Front End of Supercomputer ITO Enable TensorFlow with Jupyter in / system / ITO / frontend /). The procedure for setting up the front end and Jupyter is summarized in this article. Only bare metal can use GPU in Frontend. In order to use the GPU, you need to select bare metal when reserving front-end resources. GPUs are not available for front-end virtual machines. The installation method of TensorFlow in Linux environment using NVIDIA GPU is not limited to supercomputer ITO, but is general content.
Refer to this article and log in to the reserved frontend node. In supercomputer ITO, various software can be used by loading module. Load CUDA 10.1, which is required to use GPU with TensorFlow 2.1. Be careful with the combination of TensorFlow and GPU versions. TensorFlow 2.1 requires CUDA 10.1.
** CUDA load is required not only when installing TensorFlow, but also every time TensorFlow is executed. ** **
$ module load cuda/10.1
#Confirm that cuda has been loaded. The Intel compiler is also loaded in the author's environment.
$ module list
Currently Loaded Modulefiles:
1) intel/2019.4 2) cuda/10.1
#If you can confirm the version of NVIDIA CUDA Toolkit, it is successful.
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
First, build a virtual environment with an Intel distribution, then refer to Anaconda Cloud and install TensorFlow 2.1 on various channels including intel channel. I tried. However, the GPU was not recognized. After all, I succeeded by building a virtual environment with ʻanaconda channeland installing thetensorflow-gpu package with ʻanaconda channel.
I used the Miniconda prepared in this article. Prepare a new virtual environment tf and proceed with the work.
$ conda create -c anaconda -n tf
$ conda activate tf
$ conda install -c anaconda tensorflow-gpu
Let's check if TensorFlow recognizes the GPU. Execute the following in the Python environment.
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
When I actually tried it, it became as follows. In the Python environment, press Shift-Enter to execute. At the bottom, device_type:" GPU " appears, and you can see that it recognizes the GPU. To get out of the Python environment, run quit ().
$ python
Python 3.7.7 (default, Mar 26 2020, 15:48:22)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from tensorflow.python.client import device_lib
>>> device_lib.list_local_devices()
2020-04-12 10:07:00.887858: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
2020-04-12 10:07:01.650004: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz
2020-04-12 10:07:01.674747: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd2f0c37f20 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-12 10:07:01.674811: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-04-12 10:07:01.862541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-04-12 10:07:02.015789: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:37:00.0 name: Quadro P4000 computeCapability: 6.1
coreClock: 1.48GHz coreCount: 14 deviceMemorySize: 7.93GiB deviceMemoryBandwidth: 226.62GiB/s
2020-04-12 10:07:02.066758: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-12 10:07:07.009068: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-12 10:07:10.726880: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-12 10:07:11.122902: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-12 10:07:16.866789: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-12 10:07:17.233256: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-12 10:07:21.609688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-12 10:07:21.630634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-04-12 10:07:21.653310: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-12 10:07:21.868090: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-04-12 10:07:21.868135: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] 0
2020-04-12 10:07:21.881889: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0: N
2020-04-12 10:07:21.953254: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/device:GPU:0 with 7609 MB memory) -> physical GPU (device: 0, name: Quadro P4000, pci bus id: 0000:37:00.0, compute capability: 6.1)
2020-04-12 10:07:22.064372: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd2f185b0d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-04-12 10:07:22.064411: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Quadro P4000, Compute Capability 6.1
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 604661095847797083
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 8073603212876973691
physical_device_desc: "device: XLA_CPU device"
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 7979450368
locality {
bus_id: 1
links {
}
}
incarnation: 4571149872542587611
physical_device_desc: "device: 0, name: Quadro P4000, pci bus id: 0000:37:00.0, compute capability: 6.1"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 3629064078974056009
physical_device_desc: "device: XLA_GPU device"
]
>>>
Install the necessary packages in addition to TensorFlow in the tf virtual environment. channel specified ʻanaconda. In addition to the required jupyterlab`, the following has been installed for remote sensing analysis.
$ conda install -c anaconda jupyterlab matplotlib rasterio scikit-learn
Start Jupyter and import TensorFlow to use it. Details of the method are summarized in this article. In summary, the procedure is as follows.
