| /external/skia/docker/ |
| D | README.md | 5 ## skia-release
7 This image is used to build Skia at TOT with SwiftShader.
10 need to manually push a verison, then run the following commands:
12 docker build -t skia-release ./docker/skia-release/
13 docker tag skia-release gcr.io/skia-public/skia-release:prod
14 docker push gcr.io/skia-public/skia-release:prod
16 ## skia-wasm-release
18 This image is used to build the Web Assembly (WASM) libraries of Skia
22 need to manually push a verison, then run the following commands:
24 docker build -t skia-wasm-release ./docker/skia-wasm-release/
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| /external/skia/docker/skia-with-swift-shader-base/ |
| D | build-with-swift-shader-and-run | 9 # to use Swiftshade w/o having to over-write /usr/local/lib/libEGL.so and related on the
19 # build-with-swift-shader-and-run "out/with-swift-shader/fuzz --gpuInfo -t api -n NativeGLCanvas"
22 # After running the above, feel free to check out $SKIA_OUT/out/with-swift-shader. It has binaries
23 # but if you try to run out/with-swift-shader/fuzz --gpuInfo -t api -n NativeGLCanvas w/o using
28 # build-with-swift-shader-and-run "out/with-swift-shader/fuzz -t filter_fuzz -b /skia/skbug_1234"
30 # $SKIA_ROOT gets mapped to /skia - other than that, the docker container does not have
34 IMAGE = 'gcr.io/skia-public/skia-with-swift-shader-base:prod'
36 BUILD_SCRIPT_PATH = '/skia/docker/skia-with-swift-shader-base/build.sh'
37 EXECUTABLE_DIR = 'out/with-swift-shader/'
40 parser.add_argument('--sync_deps', action='store_true', help='Sync the deps before building?')
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| /external/tensorflow/tensorflow/lite/swift/ |
| D | README.md | 1 # TensorFlow Lite for Swift
4 solution for Swift developers. It enables low-latency inference of on-device
8 ## Build TensorFlow with iOS support
10 To build the Swift TensorFlow Lite library on Apple platforms,
20 Follow the prompts and when asked to build TensorFlow with iOS support, enter `y`.
30 Then, run `pod install`.
32 In your Swift files, import the module:
34 ```swift
40 In your `BUILD` file, add the `TensorFlowLite` dependency to your target:
45 "//tensorflow/lite/swift:TensorFlowLite",
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| /external/nanopb-c/.github/workflows/ |
| D | spm.yml | 8 swift-build-run:
9 runs-on: macOS-latest
11 - uses: actions/checkout@v2
12 - name: Build
13 run: swift build
14 - name: Run
15 run: swift test
|
| /external/tensorflow/tensorflow/lite/g3doc/guide/ |
| D | build_ios.md | 1 # Build TensorFlow Lite for iOS
3 This document describes how to build TensorFlow Lite iOS library on your own.
4 Normally, you do not need to locally build TensorFlow Lite iOS library. If you
11 In some cases, you might wish to use a local build of TensorFlow Lite, for
15 you need to build it using Bazel on a macOS machine.
20 using `xcode-select`:
23 xcode-select --install
30 sudo xcodebuild -license accept
35 Bazel is the primary build system for TensorFlow. Install Bazel as per the
36 [instructions on the Bazel website][bazel-install]. Make sure to choose a
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| D | inference.md | 4 on-device in order to make predictions based on input data. To perform an
5 inference with a TensorFlow Lite model, you must run it through an
7 The interpreter uses a static graph ordering and a custom (less-dynamic) memory
12 [supported platform](#supported-platforms).
44 end-user.
49 such as [Android](#android-platform), [iOS](#ios-platform) and
50 [Linux](#linux-platform), in multiple programming languages.
68 See below for details about using [C++](#load-and-run-a-model-in-c) and
69 [Java](#load-and-run-a-model-in-java), or follow the
88 [Swift](https://www.tensorflow.org/code/tensorflow/lite/swift)
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| D | ios.md | 6 <a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/exam…
14 to continuously classify whatever it sees from the device's rear-facing camera,
23 ## Add TensorFlow Lite to your Swift or Objective-C project
26 [Swift](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/swift)
28 [Objective-C](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/objc).
