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TensorFlow I/O

Currently we have to use python 3.8 to compile and copy .so file to miniconda library path. And swift modules compilation is still an open issue.

GitHub CI PyPI License Documentation

TensorFlow I/O is a collection of file systems and file formats that are not available in TensorFlow's built-in support. A full list of supported file systems and file formats by TensorFlow I/O can be found here.

The use of tensorflow-io is straightforward with keras. Below is an example to Get Started with TensorFlow with the data processing aspect replaced by tensorflow-io:

import tensorflow as tf
import tensorflow_io as tfio

# Read the MNIST data into the IODataset.
dataset_url = "https://storage.googleapis.com/cvdf-datasets/mnist/"
d_train = tfio.IODataset.from_mnist(
    dataset_url + "train-images-idx3-ubyte.gz",
    dataset_url + "train-labels-idx1-ubyte.gz",
)

# Shuffle the elements of the dataset.
d_train = d_train.shuffle(buffer_size=1024)

# By default image data is uint8, so convert to float32 using map().
d_train = d_train.map(lambda x, y: (tf.image.convert_image_dtype(x, tf.float32), y))

# prepare batches the data just like any other tf.data.Dataset
d_train = d_train.batch(32)

# Build the model.
model = tf.keras.models.Sequential(
    [
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(512, activation=tf.nn.relu),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(10, activation=tf.nn.softmax),
    ]
)

# Compile the model.
model.compile(
    optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
)

# Fit the model.
model.fit(d_train, epochs=5, steps_per_epoch=200)

In the above MNIST example, the URL's to access the dataset files are passed directly to the tfio.IODataset.from_mnist API call. This is due to the inherent support that tensorflow-io provides for HTTP/HTTPS file system, thus eliminating the need for downloading and saving datasets on a local directory.

NOTE: Since tensorflow-io is able to detect and uncompress the MNIST dataset automatically if needed, we can pass the URL's for the compressed files (gzip) to the API call as is.

Please check the official documentation for more detailed and interesting usages of the package.

Installation

How to build

conda create -n tf_io python=3.8
# only python 3.8 is supported with partool.par, so create a new env
conda activate tf_io
pip install --no-deps /tmp/tensorflow_pkg/tensorflow-2.9.0-cp38-cp38-macosx_10_13_x86_64.whl
sh ./configure.sh
export TF_HEADER_DIR=/Users/llv23/opt/miniconda3/lib/python3.10/site-packages/tensorflow/include
export TF_SHARED_LIBRARY_DIR=/Users/llv23/opt/miniconda3/lib/python3.10/site-packages/tensorflow
export TF_SHARED_LIBRARY_NAME=tensorflow_framework
# cp /Users/llv23/Documents/05_machine_learning/dl_gpu_mac/tensorflow-io-macOS/bazel-bin/tensorflow_io_gcs_filesystem/core/python/ops/libtensorflow_io_gcs_filesystem.so tensorflow_io_gcs_filesystem/core/python/ops
# https://stackoverflow.com/questions/40260242/how-to-set-c-standard-version-when-build-with-bazel
bazel build --verbose_failures --experimental_repo_remote_exec --cxxopt='-std=c++14' --macos_sdk_version=10.14 //tensorflow_io_gcs_filesystem/...
# comment out xcode version check in /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/external/build_bazel_rules_swift/swift/internal/xcode_swift_toolchain.bzl
# comment out /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/external/com_github_azure_azure_sdk_for_cpp/sdk/core/azure-core/inc/azure/core/http/policies/policy.hpp and /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/external/com_github_azure_azure_sdk_for_cpp/sdk/storage/azure-storage-common/inc/azure/storage/common/internal/storage_per_retry_policy.hpp for std::make_unique
bazel build --verbose_failures --experimental_repo_remote_exec --cxxopt='-std=c++14' --macos_sdk_version=10.14 //tensorflow_io/...
# refer to https://stackoverflow.com/questions/73141963/cannot-build-tensorflow-io-linking-tensorflow-io-python-ops-libtensorflow-io-g
bazel build -s --verbose_failures $BAZEL_OPTIMIZATION --experimental_repo_remote_exec --cxxopt='-std=c++14' --macos_sdk_version=10.14 //tensorflow_io/... //tensorflow_io_gcs_filesystem/...
python setup.py bdist_wheel
python setup.py bdist_wheel --project tensorflow-io-gcs-filesystem``