# 1.Enter the frontend node by doing the following in the terminal of the login node
$ ssh -A -Y Floating_IP
# Floating_IP is emailed, 172.18.32.An IP address such as 191.
# 2.Start Jupyter on the frontend node
#For Jupyter Notebook
$ jupyter notebook --ip=127.0.0.1 --port=8888 --no-browser
#For JupterLab
$ jupyter lab --ip=127.0.0.1 --port=8888 --no-browser
# 3. 1.Paste the following url that appears in the client browser url and execute
http://127.0.0.1:8888/?token=...
** Note: The cell sometimes stopped in the * state while running on JupyterLab. In that case, it was solved by Restart Kernel of Kernel. ** **
Since the package is updated daily, the behavior differs depending on the installation date and time. Here is a list of packages by the above method as of April 11, 2020. However, scikit-learn is not included in the above installations.
$ conda list
# packages in environment at /home/usr1/m00000a/local/miniconda3/envs/tf:
#
# Name Version Build Channel
_tflow_select 2.1.0 gpu anaconda
absl-py 0.9.0 py37_0 anaconda
affine 2.3.0 py_0 anaconda
asn1crypto 1.3.0 py37_0 anaconda
astor 0.8.0 py37_0 anaconda
attrs 19.3.0 py_0 anaconda
backcall 0.1.0 py37_0 anaconda
blas 1.0 mkl anaconda
bleach 3.1.0 py37_0 anaconda
blinker 1.4 py37_0 anaconda
boost-cpp 1.72.0 h8e57a91_0 conda-forge
bzip2 1.0.8 h7b6447c_0 anaconda
c-ares 1.15.0 h7b6447c_1001 anaconda
ca-certificates 2020.1.1 0 anaconda
cachetools 3.1.1 py_0 anaconda
cairo 1.16.0 hcf35c78_1003 conda-forge
certifi 2020.4.5.1 py37_0 anaconda
cffi 1.14.0 py37h2e261b9_0 anaconda
cfitsio 3.470 hb7c8383_2 anaconda
chardet 3.0.4 py37_1003 anaconda
click 7.1.1 py_0 anaconda
click-plugins 1.1.1 py_0 anaconda
cligj 0.5.0 py37_0 anaconda
cryptography 2.8 py37h1ba5d50_0 anaconda
cudatoolkit 10.1.243 h6bb024c_0 anaconda
cudnn 7.6.5 cuda10.1_0 anaconda
cupti 10.1.168 0 anaconda
curl 7.69.1 hbc83047_0 anaconda
cycler 0.10.0 py37_0 anaconda
decorator 4.4.2 py_0 anaconda
defusedxml 0.6.0 py_0 anaconda
entrypoints 0.3 py37_0 anaconda
expat 2.2.9 he1b5a44_2 conda-forge
fontconfig 2.13.1 h86ecdb6_1001 conda-forge
freetype 2.10.1 1 intel
freexl 1.0.5 h14c3975_0 anaconda
gast 0.2.2 py37_0 anaconda
geos 3.8.0 he6710b0_0 anaconda
geotiff 1.5.1 h38872f0_8 conda-forge
giflib 5.2.1 h516909a_2 conda-forge
glib 2.63.1 h5a9c865_0 anaconda
gmp 6.1.2 hb3b607b_0 anaconda
google-auth 1.13.1 py_0 anaconda
google-auth-oauthlib 0.4.1 py_2 anaconda
google-pasta 0.2.0 py_0 anaconda
grpcio 1.27.2 py37hf8bcb03_0 anaconda
h5py 2.10.0 nompi_py37h513d04c_102 conda-forge
hdf4 4.2.13 h3ca952b_2