30 [Swift image classification example](https://github.com/tensorflow/examples/tree/master/lite/exampl…
33 The sections below demonstrate how to add TensorFlow Lite Swift or Objective-C
38 In your `Podfile`, add the TensorFlow Lite pod. Then, run `pod install`.
40 #### Swift subsubsection
47 #### Objective-C
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| D | index.md | 3 TensorFlow Lite is a set of tools that enables on-device machine learning by
4 helping developers run their models on mobile, embedded, and IoT devices.
8 - *Optimized for on-device machine learning*, by addressing 5 key constraints:
9 latency (there's no round-trip to a server), privacy (no personal data
13 - *Multiple platform support*, covering [Android](android) and [iOS](ios)
16 - *Diverse language support*, which includes Java, Swift, Objective-C, C++,
18 - *High performance*, with [hardware acceleration](../performance/delegates)
20 - *End-to-end [examples](../examples)*, for common machine learning tasks such
25 are linked (for 32-bit ARM builds), and less than 300KB when using only the
48 human-readable model description and machine-readable data for automatic
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| /external/tensorflow/tensorflow/lite/swift/docsgen/ |
| D | README.md | 6 To browse the Swift reference documentation visit
7 https://www.tensorflow.org/lite/api_docs/swift.
10 TensorFlowLiteSwift via Jazzy, an open-source tool that hooks into Xcode's
11 build tooling to parse doc comments. Unfortunately, TensorFlowLiteSwift is not
12 primarily developed via xcodebuild, so the docs build can potentially become
16 - Every new file added to TensorFlowLiteSwift's BUILD must also manually be
18 - This project (and the resulting documentation) does not split types by
21 - The TensorFlowLiteC dependency is included in binary form, contributing
25 To generate documentation outside of Google, run jazzy as you would on any other
26 Swift module:
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| /external/tensorflow/tensorflow/lite/g3doc/performance/ |
| D | gpu_advanced.md | 13 workloads. Thus, they are well-suited for deep neural nets, which consist of a
17 on the GPU may run fast enough to become suitable for real-time applications
22 GPUs do their computation with 16-bit or 32-bit floating point numbers and
31 power and generating less heat than the same task run on a CPU.
35 TensorFlow Lite on GPU supports the following ops in 16-bit and 32-bit float
42 * `DEPTHWISE_CONV_2D v1-2`
56 * `RESIZE_BILINEAR v1-3`
63 [experimental quantization support](gpu_advanced.md#running-quantized-models-experimental-android-o…
75 Add the `tensorflow-lite-gpu` package alongside the existing `tensorflow-lite`
81 implementation 'org.tensorflow:tensorflow-lite:2.3.0'
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| D | gpu.md | 8 workloads. Thus, they are well-suited for deep neural nets, which consist of a
12 run fast enough for previously not available real-time applications.
14 Unlike CPUs, GPUs compute with 16-bit or 32-bit floating point numbers and do
15 not require quantization for optimal performance. The delegate does accept 8-bit
21 power and generate less heat than when the same task is run on CPUs.
27 The GPU code is only binary for now; it will be open-sourced soon. Once you
33 For a step-by-step tutorial, watch the
44 #### Step 2. Edit `app/build.gradle` to use the nightly GPU AAR
54 Add the `tensorflow-lite-gpu` package alongside the existing `tensorflow-lite`
60 implementation 'org.tensorflow:tensorflow-lite:2.3.0'
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| /external/oss-fuzz/projects/swift-nio/ |
| D | Dockerfile | 7 # http://www.apache.org/licenses/LICENSE-2.0
17 FROM gcr.io/oss-fuzz-base/base-builder-swift
19 # specific swift-nio
20 RUN git clone --depth 1 https://github.com/google/fuzzing
21 RUN git clone --depth 1 https://github.com/apple/swift-nio.git
22 COPY build.sh $SRC
23 COPY *.swift $SRC/
24 WORKDIR $SRC/swift-nio
|
| /external/oss-fuzz/projects/grpc-swift/ |
| D | Dockerfile | 7 # http://www.apache.org/licenses/LICENSE-2.0
17 FROM gcr.io/oss-fuzz-base/base-builder-swift
20 RUN apt-get update && apt-get install -y zlib1g-dev
21 RUN git clone --depth 1 https://github.com/grpc/grpc-swift
22 COPY build.sh $SRC
23 WORKDIR $SRC/grpc-swift
|
| /external/flatbuffers/docs/source/ |
| D | SwiftUsage.md | 1 Use in Swift {#flatbuffers_guide_use_swift}
6 Before diving into the FlatBuffers usage in Swift, it should be noted that
8 to general FlatBuffers usage in all of the supported languages (including Swift).
