I need to run my application on kvm.
The image(centos 6.3) that run on kvm does not contain avx.
But the computer i compile dpdk on it, have kvm.
I think i should compile dpdk without avx,
Is this possible, and how can i do it?
I know that there is or not avx by running the following command:
cat /proc/cpuinfo | grep avx --color
You can compile DPDK without AVX by commenting/removing the following lines in mk/rte.cpuflags.mk
ifneq ($(filter $(AUTO_CPUFLAGS),__AVX__),)
CPUFLAGS += AVX
endif
Related
I got the following error when trying to train my tensorflow model on sagemaker ml.p2.xlarge instance. I use tensorflow==2.3.0. I wonder whether this is because of the tensorflow version incompatibility with cuda. sagemaker ml.p2.xlarge seems to use cuda 10.0
GPU error:
2020-08-31 08:46:46.429756: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/openmpi/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
2020-08-31 08:47:02.170819: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/openmpi/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64
2020-08-31 08:47:02.764874: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1753] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
This question is probably old, but it falls back on an open issue found at the beginning of choosing which versions of frameworks to use.
The problem does not depend on the type of instance that you specified (which has NVidia GPU).
From the official documentation "Available Deep Learning Containers Images", to date 20/10/2022, precompiled versions higher than 2.2 do not seem to be usable:
Framework
Job Type
Horovod Options
CPU/GPU
Python Version Options
Example URL
TensorFlow 2.2 (Cuda 10.2)
training
Yes
GPU
3.7 (py37)
763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:2.2.0-gpu-py37-cu102-ubuntu18.04
TensorFlow 2.2
inference
No
GPU
3.7 (py37)
763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-inference:2.2.0-gpu-py37-cu102-ubuntu18.04
Within the dockerfile that is used to use the container is the instruction to install the libraries that your custom version is missing:
RUN apt-get update && apt-get install -y --no-install-recommends --allow-unauthenticated \
python3-dev \
python3-pip \
python3-setuptools \
ca-certificates \
cuda-command-line-tools-10-1 \
cuda-cudart-dev-10-1 \
cuda-cufft-dev-10-1 \
cuda-curand-dev-10-1 \
cuda-cusolver-dev-10-1 \
cuda-cusparse-dev-10-1 \
curl \
libcudnn7=7.6.2.24-1+cuda10.1 \
# TensorFlow doesn't require libnccl anymore but Open MPI still depends on it
libnccl2=2.4.7-1+cuda10.1 \
libgomp1 \
libnccl-dev=2.4.7-1+cuda10.1 \
....
Then you can install the required libraries from your custom version directly with a requirements.txt file or run the install command directly in the training script.
If there are no special project requirements, I recommend using the precompiled versions of sagemaker. Otherwise, build a docker image from scratch instead of installing libraries this way..
I'm looking the TF Lite Android App
Which can be found on GIT: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo
How can I compile the tensorflow lite framework to use the optimized "atom" cpu type?
Is it possible to compile it on a MAC os with the CPU optimizations for the "atom" cpu?
I want to run the app on an Android device (SDK 22) with an "Intel Atom" Processor.
When I run the application without any changes through Android Studio the rate was about 1200ms per frame.
Compering the same APK installed on my Galaxy S9 (arm - snapdragon processor) was about 30ms per frame.
In the "build.gradle" there is this section:
dependencies {
...
compile 'org.tensorflow:tensorflow-lite:0.0.0-nightly'
...
}
So it's seems that it's downloading the framework,
How can I compile it locally with the CPU optimization and set the app to use it instead of downloading the non optimized nightly version?
I tried to run this tutorial :
Installing TensorFlow from Sources with the cpu flags but not sure exactly how it's helping me with the Android scenario..
Assuming that your Atom device is x86, use the --fat_apk_cpu flag to specify the x86 ABI:
$ bazel build -c opt --cxxopt='--std=c++11' \
--fat_apk_cpu=x86 \
//tensorflow/contrib/lite/java/demo/app/src/main:TfLiteCameraDemo
Switch x86 with x86_64 if you're building for a 64-bit device.
The built APK, available at bazel-bin/tensorflow/contrib/lite/java/demo/app/src/main/TfLiteCameraDemo.apk, will contain the x86 .so file:
$ zipinfo bazel-bin/tensorflow/contrib/lite/java/demo/app/src/main/TfLiteCameraDemo.apk | grep lib
-rw---- 2.0 fat 1434712 b- defN 80-Jan-01 00:00 lib/x86/libtensorflowlite_jni.so
If your device is connected, you can use bazel mobile-install instead of bazel build to directly install the app:
$ bazel mobile-install -c opt --cxxopt='--std=c++11' \
--fat_apk_cpu=x86 \
--start_app \
//tensorflow/contrib/lite/java/demo/app/src/main:TfLiteCameraDemo
Is there a command to get the sm version of the gpu in given machine. Here is my use case: I build and run same cuda kernel on multiple machines. So I was wondering if there is a command which can detect sm version of gpu on the given system and pass that as arguement to nvcc:
$ nvcc -arch=`gpuarch -device 0` mykernel.cu
I found https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549. I find this is best solution. You just need python.
