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Participants will explore\nthe fundamentals of distributed computing with Python, learning how to leverage\nDask for efficient task parallelism and Ray for scalable machine learning and\nreinforcement learning applications. Through hands-on exercises, attendees will\ngain practical experience in deploying these tools to handle massive datasets,\noptimize computational resources, and streamline workflows.\n\n## Overview\n\n## Key Topics\n\n1. **Introduction to Parallel Programming:**\n   - Understanding the basics of parallel computing.\n   - Advantages of parallelism in software development.\n   - Overview of parallel hardware and software architectures.\n\n2. **Dask:**\n\n\n4. **Ray:**\n\n\n## Getting Started\n\n- Prerequisites: Basic understanding of Python programming.\n- Environment Setup: Guidance on setting up a development environment.\n- Example Projects: Hands-on examples provided to practice the concepts\n  learned.\n\n## Resources\n\n- **Documentation:** Detailed explanations and examples of the concepts\n  covered.\n- **Examples:** Sample code demonstrating the use of parallel constructs in\n  real-world scenarios.\n- **Exercises:** Problems and projects to test your understanding and skills.\n\n## Contribution\n\nThis workshop is open for contributions from the community. You can suggest\nchanges, report issues, or add new content to help enhance the learning\nexperience.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsouthernmethodistuniversity%2Fdistributed_python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsouthernmethodistuniversity%2Fdistributed_python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsouthernmethodistuniversity%2Fdistributed_python/lists"}