标签：#scheduling

Scheduling features look simple until you build them. Google Calendar speaks its own REST API with events.insert ; Microsoft 365 wants Graph and POST /me/calendar/events ; Apple and a long tail of providers expect CalDAV. The moment your app needs to read a user's events, drop a meeting on their calendar, or check whether three people are free at 2pm, you're staring down three integrations that disagree on field names, time formats, and recurrence rules. The Nylas Calendar API gives you one interface over all of them. Connect a user's account once, get a grant_id , and read calendars, manage events, send RSVPs, and compute free/busy with the same request shape whether the backing provider is Google or Microsoft. This post walks the calendar surface from both sides: the HTTP API your backend calls, and the Nylas CLI for testing the same operations in a terminal. I work on the CLI, so the terminal snippets below are the commands I actually run when I'm poking at a calendar. Calendars, events, and the calendar_id A connected account has one or more calendars , and every event belongs to exactly one of them. Most operations take a calendar_id , and the special value primary resolves to the account's default calendar — so you don't need to look up an ID to act on the main calendar. One exception: iCloud doesn't support primary , so for iCloud accounts you pass a real calendar ID from nylas calendar list . An event carries a title , a when object holding its start and end times, a list of participants , an optional location , and flags like busy . That schema is identical across providers, which is the whole point: you read a Google event and a Microsoft event into the same struct. See the Calendar API overview for how calendars, events, and availability fit together. Before you begin You need a Nylas API key and a connected account with calendar scopes. The CLI gets you there in two commands: nylas init # create an account, generate an API key nylas auth login # connect

How GPU scheduling complexity and MLOps integration are forcing platform teams to rearchitect Kubernetes clusters before operational debt becomes insurmountable. As AI workloads consume roughly 40% of enterprise Kubernetes clusters by 2026, the platform's default scheduler is proving fundamentally mismatched with the topology-aware, gang-scheduled demands of GPU-intensive training and inference. Platform engineering teams that invest now in purpose-built GPU scheduling layers, multi-tenant partitioning, and FinOps-driven autoscaling will separate themselves from organizations drowning in 30-45% GPU utilization rates and mounting infrastructure costs. Why the Default Kubernetes Scheduler Fails GPU Workloads Kubernetes was designed for stateless, CPU-bound services, and its pod-by-pod bin-packing scheduler has no native awareness of GPU topology, NUMA boundaries, or NVLink interconnect bandwidth. This becomes a critical failure point with NVIDIA H100 SXM5 nodes, where achieving full-bandwidth tensor parallelism requires all 8 GPUs on a node to be scheduled as a single atomic unit. The default scheduler cannot guarantee this co-placement, meaning distributed PyTorch FSDP or MPI training jobs frequently land on suboptimal node configurations, wasting expensive NVLink bandwidth and forcing teams to over-provision GPU capacity. Idle GPU memory stranded across partially-utilized nodes is the primary driver behind the 30-45% utilization rates reported in 2025 surveys by Gradient Dissent and Weights and Biases, representing millions of dollars in annual wasted spend for mid-to-large enterprises running mixed AI workloads. Building the GPU Scheduling Stack: Volcano, KAI Scheduler, and MIG Platform teams are converging on a layered scheduling architecture that replaces or augments the default Kubernetes scheduler with GPU-aware primitives. Volcano has become the dominant choice for distributed training workloads, using its PodGroup abstraction to enforce gang scheduling across