10 Swift.
13 documentation to build `flatc` and should be familiar with
17 ## FlatBuffers Swift library code location
19 The code for the FlatBuffers Swift library can be found at
20 `flatbuffers/swift`. You can browse the library code on the [FlatBuffers
21 GitHub page](https://github.com/google/flatbuffers/tree/master/swift).
23 ## Testing the FlatBuffers Swift library
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|
| /external/grpc-grpc/src/objective-c/examples/SwiftSample/SwiftSample.xcodeproj/ |
| D | project.pbxproj | 11 …B210007E424 /* AppDelegate.swift in Sources */ = {isa = PBXBuildFile; fileRef = 633BFFC71B950B2100…
12 …007E424 /* ViewController.swift in Sources */ = {isa = PBXBuildFile; fileRef = 633BFFC91B950B21000…
20 …B210007E424 /* AppDelegate.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swif…
21 …007E424 /* ViewController.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift…
24 …-SwiftSample.debug.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType = …
26 …-SwiftSample.release.xcconfig */ = {isa = PBXFileReference; includeInIndex = 1; lastKnownFileType …
44 A7E614A494D89D01BB395761 /* Pods-SwiftSample.debug.xcconfig */,
45 C314E3E246AF23AC29B38FCF /* Pods-SwiftSample.release.xcconfig */,
71 633BFFC71B950B210007E424 /* AppDelegate.swift */,
72 633BFFC91B950B210007E424 /* ViewController.swift */,
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|
| /external/oss-fuzz/projects/swift-protobuf/ |
| D | Dockerfile | 7 # http://www.apache.org/licenses/LICENSE-2.0
17 FROM gcr.io/oss-fuzz-base/base-builder-swift
19 RUN git clone --depth 1 https://github.com/apple/swift-protobuf.git
20 COPY build.sh $SRC
21 WORKDIR $SRC/swift-protobuf
|
| /external/protobuf/objectivec/DevTools/ |
| D | full_mac_build.sh | 3 # Helper to do build so you don't have to remember all the steps/args.
6 set -eu
9 readonly ScriptDir=$(dirname "$(echo $0 | sed -e "s,^\([^/]\),$(pwd)/\1,")")
17 This script does the common build steps needed.
23 -h, --help
25 -c, --clean
26 Issue a clean before the normal build.
27 -a, --autogen
29 -r, --regenerate-descriptors
30 Run generate_descriptor_proto.sh to regenerate all the checked in
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| /external/tensorflow/tensorflow/lite/g3doc/inference_with_metadata/task_library/ |
| D | nl_classifier.md | 28 [model compatibility requirements](#model-compatibility-requirements).
30 ## Run inference in Java
39 where the model will be run. Specify that the file should not be compressed, and
40 add the TensorFlow Lite library to the module’s `build.gradle` file:
57 implementation 'org.tensorflow:tensorflow-lite-task-text:0.2.0'
65 ### Step 2: Run inference using the API
69 …).setInputTensorName(INPUT_TENSOR_NAME).setOutputScoreTensorName(OUTPUT_SCORE_TENSOR_NAME).build();
72 // Run inference
77 [source code](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/java…
80 ## Run inference in Swift
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| D | bert_nl_classifier.md | 13 * Performs out-of-graph
14 …[Wordpiece](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/cc/te…
16 …[Sentencepiece](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/c…
27 [model compatibility requirements](#model-compatibility-requirements).
