Principle:Volcengine Verl Parquet Export

Knowledge Sources	Apache Parquet Documentation HuggingFace Datasets Documentation
Domains	Data_Engineering, Storage, NLP
Last Updated	2026-02-07 14:00 GMT

Overview

The final step of data preprocessing that serializes transformed datasets to Apache Parquet format for efficient loading during training.

Description

Parquet Export converts the processed HuggingFace Dataset objects into Apache Parquet files. Parquet is chosen for:

• Columnar storage: Efficient for reading specific columns during training
• Compression: Reduces storage requirements
• Schema preservation: Maintains complex nested types (lists of dicts, images)
• Broad compatibility: Loadable by pandas, Arrow, and HuggingFace datasets

The export produces separate files for each data split (train, test) and optionally copies them to HDFS for distributed access.

Usage

Use parquet export as the final step in any data preprocessing pipeline. Output files are consumed by verl's dataset classes (RLHFDataset, SFTDataset).

Theoretical Basis

Parquet export uses HuggingFace's built-in serialization:

# Abstract parquet export
processed_dataset = raw_dataset.map(transform_fn)
for split in ["train", "test"]:
    output_path = os.path.join(save_dir, f"{split}.parquet")
    processed_dataset[split].to_parquet(output_path)
    # Optional: copy to HDFS
    if hdfs_dir:
        copy_to_hdfs(output_path, hdfs_dir)

Related Pages

Implemented By

• Implementation:Volcengine_Verl_Dataset_To_Parquet