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Jupyter VS runc

Compare Jupyter VS runc and see what are their differences

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Jupyter logo Jupyter

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

runc logo runc

CLI tool for spawning and running containers according to the OCI specification - opencontainers/runc
  • Jupyter Landing page
    Landing page //
    2023-06-22
  • runc Landing page
    Landing page //
    2023-08-21

Jupyter features and specs

  • Interactive Computing
    Jupyter allows real-time interaction with the data and code, providing immediate feedback and making it easier to experiment and iterate.
  • Rich Media Output
    It supports output in various formats including HTML, images, videos, LaTeX, and more, enhancing the ability to visualize and interpret results.
  • Language Agnostic
    Jupyter supports multiple programming languages through its kernel system (e.g., Python, R, Julia), allowing flexibility in the choice of tools.
  • Collaborative Features
    It enables collaboration through shared notebooks, version control, and platform integrations like GitHub.
  • Educational Tool
    Jupyter is widely used for teaching, thanks to its easy-to-use interface and ability to combine narrative text with code, making it ideal for assignments and tutorials.
  • Extensibility
    Jupyter is highly extensible with a large ecosystem of plugins and extensions available for various functionalities.

Possible disadvantages of Jupyter

  • Performance Issues
    For larger datasets and more complex computations, Jupyter can be slower compared to running scripts directly in a dedicated IDE.
  • Version Control Challenges
    Managing version control for Jupyter notebooks can be cumbersome, as they are not plain text files and include metadata that can make diffing and merging complex.
  • Resource Intensive
    Running Jupyter notebooks can be resource-intensive, especially when working with multiple large notebooks simultaneously.
  • Security Concerns
    Because Jupyter allows code execution in the browser, it can be a potential security risk if notebooks from untrusted sources are run without restrictions.
  • Dependency Management
    Managing dependencies and ensuring that the notebook runs consistently across different environments can be challenging.
  • Less Suitable for Production
    Jupyter is often considered more as a research and educational tool rather than a production environment; transitioning from a notebook to production code can require significant refactoring.

runc features and specs

  • Standardization
    runc is part of the Open Containers Initiative (OCI), promoting standardization across container runtimes. This ensures interoperability and broad community support.
  • Lightweight
    As a lightweight and fast CLI tool, runc provides a minimal runtime for environments where resource efficiency is critical.
  • Security
    runc adheres to principles of secure software development and incorporates Linux kernel features like namespaces and cgroups to enhance security.
  • Broad Adoption
    As the reference implementation for OCI, runc is widely adopted and tested in production environments, ensuring reliability.
  • Flexibility
    runc offers the flexibility to handle low-level container configurations, making it suitable for advanced users needing granular control.

Possible disadvantages of runc

  • Complexity for Beginners
    The low-level nature of runc can be daunting for beginners who might prefer higher-level tools like Docker that abstract away complexities.
  • Minimalist Design
    While its simplicity is an advantage, runc lacks some of the advanced features and orchestration capabilities found in other container platforms.
  • Manual Configurations
    Users need to manually handle configurations, which can be error-prone and time-consuming compared to automated solutions.
  • Ecosystem Integration
    runc does not provide direct integration with tools and platforms by default, requiring additional setup for comprehensive ecosystem support.
  • Limited Features
    Compared to complete container platforms, runc offers fewer built-in features, requiring supplementary tools to achieve similar functionalities.

Jupyter videos

What is Jupyter Notebook?