1, change /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/external/build_bazel_rules_swift/swift/internal/xcode_swift_toolchain.bzl

    # # Xcode 11.0 implies Swift 5.1.
    # if _is_xcode_at_least_version(xcode_config, "11.0"):
    #     requested_features.append(SWIFT_FEATURE_SUPPORTS_LIBRARY_EVOLUTION)
    #     requested_features.append(SWIFT_FEATURE_SUPPORTS_PRIVATE_DEPS)

    # # Xcode 11.4 implies Swift 5.2.
    # if _is_xcode_at_least_version(xcode_config, "11.4"):
    #     requested_features.append(SWIFT_FEATURE_ENABLE_SKIP_FUNCTION_BODIES)

    # # Xcode 12.5 implies Swift 5.4.
    # if _is_xcode_at_least_version(xcode_config, "12.5"):
    #     requested_features.append(SWIFT_FEATURE_SUPPORTS_SYSTEM_MODULE_FLAG)
    # # Xcode 12.0 implies Swift 5.3.
    # if _is_xcode_at_least_version(xcode_config, "12.0"):
    #     tool_configs[swift_action_names.PRECOMPILE_C_MODULE] = (
    #         swift_toolchain_config.driver_tool_config(
    #             driver_mode = "swiftc",
    #             env = env,
    #             execution_requirements = execution_requirements,
    #             swift_executable = swift_executable,
    #             toolchain_root = toolchain_root,
    #             use_param_file = True,
    #             worker_mode = "wrap",
    #         )
    #     )

2, change /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/external/com_github_azure_azure_sdk_for_cpp/sdk/core/azure-core/src/cryptography/md5.cpp /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/external/com_github_azure_azure_sdk_for_cpp/sdk/core/azure-core/inc/azure/core/http/policies/policy.hpp /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/external/com_github_azure_azure_sdk_for_cpp/sdk/core/azure-core/inc/azure/core/internal/http/pipeline.hpp

#include "absl/memory/memory.h" 
std::make_unique to absl::make_unique

3, issue

Use --sandbox_debug to see verbose messages from the sandbox and retain the sandbox build root for debugging
ld: illegal thread local variable reference to regular symbol __ZN9grpc_core7ExecCtx9exec_ctx_E for architecture x86_64
clang: error: linker command failed with exit code 1 (use -v to see invocation)

Solution: grpc/grpc#13856 change /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/external/com_github_grpc_grpc/include/grpc/impl/codegen/port_platform.h just change MACRO declaration for GPR_GCC_TLS to GPR_PTHREAD_TLS (#define GPR_GCC_TLS 1) -> (#define GPR_PTHREAD_TLS )

4, issue

ERROR: /Users/llv23/Documents/05_machine_learning/dl_gpu_mac/tensorflow-io-macOS/tools/build/swift/BUILD:5:14: Compiling Swift module //tools/build/swift:audio_video_swift failed: Exec failed due to IOException: xcrun failed with code 1.
This most likely indicates that SDK version [10.10] for platform [MacOSX] is unsupported for the target version of xcode.
Process exited with status 1
stdout: stderr: xcodebuild: error: SDK "macosx10.10" cannot be located.
xcodebuild: error: SDK "macosx10.10" cannot be located.
xcrun: error: unable to lookup item 'Path' in SDK 'macosx10.10'

