We have an existing implementation of a general type system encoding the graph structure of a given DSL (satisfying certain type restrictions) and also an existing PyTorch-implementation of the proposed learning architecture. Next we need to use this to develop a more robust implementation and data pipeline for a fully parallellized training run across GPU clusters. This includes thorough testing of the type system and DSL parsing mechanisms to ensure robust and efficient performance across general DSLs adhering to required type restrictions. The model architecture will be implemented using PyTorch for the model components later used in the distributed training run while developing custom vector database indexing structures adhering to the requirements of the mathematical sheaf structure and vector-representations of index groups. For this we plan to build custom PyTorch modules on top of the Faiss vector database framework ensuring an efficient and consistent C++ framework for our implementation.

The implementation will provide a production-ready codebase encompassing the complete HSN architecture and its data inference environment. This includes fully documented PyTorch modules implementing the core neural network components custom C++ extensions integrating with the Faiss framework for efficient vector operations and a comprehensive type system implementation for DSL parsing and validation. The deliverable will include automated data pipeline configurations for distributed training complete with efficient data loading mechanisms and custom collation functions optimized for GPU processing. The codebase will be accompanied by extensive technical documentation covering the architecture design implementation details and deployment procedures. This documentation will include detailed API specifications system architecture diagrams and comprehensive guides for extending the type system with new DSL features. Additionally we will provide benchmark results demonstrating the system's performance characteristics across various operational scenarios and data scales.

$10,000 USD

The implementation must satisfy rigorous technical and performance requirements to be considered successful. All components must pass a comprehensive suite of unit tests with at least 95% code coverage, including specific tests for type system validation, DSL parsing efficiency, and neural network operations. The system must demonstrate stable performance in distributed training scenarios, maintaining consistent throughput across multiple GPU nodes with scaling efficiency above 80%. Performance benchmarks must show that the custom vector database indexing structures achieve query times within 150% of baseline Faiss performance while maintaining the required mathematical properties. The type system implementation must successfully validate and process DSL specifications with parse times under 100ms for typical cases and maintain linear scaling with DSL complexity. The data pipeline must sustain a minimum throughput of 10,000 samples per second per GPU when operating in a distributed environment. Integration tests must verify seamless interaction between all system components, including proper handling of edge cases in the type system, correct propagation of gradients through custom PyTorch modules, and efficient data flow through the vector database infrastructure. The system must demonstrate stable convergence behavior across multiple training runs with different random seeds, maintaining consistent performance metrics within a 5% variance.

The HSN learning system is deployed in a self-reinforcing learning environment utilising a network of publicly available APIs across a variety of signals and input/output- data types. Extensive transfer-learning is performed from domain-specialised pre-trained FN-models across multiple domains related to the variety of input/outputs. These APIs and corresponding pre-trained FN-models include without limitations large corpora of symbolic datasets and language datasets (X) SOTA LLMs computer vision datasets and audio datasets in particular relating to human emotional intent-driven input-output mappings such as e.g. audio-visual data relating to musing and dance large physically grounded datasets in particular extensively publicly available weather data (see provided weather sheaf POC!) as well as datasets from chemistry and microphysics in order to cover data-mappings across multiple levels of physical magnitudes.

A developed reward model for the HSN learning system which balances R1: supervised performance in particular input-output example tasks across the varying I/O-types with R2: model-internal reward signals using various information-theoretically grounded measures (generalisations of evidence lower bound using total correlation across I/O-signals) encoding model perplexity and curiosity towards information-rich regions of the input data manifold with respect to the input/output data types and tasks.

$15,000 USD

Observable improvements in performance metrics across supervised sample-tasks weighted with metrics assessing explorative qualities in the models internal reward system with respect to the input data manifold.

Execute full scale training run on distributed GPU cluster via AWS sage maker with online training data pipelines connect to a variety of APIs. This milestone focuses on implementing distributed training for our deep learning model using AWS SageMaker's distributed computing capabilities. The implementation will leverage SageMaker's data parallelism library to partition training data across multiple GPU instances while maintaining model synchronization through AllReduce operations. Our configuration will utilize ml.p4d.24xlarge instances with 8 NVIDIA A100 GPUs each orchestrated in a cluster architecture to maximize throughput and minimize communication overhead. The scope includes adapting the PyTorch training script for SageMaker's distributed package implementing gradient compression for bandwidth optimization and establishing robust checkpointing mechanisms. We will fine-tune distributed training parameters including gradient accumulation steps local batch sizes and learning rate scaling to ensure consistent convergence. The implementation will include comprehensive monitoring through SageMaker metrics and custom CloudWatch dashboards to track training efficiency and resource utilization.

