federer/dist/src/coordinator/preprocess/shard.d.ts

Experiments in asynchronous federated learning and decentralized learning

import { ShardFn } from "./PreprocessPipeline";
/**
 * Split a dataset into shards, where each shard holds a different label.
 *
 * @param numShards Number of shards to split a dataset into
 * @returns A function that takes a {@link DataSubset}, and returns a list of
 * shards, where each shard holds a different label.
 */
export declare function shardByLabel(numShards: number): ShardFn;
/**
 * Splits a dataset into shards, by sorting the dataset by label, splitting into
 * `numberBatches` batches, and then combining `numberBatchesPerShard` batches
 * per shard.
 *
 * @param numberLabelClasses Number of different labels in the dataset
 * @param numberBatches Number of batches to produce
 * @param numberBatchesPerShard Number of batches to place in a shard
 * @returns A function that takes a {@link DataSubset}, and returns a list of
 * shards, where each shard is as described above.
 */
export declare function shardIntoSortedLabelBatches(numberLabelClasses: number, numberBatches: number, numberBatchesPerShard: number): ShardFn;
export declare function shardIntoSkedwedBatches(numberLabelClasses: number, numberShards: number, skewFactorS: number): ShardFn;
/**
 * Combines sortedLables and skewed functions to create unbalanced
 * shards with lowerbound on samples per shard.
 *
 * @returns a shard function
 */
export declare function shardIntoUnbalancedBatchesPerBatch(sortedLabelBatches: ShardFn, skewedBatches: ShardFn, sortedLabelSplit: number): ShardFn;
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