Design a hybrid-cloud GPU allocation service
Design a Hybrid-Cloud GPU Allocation Service
Design a service responsible for allocating and managing GPU resources specifically for Machine Learning (ML) training jobs within a hybrid cloud environment. This environment comprises both on-premise GPU infrastructure and public cloud GPU instances.
Core Requirements
- Hybrid Cloud Scheduling: The service must intelligently decide where to execute jobs (on-premise vs. public cloud) based on real-time GPU availability and predefined organizational policies.
- User Job Submission: Users should be able to submit ML training jobs by providing:
- The training program (as code or a container image).
- The training data.
- Detailed resource requirements (e.g., specific GPU type, GPU count, CPU, RAM).
- Optional constraints such as preferred region, cost limits, or job priority.
- Long-Running Job Monitoring: The service needs to track and report the status of long-running training jobs, including states like queued, running, completed, failed, or canceled. It should also provide access to job logs and metrics, and implement basic retry semantics for transient failures.
Non-Functional Requirements
Candidates are expected to discuss and make reasonable assumptions regarding these non-functional aspects:
- Multi-tenancy: Ensure fair resource allocation and enforce quotas across multiple users or teams.
- Reliability: Implement robust failure handling mechanisms for scenarios such as compute node failures or public cloud API outages.
- Security: Guarantee secure isolation for user code and data, incorporating appropriate Identity and Access Management (IAM) principles.
- Scalability: The system must be designed to handle a large number of concurrent users and training jobs efficiently.
Deliverables
The candidate should present:
- A high-level architecture outlining the main components of the service.
- Definitions for key APIs (e.g., submit job, get status, list jobs, cancel job).
- A detailed scheduling strategy and the underlying data model.
- A plan for how training data and code are stored, accessed, and made available to the compute instances.
- Considerations for observability (monitoring, logging, alerting) and general operational concerns.
AI interview prep
Core dilemma
Balancing the dynamic, cost-optimized allocation of specialized, scarce GPU resources across disparate on-prem and cloud environments, while ensuring job reliability, data security, and multi-tenant fairness under varying loads and policies.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a hybrid-cloud GPU allocation service
Design a Hybrid-Cloud GPU Allocation Service
Design a service responsible for allocating and managing GPU resources specifically for Machine Learning (ML) training jobs within a hybrid cloud environment. This environment comprises both on-premise GPU infrastructure and public cloud GPU instances.
Core Requirements
- Hybrid Cloud Scheduling: The service must intelligently decide where to execute jobs (on-premise vs. public cloud) based on real-time GPU availability and predefined organizational policies.
- User Job Submission: Users should be able to submit ML training jobs by providing:
- The training program (as code or a container image).
- The training data.
- Detailed resource requirements (e.g., specific GPU type, GPU count, CPU, RAM).
- Optional constraints such as preferred region, cost limits, or job priority.
- Long-Running Job Monitoring: The service needs to track and report the status of long-running training jobs, including states like queued, running, completed, failed, or canceled. It should also provide access to job logs and metrics, and implement basic retry semantics for transient failures.
Non-Functional Requirements
Candidates are expected to discuss and make reasonable assumptions regarding these non-functional aspects:
- Multi-tenancy: Ensure fair resource allocation and enforce quotas across multiple users or teams.
- Reliability: Implement robust failure handling mechanisms for scenarios such as compute node failures or public cloud API outages.
- Security: Guarantee secure isolation for user code and data, incorporating appropriate Identity and Access Management (IAM) principles.
- Scalability: The system must be designed to handle a large number of concurrent users and training jobs efficiently.
Deliverables
The candidate should present:
- A high-level architecture outlining the main components of the service.
- Definitions for key APIs (e.g., submit job, get status, list jobs, cancel job).
- A detailed scheduling strategy and the underlying data model.
- A plan for how training data and code are stored, accessed, and made available to the compute instances.
- Considerations for observability (monitoring, logging, alerting) and general operational concerns.