├── README.md ├── monitor_queue_status.sh ├── launch_jupyter.sh ├── check_gpus.ipynb └── environment_AI.yml /README.md: -------------------------------------------------------------------------------- 1 | # slurm 2 | Basic SLURM scripts for jobs on bridges 3 | -------------------------------------------------------------------------------- /monitor_queue_status.sh: -------------------------------------------------------------------------------- 1 | while true 2 | do 3 | squeue -u khan74 4 | sleep 10 5 | done 6 | -------------------------------------------------------------------------------- /launch_jupyter.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #SBATCH -N 1 3 | #SBATCH -p GPU 4 | #SBATCH --ntasks-per-node 1 5 | #SBATCH --gres=gpu:k80:4 6 | #SBATCH -t 01:00:00 7 | #SBATCH --verbose 8 | 9 | #SBATCH --mail-user=khan74@illinois.edu 10 | #SBATCH --mail-type=BEGIN 11 | 12 | #SBATCH --job-name="test" 13 | #SBATCH -C EGRESS 14 | 15 | #echo commands to stdout 16 | set -x 17 | 18 | hostname 19 | 20 | #run GPU program 21 | cd /home/khan74 22 | 23 | jupyter notebook --no-browser --ip=0.0.0.0 24 | -------------------------------------------------------------------------------- /check_gpus.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "Using TensorFlow backend.\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "import keras" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 7, 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "import os" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 2, 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "from keras.applications.vgg16 import VGG16" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 3, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "model = VGG16(include_top=False, weights='imagenet')" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 2, 50 | "metadata": { 51 | "scrolled": true 52 | }, 53 | "outputs": [ 54 | { 55 | "name": "stdout", 56 | "output_type": "stream", 57 | "text": [ 58 | "[name: \"/device:CPU:0\"\n", 59 | "device_type: \"CPU\"\n", 60 | "memory_limit: 268435456\n", 61 | "locality {\n", 62 | "}\n", 63 | "incarnation: 14777699627553829671\n", 64 | ", name: \"/device:GPU:0\"\n", 65 | "device_type: \"GPU\"\n", 66 | "memory_limit: 11280557671\n", 67 | "locality {\n", 68 | " bus_id: 2\n", 69 | " numa_node: 1\n", 70 | " links {\n", 71 | " link {\n", 72 | " device_id: 1\n", 73 | " type: \"StreamExecutor\"\n", 74 | " strength: 1\n", 75 | " }\n", 76 | " link {\n", 77 | " device_id: 2\n", 78 | " type: \"StreamExecutor\"\n", 79 | " strength: 1\n", 80 | " }\n", 81 | " link {\n", 82 | " device_id: 3\n", 83 | " type: \"StreamExecutor\"\n", 84 | " strength: 1\n", 85 | " }\n", 86 | " }\n", 87 | "}\n", 88 | "incarnation: 5809529027750183147\n", 89 | "physical_device_desc: \"device: 0, name: Tesla K80, pci bus id: 0000:83:00.0, compute capability: 3.7\"\n", 90 | ", name: \"/device:GPU:1\"\n", 91 | "device_type: \"GPU\"\n", 92 | "memory_limit: 11280557671\n", 93 | "locality {\n", 94 | " bus_id: 2\n", 95 | " numa_node: 1\n", 96 | " links {\n", 97 | " link {\n", 98 | " type: \"StreamExecutor\"\n", 99 | " strength: 1\n", 100 | " }\n", 101 | " link {\n", 102 | " device_id: 2\n", 103 | " type: \"StreamExecutor\"\n", 