tfrecord-rust

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

Features

• Provide both high level Example 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, image, ndarray and tch.
• TensorBoard support.

Usage

Use this crate in your project

Append this line to your Cargo.toml.

tfrecord = "0.2.0"

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" }

Notice on TensorFlow updates

The crate compiles the pre-generated ProtocolBuffer code from TensorFlow. In case of TensorFlow updates or custom patches, please run the code generation manually, see Generate ProtocolBuffer code from TensorFlow section for details.

Available Cargo features

Module features

• full: Enable all features.
• async_: Enable async/await feature.
• dataset: Enable the dataset API that can load records from multiple TFRecord files.
• summary: Enable the summary and event types and writters, mainly for TensorBoard.

Third-party crate support features

• serde: Enable support with serde crate.
• image: Enable support with image crate.
• ndarray: Enable support with ndarray crate.
• tch: Enable support with tch crate.

Documentation

See docs.rs for the API.

Example

File reading example

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

use tfrecord::{Error, ExampleReader, Feature, RecordReaderInit};

fn main() -> Result<(), Error> {
    // use init pattern to construct the tfrecord reader
    let reader: ExampleReader<_> = RecordReaderInit::default().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 {
                Feature::BytesList(list) => {
                    println!("bytes\t{}", list.len());
                }
                Feature::FloatList(list) => {
                    println!("float\t{}", list.len());
                }
                Feature::Int64List(list) => {
                    println!("int64\t{}", list.len());
                }
                Feature::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::{Error, Feature, RecordStreamInit};

pub async fn _main() -> Result<(), Error> {
    // use init pattern to construct the tfrecord stream
    let stream = RecordStreamInit::default()
        .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 {
                        Feature::BytesList(list) => {
                            println!("bytes\t{}", list.len());
                        }
                        Feature::FloatList(list) => {
                            println!("float\t{}", list.len());
                        }
                        Feature::Int64List(list) => {
                            println!("int64\t{}", list.len());
                        }
                        Feature::None => {
                            println!("none");
                        }
                    }
                }

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

    Ok(())
}

Work with TensorBoard

This is a simplified example of examples/tensorboard.rs that sends summary data to log_dir directory. After running the example, launch tensorboard --logdir log_dir to watch the outcome in TensorBoard.

use super::*;
use rand::seq::SliceRandom;
use rand_distr::{Distribution, Normal};
use std::{f32::consts::PI, thread, time::Duration};
use tfrecord::{EventInit, EventWriterInit};

pub fn _main() -> Fallible<()> {
    // show log dir
    let prefix = "log_dir/my_prefix";

    // download image files
    println!("downloading images...");
    let images = IMAGE_URLS
        .iter()
        .cloned()
        .map(|url| {
            let bytes = reqwest::blocking::get(url)?.bytes()?;
            let image = image::load_from_memory(bytes.as_ref())?;
            Ok(image)
        })
        .collect::<Fallible<Vec<_>>>()?;

    // init writer
    let mut writer = EventWriterInit::from_prefix(prefix, None)?;
    let mut rng = rand::thread_rng();

    // loop
    for step in 0..30 {
        println!("step: {}", step);

        // scalar
        {
            let value: f32 = (step as f32 * PI / 8.0).sin();
            writer.write_scalar("scalar", EventInit::with_step(step), value)?;
        }

        // histogram
        {
            let normal = Normal::new(-20.0, 50.0).unwrap();
            let values = normal
                .sample_iter(&mut rng)
                .take(1024)
                .collect::<Vec<f32>>();
            writer.write_histogram("histogram", EventInit::with_step(step), values)?;
        }

        // image
        {
            let image = images.choose(&mut rng).unwrap();
            writer.write_image("image", EventInit::with_step(step), image)?;
        }

        thread::sleep(Duration::from_millis(100));
    }

    Ok(())
}

More examples

You can visit the examples and tests directories to see more verbose examples.

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.