hdf5 1.10.5 nompi_h3c11f04_1104 conda-forge
icu 64.2 he1b5a44_1 conda-forge
idna 2.9 py_1 anaconda
importlib_metadata 1.5.0 py37_0 anaconda
intel-openmp 2020.0 166 anaconda
intelpython 2020.1 0 intel
ipykernel 5.1.4 py37h39e3cac_0 anaconda
ipython 7.13.0 py37h5ca1d4c_0 anaconda
ipython_genutils 0.2.0 py37_0 anaconda
jedi 0.16.0 py37_1 anaconda
jinja2 2.11.1 py_0 anaconda
jpeg 9c h14c3975_1001 conda-forge
json-c 0.13.1 h1bed415_0 anaconda
json5 0.9.4 py_0 anaconda
jsonschema 3.2.0 py37_0 anaconda
jupyter_client 6.1.2 py_0 anaconda
jupyter_core 4.6.3 py37_0 anaconda
jupyterlab 1.2.6 pyhf63ae98_0 anaconda
jupyterlab_server 1.1.0 py_0 anaconda
kealib 1.4.13 hec59c27_0 conda-forge
keras-applications 1.0.8 py_0 anaconda
keras-preprocessing 1.1.0 py_1 anaconda
kiwisolver 1.1.0 py37he6710b0_0 anaconda
krb5 1.17.1 h173b8e3_0 anaconda
ld_impl_linux-64 2.33.1 h53a641e_7 anaconda
libblas 3.8.0 14_mkl conda-forge
libcblas 3.8.0 14_mkl conda-forge
libcurl 7.69.1 h20c2e04_0 anaconda
libdap4 3.20.4 hd3bb157_0 conda-forge
libedit 3.1.20181209 hc058e9b_0 anaconda
libffi 3.2.1 h4deb6c0_3 anaconda
libgcc-ng 9.1.0 hdf63c60_0 anaconda
libgdal 3.0.4 h20022a4_0 conda-forge
libgfortran-ng 7.3.0 hdf63c60_0 anaconda
libiconv 1.15 h63c8f33_5 anaconda
libkml 1.3.0 hb574062_1011 conda-forge
liblapack 3.8.0 14_mkl conda-forge
libnetcdf 4.7.3 nompi_h9f9fd6a_101 conda-forge
libpng 1.6.37 hbc83047_0 anaconda
libpq 12.2 h20c2e04_0 anaconda
libprotobuf 3.11.4 hd408876_0 anaconda
libsodium 1.0.16 h1bed415_0 anaconda
libspatialite 4.3.0a ha48a99a_1034 conda-forge
libssh2 1.9.0 h1ba5d50_1 anaconda
libstdcxx-ng 9.1.0 hdf63c60_0 anaconda
libtiff 4.1.0 hc3755c2_3 conda-forge
libuuid 2.32.1 h14c3975_1000 conda-forge
libwebp 1.0.2 h56121f0_5 conda-forge
libxcb 1.13 h1bed415_1 anaconda
libxml2 2.9.10 hee79883_0 conda-forge
lz4-c 1.8.3 he1b5a44_1001 conda-forge
markdown 3.1.1 py37_0 anaconda
markupsafe 1.1.1 py37h7b6447c_0 anaconda
matplotlib 3.1.2 py37_3 intel
mistune 0.8.4 py37h7b6447c_0 anaconda
mkl 2019.5 281 anaconda
mkl-service 2.3.0 py37he904b0f_0 anaconda
mkl_fft 1.0.15 py37ha843d7b_0 anaconda
mkl_random 1.1.0 py37hd6b4f25_0 anaconda
nbconvert 5.6.1 py37_0 anaconda
nbformat 5.0.4 py_0 anaconda
ncurses 6.2 he6710b0_0 anaconda
notebook 6.0.3 py37_0 anaconda
numpy 1.17.5 py37h95a1406_0 conda-forge
numpy-base 1.18.1 py37hde5b4d6_1 anaconda
oauthlib 3.1.0 py_0 anaconda
openjpeg 2.3.1 h981e76c_3 conda-forge
openssl 1.1.1 h7b6447c_0 anaconda
opt_einsum 3.1.0 py_0 anaconda
pandoc 2.2.3.2 0 anaconda
pandocfilters 1.4.2 py37_1 anaconda
parso 0.6.2 py_0 anaconda
