Understanding Parallel and Distributed Training

OpenAI to launch its first ‘open-weights’ model since 2019

April 02, 2025

In partnership with

Welcome to learning edition of the Data Pragmatist, your dose of all things data science and AI.

📖 Estimated Reading Time: 5 minutes. Missed our previous editions?

💥 OpenAI to launch its first ‘open-weights’ model since 2019 LINK

  • OpenAI is planning to release its first "open-weight" language model since 2019, allowing developers to run the powerful AI system on their own hardware rather than through OpenAI's subscription-based cloud service.

  • The strategic shift comes amid economic pressure from efficient alternatives like DeepSeek's open-source model from China and Meta's Llama models, which have reached one billion downloads while operating at a fraction of OpenAI's costs.

  • For enterprise customers, especially in regulated industries like healthcare and finance, this move addresses concerns about data sovereignty and vendor lock-in, potentially enabling AI implementation in previously restricted contexts.

🚀 SpaceX launches the first crewed spaceflight to explore Earth's polar regions LINK

  • SpaceX successfully launched the Fram2 private crewed mission on March 31, marking the first human spaceflight dedicated to exploring Earth's polar regions from an altitude of 430 km.

  • The mission crew will observe unusual light emissions like auroras and STEVEs while conducting 22 experiments to better understand human health in space for future long-duration missions.

  • The four-person crew includes cryptocurrency investor Chun Wang who funded the trip, filmmaker Jannicke Mikkelsen as vehicle commander, robotics researcher Rabea Rogge as pilot, and polar adventurer Eric Philips as medical officer.

Find out why 1M+ professionals read Superhuman AI daily.

In 2 years you will be working for AI

Or an AI will be working for you

Here's how you can future-proof yourself:

  1. Join the Superhuman AI newsletter – read by 1M+ people at top companies

  2. Master AI tools, tutorials, and news in just 3 minutes a day

  3. Become 10X more productive using AI

Join 1,000,000+ pros at companies like Google, Meta, and Amazon that are using AI to get ahead.

🧠Parallel and Distributed Training: Techniques like Horovod and DeepSpeed

In deep learning, training large-scale models requires significant computational resources. Parallel and distributed training techniques enable faster and more efficient model training by leveraging multiple GPUs or even multiple machines. Two popular frameworks for distributed training are Horovod and DeepSpeed.

Parallel vs. Distributed Training

  • Parallel Training: Uses multiple GPUs on a single machine to speed up computation by splitting workloads.

  • Distributed Training: Extends parallelism across multiple machines, allowing scalability for massive datasets and deep networks.

Parallelism can be further categorized into:

  1. Data Parallelism: Each worker (GPU or machine) gets a subset of the data and computes gradients independently before averaging updates.

  2. Model Parallelism: Different parts of the model are distributed across multiple devices, useful for extremely large models.

  3. Pipeline Parallelism: Layers of the model are divided into stages processed sequentially across different devices.

Horovod

Horovod, developed by Uber, is a distributed deep learning framework that simplifies multi-GPU and multi-node training. It uses:

  • Ring-AllReduce: Efficient communication for gradient averaging across devices.

  • Framework-Agnostic Integration: Supports TensorFlow, PyTorch, and MXNet.

  • Scalability: Easily extends training from a single GPU to multiple nodes.

DeepSpeed

DeepSpeed, developed by Microsoft, optimizes large-scale training with features like:

  • Zero Redundancy Optimizer (ZeRO): Reduces memory consumption by distributing optimizer states, gradients, and parameters across devices.

  • Efficient Mixed Precision Training: Uses lower-precision computations for speed without losing accuracy.

  • Memory Optimization: Enables training models with hundreds of billions of parameters.

Conclusion

Both Horovod and DeepSpeed are powerful tools for parallel and distributed deep learning. Horovod is excellent for ease of use and broad framework compatibility, while DeepSpeed is ideal for training extremely large models with memory optimizations.

Top 5 AI for Software Development and Code Generation

1. GitHub Copilot

GitHub Copilot, powered by OpenAI’s Codex, is an AI-driven coding assistant that helps developers write code faster by providing real-time suggestions and autocompletions.

Features:

  • Autocompletes functions and entire code blocks.

  • Supports multiple programming languages.

  • Learns from context to suggest relevant code.

Use Cases:

  • Writing boilerplate code quickly.

  • Enhancing productivity in software development.

  • Assisting beginners in learning coding best practices.

2. OpenAI Codex

OpenAI Codex is the underlying model behind GitHub Copilot, capable of understanding and generating human-like code in multiple programming languages.

Features:

  • Converts natural language instructions into working code.

  • Supports various languages, including Python, JavaScript, and C++.

  • Can debug and optimize existing code.

Use Cases:

  • Automating repetitive coding tasks.

  • Assisting in API integration and documentation.

  • Generating functional scripts from plain-text descriptions.

3. Tabnine

Tabnine is an AI-powered code completion tool designed to enhance productivity by predicting code snippets and function structures.

Features:

  • Supports multiple IDEs like VS Code, IntelliJ, and PyCharm.

  • Offers privacy-focused on-premise deployment.

  • Learns from private repositories for personalized suggestions.

Use Cases:

  • Speeding up coding workflows.

  • Reducing syntax and logical errors.

  • Assisting teams with custom AI-powered code suggestions.

4. Amazon CodeWhisperer

Amazon CodeWhisperer is an AI-powered coding assistant developed by AWS, offering intelligent code suggestions and security recommendations.

Features:

  • Provides code recommendations based on developer input.

  • Detects security vulnerabilities in code.

  • Supports multiple programming languages.

Use Cases:

  • Accelerating cloud-based development.

  • Improving security practices in coding.

  • Enhancing team collaboration with AI-assisted development.

5. ChatGPT for Coding

ChatGPT, powered by OpenAI, assists developers by generating, debugging, and optimizing code based on user queries.

Features:

  • Generates code snippets and complete functions.

  • Explains coding concepts and algorithms.

  • Helps debug and optimize code.

Use Cases:

  • Assisting in learning new programming languages.

  • Debugging complex errors with AI-driven insights.

  • Generating test cases and documentation.

If you are interested in contributing to the newsletter, respond to this email. We are looking for contributions from you — our readers to keep the community alive and going.