tfd-utils 0.2.1

TensorFlow utilities for efficient TFRecord processing and random access

TFD Utils

A Python library for efficient TensorFlow TFRecord processing and random access.

Features

• Random Access to TFRecord Files: Efficiently access specific records in TFRecord files without reading the entire file
• Automatic Index Caching: Builds and caches an index on first access for fast subsequent lookups
• Multiple File Support: Handle single files, lists of files, or glob patterns
• Flexible Key Types: Support for string, integer, and float keys
• Memory Efficient: Only loads requested records into memory

Quick Start

from tfd_utils.random_access import TFRecordRandomAccess

# Initialize with a single file
reader = TFRecordRandomAccess("path/to/your/file.tfrecord")

# Or with multiple files
reader = TFRecordRandomAccess([
    "path/to/file1.tfrecord",
    "path/to/file2.tfrecord"
])

# Or with a glob pattern
reader = TFRecordRandomAccess("path/to/data_*.tfrecord")

# Get a record by key
record = reader.get_record("your_key")

# Get a specific feature from a record
image_bytes = reader.get_feature("your_key", "image")

# Check if key exists
if "your_key" in reader:
    print("Key exists!")

# Get statistics
stats = reader.get_stats()
print(f"Total records: {stats['total_records']}")

Advanced Usage

Custom Key Feature

By default, the library looks for keys in a feature named 'key'. You can specify a different feature name:

# Use 'id' feature as the key
reader = TFRecordRandomAccess("file.tfrecord", key_feature_name="id")

Custom Index File

You can specify where to save the index cache:

reader = TFRecordRandomAccess(
    "file.tfrecord",
    index_file="my_custom_index.cache"
)

Rebuilding Index

If your TFRecord files change, you can rebuild the index:

reader.rebuild_index()

License

MIT License