Python in DevOps: Automation, Efficiency, and Scalability

This page summarizes the projects mentioned and recommended in the original post on dev.to

Nutrient – The #1 PDF SDK Library, trusted by 10K+ developers
Other PDF SDKs promise a lot - then break. Laggy scrolling, poor mobile UX, tons of bugs, and lack of support cost you endless frustrations. Nutrient’s SDK handles billion-page workloads - so you don’t have to debug PDFs. Used by ~1 billion end users in more than 150 different countries.
www.nutrient.io
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CodeRabbit: AI Code Reviews for Developers
Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
coderabbit.ai
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  1. terraform

    Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.

    Terraform and Pulumi automate the provisioning of cloud resources. IaC ensures consistency and eliminates manual errors when setting up infrastructure.

  2. Nutrient

    Nutrient – The #1 PDF SDK Library, trusted by 10K+ developers. Other PDF SDKs promise a lot - then break. Laggy scrolling, poor mobile UX, tons of bugs, and lack of support cost you endless frustrations. Nutrient’s SDK handles billion-page workloads - so you don’t have to debug PDFs. Used by ~1 billion end users in more than 150 different countries.

    Nutrient logo
  3. kubeflow

    Machine Learning Toolkit for Kubernetes

    MLOps elevates DevOps principles to AI workloads. The new frontiers of DevOps are managing large model training jobs, handling GPUs, and automating ML pipelines. Python tools like MLflow and Kubeflow make this possible.

  4. Jenkins

    Jenkins automation server

    Jenkins and GitHub Actions are go-to tools for automating tasks like testing and deployment. These pipelines ensure that the latest version of your software gets built, tested, and delivered efficiently.

  5. docker

    FreeBSD port of docker, take a look at PORTING-FREEBSD.md in freebsd-compat branch (by kvasdopil)

    Containers package applications and their dependencies, ensuring consistency across environments. Using Docker makes it easy to "build once, run anywhere." Kubernetes manages multiple containers to keep complex apps running smoothly.

  6. kubespy

    Tools for observing Kubernetes resources in real time, powered by Pulumi.

    Terraform and Pulumi automate the provisioning of cloud resources. IaC ensures consistency and eliminates manual errors when setting up infrastructure.

  7. MLflow

    Open source platform for the machine learning lifecycle

    MLOps elevates DevOps principles to AI workloads. The new frontiers of DevOps are managing large model training jobs, handling GPUs, and automating ML pipelines. Python tools like MLflow and Kubeflow make this possible.

  8. kubernetes

    Production-Grade Container Scheduling and Management

    Containers package applications and their dependencies, ensuring consistency across environments. Using Docker makes it easy to "build once, run anywhere." Kubernetes manages multiple containers to keep complex apps running smoothly.

  9. CodeRabbit

    CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.

    CodeRabbit logo
  10. Grafana

    The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.

    Tools like Prometheus, Grafana, and ELK Stack are becoming popular as they offer insights into live systems. These tools help monitor metrics such as server usage, request latency, and CPU load to prevent downtime.

  11. starter-workflows

    Accelerating new GitHub Actions workflows

    Jenkins and GitHub Actions are go-to tools for automating tasks like testing and deployment. These pipelines ensure that the latest version of your software gets built, tested, and delivered efficiently.

  12. dagger

    An engine to run your pipelines in containers (by dagger)

    While YAML has been a traditional choice for defining pipelines, Python SDKs like Dagger provide developers with programmatic control. Instead of static files, you can write functions to configure, test, and deploy applications dynamically.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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