Machine Learning Engineers Should Use Docker

Why Machine Learning Engineers Need Docker

As a machine learning engineer, if you have trained a model in a Jupyter notebook that comprises various libraries with different versions, Docker can aid in easily replicating your working environment so that colleagues or others can run the code and reproduce consistent results. Utilizing the same version of libraries, random seeds, and even the operating system, ensures that the results will remain consistent. Furthermore, if you intend to use the same model in a product, Docker can facilitate reproducing the results in a seamless manner.

Another benefit of using Docker for machine learning engineers is the ability to easily move code and dependencies between systems with varying hardware resources. Without Docker, adjusting and preparing a new system for the code to run can be time-consuming and undesirable. Docker allows machine learning engineers to bundle their code and dependencies into containers, which enables the portability of the code to a wide range of computers, regardless of the underlying hardware or operating system. This allows for the immediate initiation of work on a project, once it has been “Dockerized.”

Deployment of machine learning models is typically a complex process, but Docker simplifies it by allowing for the wrapping of models in an API and placement in a container through the use of container management technologies such as Kubernetes. This makes the deployment process more straightforward.

Furthermore, many machine learning engineers utilize Docker to prototype deep learning models on a single GPU and then scale up to use more powerful resources, such as those available on Amazon Web Server. Nvidia-Docker facilitates the quick creation of repositories necessary to run deep learning libraries, such as Tensorflow and Pytorch, on a GPU, making the process more manageable.