I'm new to Tensorflow.
I am using a 64 bit version of Windows 10 and I would like to install Tensorflow for the CPU.
I don't remember the exact steps that I followed to install it, however when I checked for the installation using:
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
I have the following output:
2017-10-18 09:56:21.656601: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-10-18 09:56:21.656984: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
b'Hello, TensorFlow!'
I am running python in Sublime Text 3 using the package SublimeREPL.
I tried to search these errors and found out that it means that the tensorflow is built without these instructions which could improve performances for the CPU. I also found the code to hide these warnings, but I actually I want to use these instructions.
The code that I found that enables this is:
bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-msse4.2 --copt=-msse4.1 --copt=-msse3 --copt=-mfma -k //tensorflow/tools/pip_package:build_pip_package
but I got this output:
ERROR: Skipping '//tensorflow/tools/pip_package:build_pip_package': no such package 'tensorflow/tools/pip_package': BUILD file not found on package path.
WARNING: Target pattern parsing failed. Continuing anyway.
INFO: Found 0 targets...
ERROR: command succeeded, but there were errors parsing the target pattern.
INFO: Elapsed time: 8,147s, Critical Path: 0,02s
How can I solve this problem?
Lastly, I don't understand what pip, wheel and bazel are so I need a step by step instructions.
Thank you a lot!
if you want to download TensorFlow source, compile+install, use this link. If you want to download binaries, then use this link.
I am trying to build MKL-accelerated version of TensorFlow using bazel 0.5.1, gcc 6.2, binutils 2.28, Anaconda2 python on Scientific Linux 7.2.
Apparently the system /lib64/libstdc++.so.6 is too old, so I am trying to use gcc installed in another directory. PATH, LD_LIBRARY_PATH are modified to prepend the corresponding paths (using modules). However, while bazel has no trouble picking up correctly executables for gcc, ld, python, it still tries to load old system /lib64/libstdc++.so.6. How to force it to use the one from gcc 6.2? Why does not it pick it up from LD_LIBRARY_PATH?
According to google many people are having trouble with this but I could not find a solution that would work for me. I had no trouble building TensorFlow under Ubuntu 16.04 that has sufficiently new gcc in the standard location.
I do:
1) ./configure
The only non-default options I choose is use MKL and download MKL
2) bazel build --config=mkl --copt="-DEIGEN_USE_VML" -s -c opt //tensorflow/tools/pip_package:build_pip_package
.....
example/example_parser_configuration.proto tensorflow/core/protobuf/control_flow.proto tensorflow/core/protobuf/meta_graph.proto tensorflow/core/protobuf/named_tensor.proto tensorflow/core/protobuf/saved_model.proto tensorflow/core/protobuf/tensorflow_server.proto tensorflow/core/util/event.proto tensorflow/core/util/test_log.proto)
ERROR: /scratch/midway2/ivy2/TF_intel/tensorflow/tensorflow/tools/tfprof/BUILD:42:1: null failed: protoc failed: error executing command bazel-out/host/bin/external/protobuf/protoc '--python_out=bazel-out/local-opt/genfiles/' -I. -I. -Iexternal/protobuf/python -Ibazel-out/local-opt/genfiles/external/protobuf/python ... (remaining 5 argument(s) skipped): com.google.devtools.build.lib.shell.BadExitStatusException: Process exited with status 1.
bazel-out/host/bin/external/protobuf/protoc: /lib64/libstdc++.so.6: version GLIBCXX_3.4.20' not found (required by bazel-out/host/bin/external/protobuf/protoc)
bazel-out/host/bin/external/protobuf/protoc: /lib64/libstdc++.so.6: versionCXXABI_1.3.8' not found (required by bazel-out/host/bin/external/protobuf/protoc)
bazel-out/host/bin/external/protobuf/protoc: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.21' not found (required by bazel-out/host/bin/external/protobuf/protoc)
.....
Thank you,
Igor
sorry for the slow reply. Bazel by design ignores LD_LIBRARY_PATH when running actions. It doesn't have to ignore them during C++ toolchain detection, but at the moment, it does :/ To help you forward, I would try adding --sysroot= as linkopt or using bazel grte_top flag. Depending on where your libstdc++.so lives, you might need to disable sandbox. The principled solution would be to write a custom CROSSTOOL that specifies builtin_sysroot or grte_top. But that is not an easy task.
Let me know if I lost you somewhere in that paragraph :)