kronos

Overview

Kronos is a docker based command for executing machine learning projects which can automatically differenciate between a local cpu environment and a CUDA GPU environment and run accordingly.

Installation

1. Install pip.
2. Install kronos via pip

pip install kronos-ml

Command

Introduce kronos to your project

In order to use kronos, you need to create the kronos configuration files in your project directory (current directory) by executing the following init command.

kronos init [OPTIONS]

The init command allows users to set the location of the project directory with the option --dir.

kronos init --dir {DIRECTORY_LOCATION}

Build the container

The following commands build the docker-compose container for Python in Ubuntu 18.04 environment. For CPU environments, type in

kronos build

For GPU environments, type in

kronos build --gpu

Run the Python file in the docker container you built

For CPU environments, use

kronos run {YOUR_PYTHON_FILE.py}

For GPU environments, use

kronos run --gpu {python file}

Tutorial

Creating a Sample Test Project

Skip this step if you already have a project that you want to implement to Kronos.

Create an empty directory to be used by kronos as a project directory and move into it.

mkdir test
cd test

Generate a sample Python file using a text editor.

nano sample_code.py

Inside the file, insert the following code.

import torch

a = torch.randn((3, 2))
b = torch.randn((2, 4))

c = torch.matmul(a, b)

print(c.size())

Save and exit. If you are using Nano, save the file by pressing Ctrl + O, and exit using Ctrl + X.

Running the Project with kronos

Initialize the kronos project directory.

kronos init

The test directory should now have a docker directory, kronos-config.yml, and sample_code.py. The next step is to let Docker/kronos know about the libraries that you will be using while running your Python file. This is done using the requirements file in the docker folder. List all items in the docker directory.

ls docker

Out of all the files in the directory, the ones that we will be focusing on are cpu_requirements.txt and gpu_requirements.txt. Choose the requirement file based on whether your environment is using CPU or GPU processors. cpu_requirements.txt is used in this tutorial. Open the text file.

nano docker/cpu_requirements.txt

This is the list of all the libraries that the Docker will be installing into its virtual environment. Add the libraries in your project that will be used bt Python. In the context of the sample project explained above, since sample_code.py imports Pytorch (specified by import torch), Pytorch should be added to the list. Note that unnecessary libraries, such as Tensorflow in this case, should be omitted in order to reduce unnecessary install time. The text file should now read as below.

torch

Specific versions of the library can be specified optionally.

torch==1.1.0

Save and exit. Using the build command, build a docker container and install all libraries in it. Add --gpu for use in GPU environments.

kronos build

Finally, run the Python file. Add --gpu for use in GPU environments.

kronos run sample_code.py