Solution: google-ai-edge/mediapipe#130

bazel build --verbose_failures --experimental_repo_remote_exec --cxxopt='-std=c++14' --macos_sdk_version=10.14 //tensorflow_io_gcs_filesystem/...
bazel build --verbose_failures --experimental_repo_remote_exec --cxxopt='-std=c++14' --macos_sdk_version=10.14 //tensorflow_io/...

check version

(tf_io) Orlando:tensorflow-io-macOS llv23$ xcodebuild -showsdks
iOS SDKs:
	iOS 12.1                      	-sdk iphoneos12.1

iOS Simulator SDKs:
	Simulator - iOS 12.1          	-sdk iphonesimulator12.1

macOS SDKs:
	macOS 10.14                   	-sdk macosx10.14
	macOS 10.14                   	-sdk macosx10.14

tvOS SDKs:
	tvOS 12.1                     	-sdk appletvos12.1

tvOS Simulator SDKs:
	Simulator - tvOS 12.1         	-sdk appletvsimulator12.1

watchOS SDKs:
	watchOS 5.1                   	-sdk watchos5.1

watchOS Simulator SDKs:
	Simulator - watchOS 5.1       	-sdk watchsimulator5.1

5, issue

/private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/external/build_bazel_rules_swift/tools/worker/swift_runner.cc

comment out for library name = "python/ops/libtensorflow_io.so" in Line 16

/Users/llv23/Documents/05_machine_learning/dl_gpu_mac/tensorflow-io-macOS/tensorflow_io/BUILD

cp /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b//execroot/org_tensorflow_io/bazel-out/darwin-fastbuild/bin/tensorflow_io/python/ops/libtensorflow_io.so /Users/llv23/Documents/05_machine_learning/dl_gpu_mac/dl-built-libraries/tensorflow-built/2.9.1-cuda10.1-py3.10/ios/tensorflow_io_gcs_filesystem/core/python/ops

cp /Users/llv23/Documents/05_machine_learning/dl_gpu_mac/dl-built-libraries/tensorflow-built/2.9.1-cuda10.1-py3.10/ios/tensorflow_io_gcs_filesystem/core/python/ops/*.so /Users/llv23/opt/miniconda3/lib/python3.10/site-packages/tensorflow_io/python/ops/

6, now temporiarly disable audio and video module

ERROR: /Users/llv23/Documents/05_machine_learning/dl_gpu_mac/tensorflow-io-macOS/tools/build/swift/BUILD:5:14: Compiling Swift module //tools/build/swift:audio_video_swift failed: (Exit 1): worker failed: error executing command 
  (cd /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/execroot/org_tensorflow_io && \
  exec env - \
    APPLE_SDK_PLATFORM=MacOSX \
    APPLE_SDK_VERSION_OVERRIDE=10.14 \
  bazel-out/darwin-opt-exec-50AE0418/bin/external/build_bazel_rules_swift/tools/worker/worker swiftc @bazel-out/darwin-fastbuild/bin/tools/build/swift/audio_video.swiftmodule-0.params)
# Configuration: 8e768e62908f0dc2c00112f94b5a81081c8500d096777954be71a7758c743006
# Execution platform: @local_execution_config_platform//:platform
<unknown>:0: error: no such file or directory: 'bazel-out/darwin-fastbuild/bin/tools/build/swift/audio_video.swiftmodule'
swift_worker: Could not copy bazel-out/darwin-fastbuild/bin/_swift_incremental/tools/build/swift/audio_video_swift_objs/swift/audio.swift.o to bazel-out/darwin-fastbuild/bin/tools/build/swift/audio_video_swift_objs/swift/audio.swift.o (errno 2)

