inference-logging-client 0.1.0

Decode MPLog feature logs from proto, arrow, or parquet format

Inference Logging Client

A Python package for decoding MPLog feature logs from proto, arrow, or parquet format.

Features

• Decode MPLog feature logs from multiple encoding formats:
  • Proto: Custom binary encoding with generated flag + sequential features
  • Arrow: Arrow IPC format with binary columns
  • Parquet: Parquet format with feature map
• Automatic format detection from metadata
• Support for zstd compression
• Fetch feature schemas from inference API
• Convert decoded logs to pandas DataFrames
• Command-line interface for easy usage

Installation

pip install inference-logging-client

Quick Start

Python API

import inference_logging_client

# Decode MPLog from bytes
with open("log.bin", "rb") as f:
    data = f.read()

df = inference_logging_client.decode_mplog(
    log_data=data,
    model_proxy_id="my-model",
    version=1
)

print(df.head())

Decode from DataFrame

import pandas as pd
import inference_logging_client

# Read DataFrame with MPLog columns
df = pd.read_parquet("logs.parquet")

# Decode features
decoded_df = inference_logging_client.decode_mplog_dataframe(df)

print(decoded_df.head())

Command Line Interface

# Decode with auto-detection
inference-logging-client --model-proxy-id my-model --version 1 input.bin

# Specify format explicitly
inference-logging-client --model-proxy-id my-model --version 1 --format proto input.bin

# Output to CSV
inference-logging-client --model-proxy-id my-model --version 1 input.bin -o output.csv

# Decode from stdin (base64)
echo "BASE64_DATA" | inference-logging-client --model-proxy-id my-model --version 1 --base64 -

Configuration

The package uses environment variables for configuration:

• INFERENCE_HOST: Inference service host URL (default: http://localhost:8082)
• INFERENCE_PATH: API path for schema fetching (default: /api/v1/inference/mp-config-registry/get_feature_schema)

API Reference

decode_mplog()

Decode MPLog bytes to a pandas DataFrame.

Parameters:

• log_data (bytes): The MPLog bytes (possibly compressed)
• model_proxy_id (str): The model proxy config ID
• version (int): The schema version
• format_type (Format, optional): The encoding format. If None, auto-detect from metadata.
• inference_host (str, optional): The inference service host URL
• decompress (bool): Whether to attempt zstd decompression (default: True)
• schema (list, optional): Pre-fetched schema (list of FeatureInfo). If provided, skips schema fetch.

Returns:

• pd.DataFrame: DataFrame with entity_id as first column and features as remaining columns

decode_mplog_dataframe()

Decode MPLog features from a DataFrame with specific column structure.

Parameters:

• df (pd.DataFrame): Input DataFrame with MPLog data columns
• inference_host (str, optional): The inference service host URL
• decompress (bool): Whether to attempt zstd decompression (default: True)
• features_column (str): Name of the column containing encoded features (default: "features")
• metadata_column (str): Name of the column containing metadata byte (default: "metadata")
• mp_config_id_column (str): Name of the column containing model proxy config ID (default: "mp_config_id")

Returns:

• pd.DataFrame: DataFrame with decoded features, one row per entity

get_mplog_metadata()

Extract metadata from MPLog bytes without full decoding.

Parameters:

• log_data (bytes): The MPLog bytes (possibly compressed)
• decompress (bool): Whether to attempt zstd decompression (default: True)

Returns:

• DecodedMPLog: Object with metadata fields populated

Supported Feature Types

Scalar Types

• Integer: INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64
• Float: FP16, FP32, FP64, FP8E5M2, FP8E4M3
• Boolean: BOOL
• String: STRING

Vector Types

• All scalar types can be vectors (e.g., FP32VECTOR, INT64VECTOR)
• Vectors can be binary-encoded or JSON-encoded

Encoding Formats

Proto Format

• First byte: generated flag
• Scalars: fixed size bytes based on type
• Strings/Vectors: 2-byte little-endian size prefix + data bytes

Arrow Format

• Arrow IPC format with binary columns
• Column names are feature indices ("0", "1", ...)
• Each column contains raw feature value bytes

Parquet Format

• Parquet file with Features column (map[int][]byte)
• Each row represents an entity

Metadata Byte Layout

The metadata byte encodes:

• Bit 0: compression flag (0=disabled, 1=enabled)
• Bit 1: reserved
• Bits 2-5: version (4 bits, 0-15)
• Bits 6-7: format type (00=proto, 01=arrow, 10=parquet)

Development

# Install in development mode
pip install -e .

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black src/

# Lint code
ruff check src/

License

MIT License

Repository

https://github.com/Meesho/BharatMLStack

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.