104 | " strength: 1\n", 105 | " }\n", 106 | " link {\n", 107 | " device_id: 3\n", 108 | " type: \"StreamExecutor\"\n", 109 | " strength: 1\n", 110 | " }\n", 111 | " }\n", 112 | "}\n", 113 | "incarnation: 11258002698626366590\n", 114 | "physical_device_desc: \"device: 1, name: Tesla K80, pci bus id: 0000:84:00.0, compute capability: 3.7\"\n", 115 | ", name: \"/device:GPU:2\"\n", 116 | "device_type: \"GPU\"\n", 117 | "memory_limit: 11280557671\n", 118 | "locality {\n", 119 | " bus_id: 2\n", 120 | " numa_node: 1\n", 121 | " links {\n", 122 | " link {\n", 123 | " type: \"StreamExecutor\"\n", 124 | " strength: 1\n", 125 | " }\n", 126 | " link {\n", 127 | " device_id: 1\n", 128 | " type: \"StreamExecutor\"\n", 129 | " strength: 1\n", 130 | " }\n", 131 | " link {\n", 132 | " device_id: 3\n", 133 | " type: \"StreamExecutor\"\n", 134 | " strength: 1\n", 135 | " }\n", 136 | " }\n", 137 | "}\n", 138 | "incarnation: 15144344017742222817\n", 139 | "physical_device_desc: \"device: 2, name: Tesla K80, pci bus id: 0000:8a:00.0, compute capability: 3.7\"\n", 140 | ", name: \"/device:GPU:3\"\n", 141 | "device_type: \"GPU\"\n", 142 | "memory_limit: 11280557671\n", 143 | "locality {\n", 144 | " bus_id: 2\n", 145 | " numa_node: 1\n", 146 | " links {\n", 147 | " link {\n", 148 | " type: \"StreamExecutor\"\n", 149 | " strength: 1\n", 150 | " }\n", 151 | " link {\n", 152 | " device_id: 1\n", 153 | " type: \"StreamExecutor\"\n", 154 | " strength: 1\n", 155 | " }\n", 156 | " link {\n", 157 | " device_id: 2\n", 158 | " type: \"StreamExecutor\"\n", 159 | " strength: 1\n", 160 | " }\n", 161 | " }\n", 162 | "}\n", 163 | "incarnation: 15663464147242643094\n", 164 | "physical_device_desc: \"device: 3, name: Tesla K80, pci bus id: 0000:8b:00.0, compute capability: 3.7\"\n", 165 | "]\n" 166 | ] 167 | } 168 | ], 169 | "source": [ 170 | "from tensorflow.python.client import device_lib\n", 171 | "print(device_lib.list_local_devices())" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": 3, 177 | "metadata": {}, 178 | "outputs": [ 179 | { 180 | "data": { 181 | "text/plain": [ 182 | "['/job:localhost/replica:0/task:0/device:GPU:0',\n", 183 | " '/job:localhost/replica:0/task:0/device:GPU:1',\n", 184 | " '/job:localhost/replica:0/task:0/device:GPU:2',\n", 185 | " '/job:localhost/replica:0/task:0/device:GPU:3']" 186 | ] 187 | }, 188 | "execution_count": 3, 189 | "metadata": {}, 190 | "output_type": "execute_result" 191 | } 192 | ], 193 | "source": [ 194 | "from keras import backend as K\n", 195 | "K.tensorflow_backend._get_available_gpus()" 196 | ] 197 | }, 198 | { 199 | "cell_type": "code", 200 | "execution_count": 4, 201 | "metadata": {}, 202 | "outputs": [ 203 | { 204 | "name": "stdout", 205 | "output_type": "stream", 206 | "text": [ 207 | "Fri Oct 12 09:18:45 2018 \r\n", 208 | "+-----------------------------------------------------------------------------+\r\n", 209 | "+-----------------------------------------------------------------------------+\r\n", 210 | "| NVIDIA-SMI 396.37 Driver Version: 396.37 |\r\n", 211 | "|-------------------------------+----------------------+----------------------+\r\n", 212 | "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n", 213 | "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n", 