The milestone will deliver a production-ready distributed training infrastructure with the following components: A fully configured distributed training pipeline integrated with AWS SageMaker complete with automated node initialization and fault tolerance mechanisms. Comprehensive technical documentation detailing the distributed architecture including node configuration communication patterns and deployment procedures. A suite of monitoring tools and dashboards for real-time tracking of training metrics GPU utilization and inter-node communication performance. Custom utilities for managing distributed checkpointing and model synchronization will be provided along with scripts for automated deployment and scaling of training clusters. Performance analysis reports will document scaling efficiency resource utilization and cost optimization strategies. The codebase will include unit tests and integration tests specific to distributed operations ensuring reliability and reproducibility of training runs.

$30,000 USD

The implementation will be considered successful upon meeting the following quantitative and qualitative metrics: Achievement of linear scaling efficiency of at least 75% when scaling from 1 to 8 nodes, demonstrated through comprehensive benchmarking tests. Maintenance of model convergence metrics within 1% deviation compared to single-node training results, verified through multiple training runs. The system must demonstrate fault tolerance by successfully recovering from simulated node failures without data loss. Training throughput should show at least 6x improvement when scaling from single-node to 8-node configuration. Resource utilization metrics must indicate GPU utilization above 85% during training. The implementation must maintain consistent convergence behavior across different cluster sizes, verified through loss curve analysis and final model performance metrics. Network bandwidth utilization should remain within 80% of theoretical limits during all-reduce operations, and gradient compression must achieve at least a 3x reduction in communication overhead without impacting model accuracy.

Deploy the pre-trained system to the continual learning inference environment and integration with the SNET platform on the Ethereum blockchain. This includes smart contract integrations allowing developers to develop custom learning tasks/criterions for the AI-system following a blue print template for developing custom DSL functions and custom n-ary criterion vectors in order to apply the system on particular tasks while improving it on all tasks through the continual learning process. A MVP will be developed and deployed allowing any users of the SNET platform to access model inference by transacting AGIX tokens to the smart contract and the earned tokens will be distributed via smart contracts across contributing developers in proportion to measurable improvements in model performance due to optimizing on tasks and data pipelines they created.

A production-ready system deployed in the inference environment integrated with the SNET platform encompassing a suite of smart contracts for token management and reward distribution. The system will include an automated testing framework validating all blockchain interactions token transactions and reward distributions. API documentation will detail the integration points between the AI system and blockchain components including interfaces for custom task development and performance tracking. The deployment package will contain Docker containers for both the inference environment and blockchain nodes ensuring consistent deployment across environments. Developer documentation will provide detailed guidelines for creating custom DSL functions and criterion vectors complete with example implementations and best practices. The system will include monitoring dashboards tracking model performance token transactions and developer contributions with automated reporting mechanisms for transparency in reward distribution.

$15,000 USD

The deployment will be considered successful upon meeting specific technical and operational benchmarks. The system must demonstrate seamless integration between the AI inference environment and the Ethereum blockchain, with transaction processing times under 30 seconds for model inference requests. Smart contract execution costs must remain below 0.05 ETH per transaction under normal network conditions. The reward distribution mechanism must accurately track and attribute performance improvements to individual developers with 99.9% accuracy. System availability should maintain 99.9% uptime, with automated failover mechanisms for both inference and blockchain components. The platform must successfully process at least 1000 concurrent inference requests while maintaining response times under 2 seconds. All smart contracts must pass comprehensive security audits and demonstrate resistance to common attack vectors. The token management system must accurately track and distribute rewards with zero discrepancies in token accounting. Developer onboarding metrics should show that new contributors can successfully deploy custom tasks within 48 hours of following the documentation. The system should demonstrate the ability to track and validate performance improvements across at least 50 concurrent custom tasks while maintaining accurate reward attribution.