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Docfix: Document usage of --config=opt for SIMD instruction sets. (#7211)

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* Docfix: Document usage of --config=opt for SIMD instruction sets.
This commit is contained in:
gunan 2017-02-02 09:27:52 -08:00 committed by Martin Wicke
parent 85235cbcb4
commit b47dc70e54
14 changed files with 31 additions and 29 deletions
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3
configure vendored
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@ -76,7 +76,8 @@ done
## Set up architecture-dependent optimization flags.
if [ -z "$CC_OPT_FLAGS" ]; then
default_cc_opt_flags="-march=native"
read -p "Please specify optimization flags to use during compilation [Default is $default_cc_opt_flags]: " CC_OPT_FLAGS
read -p "Please specify optimization flags to use during compilation when bazel option "\
"\"--config=opt\" is specified [Default is $default_cc_opt_flags]: " CC_OPT_FLAGS
if [ -z "$CC_OPT_FLAGS" ]; then
CC_OPT_FLAGS=$default_cc_opt_flags
fi

4
tensorflow/core/kernels/BUILD
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@ -7,9 +7,9 @@
# append "_gpu" to the test name to invoke the GPU benchmarks. Example:
#
# # for CPU tests
# $ bazel test -c opt --copt=-mavx //third_party/tensorflow/core/kernels:my_op_test
# $ bazel test --config opt //third_party/tensorflow/core/kernels:my_op_test
# # for GPU benchmarks
# $ bazel run -c opt --copt=-mavx --config=cuda //third_party/tensorflow/core/kernels:my_op_test_gpu -- --benchmarks=..
# $ bazel run --config opt --config=cuda //third_party/tensorflow/core/kernels:my_op_test_gpu -- --benchmarks=..
#
package(default_visibility = ["//visibility:public"])

2
tensorflow/examples/how_tos/reading_data/fully_connected_preloaded.py
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@ -17,7 +17,7 @@
Run using bazel:
bazel run -c opt \
bazel run --config opt \
<...>/tensorflow/examples/how_tos/reading_data:fully_connected_preloaded
or, if installed via pip:

2
tensorflow/examples/how_tos/reading_data/fully_connected_preloaded_var.py
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@ -17,7 +17,7 @@
Run using bazel:
bazel run -c opt \
bazel run --config opt \
<...>/tensorflow/examples/how_tos/reading_data:fully_connected_preloaded_var
or, if installed via pip:

2
tensorflow/examples/multibox_detector/README.md
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@ -32,7 +32,7 @@ normalized from 0-1 in left top right bottom order.
To build it, run this command:
```bash
$ bazel build -c opt tensorflow/examples/multibox_detector/...
$ bazel build --config opt tensorflow/examples/multibox_detector/...
```
That should build a binary executable that you can then run like this:

27
tensorflow/g3doc/get_started/os_setup.md
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@ -856,13 +856,13 @@ default and if you want to limit RAM usage you can add `--local_resources
2048,.5,1.0` while invoking bazel.
```bash
$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
$ bazel build --config opt //tensorflow/tools/pip_package:build_pip_package
# To build with support for CUDA:
$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
$ bazel build --config opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
# Alternatively, to build with support for OpenCL:
$ bazel build -c opt --config=sycl //tensorflow/tools/pip_package:build_pip_package
# Alternatively, to build with support for OpenCL (Experimental):
$ bazel build --config opt --config=sycl //tensorflow/tools/pip_package:build_pip_package
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
@ -873,20 +873,21 @@ $ sudo pip install /tmp/tensorflow_pkg/tensorflow-0.12.1-py2-none-any.whl
## Optimizing CPU performance
To be compatible with as wide a range of machines as possible, TensorFlow
defaults to only using SSE4.1 SIMD instructions on x86 machines. Most modern PCs
and Macs support more advanced instructions, so if you're building a binary
that you'll only be running on your own machine, you can enable these by using
`--copt=-march=native` in your bazel build command. For example:
defaults to only using SSE4 SIMD instructions. Most modern computers support
more advanced instructions. So if you're building a binary that you'll only
be running on your own machine, you can enable these by using `-march=native`
for optimization options when running `configure`. Then you can build your
optimized binaries with the following command:
``` bash
$ bazel build --copt=-march=native -c opt //tensorflow/tools/pip_package:build_pip_package
$ bazel build --config opt //tensorflow/tools/pip_package:build_pip_package
```
If you are distributing a binary but know the capabilities of the machines
you'll be running on, you can manually choose the right instructions with
something like `--copt=-march=avx`. You may also want to enable multiple
something like `-march=avx`. You may also want to enable multiple
features using several arguments, for example
`--copt=-mavx2 --copt=-mfma`.
`-mavx2,-mfma`.
If you run a binary built using SIMD instructions on a machine that doesn't
support them, you'll see an illegal instruction error when that code is
@ -902,10 +903,10 @@ system directories, run the following commands inside the TensorFlow root
directory:
```bash
bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
bazel build --config opt //tensorflow/tools/pip_package:build_pip_package
# To build with GPU support:
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel build --config opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
mkdir _python_build
cd _python_build

