I know that this question has been asked a lot, but none of the suggestions seem to work, probably since my setup is somewhat different:
Ubuntu 22.04
python 3.10.8
tensorflow 2.11.0
cudatoolkit 11.2.2
cudnn 8.1.0.77
nvidia-tensorrt 8.4.3.1
nvidia-pyindex 1.0.9
Having created a conda environment 'tf', in the directory home/dan/anaconda3/envs/tf/lib/python3.10/site-packages/tensorrt I have
libnvinfer_builder_resource.so.8.4.3
libnvinfer_plugin.so.8
libnvinfer.so.8
libnvonnxparser.so.8
libnvparsers.so.8
tensorrt.so
When running python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" I get
tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7';
dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory;
LD_LIBRARY_PATH: :/home/dan/anaconda3/envs/tf/lib
tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7';
dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory;
LD_LIBRARY_PATH: :/home/dan/anaconda3/envs/tf/lib
tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
I'm guessing I should downgrade nvidia-tensorrt, but nothing I've tried seems to work, any advice would be much appreciated.
Solution: follow the steps listed here https://github.com/tensorflow/tensorflow/issues/57679#issuecomment-1249197802.
Add the following to ~/.bashrc (for the conda envs as described in my scenario):
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/dan/anaconda3/lib/
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/dan/anaconda3/lib/python3.8/site-packages/tensorrt/
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/dan/anaconda3/envs/tf/lib
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/dan/anaconda3/envs/tf/lib/python3.8/site-packages/tensorrt/
For me the setting a symbolic link from libnvinfer version 7 to 8 worked:
# the follwoing path will be different for you - depending on your install method
$ cd env/lib/python3.10/site-packages/tensorrt
# create symbolic links
$ ln -s libnvinfer_plugin.so.8 libnvinfer_plugin.so.7
$ ln -s linvinfer.so.8 libnvinfer.so.7
# add tensorrt to library path
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/env/lib/python3.10/site-packages/tensorrt/
Related
I've installed tensorflow in a remote machine, then connected it via remote VSCode.
I've also installed tensorflow following the installation guide that I have the conda environment with the name atnwf in the remote machine.
I also learned that jupyter notebook does not keep environment variables so that I set the proper LD_LIBRARY_PATH using %env in my notebook.
%env LD_LIBRARY_PATH=/home/grad/tomandjerry/anaconda3/envs/atnwf/lib/
However still the tensorflow warning message says
2022-12-21 13:46:29.064619: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/grad/tomandjerry/anaconda3/envs/atnwf/lib/
I've already checked that the library was installed in the corresponding path. Moreover, I verified the basic installation.
(atnwf) [lib]$ python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2022-12-21 14:02:34.992430: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-12-21 14:02:36.382350: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/lib/:/home/grad/tomandjerry/anaconda3/envs/atnwf/lib/:/home/grad/tomandjerry/anaconda3/envs/atnwf/lib/:/home/grad/tomandjerry/anaconda3/envs/atnwf/lib/
2022-12-21 14:02:36.382611: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/lib/:/home/grad/tomandjerry/anaconda3/envs/atnwf/lib/:/home/grad/tomandjerry/anaconda3/envs/atnwf/lib/:/home/grad/tomandjerry/anaconda3/envs/atnwf/lib/
2022-12-21 14:02:36.382633: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU')]
How can I resolve the "not found" issue correctly?
I tried re-installing all the packages in the conda environment by resetting a new environment with another name. I've also tried the method editing kernelspec stated in How to set env variable in Jupyter notebook.
I think I read pretty much most of the guides on setting up tensorflow, tensorflow-hub, object detection on Mac M1 on BigSur v11.6. I managed to figure out most of the errors after more than 2 weeks. But I am stuck at OpenCV setup. I tried to compile it from source but seems like it can't find the modules from its core package so constantly can't make the file after the successful cmake build. It fails at different stages, crying for different libraries, despite they are there but max reached 31% after multiple cmake and deletion of the build folder or the cmake cash file. So I am not sure what to do in order to make successfully the file.