you need to check tensorflow_io/core/BUILD and comments out

cc_library(
    name = "audio_video_ops",
    srcs = [
        "kernels/audio_kernels.cc",
        "kernels/audio_kernels.h",
        "kernels/audio_video_flac_kernels.cc",
        "kernels/audio_video_mp3_kernels.cc",
        "kernels/audio_video_mp4_kernels.cc",
        "kernels/audio_video_ogg_kernels.cc",
        "kernels/audio_video_wav_kernels.cc",
        "kernels/video_kernels.cc",
        "kernels/video_kernels.h",
        "ops/audio_ops.cc",
        "ops/video_ops.cc",
    ],
    copts = tf_io_copts(),
    linkstatic = True,
    deps = [
        "@flac",
        "@minimp3",
        "@speexdsp",
        "@minimp4",
        "@vorbis",
        "//tensorflow_io/core:dataset_ops",
    ] + select({
        #
        # "@bazel_tools//src/conditions:darwin": [
        #     "//tools/build/swift:audio_video_swift",
        # ],
        "//conditions:default": [],
    }),
    alwayslink = 1,
)

Copy output of all files in /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b//execroot/org_tensorflow_io/bazel-out/darwin-fastbuild/bin/tensorflow_io/python/ops/ to /Users/llv23/Documents/05_machine_learning/dl_gpu_mac/dl-built-libraries/tensorflow-built/2.9.1-cuda10.1-py3.10/ios/tensorflow_io_gcs_filesystem/core/python/ops/

then install for

cp -rf /Users/llv23/Documents/05_machine_learning/dl_gpu_mac/dl-built-libraries/tensorflow-built/2.9.1-cuda10.1-py3.10/ios/tensorflow_io_gcs_filesystem/core/python/ops/*.so /Users/llv23/opt/miniconda3/lib/python3.10/site-packages/tensorflow_io/python/ops/

Fix for video and audio module

  1. Issue about ":0: error: unknown argument: '-debug-prefix-map'"

/private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/external/build_bazel_rules_swift/tools/worker/swift_runner.cc Line 279-280 comment out

        // consumer("-debug-prefix-map");
        // consumer(GetCurrentDirectory() + "=.");

2, Issue about ":0: error: unknown argument: '-no-clang-module-breadcrumbs'" /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/execroot/org_tensorflow_io/external/build_bazel_rules_swift/swift/internal/compiling.bzl

Line 219 comment out

        # Don't embed Clang module breadcrumbs in debug info.
        swift_toolchain_config.action_config(
            actions = [swift_action_names.COMPILE],
            configurators = [
                swift_toolchain_config.add_arg(
                    "-Xfrontend",
                    # "-no-clang-module-breadcrumbs",
                ),
            ],
        ),

/private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/execroot/org_tensorflow_io/bazel-out/darwin-fastbuild/bin/tools/build/swift/audio_video.swiftmodule-0.params

Line 11 comment out

#-no-clang-module-breadcrumbs

3, Issue ""

cd /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/execroot/org_tensorflow_io
swiftc -framework AVFoundation  -target x86_64-apple-macosx10.14 -sdk $(xcrun --sdk macosx --show-sdk-path) -emit-module-path bazel-out/darwin-fastbuild/bin/tools/build/swift/audio_video.swiftmodule -F__BAZEL_XCODE_DEVELOPER_DIR__/Platforms/MacOSX.platform/Developer/Library/Frameworks -I__BAZEL_XCODE_DEVELOPER_DIR__/Platforms/MacOSX.platform/Developer/usr/lib -emit-object -output-file-map bazel-out/darwin-fastbuild/bin/tools/build/swift/audio_video_swift.output_file_map.json -Xfrontend -DDEBUG -Onone -Xfrontend -serialize-debugging-options -enable-testing -gline-tables-only -Xcc -iquote. -Xcc -iquotebazel-out/darwin-fastbuild/bin -Xfrontend -color-diagnostics -enable-batch-mode -module-name audio_video -parse-as-library -target x86_64-apple-macosx10.14 -Xcc -O0 -Xcc -DDEBUG=1 tensorflow_io/core/swift/audio.swift tensorflow_io/core/swift/video.swift
cp bazel-out/darwin-fastbuild/bin/tools/build/swift/audio_video_swift_objs/swift/* bazel-out/darwin-fastbuild/bin/_swift_incremental/tools/build/swift/audio_video_swift_objs/swift/
cd bazel-out/darwin-fastbuild/bin/tools/build/swift/audio_video_swift_objs/swift
ar rcs libaudio_video_swift.a audio.swift.o video.swift.o
swiftc -framework AVFoundation  -target x86_64-apple-macosx10.14 -sdk $(xcrun --sdk macosx --show-sdk-path) -emit-library audio.swift.o video.swift.o -o libaudio_video_swift.dylib
cd /private/var/tmp/_bazel_llv23/a82ad01ec0c5d2a91897f1531acdf67b/execroot/org_tensorflow_io
cp bazel-out/darwin-fastbuild/bin/tools/build/swift/audio_video_swift_objs/swift/libaudio_video_swift.* bazel-out/darwin-fastbuild/bin/tools/build/swift/
cp bazel-out/darwin-fastbuild/bin/tools/build/swift/libaudio_video_swift.a bazel-out/darwin-fastbuild/bin/tools/build/swift/libaudio_video.a
cp bazel-out/darwin-fastbuild/bin/tools/build/swift/libaudio_video_swift.dylib bazel-out/darwin-fastbuild/bin/tools/build/swift/libaudio_video.dylib

