← Together AI Interview Insights
This was the core question and it took the whole session.
Start by clarifying requirements and scale, then design core classes (Node, Pod, Scheduler, ClusterManager) with clear responsibilities and efficient data structures for GPU tracking. Implement scheduling logic that finds a node with sufficient free GPUs, and expose APIs for adding/removing nodes and pods, querying utilization, and listing resources.
Pro tip: Mention that GPU allocation must be atomic and thread-safe to avoid race conditions in concurrent scheduling, and discuss how to handle fragmentation by potentially bin-packing or spreading pods based on policy.
Ask about expected number of nodes/pods, concurrency needs, and whether scheduling should be first-fit, best-fit, or policy-driven. Confirm if GPU types or other constraints matter.
Define Node (total GPUs, used GPUs, list of pods), Pod (GPU requirement, assigned node), Scheduler (scheduling algorithm), and ClusterManager (collections of nodes and pods). Use efficient structures like a free-GPU index or priority queue for fast lookup.
For a pod, iterate over nodes to find one with free GPUs >= requirement. Consider first-fit for simplicity or best-fit to reduce fragmentation. Ensure atomic allocation to prevent overcommitment.
Provide methods: addNode, removeNode, addPod, removePod, schedulePod, getUtilization, listNodes, listPods. Use locks or concurrent data structures to make operations thread-safe.
Talk about handling node failures, pod preemption, GPU sharing, and scaling to multiple schedulers. Mention monitoring and metrics for utilization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The index update part is where I felt shaky.
Start by framing the placement strategy as a multi-objective optimization problem balancing resource utilization, fault tolerance, and performance. Then, describe a concrete strategy (e.g., bin packing with spreading constraints) and justify it with trade-offs. Finally, walk through the index update mechanisms for each operation, emphasizing consistency and efficiency.
Pro tip: Mention that you would use a combination of hard constraints (e.g., anti-affinity) and soft preferences (e.g., least-requested) to handle diverse workloads, and highlight how you would measure and iterate on the strategy using metrics like scheduling latency and cluster utilization.
Clarify the goals: maximize resource utilization, ensure high availability, minimize latency, and respect constraints like affinity/anti-affinity, taints/tolerations, and topology spread. Consider workload types (e.g., latency-sensitive vs. batch).
Choose a strategy such as bin packing (e.g., MostAllocated) for high utilization or spreading (e.g., LeastAllocated) for fault tolerance. Justify based on workload characteristics and trade-offs between efficiency and resilience.
Describe how indexes (e.g., node resource availability, pod-to-node mapping) are updated when pods are added or removed. Emphasize atomic updates and consistency, possibly using a centralized scheduler with a cache.
Detail how scheduling a pod updates indexes (e.g., decrement available resources, update affinity maps) and how eviction triggers re-scheduling and index rollback. Mention handling of preemption and graceful termination.
Address how to maintain index consistency across concurrent operations, possibly using optimistic concurrency or locking. Discuss scaling the scheduler horizontally and partitioning the cluster if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about per-node locks versus a global scheduler lock and the tradeoffs.
Start by clarifying the system context and requirements, then describe a layered approach using optimistic concurrency control, idempotent operations, and robust failure handling. Emphasize trade-offs and how you would validate the design under concurrent scenarios.
Pro tip: Mention that you would use Kubernetes' built-in mechanisms like resource versions and finalizers, but also discuss how you'd handle edge cases such as partial failures and retries with exponential backoff.
Ask questions to understand the scale, latency requirements, and consistency guarantees needed. Identify if the system is Kubernetes-based or custom.
Use optimistic concurrency (e.g., resource versions) to detect conflicts, and implement a queue or lock manager for serializing critical sections if needed.
Make all operations idempotent by using unique request IDs, deduplication caches, and designing APIs that can be safely retried.
Use retries with exponential backoff, circuit breakers, and dead-letter queues. Ensure operations are atomic or have compensating transactions.
Describe how you would test concurrency with stress tests and monitor for conflicts, failures, and performance bottlenecks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pseudocode was fine, I wrote it on the whiteboard pretty quickly.
First, briefly state your chosen placement strategy (e.g., best-fit, first-fit, or a custom heuristic) and justify it for Together AI's workload. Then, write clear pseudocode for schedule_pod, ensuring each API call is annotated with its time and space complexity. Finally, discuss trade-offs and potential optimizations.
Pro tip: Demonstrate awareness of real-world constraints by mentioning how your strategy handles dynamic pod arrivals and failures, and how it scales with cluster size. Also, explicitly state assumptions about data structures (e.g., heaps, balanced trees) that enable efficient operations.
Clearly name your strategy (e.g., best-fit decreasing, least-requested, or spread) and explain why it suits Together AI's needs (e.g., minimizing fragmentation, balancing load, or reducing latency).
Specify the data structures used (e.g., priority queues, maps) and any assumptions about the cluster state (e.g., number of nodes, pod resource requirements).
Provide clear, language-agnostic pseudocode for the function, including input parameters (e.g., pod, node list) and output (e.g., assigned node or failure).
For each API call within schedule_pod, state its time and space complexity in terms of relevant variables (e.g., n = number of nodes, m = number of pods).
Mention potential improvements (e.g., caching, incremental updates) and how the strategy performs under different workloads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The fragmentation one is genuinely interesting because GPU scheduling doesn't have the same physical contiguity constraint as memory paging, so I pushed back a little and said fragmentation here is more about count than layout.
Start by acknowledging that these are classic scheduling challenges in GPU clusters and that there's no single perfect solution—only trade-offs. Then walk through a layered strategy: first, handle the multi-node GPU requirement via distributed scheduling and gang scheduling; second, address fragmentation with bin-packing, defragmentation, and preemption. Finally, tie it back to Together AI's scale and the need for a balance between utilization and fairness.
Pro tip: Mention that you'd measure the impact of each strategy with metrics like scheduling latency, GPU utilization, and job success rate—showing you think in terms of continuous improvement, not just one-off fixes.
Restate the two edge cases to ensure understanding, and note that they require different but related solutions. Ask about workload characteristics (e.g., typical GPU counts per pod, job durations) if not provided.
Explain that pods needing more GPUs than a single node has must be scheduled across multiple nodes using gang scheduling to ensure all-or-nothing placement. Mention technologies like Kubernetes with device plugins, Volcano, or custom schedulers that support co-scheduling.
Describe using bin-packing algorithms to place pods efficiently and reduce fragmentation. For existing fragmentation, propose defragmentation strategies like descheduling and rescheduling small pods, or using preemption with priorities to free up contiguous capacity.
Discuss trade-offs: aggressive bin-packing may hurt fault tolerance or increase latency; preemption can disrupt lower-priority jobs. Suggest safeguards like quotas, priorities, and graceful eviction to maintain fairness and stability.
Emphasize the need for observability: track GPU utilization, fragmentation metrics, scheduling delays, and job failures. Use this data to tune policies and potentially adopt dynamic strategies like topology-aware scheduling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.