Basic Docker Compose for Machine Learning Purposes

Last update: Oct 29, 2021

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Overview

Docker-compose for Machine Learning

How to use:

cd docker-ml-jupyterlab
# on mac
docker compose up

# on linux
docker-compose up

# or 
sudo docker-compose up 
# if you didn't add your user to the docker group

And just copy & paste the URL into your browser!

Owner

Chris Chen

AI-Backend Engineer

GitHub Repository

Basic Docker Compose for Machine Learning Purposes

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Basic Docker Compose for Machine Learning Purposes

Related tags

Overview

Docker-compose for Machine Learning

How to use:

Owner

Chris Chen

Basic Docker Compose for Machine Learning Purposes

A collection of interactive machine-learning experiments: 🏋️models training + 🎨models demo

Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.

Can a machine learning project be implemented to estimate the salaries of baseball players whose salary information and career statistics for 1986 are shared?

The unified machine learning framework, enabling framework-agnostic functions, layers and libraries.

Add built-in support for quaternions to numpy

Iris-Heroku - Putting a Machine Learning Model into Production with Flask and Heroku

A simple and lightweight genetic algorithm for optimization of any machine learning model

Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)

A simple application that calculates the probability distribution of a normal distribution

Multiple Linear Regression using the LinearRegression class from sklearn.linear_model library

Python package for causal inference using Bayesian structural time-series models.

Implemented four supervised learning Machine Learning algorithms

My capstone project for Udacity's Machine Learning Nanodegree

FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

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