Python Package

The tensorflow-io Python package can be installed with pip directly using:

$ pip install tensorflow-io

People who are a little more adventurous can also try our nightly binaries:

$ pip install tensorflow-io-nightly

To ensure you have a version of TensorFlow that is compatible with TensorFlow-IO, you can specify the tensorflow extra requirement during install:

pip install tensorflow-io[tensorflow]

Similar extras exist for the tensorflow-gpu, tensorflow-cpu and tensorflow-rocm packages.

Docker Images

In addition to the pip packages, the docker images can be used to quickly get started.

For stable builds:

$ docker pull tfsigio/tfio:latest
$ docker run -it --rm --name tfio-latest tfsigio/tfio:latest

For nightly builds:

$ docker pull tfsigio/tfio:nightly
$ docker run -it --rm --name tfio-nightly tfsigio/tfio:nightly

R Package

Once the tensorflow-io Python package has been successfully installed, you can install the development version of the R package from GitHub via the following:

if (!require("remotes")) install.packages("remotes")
remotes::install_github("tensorflow/io", subdir = "R-package")

TensorFlow Version Compatibility

To ensure compatibility with TensorFlow, it is recommended to install a matching version of TensorFlow I/O according to the table below. You can find the list of releases here.

TensorFlow I/O Version TensorFlow Compatibility Release Date
0.26.0 2.9.x May 17, 2022
0.25.0 2.8.x Apr 19, 2022
0.24.0 2.8.x Feb 04, 2022
0.23.1 2.7.x Dec 15, 2021
0.23.0 2.7.x Dec 14, 2021
0.22.0 2.7.x Nov 10, 2021
0.21.0 2.6.x Sep 12, 2021
0.20.0 2.6.x Aug 11, 2021
0.19.1 2.5.x Jul 25, 2021
0.19.0 2.5.x Jun 25, 2021
0.18.0 2.5.x May 13, 2021
0.17.1 2.4.x Apr 16, 2021
0.17.0 2.4.x Dec 14, 2020
0.16.0 2.3.x Oct 23, 2020
0.15.0 2.3.x Aug 03, 2020
0.14.0 2.2.x Jul 08, 2020
0.13.0 2.2.x May 10, 2020
0.12.0 2.1.x Feb 28, 2020
0.11.0 2.1.x Jan 10, 2020
0.10.0 2.0.x Dec 05, 2019
0.9.1 2.0.x Nov 15, 2019
0.9.0 2.0.x Oct 18, 2019
0.8.1 1.15.x Nov 15, 2019
0.8.0 1.15.x Oct 17, 2019
0.7.2 1.14.x Nov 15, 2019
0.7.1 1.14.x Oct 18, 2019
0.7.0 1.14.x Jul 14, 2019
0.6.0 1.13.x May 29, 2019
0.5.0 1.13.x Apr 12, 2019
0.4.0 1.13.x Mar 01, 2019
0.3.0 1.12.0 Feb 15, 2019
0.2.0 1.12.0 Jan 29, 2019
0.1.0 1.12.0 Dec 16, 2018

Performance Benchmarking

We use github-pages to document the results of API performance benchmarks. The benchmark job is triggered on every commit to master branch and facilitates tracking performance w.r.t commits.