2
tensorflow/g3doc/how_tos/adding_an_op/index.md
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@ -177,7 +177,7 @@ tf_custom_op_library(
Run the following command to build `zero_out.so`.
```bash
$ bazel build -c opt //tensorflow/core/user_ops:zero_out.so
$ bazel build --config opt //tensorflow/core/user_ops:zero_out.so
```
> Note:

4
tensorflow/g3doc/how_tos/image_retraining/index.md
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@ -42,10 +42,10 @@ bazel build tensorflow/examples/image_retraining:retrain
If you have a machine which supports [the AVX instruction set](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions)
(common in x86 CPUs produced in the last few years) you can improve the running
speed of the retraining by building for that architecture, like this:
speed of the retraining by building for that architecture, like this (after choosing appropriate options in `configure`):
```sh
bazel build -c opt --copt=-mavx tensorflow/examples/image_retraining:retrain
bazel build --config opt tensorflow/examples/image_retraining:retrain
```
The retrainer can then be run like this:

2
tensorflow/go/README.md
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@ -40,7 +40,7 @@ Construct and execute TensorFlow graphs in Go.
```sh
cd ${GOPATH}/src/github.com/tensorflow/tensorflow
./configure
bazel build -c opt //tensorflow:libtensorflow.so
bazel build --config opt //tensorflow:libtensorflow.so
```
This can take a while (tens of minutes, more if also building for GPU).

2
tensorflow/go/genop/generate.sh
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@ -30,7 +30,7 @@ then
then
echo "Protocol buffer compiler protoc not found in PATH or in ${PROTOC}"
echo "Perhaps build it using:"
echo "bazel build -c opt @protobuf//:protoc"
echo "bazel build --config opt @protobuf//:protoc"
exit 1
fi
PROTOC=$PATH_PROTOC

2
tensorflow/java/README.md
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@ -40,7 +40,7 @@ Configure and build the Java Archive (JAR) and native library:
./configure
# Build the JAR and native library
bazel build -c opt \
bazel build --config opt \
//tensorflow/java:tensorflow \
//tensorflow/java:libtensorflow_jni
```

4
tensorflow/tools/benchmark/README.md
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@ -39,7 +39,7 @@ $adb shell "/data/local/tmp/benchmark_model \
### On desktop:
(1) build the binary
```bash
$bazel build -c opt tensorflow/tools/benchmark:benchmark_model
$bazel build --config opt tensorflow/tools/benchmark:benchmark_model
```
(2) Run on your compute graph, similar to the Android case but without the need of adb shell.
@ -54,4 +54,4 @@ $bazel-bin/tensorflow/tools/benchmark/benchmark_model \
```
The Inception graph used as an example here may be downloaded from
https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip

2
tensorflow/tools/lib_package/README.md
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@ -13,7 +13,7 @@ and [Rust](https://github.com/tensorflow/rust).
The command:
```sh
bazel build -c opt //tensorflow/tools/lib_package:libtensorflow
bazel build --config opt //tensorflow/tools/lib_package:libtensorflow
```
produces `bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz`, which

2
tensorflow/tools/tfprof/README.md
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@ -98,7 +98,7 @@ TODO(xpan): Provide graph.pbtxt, model.ckpt, tfprof_log and run_meta download.
```shell
# Build the tool.
bazel build -c opt tensorflow/tools/tfprof/...
bazel build --config opt tensorflow/tools/tfprof/...
# Help information, including detail 'option' instructions.
bazel-bin/tensorflow/tools/tfprof/tfprof help