Compute Fabric

Coordinating global GPU power to handle AI tasks at scale.

Compute Fabric within the Skyops ecosystem provides a decentralized, efficient and scalable framework for running AI workloads. This infrastructure integrates computational resources from across the globe to deliver consistent performance for tasks such as model training, inference and fine-tuning.

⬣ Geo-Distributed Resource Management

The Computing Network aggregates computational power from a variety of contributors, enabling decentralized resource sharing and maximizing efficiency.

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Example Code for Node Registration:

# Install Skyops CLI
pip install skyops-cli

# Register a GPU Worker Node
skyops register-node --gpu RTX3060 --ram 12 --bandwidth 100

# Check node status
skyops node-status --id NODE12345

Dynamic Resource Allocation Process:

⬡ A user requests computational resources to train a machine learning model.

⬡ The system evaluates available nodes based on their specifications and workload.

⬡ Nodes are dynamically assigned to tasks, ensuring optimal resource use.

⬣ Adaptive Scheduling and Workload Distribution

The Skyops scheduler optimizes workload distribution across the network by leveraging multiple parallelism techniques:

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Example Code for Scheduling a Task:

# Submit a model training task
skyops submit-task --model resnet50 --dataset images.zip --nodes 5

# Monitor the task's progress
skyops task-status --task-id TASK67890

Task Scheduling Diagram:

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⬣ Decentralized Fault Tolerance

The system is designed to handle disruptions effectively, ensuring smooth task execution.

Fault Tolerance Features:

⬡ Heartbeat Mechanism: Regular pings are sent between nodes to monitor activity.

⬡ Automated Reallocation: Tasks are reassigned to other nodes in case of failure.

⬡ Redundancy: Critical tasks are mirrored across multiple nodes.

Fault Handling Code Example:

from skyops import FaultHandler

# Initialize fault monitoring
handler = FaultHandler()

# Detect and reassign task on node failure
if not handler.node_heartbeat(node_id="NODE12345"):
    handler.reassign_task(task_id="TASK67890", new_node="NODE56789")

⬣ Node Categories

GPU Workers

Nodes contributing GPU resources for computation.

⬡ Example Setup: A user with an RTX 4090 GPU connects to the network and contributes compute power for high-resolution image generation tasks.

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⬣ Broker Nodes

Nodes responsible for task management and optimization.

⬡ Example: A Broker Node evaluates and assigns a video processing task to the nearest available GPUs to minimize latency.

⬣ Performance Validation

To maintain computational accuracy, Skyops employs a multi-layered validation mechanism.

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⬣ Code Example for Validation:

from skyops import Validator

# Perform checksum validation
validator = Validator()
if not validator.validate_checksum(task_id="TASK123", checksum="abc123"):
    print("Task validation failed. Retrying...")

# Schedule random audit
validator.schedule_audit(task_id="TASK678")

⬣ Benefits of the Computing Network

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Compute Fabric is the central pillar of Skyops ecosystem. Through efficient task scheduling, robust fault handling and comprehensive validation, it ensures that computational resources are optimally utilized for AI tasks, benefiting contributors and users alike.