import { z } from "zod"; export declare const TrainingGpuSelectionSchema: z.ZodDiscriminatedUnion<[z.ZodObject<{ mode: z.ZodLiteral<"automatic">; objective: z.ZodEnum<{ balanced: "balanced"; economical: "economical"; fast: "fast"; }>; gpuCount: z.ZodOptional, z.ZodLiteral<2>]>>; }, z.core.$strict>, z.ZodObject<{ mode: z.ZodLiteral<"manual">; availabilityId: z.ZodString; gpuType: z.ZodString; gpuCount: z.ZodOptional, z.ZodLiteral<2>]>>; }, z.core.$strict>], "mode">; /** This token binds a reviewed workload and price, without reserving resources * or approving the estimate's recommended budget. */ export declare const TrainingEstimateReviewSchema: z.ZodObject<{ estimateId: z.ZodString; inputHash: z.ZodString; requestHash: z.ZodString; pricingVersion: z.ZodString; expiresAt: z.ZodString; token: z.ZodString; }, z.core.$strict>; export type TrainingGpuSelection = z.infer; export type TrainingEstimateReview = z.infer; export declare const TrainingGpuCatalogSchema: z.ZodObject<{ schemaVersion: z.ZodLiteral<"openpond.trainingGpuCatalog.v1">; observedAt: z.ZodString; expiresAt: z.ZodString; workerImageDigest: z.ZodString; supportedLoraRanks: z.ZodArray; pricingBasis: z.ZodLiteral<"accepted_compute_and_disk_hourly_before_credits">; offerings: z.ZodArray; gpuCount: z.ZodNumber; gpuMemoryGb: z.ZodNumber; stockStatus: z.ZodEnum<{ High: "High"; Low: "Low"; Medium: "Medium"; }>; computeHourlyUsd: z.ZodNumber; diskHourlyUsd: z.ZodNumber; totalHourlyUsd: z.ZodNumber; }, z.core.$strict>>; }, z.core.$strict>; export type TrainingGpuCatalog = z.infer; //# sourceMappingURL=training-compute-selection.d.ts.map