tfrecord-rust

The crate provides the functionality to serialize and deserialize TFRecord data format from TensorFlow.

Features

Provide both high level EasyExample type as well as low level Vec<u8> bytes {,de}serialization.
Support async/await syntax. It's easy to work with futures-rs.
Interoperability with serde.

Usage

Append this line to your Cargo.toml.

tfrecord = "0.1.2"

Due to a bug in async-std 1.6.0, it cannot read file properly sometimes. Please add the patch at the end of Cargo.toml unless the issue is fixed in future release.

[patch.crates-io]
async-std = { git = "https://github.com/async-rs/async-std", branch = "master" }

The crate provides several cargo features that you can conditionally compile modules.

serde: Enable interoperability with serde to serialize and deserialize example types.
async_: Enable async/await feature.
dataset: Enable the dataset API that can load records from multiple TFRecord files.
full: Enable all features above.

By default, the crate compiles the pre-built ProtocolBuffer code in the repository. If you would like to re-run the code generation, see Generate ProtocolBuffer code from TensorFlow section.

Documentation

See docs.rs for the API.

Example

File reading example

This is a snipplet copied from examples/tfrecord_info.rs.

use tfrecord::{EasyExampleReader, EasyFeature, Error, RecordReaderInit};

fn main() -> Result<(), Error> {
    // use init pattern to construct the tfrecord reader
    let reader: EasyExampleReader<_> = RecordReaderInit {
        check_integrity: true,
    }
    .open(&*INPUT_TFRECORD_PATH)?;

    // print header
    println!("example_no\tfeature_no\tname\ttype\tsize");

    // enumerate examples
    for (example_index, result) in reader.enumerate() {
        let example = result?;

        // enumerate features in an example
        for (feature_index, (name, feature)) in example.into_iter().enumerate() {
            print!("{}\t{}\t{}\t", example_index, feature_index, name);

            match feature {
                EasyFeature::BytesList(list) => {
                    println!("bytes\t{}", list.len());
                }
                EasyFeature::FloatList(list) => {
                    println!("float\t{}", list.len());
                }
                EasyFeature::Int64List(list) => {
                    println!("int64\t{}", list.len());
                }
                EasyFeature::None => {
                    println!("none");
                }
            }
        }
    }

    Ok(())
}

Work with async/await syntax

The snipplet from examples/tfrecord_info_async.rs demonstrates the integration with async-std.

use futures::stream::TryStreamExt;
use std::{fs::File, io::BufWriter, path::PathBuf};
use tfrecord::{EasyFeature, Error, RecordStreamInit};

#[async_std::main]
async fn main() -> Result<(), Error> {
    // use init pattern to construct the tfrecord stream
    let stream = RecordStreamInit {
        check_integrity: true,
    }
    .easy_examples_open(&*INPUT_TFRECORD_PATH)
    .await?;

    // print header
    println!("example_no\tfeature_no\tname\ttype\tsize");

    // enumerate examples
    stream
        .try_fold(0, |example_index, example| {
            async move {
                // enumerate features in an example
                for (feature_index, (name, feature)) in example.into_iter().enumerate() {
                    print!("{}\t{}\t{}\t", example_index, feature_index, name);

                    match feature {
                        EasyFeature::BytesList(list) => {
                            println!("bytes\t{}", list.len());
                        }
                        EasyFeature::FloatList(list) => {
                            println!("float\t{}", list.len());
                        }
                        EasyFeature::Int64List(list) => {
                            println!("int64\t{}", list.len());
                        }
                        EasyFeature::None => {
                            println!("none");
                        }
                    }
                }

                Ok(example_index + 1)
            }
        })
        .await?;

    Ok(())
}

More examples

Also, we suggest visiting the test code for more detailed usage.

Generate ProtocolBuffer code from TensorFlow

The crate relies on ProtocolBuffer documents from TensorFlow. The crate ships pre-generated code from ProtocolBuffer documents by default. Most users don't need to bother with the code generation. The step is needed only in case of TensorFlow updates or your custom patch.

The build script accepts several ways to access the TensorFlow source code, controlled by the TFRECORD_BUILD_METHOD environment variable. The generated code will be placed under prebuild_src directory. See the examples below to understand the usage.

Build from a source tarball

export TFRECORD_BUILD_METHOD="src_file:///home/myname/tensorflow-2.2.0.tar.gz"
cargo build --release --features serde,generate_protobuf_src  # with serde
cargo build --release --features generate_protobuf_src        # without serde

Build from a source directory

export TFRECORD_BUILD_METHOD="src_dir:///home/myname/tensorflow-2.2.0"
cargo build --release --features serde,generate_protobuf_src  # with serde
cargo build --release --features generate_protobuf_src        # without serde

Build from a URL

export TFRECORD_BUILD_METHOD="url://https://github.com/tensorflow/tensorflow/archive/v2.2.0.tar.gz"
cargo build --release --features serde,generate_protobuf_src  # with serde
cargo build --release --features generate_protobuf_src        # without serde

Build from installed TensorFlow on system. The build script will search ${install_prefix}/include/tensorflow directory for protobuf documents.

export TFRECORD_BUILD_METHOD="install_prefix:///usr"
cargo build --release --features serde,generate_protobuf_src  # with serde
cargo build --release --features generate_protobuf_src        # without serde

License

MIT license. See LICENSE file for full license.

tfrecord 0.1.2