Update TFLITE RPI build document
- Fixed ordered list numbering - Added some Make option examples - Added a way to build TFLite armhf shared library PiperOrigin-RevId: 326964389 Change-Id: I1dfbe781e09a5860119a9739cf102c8b02c8cf68
This commit is contained in:
Terry Heo
TensorFlower Gardener
parent
563a0184e1
commit
f5e573abda
@ -1,66 +1,83 @@
|
||||
# Build TensorFlow Lite for Raspberry Pi
|
||||
|
||||
This page describes how to build the TensorFlow Lite static library for
|
||||
Raspberry Pi. If you just want to start using TensorFlow Lite to execute your
|
||||
models, the fastest option is to install the TensorFlow Lite runtime package as
|
||||
shown in the [Python quickstart](python.md).
|
||||
This page describes how to build the TensorFlow Lite static and shared libraries
|
||||
for Raspberry Pi. If you just want to start using TensorFlow Lite to execute
|
||||
your models, the fastest option is to install the TensorFlow Lite runtime
|
||||
package as shown in the [Python quickstart](python.md).
|
||||
|
||||
**Note:** This page shows how to compile only the C++ static library for
|
||||
**Note:** This page shows how to compile the C++ static and shared libraries for
|
||||
TensorFlow Lite. Alternative install options include:
|
||||
[install just the Python interpreter API](python.md) (for inferencing only);
|
||||
[install the full TensorFlow package from pip](https://www.tensorflow.org/install/pip);
|
||||
or
|
||||
[build the full TensorFlow package](https://www.tensorflow.org/install/source_rpi).
|
||||
|
||||
## Cross-compile for Raspberry Pi
|
||||
**Note:** This page only covers 32-bit builds. If you're looking for 64-bit
|
||||
builds, check [Build for ARM64](build_arm64.md) page.
|
||||
|
||||
## Cross-compile for Raspberry Pi with Make
|
||||
|
||||
The following instructions have been tested on Ubuntu 16.04.3 64-bit PC (AMD64)
|
||||
and TensorFlow devel docker image
|
||||
[tensorflow/tensorflow:nightly-devel](https://hub.docker.com/r/tensorflow/tensorflow/tags/).
|
||||
[tensorflow/tensorflow:devel](https://hub.docker.com/r/tensorflow/tensorflow/tags/).
|
||||
|
||||
To cross compile TensorFlow Lite follow the steps:
|
||||
|
||||
1. Clone official Raspberry Pi cross-compilation toolchain:
|
||||
#### Step 1. Clone official Raspberry Pi cross-compilation toolchain
|
||||
|
||||
```sh
|
||||
git clone https://github.com/raspberrypi/tools.git rpi_tools
|
||||
```
|
||||
```sh
|
||||
git clone https://github.com/raspberrypi/tools.git rpi_tools
|
||||
```
|
||||
|
||||
2. Clone TensorFlow repository:
|
||||
#### Step 2. Clone TensorFlow repository
|
||||
|
||||
```sh
|
||||
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
|
||||
```
|
||||
```sh
|
||||
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
|
||||
```
|
||||
|
||||
**Note:** If you're using the TensorFlow Docker image, the repo is already
|
||||
provided in `/tensorflow_src/`.
|
||||
**Note:** If you're using the TensorFlow Docker image, the repo is already
|
||||
provided in `/tensorflow_src/`.
|
||||
|
||||
3. Run following script at the root of the TensorFlow repository to download
|
||||
all the build dependencies:
|
||||
#### Step 3. Run following script at the root of the TensorFlow repository to download
|
||||
|
||||
```sh
|
||||
cd tensorflow_src && ./tensorflow/lite/tools/make/download_dependencies.sh
|
||||
```
|
||||
all the build dependencies:
