development Part:

GPU Management Libraries:

Speed-up & Scalabale Python Codes:

Hardware Notes for Deep Learning:

MLOPs Courses & Resources:

Other:

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