29 ## Run inference in Java
34 where the model will be run. Specify that the file should not be compressed, and
35 add the TensorFlow Lite library to the module’s `build.gradle` file:
52 implementation 'org.tensorflow:tensorflow-lite-task-text:0.2.0'
60 ### Step 2: Run inference using the API
66 // Run inference
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| D | overview.md | 3 TensorFlow Lite Task Library contains a set of powerful and easy-to-use
4 task-specific libraries for app developers to create ML experiences with TFLite.
5 It provides optimized out-of-box model interfaces for popular machine learning
8 performance and usability. Task Library works cross-platform and is supported on
9 Java, C++, and Swift.
13 * **Clean and well-defined APIs usable by non-ML-experts** \
15 easy-to-use APIs in the Task library as building blocks to help you easily
29 easily build your own Android/iOS inference APIs.
54 * Extend Task API infrastructure and build
57 ## Run Task Library with Delegates
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| D | bert_question_answerer.md | 5 documentation for the Question-Answer model
13 * Performs out-of-graph Wordpiece or Sentencepiece tokenizations on input
24 …ensorFlow Hub](https://tfhub.dev/tensorflow/collections/lite/task-library/bert-question-answerer/1…
27 [model compatibility requirements](#model-compatibility-requirements).
29 ## Run inference in Java
34 where the model will be run. Specify that the file should not be compressed, and
35 add the TensorFlow Lite library to the module’s `build.gradle` file:
52 implementation 'org.tensorflow:tensorflow-lite-task-text:0.2.0'
60 ### Step 2: Run inference using the API
66 // Run inference
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| /external/tensorflow/tensorflow/lite/g3doc/ |
| D | _book.yaml | 2 - name: "Install"
5 - include: /install/_toc.yaml
7 - name: "Learn"
11 - include: /learn/_menu_toc.yaml
15 - name: "Tutorials"
17 - title: "TensorFlow Lite Tutorials"
20 - heading: "Vision"
21 - title: "Recognize handwritten digits"
22 path: https://codelabs.developers.google.com/codelabs/digit-classifier-tflite/
24 - title: "Recognize flowers on Android"
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|
| /external/oss-fuzz/docs/advanced-topics/ |
| D | code_coverage.md | 1 ---
6 permalink: /advanced-topics/code-coverage/
7 ---
12 For projects written in C/C++, Rust, Go, Swift or Java and other JVM-based languages,
13 you can generate code coverage reports using Clang source-based code coverage.
19 - TOC
21 ---
25 Docker images get regularly updated with a newer version of build tools, build
33 ## Build fuzz targets
35 Code coverage report generation requires a special build configuration to be
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|
| /external/oss-fuzz/infra/ |
| D | helper.py | 8 # http://www.apache.org/licenses/LICENSE-2.0
17 """Helper script for OSS-Fuzz users. Can do common tasks like building
36 BUILD_DIR = os.path.join(OSS_FUZZ_DIR, 'build')
38 BASE_RUNNER_IMAGE = 'gcr.io/oss-fuzz-base/base-runner'
42 'gcr.io/oss-fuzz-base/base-image',
43 'gcr.io/oss-fuzz-base/base-clang',
44 'gcr.io/oss-fuzz-base/base-builder',
46 'gcr.io/oss-fuzz-base/base-runner-debug',
48 'go': ['gcr.io/oss-fuzz-base/base-builder-go'],
49 'jvm': ['gcr.io/oss-fuzz-base/base-builder-jvm'],
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| D | build_and_push_test_images.py | 8 # http://www.apache.org/licenses/LICENSE-2.0
17 """Script for building and pushing base-images to gcr.io/oss-fuzz-base/ with
18 "-test" suffix. This is useful for reusing the build infra to test image
26 TAG_PREFIX = 'gcr.io/oss-fuzz-base/'
28 IMAGES_DIR = os.path.join(INFRA_DIR, 'base-images')
35 subprocess.run(command, check=True)
40 """Builds and pushes |image| to docker registry with "-testing" suffix."""
42 testing_tag = main_tag + '-' + test_image_suffix
51 command = ['docker', 'build']
53 command.extend(['--tag', tag])
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