214 | "|===============================+======================+======================|\r\n", 215 | "| 0 Tesla K80 On | 00000000:83:00.0 Off | 0 |\r\n", 216 | "| N/A 33C P0 69W / 149W | 10879MiB / 11441MiB | 0% Default |\r\n", 217 | "+-------------------------------+----------------------+----------------------+\r\n", 218 | "| 1 Tesla K80 On | 00000000:84:00.0 Off | 0 |\r\n", 219 | "| N/A 29C P0 83W / 149W | 10879MiB / 11441MiB | 0% Default |\r\n", 220 | "+-------------------------------+----------------------+----------------------+\r\n", 221 | "| 2 Tesla K80 On | 00000000:8A:00.0 Off | 0 |\r\n", 222 | "| N/A 33C P0 65W / 149W | 10879MiB / 11441MiB | 0% Default |\r\n", 223 | "+-------------------------------+----------------------+----------------------+\r\n", 224 | "| 3 Tesla K80 On | 00000000:8B:00.0 Off | 0 |\r\n", 225 | "| N/A 31C P0 82W / 149W | 10879MiB / 11441MiB | 0% Default |\r\n", 226 | "+-------------------------------+----------------------+----------------------+\r\n", 227 | " \r\n", 228 | "+-----------------------------------------------------------------------------+\r\n", 229 | "| Processes: GPU Memory |\r\n", 230 | "| GPU PID Type Process name Usage |\r\n", 231 | "|=============================================================================|\r\n", 232 | "| 0 12912 C ...han74/.conda/envs/myanaconda/bin/python 10864MiB |\r\n", 233 | "| 1 12912 C ...han74/.conda/envs/myanaconda/bin/python 10864MiB |\r\n", 234 | "| 2 12912 C ...han74/.conda/envs/myanaconda/bin/python 10864MiB |\r\n", 235 | "| 3 12912 C ...han74/.conda/envs/myanaconda/bin/python 10864MiB |\r\n", 236 | "+-----------------------------------------------------------------------------+\r\n" 237 | ] 238 | } 239 | ], 240 | "source": [ 241 | "!nvidia-smi" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": 5, 247 | "metadata": {}, 248 | "outputs": [ 249 | { 250 | "data": { 251 | "text/plain": [ 252 | "'2.2.2'" 253 | ] 254 | }, 255 | "execution_count": 5, 256 | "metadata": {}, 257 | "output_type": "execute_result" 258 | } 259 | ], 260 | "source": [ 261 | "keras.__version__" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": 8, 267 | "metadata": {}, 268 | "outputs": [ 269 | { 270 | "data": { 271 | "text/plain": [ 272 | "'0,1,2,3'" 273 | ] 274 | }, 275 | "execution_count": 8, 276 | "metadata": {}, 277 | "output_type": "execute_result" 278 | } 279 | ], 280 | "source": [ 281 | "os.environ[\"CUDA_VISIBLE_DEVICES\"]" 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": 13, 287 | "metadata": {}, 288 | "outputs": [], 289 | "source": [ 290 | "os.environ[\"CUDA_VISIBLE_DEVICES\"]==0\n", 291 | " print('o')" 292 | ] 293 | }, 294 | { 295 | "cell_type": "code", 296 | "execution_count": null, 297 | "metadata": {}, 298 | "outputs": [], 299 | "source": [] 300 | } 301 | ], 302 | "metadata": { 303 | "kernelspec": { 304 | "display_name": "Python 3", 305 | "language": "python", 306 | "name": "python3" 307 | }, 308 | "language_info": { 309 | "codemirror_mode": { 310 | "name": "ipython", 311 | "version": 3 312 | }, 313 | "file_extension": ".py", 314 | "mimetype": "text/x-python", 315 | "name": "python", 316 | "nbconvert_exporter": "python", 317 | "pygments_lexer": "ipython3", 318 | "version": "3.6.6" 319 | } 320 | }, 321 | "nbformat": 4, 322 | "nbformat_minor": 2 323 | } 324 | -------------------------------------------------------------------------------- /environment_AI.yml: -------------------------------------------------------------------------------- 1 | name: myanaconda 2 | channels: 3 | - mgckind 4 | - defaults 5 | - conda-forge 6 | - menpo 7 | dependencies: 8 | - jupyter_contrib_core=0.3.3=py_2 9 | - jupyter_contrib_nbextensions=0.5.0=py36_1000 10 | - jupyter_highlight_selected_word=0.2.0=py36_1000 11 | - jupyter_latex_envs=1.4.4=py36_1000 12 | - jupyter_nbextensions_configurator=0.4.0=py36_1000 13 | - _ipyw_jlab_nb_ext_conf=0.1.0=py36_0 14 | - _tflow_1100_select=0.0.1=gpu 15 | - _tflow_190_select=0.0.3=mkl 16 | - absl-py=0.5.0=py36_0 17 | - alabaster=0.7.11=py36_0 18 | - anaconda-client=1.7.2=py36_0 19 | - anaconda=custom=py36hbbc8b67_0 20 | - anaconda-navigator=1.9.2=py36_0 21 | - anaconda-project=0.8.2=py36_0 22 | - appdirs=1.4.3=py36h28b3542_0 23 | - asn1crypto=0.24.0=py36_0 24 | - astor=0.7.1=py36_0 25 | - astroid=2.0.4=py36_0 26 | - astropy=3.0.4=py36h14c3975_0 27 | - atomicwrites=1.2.1=py36_0 28 | - attrs=18.2.0=py36h28b3542_0 29 | - automat=0.7.0=py36_0 30 | - babel=2.6.0=py36_0 31 | - backcall=0.1.0=py36_0 32 | - backports=1.0=py36_1 33 | - backports.shutil_get_terminal_size=1.0.0=py36_2 34 | - beautifulsoup4=4.6.3=py36_0 35 | - binutils_impl_linux-64=2.31.1=h6176602_1 36 | - binutils_linux-64=2.31.1=h6176602_3 37 | - bitarray=0.8.3=py36h14c3975_0 38 | - bkcharts=0.2=py36_0 39 | - blas=1.0=mkl 40 | - blaze=0.11.3=py36_0 41 | - bleach=2.1.4=py36_0 42 | - blosc=1.14.4=hdbcaa40_0 43 | - bokeh=0.13.0=py36_0 44 | - boto=2.49.0=py36_0 45 | - bottleneck=1.2.1=py36h035aef0_1 46 | - bzip2=1.0.6=h14c3975_5 47 | - ca-certificates=2018.03.07=0 48 | - cairo=1.14.12=h8948797_3 49 | - certifi=2018.10.15=py36_0 50 | - cffi=1.11.5=py36he75722e_1 51 | - chardet=3.0.4=py36_1 52 | - click=7.0=py36_0 53 | - cloudpickle=0.5.6=py36_0 54 | - clyent=1.2.2=py36_1 55 | - colorama=0.3.9=py36_0 56 | - conda-verify=3.1.1=py36_0 57 | - constantly=15.1.0=py36h28b3542_0 58 | - contextlib2=0.5.5=py36_0 59 | - cryptography=2.3.1=py36hc365091_0 60 | - cudatoolkit=9.0=h13b8566_0 61 | - cudnn=7.1.2=cuda9.0_0 62 | - cupti=9.0.176=0 63 | - curl=7.61.1=hbc83047_0 64 | - cycler=0.10.0=py36_0 65 | - cython=0.28.5=py36hf484d3e_0 66 | - cytoolz=0.9.0.1=py36h14c3975_1 67 | - dask=0.19.2=py36_0 68 | - dask-core=0.19.2=py36_0 69 | - datashape=0.5.4=py36_1 70 | - dbus=1.13.2=h714fa37_1 71 | - decorator=4.3.0=py36_0 72 | - distributed=1.23.2=py36_0 73 | - docutils=0.14=py36_0 74 | - entrypoints=0.2.3=py36_2 75 | - et_xmlfile=1.0.1=py36_0 76 | - expat=2.2.6=he6710b0_0 77 | - fastcache=1.0.2=py36h14c3975_2 78 | - ffmpeg=4.0=hcdf2ecd_0 79 | - filelock=3.0.8=py36_0 80 | - flask=1.0.2=py36_1 81 | - flask-cors=3.0.6=py36_0 82 | - fontconfig=2.13.0=h9420a91_0 83 | - freeglut=3.0.0=hf484d3e_5 84 | - freetype=2.9.1=h8a8886c_1 85 | - fribidi=1.0.5=h7b6447c_0 86 | - future=0.16.0=py36_0 87 | - gast=0.2.0=py36_0 88 | - gcc_impl_linux-64=7.3.0=habb00fd_1 89 | - gcc_linux-64=7.3.0=h553295d_3 90 | - get_terminal_size=1.0.0=haa9412d_0 91 | - gevent=1.3.6=py36h7b6447c_0 92 | - glib=2.56.2=hd408876_0 93 | - glob2=0.6=py36_1 94 | - gmp=6.1.2=h6c8ec71_1 