|
||||
|
||||
**Note:** You only need to do this once.
|
||||
```sh
|
||||
cd tensorflow_src && ./tensorflow/lite/tools/make/download_dependencies.sh
|
||||
```
|
||||
|
||||
4. To build ARMv7 binary for Raspberry Pi 2, 3 and 4 execute:
|
||||
**Note:** You only need to do this once.
|
||||
|
||||
```sh
|
||||
PATH=../rpi_tools/arm-bcm2708/arm-rpi-4.9.3-linux-gnueabihf/bin:$PATH ./tensorflow/lite/tools/make/build_rpi_lib.sh
|
||||
```
|
||||
#### Step 4a. To build ARMv7 binary for Raspberry Pi 2, 3 and 4
|
||||
|
||||
**Note:** This should compile a static library in:
|
||||
`tensorflow/lite/tools/make/gen/rpi_armv7l/lib/libtensorflow-lite.a`.
|
||||
```sh
|
||||
PATH=../rpi_tools/arm-bcm2708/arm-rpi-4.9.3-linux-gnueabihf/bin:$PATH \
|
||||
./tensorflow/lite/tools/make/build_rpi_lib.sh
|
||||
```
|
||||
|
||||
5. To build ARMv6 binary for Raspberry Pi Zero execute:
|
||||
**Note:** This should compile a static library in:
|
||||
`tensorflow/lite/tools/make/gen/rpi_armv7l/lib/libtensorflow-lite.a`.
|
||||
|
||||
```sh
|
||||
PATH=../rpi_tools/arm-bcm2708/arm-rpi-4.9.3-linux-gnueabihf/bin:$PATH ./tensorflow/lite/tools/make/build_rpi_lib.sh TARGET_ARCH=armv6
|
||||
```
|
||||
You can add additional Make options or target names to the `build_rpi_lib.sh`
|
||||
script since it's a wrapper of Make with TFLite
|
||||
[Makefile](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tools/make/Makefile).
|
||||
Here are some possible options:
|
||||
|
||||
**Note:** This should compile a static library in:
|
||||
`tensorflow/lite/tools/make/gen/rpi_armv6/lib/libtensorflow-lite.a`.
|
||||
```sh
|
||||
./tensorflow/lite/tools/make/build_rpi_lib.sh clean # clean object files
|
||||
./tensorflow/lite/tools/make/build_rpi_lib.sh -j 16 # run with 16 jobs to leverage more CPU cores
|
||||
./tensorflow/lite/tools/make/build_rpi_lib.sh label_image # # build label_image binary
|
||||
```
|
||||
|
||||
#### Step 4b. To build ARMv6 binary for Raspberry Pi Zero
|
||||
|
||||
```sh
|
||||
PATH=../rpi_tools/arm-bcm2708/arm-rpi-4.9.3-linux-gnueabihf/bin:$PATH \
|
||||
./tensorflow/lite/tools/make/build_rpi_lib.sh TARGET_ARCH=armv6
|
||||
```
|
||||
|
||||
**Note:** This should compile a static library in:
|
||||
`tensorflow/lite/tools/make/gen/rpi_armv6/lib/libtensorflow-lite.a`.
|
||||
|
||||
## Compile natively on Raspberry Pi
|
||||
|
||||
@ -69,32 +86,97 @@ GNU/Linux 10 (buster), gcc version 8.3.0 (Raspbian 8.3.0-6+rpi1):
|
||||
|
||||
To natively compile TensorFlow Lite follow the steps:
|
||||
|
||||
1. Log in to your Raspberry Pi and install the toolchain:
|
||||
#### Step 1. Log in to your Raspberry Pi and install the toolchain
|
||||
|
||||
```sh
|
||||
sudo apt-get install build-essential
|
||||
```
|
||||
```sh
|
||||
sudo apt-get install build-essential
|
||||
```
|
||||
|
||||
2. Clone TensorFlow repository:
|
||||
#### Step 2. Clone TensorFlow repository
|
||||
|
||||
```sh
|
||||
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
|
||||
```
|
||||
```sh
|
||||
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
|
||||
```
|
||||
|
||||
3. Run following script at the root of the TensorFlow repository to download
|
||||
all the build dependencies:
