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Registration starts

SparkTheSummer

Read about the bounty details and find code templates for Polygon here: [https://nsb.dev/polygon-bounty](https://nsb.dev/polygon-bounty).

Polygon Track

Polygon

Select this track if you're building on Ethereum to be eligible for the prizes!

Ethereum Track

ETHIndia

This track challenges participants to explore the integration of artificial intelligence (AI) technologies within the Akash Network, specifically focusing on GPU usage. As AI applications become increasingly complex and resource-intensive, the need for scalable, open source and cost-effective infrastructure is paramount. Akash Network offers a unique solution by enabling developers to deploy and scale their AI workloads on a decentralized network of GPUs.

**Track Objectives/Requirements:**

Exploration of AI Integration: Participants are encouraged to explore various ways AI technologies can be integrated with Akash Network's GPU infrastructure. 

This could include but is not limited to, machine learning model training, inference deployment, natural language processing tasks, computer vision applications, etc.

Optimization for Decentralized Environment: Develop strategies for optimizing AI workloads to leverage the decentralized nature of Akash Network. This involves considerations such as load balancing, fault tolerance, latency reduction, and efficient resource utilization across distributed GPU nodes.

Cost-Effectiveness: Design solutions that prioritize cost-effectiveness without compromising performance. Participants should explore methods for minimizing resource wastage, optimizing resource allocation, and leveraging Akash Network's competitive pricing model for GPU usage.

Scalability and Flexibility: Create solutions that can seamlessly scale with the growing demands of AI workloads. Participants should consider how their applications can dynamically adapt to fluctuations in resource availability and demand within the Akash Network ecosystem.

Open Source Licensing and Documentation: Open-source software under a permissive license (e.g., MIT, Apache) to foster collaboration, transparency, and community-driven development. Participants should provide comprehensive documentation, tutorials, and developer resources to facilitate adoption and contribution by the Akash community.
Potential Project Ideas:

Distributed Training Framework: Develop a framework for distributed training of deep learning models across multiple nodes on a single GPU provider within the Akash Network. Optimize communication overhead, synchronization mechanisms, and model parallelism to achieve faster convergence and scalability.
Edge AI Inference Platform: Build an edge AI inference platform using Akash Network's GPU infrastructure to deploy and scale AI models for real-time inference tasks. Explore techniques for minimizing inference latency, maximizing throughput, and ensuring robustness in decentralized environments.
Decentralized AI Marketplace: Create a decentralized marketplace where AI developers can deploy their pre-trained models on Akash Network's GPU infrastructure for monetization. Implement features for model discovery, deployment automation, and usage-based pricing.
Federated Learning Platform: Design a federated learning platform leveraging Akash Network's GPU resources to train machine learning models across a network of edge devices. Ensure privacy-preserving collaboration, efficient model aggregation, and incentive mechanisms for participant nodes.
Integration of LLMs with Akash Network: Participants are tasked with exploring the seamless integration of Large Language Models (LLMs), such as GPT-3, GPT-4, or similar architectures, with Akash Network's GPU infrastructure. This involves deploying pre-trained models, fine-tuning on domain-specific data, and building applications that leverage the power of LLMs for natural language understanding and generation.
Automated SDL Generation: Leveraging Akash Network's GPU capabilities, participants will develop automated tools and frameworks to streamline the generation of SDL documents, enhancing productivity and accuracy in system design and development processes.
Optimizing Akash Networks Ray Cluster Integration: Explore and enhance the integration between Akash Networks and Ray, pushing the boundaries of decentralized computing.
Integration of Flyte with Akash Network: Participants are tasked with exploring the seamless integration of Flyte, an open-source orchestrator that facilitates building production-grade data and ML pipelines with Akash Network’s GPU infrastructure.

Integration of Metaflow with Akash Network: Participants are tasked with exploring the seamless integration of Metaflow, a human-friendly library that helps scientists and engineers build and manage real-life data science projects with Akash Network’s GPU infrastructure.
Foundation AI Model Training on Akash Network: Participants will have the chance to innovate and develop novel solutions that harness the power of Akash Network for AI model training, paving the way for a more democratized and accessible AI ecosystem.