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TFRecord Format (TensorFlow)

Description​

TFRecord is a binary file format used by TensorFlow for storing training data. It's designed for efficient reading of large datasets. TFRecord files contain serialized protocol buffer messages with CRC32 checksums.

File Extensions​

  • .tfrecord - TFRecord files
  • .tfrecords - TFRecord files (alias)

Implementation Details​

Reading​

The TFRecord implementation:

  • Parses TFRecord format
  • Reads length-prefixed records with CRC32 checksums
  • Handles binary record data
  • Converts records to dictionaries
  • Supports streaming for large files

Writing​

Writing support:

  • Writes length-prefixed JSON records with CRC32 checksums
  • Nested dictionaries are JSON-encoded into each record

Key Features​

  • TensorFlow format: Designed for TensorFlow
  • CRC32 checksums: Includes data integrity checks
  • Length-prefixed: Records prefixed with length
  • Nested data: Supports complex data structures
  • Streaming: Processes files record by record

Usage​

from iterable import open_iterable

# Basic reading
with open_iterable('data.tfrecord', iterableargs={
    'value_key': 'value'
}) as source:
    for record in source:
        print(record)  # Contains record data

# Writing
with open_iterable('output.tfrecord', mode='w') as dest:
    dest.write({'id': 1, 'text': 'hello'})

Parameters​

  • value_key (str): Key name for record value (default: value)

Limitations​

  1. Binary format: Not human-readable
  2. TensorFlow-specific: Designed for TensorFlow
  3. CRC32 validation: Simplified CRC32 implementation
  4. Format complexity: TFRecord format can be complex

Compression Support​

TFRecord files can be compressed with all supported codecs:

  • GZip (.tfrecord.gz)
  • BZip2 (.tfrecord.bz2)
  • LZMA (.tfrecord.xz)
  • LZ4 (.tfrecord.lz4)
  • ZIP (.tfrecord.zip)
  • Brotli (.tfrecord.br)
  • ZStandard (.tfrecord.zst)

Note: TFRecord also supports internal compression.

Use Cases​

  • Machine learning: Training data for TensorFlow
  • Data pipelines: TensorFlow data processing
  • Model training: Preparing training datasets
  • Data storage: Efficient storage of training data

Error Handling​

  • Missing dependency: optional libraries raise ImportError with an install hint (pip install 'iterabledata[<extra>]' when an extra exists).
  • Write mode: read-only formats raise WriteNotSupportedError or ValueError when opened with mode="w".
  • Bad or unsupported input: may raise ValueError, OSError, or library-specific errors.
  • See Troubleshooting for decoding, detection, and engine issues.