I git cloned and unzipped the opencv-4.5.0 and opencv_contrib-4.5.0 in my miniforge3 directory. Then I created a folder "build" in my opencv-4.5.0 folder and the cmake command I use in it is (my miniforge conda environment is called silicon and made sure I am using arch arm64 in bash environment):
cmake -DCMAKE_SYSTEM_PROCESSOR=arm64 -DCMAKE_OSX_ARCHITECTURES=arm64 -DWITH_OPENJPEG=OFF -DWITH_IPP=OFF -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D OPENCV_EXTRA_MODULES_PATH=/Users/adi/miniforge3/opencv_contrib-4.5.0/modules -D PYTHON3_EXECUTABLE=/Users/adi/miniforge3/envs/silicon/bin/python3.8 -D BUILD_opencv_python2=OFF -D BUILD_opencv_python3=ON -D INSTALL_PYTHON_EXAMPLES=ON -D INSTALL_C_EXAMPLES=OFF -D OPENCV_ENABLE_NONFREE=ON -D BUILD_EXAMPLES=ON /Users/adi/miniforge3/opencv-4.5.0
So it cries like:
[ 20%] Linking CXX shared library ../../lib/libopencv_core.dylib
[ 20%] Built target opencv_core
make: *** [all] Error 2
or also like in another tries was initially asking for calib3d or dnn but those libraries are there in the main folder opencv-4.5.0.
The other way I try to install openCV is with conda:
conda install opencv
But then when I test with
python -c "import cv2; cv2.__version__"
it seems like it searches for the ffmepg via homebrew (I didn't install any of these via homebrew but with conda). So it complained:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/Users/adi/miniforge3/envs/silicon/lib/python3.8/site-packages/cv2/__init__.py", line 5, in <module>
from .cv2 import *
ImportError: dlopen(/Users/adi/miniforge3/envs/silicon/lib/python3.8/site-packages/cv2/cv2.cpython-38-darwin.so, 2): Library not loaded: /opt/homebrew/opt/ffmpeg/lib/libavcodec.58.dylib
Referenced from: /Users/adi/miniforge3/envs/silicon/lib/python3.8/site-packages/cv2/cv2.cpython-38-darwin.so
Reason: image not found
Though I have these libs, so when I searched with: find /usr/ -name 'libavcodec.58.dylib' I could find many locations:
find: /usr//sbin/authserver: Permission denied
find: /usr//local/mysql-8.0.22-macos10.15-x86_64/keyring: Permission denied
find: /usr//local/mysql-8.0.22-macos10.15-x86_64/data: Permission denied
find: /usr//local/hw_mp_userdata/Internet_Manager/OnlineUpdate: Permission denied
/usr//local/lib/libavcodec.58.dylib
/usr//local/Cellar/ffmpeg/4.4_2/lib/libavcodec.58.dylib
(silicon) MacBook-Pro:opencv-4.5.0 adi$ ln -s /usr/local/Cellar/ffmpeg/4.4_2/lib/libavcodec.58.dylib /opt/homebrew/opt/ffmpeg/lib/libavcodec.58.dylib
ln: /opt/homebrew/opt/ffmpeg/lib/libavcodec.58.dylib: No such file or directory
One of the guides said to install homebrew also in arm64 env, so I did it with:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
export PATH="/opt/homebrew/bin:/usr/local/bin:$PATH"
alias ibrew='arch -x86_64 /usr/local/bin/brew' # create brew for intel (ibrew) and arm/ silicon
Not sure if that is affecting it but seems like it didn't do anything because still uses /opt/homebrew/ instead of /usr/local/.
So any help would be highly appreciated if I can make any of the ways work. Ultimately I want to use Tenserflow Model Zoo Object Detection models. So all the other dependencies seems fine (for now) besides either OpenCV not working or if it is working with conda install then it seems that scipy and scikit-learn don't work.
