Principle:Huggingface Datasets Work Sharding
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, NLP |
| Last Updated | 2026-02-14 18:00 GMT |
Overview
Work Sharding provides the logic for distributing dataset generation work across multiple shards and workers, enabling parallel processing by splitting file lists and generation parameters into balanced workload partitions.
Description
When generating large datasets, processing all source files sequentially on a single worker can be prohibitively slow. Work Sharding addresses this by dividing the generation workload into discrete shards that can be processed independently and in parallel. The sharding system operates on the generation keyword arguments (gen_kwargs) that define the input to a dataset builder's generation function, splitting lists of files and associated parameters into subsets that each worker can process independently.
The _number_of_shards_in_gen_kwargs function analyzes the generation parameters to determine how many shards are available based on the length of the list-valued arguments. This establishes the maximum degree of parallelism: if a dataset is built from 100 source files, there are 100 potential shards. The _distribute_shards function then assigns contiguous ranges of these shards to individual workers, ensuring that each worker receives a roughly equal portion of the total workload.
The distribution algorithm handles the common case where the number of shards does not divide evenly among workers by assigning one extra shard to the first N workers (where N is the remainder of the division). This ensures balanced workload distribution without leaving any shards unprocessed. The result is a set of per-worker generation parameter dictionaries that can be passed directly to parallel worker processes, each containing only the subset of files and parameters that worker is responsible for.
Usage
Use Work Sharding when:
- You are generating a large dataset from many source files and want to parallelize the generation across multiple CPU cores or machines.
- You need to split dataset generation parameters into balanced partitions for distributed processing.
- You are implementing a custom dataset builder that supports multi-process generation via the
num_procparameter. - You want to understand how the library distributes work when
dataset.map()orload_dataset()is called with multiple workers.
Theoretical Basis
Work sharding is an application of the data parallelism pattern, where the same operation is applied independently to different subsets of the input data. The key challenge is partitioning the input so that each worker receives a balanced share of the total work. The library uses a simple but effective round-robin-remainder strategy: given S shards and W workers, each worker receives floor(S/W) shards, and the first S mod W workers each receive one additional shard.
This approach assumes that each shard represents roughly equal work, which holds when source files are of similar size. For datasets with highly variable file sizes, more sophisticated load-balancing strategies could be employed, but the simple partitioning approach provides a good default that avoids the overhead of dynamic work stealing or size-aware scheduling. The contiguous assignment of shard ranges (rather than interleaved assignment) also maximizes data locality, which can improve I/O performance when reading from sequential storage.