Contributing

Tensorflow I/O is a community led open source project. As such, the project depends on public contributions, bug-fixes, and documentation. Please see:

Build Status and CI

Build Status
Linux CPU Python 2 Status
Linux CPU Python 3 Status
Linux GPU Python 2 Status
Linux GPU Python 3 Status

Because of manylinux2010 requirement, TensorFlow I/O is built with Ubuntu:16.04 + Developer Toolset 7 (GCC 7.3) on Linux. Configuration with Ubuntu 16.04 with Developer Toolset 7 is not exactly straightforward. If the system have docker installed, then the following command will automatically build manylinux2010 compatible whl package:

#!/usr/bin/env bash

ls dist/*
for f in dist/*.whl; do
  docker run -i --rm -v $PWD:/v -w /v --net=host quay.io/pypa/manylinux2010_x86_64 bash -x -e /v/tools/build/auditwheel repair --plat manylinux2010_x86_64 $f
done
sudo chown -R $(id -nu):$(id -ng) .
ls wheelhouse/*

It takes some time to build, but once complete, there will be python 3.5, 3.6, 3.7 compatible whl packages available in wheelhouse directory.

On macOS, the same command could be used. However, the script expects python in shell and will only generate a whl package that matches the version of python in shell. If you want to build a whl package for a specific python then you have to alias this version of python to python in shell. See .github/workflows/build.yml Auditwheel step for instructions how to do that.

Note the above command is also the command we use when releasing packages for Linux and macOS.

TensorFlow I/O uses both GitHub Workflows and Google CI (Kokoro) for continuous integration. GitHub Workflows is used for macOS build and test. Kokoro is used for Linux build and test. Again, because of the manylinux2010 requirement, on Linux whl packages are always built with Ubuntu 16.04 + Developer Toolset 7. Tests are done on a variatiy of systems with different python3 versions to ensure a good coverage:

Python Ubuntu 18.04 Ubuntu 20.04 macOS + osx9 Windows-2019
2.7 ✔️ ✔️ ✔️ N/A
3.7 ✔️ ✔️ ✔️ ✔️
3.8 ✔️ ✔️ ✔️ ✔️

TensorFlow I/O has integrations with many systems and cloud vendors such as Prometheus, Apache Kafka, Apache Ignite, Google Cloud PubSub, AWS Kinesis, Microsoft Azure Storage, Alibaba Cloud OSS etc.

We tried our best to test against those systems in our continuous integration whenever possible. Some tests such as Prometheus, Kafka, and Ignite are done with live systems, meaning we install Prometheus/Kafka/Ignite on CI machine before the test is run. Some tests such as Kinesis, PubSub, and Azure Storage are done through official or non-official emulators. Offline tests are also performed whenever possible, though systems covered through offine tests may not have the same level of coverage as live systems or emulators.

Live System Emulator CI Integration Offline
Apache Kafka ✔️ ✔️
Apache Ignite ✔️ ✔️
Prometheus ✔️ ✔️
Google PubSub ✔️ ✔️
Azure Storage ✔️ ✔️
AWS Kinesis ✔️ ✔️
Alibaba Cloud OSS ✔️
Google BigTable/BigQuery to be added
Elasticsearch (experimental) ✔️ ✔️
MongoDB (experimental) ✔️ ✔️

References for emulators:

Community

Additional Information

License

Apache License 2.0