In my case I also had lot of trouble trying to install both modules. I finally managed to do so but to be honest not really sure how and why. I leave below the requirements in case you might want to recreate the environment that worked in my case. You should have the conda Miniforge 3 installed :
# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: osx-arm64
absl-py=1.0.0=pypi_0
astunparse=1.6.3=pypi_0
autocfg=0.0.8=pypi_0
blas=2.113=openblas
blas-devel=3.9.0=13_osxarm64_openblas
boto3=1.22.10=pypi_0
botocore=1.25.10=pypi_0
c-ares=1.18.1=h1a28f6b_0
ca-certificates=2022.2.1=hca03da5_0
cachetools=5.0.0=pypi_0
certifi=2021.10.8=py39hca03da5_2
charset-normalizer=2.0.12=pypi_0
cycler=0.11.0=pypi_0
expat=2.4.4=hc377ac9_0
flatbuffers=2.0=pypi_0
fonttools=4.31.1=pypi_0
gast=0.5.3=pypi_0
gluoncv=0.10.5=pypi_0
google-auth=2.6.0=pypi_0
google-auth-oauthlib=0.4.6=pypi_0
google-pasta=0.2.0=pypi_0
grpcio=1.42.0=py39h95c9599_0
h5py=3.6.0=py39h7fe8675_0
hdf5=1.12.1=h5aa262f_1
idna=3.3=pypi_0
importlib-metadata=4.11.3=pypi_0
jmespath=1.0.0=pypi_0
keras=2.8.0=pypi_0
keras-preprocessing=1.1.2=pypi_0
kiwisolver=1.4.0=pypi_0
krb5=1.19.2=h3b8d789_0
libblas=3.9.0=13_osxarm64_openblas
libcblas=3.9.0=13_osxarm64_openblas
libclang=13.0.0=pypi_0
libcurl=7.80.0=hc6d1d07_0
libcxx=12.0.0=hf6beb65_1
libedit=3.1.20210910=h1a28f6b_0
libev=4.33=h1a28f6b_1
libffi=3.4.2=hc377ac9_2
libgfortran=5.0.0=11_1_0_h6a59814_26
libgfortran5=11.1.0=h6a59814_26
libiconv=1.16=h1a28f6b_1
liblapack=3.9.0=13_osxarm64_openblas
liblapacke=3.9.0=13_osxarm64_openblas
libnghttp2=1.46.0=h95c9599_0
libopenblas=0.3.18=openmp_h5dd58f0_0
libssh2=1.9.0=hf27765b_1
llvm-openmp=12.0.0=haf9daa7_1
markdown=3.3.6=pypi_0
matplotlib=3.5.1=pypi_0
mxnet=1.6.0=pypi_0
ncurses=6.3=h1a28f6b_2
numpy=1.21.2=py39hb38b75b_0
numpy-base=1.21.2=py39h6269429_0
oauthlib=3.2.0=pypi_0
openblas=0.3.18=openmp_h3b88efd_0
opencv-python=4.5.5.64=pypi_0
openssl=1.1.1m=h1a28f6b_0
opt-einsum=3.3.0=pypi_0
packaging=21.3=pypi_0
pandas=1.4.1=pypi_0
pillow=9.0.1=pypi_0
pip=22.0.4=pypi_0
portalocker=2.4.0=pypi_0
protobuf=3.19.4=pypi_0
pyasn1=0.4.8=pypi_0
pyasn1-modules=0.2.8=pypi_0
pydot=1.4.2=pypi_0
pyparsing=3.0.7=pypi_0
python=3.9.7=hc70090a_1
python-dateutil=2.8.2=pypi_0
python-graphviz=0.8.4=pypi_0
pytz=2022.1=pypi_0
pyyaml=6.0=pypi_0
readline=8.1.2=h1a28f6b_1
requests=2.27.1=pypi_0
requests-oauthlib=1.3.1=pypi_0
rsa=4.8=pypi_0
s3transfer=0.5.2=pypi_0
scipy=1.8.0=pypi_0
setuptools=58.0.4=py39hca03da5_1
six=1.16.0=pyhd3eb1b0_1
sqlite=3.38.0=h1058600_0
tensorboard=2.8.0=pypi_0
tensorboard-data-server=0.6.1=pypi_0
tensorboard-plugin-wit=1.8.1=pypi_0
tensorflow-deps=2.8.0=0
tensorflow-macos=2.8.0=pypi_0
termcolor=1.1.0=pypi_0
tf-estimator-nightly=2.8.0.dev2021122109=pypi_0
tk=8.6.11=hb8d0fd4_0
tqdm=4.63.1=pypi_0
typing-extensions=4.1.1=pypi_0
tzdata=2021e=hda174b7_0
urllib3=1.26.9=pypi_0
werkzeug=2.0.3=pypi_0
wheel=0.37.1=pyhd3eb1b0_0
wrapt=1.14.0=pypi_0
xz=5.2.5=h1a28f6b_0
yacs=0.1.8=pypi_0
zipp=3.7.0=pypi_0
zlib=1.2.11=h5a0b063_4
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 was trying to using syntaxnet and I have finished most of processes. Upgrade bazel version to 0.43 in case of errors (Ubuntu 16.04 Ver, Anaconda python 2.7).
However, I am having a troubles with ./configure part. I am reading the official instruction via tensorflow github.
git clone --recursive https://github.com/tensorflow/models.git
cd models/syntaxnet/tensorflow
**./configure**
cd ..
bazel test syntaxnet/... util/utf8/...
# On Mac, run the following:
bazel test --linkopt=-headerpad_max_install_names \
syntaxnet/... util/utf8/...