More videos:

  • Tutorial - Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
  • Review - JupyterLab: The Next Generation Jupyter Web Interface

runc videos

2/21/19 RunC Vulnerability Gives Root Access on Container Systems| AT&T ThreatTraq

More videos:

  • Review - Demo MONEY,TIME - RunC

Category Popularity

0-100% (relative to Jupyter and runc)
Data Science And Machine Learning
Web Servers
0 0%
100% 100
Data Dashboard
100 100%
0% 0
Web And Application Servers

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Jupyter and runc

Jupyter Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
Source: lakefs.io
7 best Colab alternatives in 2023
JupyterLab is the next-generation user interface for Project Jupyter. Like Colab, it's an interactive development environment for working with notebooks, code, and data. However, JupyterLab offers more flexibility as it can be self-hosted, enabling users to use their own hardware resources. It also supports extensions for integrating other services, making it a highly...
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Jupyter Notebook is a widely popular tool for data scientists to work on data science projects. This article reviews the top 12 alternatives to Jupyter Notebook that offer additional features and capabilities.
Source: noteable.io
15 data science tools to consider using in 2021
Jupyter Notebook's roots are in the programming language Python -- it originally was part of the IPython interactive toolkit open source project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name; along with supporting those three languages, Jupyter has modular kernels for dozens of others.
Top 4 Python and Data Science IDEs for 2021 and Beyond
Yep — it’s the most popular IDE among data scientists. Jupyter Notebooks made interactivity a thing, and Jupyter Lab took the user experience to the next level. It’s a minimalistic IDE that does the essentials out of the box and provides options and hacks for more advanced use.

runc Reviews

We have no reviews of runc yet.
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Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than runc. While we know about 216 links to Jupyter, we've tracked only 11 mentions of runc. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Jupyter mentions (216)

  • The 3 Best Python Frameworks To Build UIs for AI Apps
    Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 2 months ago
  • LangChain: From Chains to Threads
    LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago
  • Applied Artificial Intelligence & its role in an AGI World
    Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago
  • Jupyter Notebook for Java
    Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 9 months ago
  • JIRA Analytics with Pandas
    One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago
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runc mentions (11)

  • Setup multi node kubernetes cluster using kubeadm
    For kubeadm , kubetlet , kubectl should same version package in this lab I used v1.31 to have 1.31.7 References: Https://kubernetes.io/docs/reference/networking/ports-and-protocols/ Https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/install-kubeadm/ Https://github.com/opencontainers/runc/releases/... - Source: dev.to / 2 months ago
  • Comparing 3 Docker container runtimes - Runc, gVisor and Kata Containers
    Previously I wrote about the multiple variants of Docker and also the dependencies behind the Docker daemon. One of the dependencies was the container runtime called runc. That is what creates the usual containers we are all familiar with. When you use Docker, this is the default runtime, which is understandable since it was started by Docker, Inc. - Source: dev.to / 7 months ago
  • You run containers, not dockers - Discussing Docker variants, components and versioning
    Now we have dockerd which uses containerd, but containerd will not create containers directly. It needs a runtime and the default runtime is runc, but that can be changed. Containerd actually doesn't have to know the parameters of the runtime. There is a shim process between containerd and runc, so containerd knows the parameters of the shim, and the shim knows the parameters of runc or other runtimes. - Source: dev.to / 7 months ago
  • US Cybersecurity: The Urgent Need for Memory Safety in Software Products
    It's interesting that, in light of things like this, you still see large software companies adding support for new components written in non-memory safe languages (e.g. C) As an example Red Hat OpenShift added support for crun(https://github.com/containers/crun), which is written in C as an alternative to runc, which is written in Go( - Source: Hacker News / over 1 year ago
  • Why did the Krustlet project die?
    Yeah, runtimeClass lets you specify which CRI plugin you want based on what you have available. Here's an example from the containerd documentation - you could have one node that can run containers under standard runc, gvisor, kata containers, or WASM. Without runtimeClass, you'd need either some form of custom solution or four differently configured nodes to run those different runtimes. That's how krustlet did... Source: over 2 years ago
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What are some alternatives?

When comparing Jupyter and runc, you can also consider the following products

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Docker Hub - Docker Hub is a cloud-based registry service

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Apache Thrift - An interface definition language and communication protocol for creating cross-language services.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Eureka - Eureka is a contact center and enterprise performance through speech analytics that immediately reveals insights from automated analysis of communications including calls, chat, email, texts, social media, surveys and more.