|
||||
#### Step 3. Run following script at the root of the TensorFlow repository to download all the build dependencies
|
||||
|
||||
```sh
|
||||
cd tensorflow_src && ./tensorflow/lite/tools/make/download_dependencies.sh
|
||||
```
|
||||
```sh
|
||||
cd tensorflow_src && ./tensorflow/lite/tools/make/download_dependencies.sh
|
||||
```
|
||||
|
||||
**Note:** You only need to do this once.
|
||||
**Note:** You only need to do this once.
|
||||
|
||||
4. You should then be able to compile TensorFlow Lite with:
|
||||
#### Step 4. You should then be able to compile TensorFlow Lite with:
|
||||
|
||||
```sh
|
||||
./tensorflow/lite/tools/make/build_rpi_lib.sh
|
||||
```
|
||||
```sh
|
||||
./tensorflow/lite/tools/make/build_rpi_lib.sh
|
||||
```
|
||||
|
||||
**Note:** This should compile a static library in:
|
||||
`tensorflow/lite/tools/make/gen/lib/rpi_armv6/libtensorflow-lite.a`.
|
||||
**Note:** This should compile a static library in:
|
||||
`tensorflow/lite/tools/make/gen/lib/rpi_armv6/libtensorflow-lite.a`.
|
||||
|
||||
## Cross-compile for armhf with Bazel
|
||||
|
||||
You can use
|
||||
[ARM GCC toolchains](https://github.com/tensorflow/tensorflow/tree/master/third_party/toolchains/embedded/arm-linux)
|
||||
with Bazel to build an armhf shared library which is compatibile with Raspberry
|
||||
Pi 2, 3 and 4.
|
||||
|
||||
Note: The generated shared library requires glibc 2.28 or higher to run.
|
||||
|
||||
The following instructions have been tested on Ubuntu 16.04.3 64-bit PC (AMD64)
|
||||
and TensorFlow devel docker image
|
||||
[tensorflow/tensorflow:devel](https://hub.docker.com/r/tensorflow/tensorflow/tags/).
|
||||
|
||||
To cross compile TensorFlow Lite with Bazel, follow the steps:
|
||||
|
||||
#### Step 1. Install Bazel
|
||||
|
||||
Bazel is the primary build system for TensorFlow. Install the latest version of
|
||||
the [Bazel build system](https://bazel.build/versions/master/docs/install.html).
|
||||
|
||||
**Note:** If you're using the TensorFlow Docker image, Bazel is already
|
||||
available.
|
||||
|
||||
#### Step 2. Clone TensorFlow repository
|
||||
|
||||
```sh
|
||||
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
|
||||
```
|
||||
|
||||
**Note:** If you're using the TensorFlow Docker image, the repo is already
|
||||
provided in `/tensorflow_src/`.
|
||||
|
||||
#### Step 3. Build ARMv7 binary for Raspberry Pi 2, 3 and 4
|
||||
|
||||
##### C library
|
||||
|
||||
```bash
|
||||
bazel build --config=elinux_armhf -c opt //tensorflow/lite/c:libtensorflowlite_c.so
|
||||
```
|
||||
|
||||
Check
|
||||
[TensorFlow Lite C API](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/c)
|
||||
page for the detail.
|
||||
|
||||
##### C++ library
|
||||
|
||||
```bash
|
||||
bazel build --config=elinux_armhf -c opt //tensorflow/lite:libtensorflowlite.so
|
||||
```
|
||||
|
||||
You can find a shared library library in:
|
||||
`bazel-bin/tensorflow/lite/libtensorflowlite.so`.
|
||||
|
||||
Currently, there is no straightforward way to extract all header files needed,
|
||||
so you must include all header files in tensorflow/lite/ from the TensorFlow
|
||||
repository. Additionally, you will need header files from FlatBuffers and
|
||||
Abseil.
|
||||
|
||||
##### Etc
|
||||
|
||||
You can also build other Bazel targets with the toolchain. Here are some useful
|
||||
targets.
|
||||
|
||||
* //tensorflow/lite/tools/benchmark:benchmark_model
|
||||
* //tensorflow/lite/examples/label_image:label_image
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user