pcre 8.43 he6710b0_0 anaconda
pexpect 4.8.0 py37_0 anaconda
pickleshare 0.7.5 py37_0 anaconda
pip 20.0.2 py37_1 anaconda
pixman 0.38.0 h7b6447c_0 anaconda
poppler 0.67.0 h14e79db_8 conda-forge
poppler-data 0.4.9 0 anaconda
postgresql 12.2 h20c2e04_0 anaconda
proj 6.3.0 hc80f0dc_0 conda-forge
prometheus_client 0.7.1 py_0 anaconda
prompt-toolkit 3.0.4 py_0 anaconda
prompt_toolkit 3.0.4 0 anaconda
protobuf 3.11.4 py37he6710b0_0 anaconda
ptyprocess 0.6.0 py37_0 anaconda
pyasn1 0.4.8 py_0 anaconda
pyasn1-modules 0.2.7 py_0 anaconda
pycparser 2.20 py_0 anaconda
pygments 2.6.1 py_0 anaconda
pyjwt 1.7.1 py37_0 anaconda
pyopenssl 19.1.0 py37_0 anaconda
pyparsing 2.4.6 py_0 anaconda
pyrsistent 0.16.0 py37h7b6447c_0 anaconda
pysocks 1.7.1 py37_0 anaconda
python 3.7.7 hcf32534_0_cpython anaconda
python-dateutil 2.8.1 py_0 anaconda
pytz 2019.3 py_0 anaconda
pyzmq 18.1.1 py37he6710b0_0 anaconda
rasterio 1.1.0 py37h41e4f33_0 anaconda
readline 8.0 h7b6447c_0 anaconda
requests 2.23.0 py37_0 anaconda
requests-oauthlib 1.3.0 py_0 anaconda
rsa 4.0 py_0 anaconda
scipy 1.4.1 py37h0b6359f_0 anaconda
send2trash 1.5.0 py37_0 anaconda
setuptools 46.1.3 py37_0 anaconda
six 1.14.0 py37_0 anaconda
snuggs 1.4.7 py_0 anaconda
sqlite 3.31.1 h7b6447c_0 anaconda
tbb 2018.0.5 h6bb024c_0 anaconda
tcl 8.6.9 24 intel
tensorboard 2.1.0 py3_0 anaconda
tensorflow 2.1.0 gpu_py37h7a4bb67_0 anaconda
tensorflow-base 2.1.0 gpu_py37h6c5654b_0 anaconda
tensorflow-estimator 2.1.0 pyhd54b08b_0 anaconda
tensorflow-gpu 2.1.0 h0d30ee6_0 anaconda
termcolor 1.1.0 py37_1 anaconda
terminado 0.8.3 py37_0 anaconda
testpath 0.4.4 py_0 anaconda
tiledb 1.7.0 hcde45ca_2 conda-forge
tk 8.6.8 hbc83047_0 anaconda
tornado 6.0.4 py37h7b6447c_1 anaconda
traitlets 4.3.3 py37_0 anaconda
urllib3 1.25.8 py37_0 anaconda
wcwidth 0.1.9 py_0 anaconda
webencodings 0.5.1 py37_1 anaconda
werkzeug 1.0.0 py_0 anaconda
wheel 0.34.2 py37_0 anaconda
wrapt 1.12.1 py37h7b6447c_1 anaconda
xerces-c 3.2.2 h8412b87_1004 conda-forge
xorg-kbproto 1.0.7 h14c3975_1002 conda-forge
xorg-libice 1.0.10 h516909a_0 conda-forge
xorg-libsm 1.2.3 h84519dc_1000 conda-forge
xorg-libx11 1.6.9 h516909a_0 conda-forge
xorg-libxext 1.3.4 h516909a_0 conda-forge
xorg-libxrender 0.9.10 h516909a_1002 conda-forge
xorg-renderproto 0.11.1 h14c3975_1002 conda-forge
xorg-xextproto 7.3.0 h14c3975_1002 conda-forge
xorg-xproto 7.0.31 h14c3975_1007 conda-forge
xz 5.2.4 h14c3975_4 anaconda
zeromq 4.3.1 he6710b0_3 anaconda
zipp 2.2.0 py_0 anaconda
zlib 1.2.11 h7b6447c_3 anaconda
zstd 1.4.4 h3b9ef0a_2 conda-forge
Recommended Posts