salad_mcp

This capability allows users to manage GPU workloads on SaladCloud by leveraging a centralized control plane that orchestrates container groups and inference endpoints. It utilizes a job queue system to handle task distribution effectively, ensuring that resources are allocated based on current CPU/GPU availability. The architecture is designed for scalability, allowing users to monitor and adjust workloads dynamically as demand fluctuates.

This capability orchestrates jobs using a queue-based architecture that prioritizes tasks based on resource availability and user-defined parameters. It employs a lightweight messaging system to communicate between job producers and consumers, ensuring that jobs are executed in an optimal order while minimizing idle resources. This design allows for high throughput and responsiveness in job execution.

This capability continuously monitors CPU and GPU resource availability to provide real-time insights into the capacity of the SaladCloud environment. It employs a polling mechanism that queries the cloud infrastructure for resource status and updates the system accordingly. This allows users to make informed decisions about scaling and resource allocation based on current usage patterns.

This capability provides tools for managing and analyzing logs generated by GPU workloads and jobs. It integrates with existing logging frameworks to collect, store, and analyze logs in a centralized manner. Users can query logs using a structured query language, enabling them to identify issues and optimize performance based on historical data.

This capability allows users to set and manage quotas for GPU and CPU resource allocation across different projects or teams. It employs a policy-based approach where administrators can define limits based on usage patterns and project requirements. The system tracks resource consumption against these quotas, providing alerts when limits are approached or exceeded.