The hottest Docker Substack posts right now

anyGPT is a Python library for GPT-style language models
It supports GPT-1, GPT-2, and GPT-3 models for versatility
The library offers easy installation, Docker support, and extensive features for training large models

In development, replicate only a minimal set of required dependencies.
Replicas of dependencies in a local environment can function with less memory and CPU than the original.
Replicating only a minimal set of dependencies allows for faster testing and development cycles.

Building docker images can be time-consuming, especially in CI/CD pipelines.
To speed up the process, consider using cache, layering your Dockerfile, and utilizing multi-stage builds.
Ensure your Dockerfile is correctly set up, specifying versions, exposing necessary ports, and addressing platform compatibility.

Rupesh Mishra is a software engineer with 8 years of experience working with Python, Django, PostgreSQL, MongoDB, AWS, Docker, and Kubernetes.
Rupesh writes articles explaining complex technical concepts like memory management and object instantiation in Python in a simple and easy-to-understand manner.
Rupesh's newsletter covers topics like Python, Docker, Kubernetes, MongoDB, PostgreSQL, Architecture Design, and Microservices, offering a course on Microservices, Docker, and Kubernetes.

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Approach data engineering like software products, applying software engineering SDLC principles can help automate Google Cloud Dataflow with GitLab CI/CD pipelines.
A Dataflow flex-template consists of a Dockerfile and a template specification JSON file, offering advantages like separating implementation from deployment and enabling different teams to work on the pipeline.
Using GitLab's CI/CD for deploying Dataflow flex-templates is beneficial due to its intuitive UI, CI Linting feature, out-of-the-box security, and environment integration tools.