95 | - gmpy2=2.0.8=py36h10f8cd9_2 96 | - graphite2=1.3.12=h23475e2_2 97 | - greenlet=0.4.15=py36h7b6447c_0 98 | - grpcio=1.12.1=py36hdbcaa40_0 99 | - gst-plugins-base=1.14.0=hbbd80ab_1 100 | - gstreamer=1.14.0=hb453b48_1 101 | - gxx_impl_linux-64=7.3.0=hdf63c60_1 102 | - gxx_linux-64=7.3.0=h553295d_3 103 | - h5py=2.8.0=py36h989c5e5_3 104 | - harfbuzz=1.8.8=hffaf4a1_0 105 | - hdf5=1.10.2=hba1933b_1 106 | - heapdict=1.0.0=py36_2 107 | - html5lib=1.0.1=py36_0 108 | - hyperlink=18.0.0=py36_0 109 | - icu=58.2=h9c2bf20_1 110 | - idna=2.7=py36_0 111 | - imageio=2.4.1=py36_0 112 | - imagesize=1.1.0=py36_0 113 | - incremental=17.5.0=py36_0 114 | - intel-openmp=2019.0=118 115 | - ipykernel=5.0.0=py36h39e3cac_0 116 | - ipython=7.0.1=py36h39e3cac_0 117 | - ipython_genutils=0.2.0=py36_0 118 | - ipywidgets=7.4.2=py36_0 119 | - isort=4.3.4=py36_0 120 | - itsdangerous=0.24=py36_1 121 | - jasper=2.0.14=h07fcdf6_1 122 | - jbig=2.1=hdba287a_0 123 | - jdcal=1.4=py36_0 124 | - jedi=0.12.1=py36_0 125 | - jeepney=0.4=py36_0 126 | - jinja2=2.10=py36_0 127 | - jpeg=9b=h024ee3a_2 128 | - jsonschema=2.6.0=py36_0 129 | - jupyter=1.0.0=py36_7 130 | - jupyter_client=5.2.3=py36_0 131 | - jupyter_console=5.2.0=py36_1 132 | - jupyter_core=4.4.0=py36_0 133 | - jupyterlab=0.34.12=py36_0 134 | - jupyterlab_launcher=0.13.1=py36_0 135 | - keras=2.2.2=0 136 | - keras-applications=1.0.4=py36_1 137 | - keras-base=2.2.2=py36_0 138 | - keras-gpu=2.2.2=0 139 | - keras-preprocessing=1.0.2=py36_1 140 | - keyring=13.2.1=py36_0 141 | - kiwisolver=1.0.1=py36hf484d3e_0 142 | - lazy-object-proxy=1.3.1=py36h14c3975_2 143 | - libcurl=7.61.1=heec0ca6_0 144 | - libedit=3.1.20170329=h6b74fdf_2 145 | - libffi=3.2.1=hd88cf55_4 146 | - libgcc-ng=8.2.0=hdf63c60_1 147 | - libgfortran-ng=7.3.0=hdf63c60_0 148 | - libglu=9.0.0=hf484d3e_1 149 | - libgpuarray=0.7.6=h14c3975_0 150 | - libopencv=3.4.2=hb342d67_1 151 | - libopus=1.2.1=hb9ed12e_0 152 | - libpng=1.6.34=hb9fc6fc_0 153 | - libprotobuf=3.6.0=hdbcaa40_0 154 | - libsodium=1.0.16=h1bed415_0 155 | - libssh2=1.8.0=h9cfc8f7_4 156 | - libstdcxx-ng=8.2.0=hdf63c60_1 157 | - libtiff=4.0.9=he85c1e1_2 158 | - libtool=2.4.6=h7b6447c_5 159 | - libuuid=1.0.3=h1bed415_2 160 | - libvpx=1.7.0=h439df22_0 161 | - libxcb=1.13=h1bed415_1 162 | - libxml2=2.9.8=h26e45fe_1 163 | - libxslt=1.1.32=h1312cb7_0 164 | - llvmlite=0.25.0=py36hd408876_0 165 | - locket=0.2.0=py36_1 166 | - lxml=4.2.5=py36hefd8a0e_0 167 | - lzo=2.10=h49e0be7_2 168 | - mako=1.0.7=py36_0 169 | - markdown=2.6.11=py36_0 170 | - markupsafe=1.0=py36h14c3975_1 171 | - matplotlib=3.0.0=py36h5429711_0 172 | - mccabe=0.6.1=py36_1 173 | - mistune=0.8.3=py36h14c3975_1 174 | - mkl=2019.0=118 175 | - mkl-service=1.1.2=py36h90e4bf4_5 176 | - mkl_fft=1.0.6=py36h7dd41cf_0 177 | - mkl_random=1.0.1=py36h4414c95_1 178 | - more-itertools=4.3.0=py36_0 179 | - mpc=1.1.0=h10f8cd9_1 180 | - mpfr=4.0.1=hdf1c602_3 181 | - mpmath=1.0.0=py36_2 182 | - msgpack-python=0.5.6=py36h6bb024c_1 183 | - multipledispatch=0.6.0=py36_0 184 | - navigator-updater=0.2.1=py36_0 185 | - nbconvert=5.3.1=py36_0 186 | - nbformat=4.4.0=py36_0 187 | - nccl=1.3.5=cuda9.0_0 188 | - ncurses=6.1=hf484d3e_0 189 | - networkx=2.2=py36_1 190 | - 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