Following logs will help you to understand what’s going on my machine. Thanks for the advice
Please specify the location of python. [Default is /home/ryan/anaconda2/bin/python]:
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] n
No Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with Hadoop File System support? [y/N] n
No Hadoop File System support will be enabled for TensorFlow
Found possible Python library paths:
/home/ryan
/home/ryan/pynaoqi-python2.7
/home/ryan/anaconda2/lib/python2.7/site-packages
Please input the desired Python library path to use. Default is [/home/ryan]
/home/ryan/anaconda2/lib/python2.7/site-packages
Do you wish to build TensorFlow with GPU support? [y/N] y
GPU support will be enabled for TensorFlow
Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]: 8.0
Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify the Cudnn version you want to use. [Leave empty to use system default]: 5.0
Please specify the location where cuDNN 5.0 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Invalid path to cuDNN toolkit. Neither of the following two files can be found:
/usr/local/cuda-8.0/lib64/libcudnn.so.5.0
/usr/local/cuda-8.0/libcudnn.so.5.0
.5.0
Please specify the Cudnn version you want to use. [Leave empty to use system default]:
Please specify the location where cuDNN library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
libcudnn.so resolves to libcudnn.5
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size.
[Default is: "3.5,5.2"]:
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=120
INFO: Reading options for 'clean' from /home/ryan/git_ryan/models/syntaxnet/tensorflow/tools/bazel.rc:
Inherited 'build' options: --force_python=py2 --host_force_python=py2 --python2_path=/home/ryan/anaconda2/bin/python --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --define PYTHON_BIN_PATH=/home/ryan/anaconda2/bin/python --spawn_strategy=standalone --genrule_strategy=standalone
**INFO: Reading options for 'clean' from /etc/bazel.bazelrc:
Inherited 'build' options: --action_env=PATH --action_env=LD_LIBRARY_PATH --action_env=TMPDIR --test_env=PATH --test_env=LD_LIBRARY_PATH
Unrecognized option: --action_env=PATH
ERROR: /home/ryan/git_ryan/models/syntaxnet/tensorflow/tensorflow/tensorflow.bzl:568:26: Traceback (most recent call last):
File "/home/ryan/git_ryan/models/syntaxnet/tensorflow/tensorflow/tensorflow.bzl", line 562
rule(attrs = {"srcs": attr.label_list..."), <3 more arguments>)}, <2 more arguments>)
File "/home/ryan/git_ryan/models/syntaxnet/tensorflow/tensorflow/tensorflow.bzl", line 568, in rule
attr.label_list(cfg = "data", allow_files = True)
expected ConfigurationTransition or NoneType for 'cfg' while calling label_list but got string instead: data.
ERROR: com.google.devtools.build.lib.packages.BuildFileContainsErrorsException: error loading package '': Extension file 'tensorflow/tensorflow.bzl' has errors.
Configuration finished**
I think the version of your bazel is too high for Syntaxnet. you can try bazel-0.3.1 please.
TLDR: Can I use static ATLAS/LAPACK libraries with NumPy & SciPy?
Background:
After building ATLAS with LAPACK with the following:
wget http://sourceforge.net/projects/math-atlas/files/Stable/3.10.1/atlas3.10.1.tar.bz2/download
wget http://www.netlib.org/lapack/lapack-3.4.2.tgz
tar -jxvf atlas3.10.1.tar.bz2
mkdir BUILD
cd BUILD
../ATLAS/configure -b 64 -Fa alg -fPIC \
--with-netlib-lapack-tarfile=../lapack-3.4.2.tgz \
--prefix=<ATLAS_INSTALL_PATH>
make
cd lib
make shared
make ptshared
cd ..
make install
I got the following files under BUILD/lib:
Make.inc#
Makefile
.a files:
libatlas.a
libcblas.a
libf77blas.a
libptf77blas.a
libtstatlas.a
liblapack.a
libf77refblas.a
libptlapack.a
libptcblas.a
.so files:
libsatlas.so*
libtatlas.so*
My first question is, why don't I have .so (shared dynamic library) files for lapack and cblas?
My second question is, which of the following two files does NumPy use?
libsatlas.so*
libtatlas.so*
Finally, if I define:
BLAS=/path_to_BUILD/lib/libcblas.a
LAPACK=/path_to_BUILD/lib/liblapack.a
ATLAS=/path_to_BUILD/lib/libatlas.a
and add /path_to_BUILD/lib to LD_LIBRARY_PATH and to the library_dirs variable within the site.cfg file in NumPy. Would NumPy and SciPy use my libraries? (even though they are static?).
You should be able to. Add
[DEFAULT]
search_static_first = true
to your site.cfg file and you should be good to go.