Difference between revisions of "SOSCIP GPU"

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'''WARNING: SciNet is in the process of replacing this wiki with a new documentation site. For current information, please go to [https://docs.scinet.utoronto.ca https://docs.scinet.utoronto.ca]'''
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{{Infobox Computer
 
{{Infobox Computer
 
|image=[[Image:S882lc.png|center|300px|thumb]]
 
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|vendorcompilers=xlc/xlf, nvcc
 
|vendorcompilers=xlc/xlf, nvcc
 
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== New Documentation Site ==
 +
Please visit the new documentation site: [https://docs.scinet.utoronto.ca/index.php/SOSCIP_GPU https://docs.scinet.utoronto.ca/index.php/SOSCIP_GPU] for updated information.
  
 
== SOSCIP ==
 
== SOSCIP ==
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Please use [mailto:soscip-support@scinet.utoronto.ca <soscip-support@scinet.utoronto.ca>] for SOSCIP GPU specific inquiries.
 
Please use [mailto:soscip-support@scinet.utoronto.ca <soscip-support@scinet.utoronto.ca>] for SOSCIP GPU specific inquiries.
  
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<!--
 
== Specifications==
 
== Specifications==
  
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</pre>
 
</pre>
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===Packing single-GPU jobs within one SLURM job submission===
 +
Jobs are scheduled by node (4 GPUs) on SOSCIP GPU cluster. If user's code/program cannot utilize all 4 GPUs, user can use GNU Parallel tool to pack 4 or more single-GPU jobs into one SLURM job. Below is an example of submitting 4 single-GPU python codes within one job:  (When using GNU parallel for a publication please cite as per '''''parallel --citation''''')
 +
<pre>
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#!/bin/bash
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#SBATCH --nodes=1
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#SBATCH --ntasks=20  # MPI tasks (needed for srun)
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#SBATCH --time=00:10:00  # H:M:S
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#SBATCH --gres=gpu:4    # Ask for 4 GPUs per node
 +
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module load gnu-parallel/20180422
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cd $SLURM_SUBMIT_DIR
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 +
parallel -a jobname-params.input --colsep ' ' -j 4 'CUDA_VISIBLE_DEVICES=$(( {%} - 1 )) numactl -N $(( ({%} -1) / 2 )) python {1} {2} {3} &> jobname-{#}.out'
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</pre>
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The jobname-params.input file contains:
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<pre>
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code-1.py --param1=a --param2=b
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code-2.py --param1=c --param2=d
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code-3.py --param1=e --param2=f
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code-4.py --param1=g --param2=h
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</pre>
 +
*In the above example, GNU Parallel tool will read '''jobname-params.input''' file and separate parameters. Each row in the input file has to contain exact 3 parameters to '''python'''. code-N.py is also considered as a parameter. User can change parameter number in the '''parallel''' command ({1} {2} {3}...).
 +
*'''"-j 4"''' flag limits the max number of jobs to be 4. User can have more rows in the input file, but GNU Parallel tool only executes maximum of 4 at the same time.
 +
*'''"CUDA_VISIBLE_DEVICES=$(( {%} - 1 ))"''' will set one GPU for each job. '''"numactl -N $(( ({%} -1) / 2 ))"''' will bind 2 jobs on CPU socket 0, other 2 jobs on socket 1. {%} is job slot which will be translated to 1 or 2 or 3 or 4 in this case.
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*Outputs will be  jobname-1.out, jobname-2.out,jobname-3.out,jobname-4.out... {#} is job number which will be translated to the row number in the input file.
  
 
== Software Installed ==
 
== Software Installed ==
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The PowerAI platform contains popular open machine learning frameworks such as '''Caffe, TensorFlow, and Torch'''. Run the <tt>module avail</tt> command for a complete listing. More information is available at this link: https://developer.ibm.com/linuxonpower/deep-learning-powerai/releases/. Release 4.0 is currently installed.
 
The PowerAI platform contains popular open machine learning frameworks such as '''Caffe, TensorFlow, and Torch'''. Run the <tt>module avail</tt> command for a complete listing. More information is available at this link: https://developer.ibm.com/linuxonpower/deep-learning-powerai/releases/. Release 4.0 is currently installed.
  
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==== GNU Compilers ====
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===GNU Compilers ===
  
−
More recent versions of the GNU Compiler Collection (C/C++/Fortran) are provided in the IBM Advanced Toolchain with enhancements for the POWER8 CPU. To load the newer advance toolchain version use:
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System default compiler is GCC/5.4.0. More recent versions of the GNU Compiler Collection (C/C++/Fortran) are provided in the IBM Advance Toolchain with enhancements for the POWER8 CPU. To load the newer advance toolchain version use:
  
−
Advanced Toolchain V10.0
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Advance Toolchain V10.0
 
<pre>
 
<pre>
−
module load gcc/6.3.1
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module load gcc/6.4.1
 
</pre>
 
</pre>
  
−
Advanced Toolchain V11.0
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Advance Toolchain V11.0
 
<pre>
 
<pre>
−
module load gcc/7.2.1
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module load gcc/7.3.1
 
</pre>
 
</pre>
  
−
More information about the IBM Advanced Toolchain can be found here: [https://developer.ibm.com/linuxonpower/advance-toolchain/ https://developer.ibm.com/linuxonpower/advance-toolchain/]
+
More information about the IBM Advance Toolchain can be found here: [https://developer.ibm.com/linuxonpower/advance-toolchain/ https://developer.ibm.com/linuxonpower/advance-toolchain/]
  
−
==== IBM XL Compilers ====
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=== IBM XL Compilers ===
  
 
To load the native IBM xlc/xlc++ and xlf (Fortran) compilers, run
 
To load the native IBM xlc/xlc++ and xlf (Fortran) compilers, run
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[https://www.ibm.com/support/knowledgecenter/SSAT4T_15.1.5/com.ibm.compilers.linux.doc/welcome.html IBM XL Fortran]
 
[https://www.ibm.com/support/knowledgecenter/SSAT4T_15.1.5/com.ibm.compilers.linux.doc/welcome.html IBM XL Fortran]
  
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==== NVIDIA GPU Driver Version ====
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=== NVIDIA GPU Driver ===
  
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The current NVIDIA driver version is 384.66
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The current NVIDIA driver version is 396.26
  
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==== CUDA ====
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=== CUDA ===
  
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The current installed CUDA Tookits is are version 8.0 and version 9.0
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The current installed CUDA Tookits is are version 8.0, 9.0 and 9.1.
  
 
<pre>
 
<pre>
 
module load cuda/8.0
 
module load cuda/8.0
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</pre>
 
−
 
 
or  
 
or  
−
 
−
<pre>
 
 
module load cuda/9.0
 
module load cuda/9.0
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or
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module load cuda/9.1
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or
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module load cuda/9.2
 
</pre>
 
</pre>
  
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/usr/local/cuda-8.0
 
/usr/local/cuda-8.0
 
/usr/local/cuda-9.0
 
/usr/local/cuda-9.0
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/usr/local/cuda-9.1
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/usr/local/cuda-9.2
 
</pre>
 
</pre>
  
−
Note that the <tt>/usr/local/cuda</tt> directory is linked to the <tt>/usr/local/cuda-9.0</tt> directory.
+
Note that the <tt>/usr/local/cuda</tt> directory is linked to the <tt>/usr/local/cuda-9.2</tt> directory.
  
 
Documentation and API reference information for the CUDA Toolkit can be found here: [http://docs.nvidia.com/cuda/index.html http://docs.nvidia.com/cuda/index.html]
 
Documentation and API reference information for the CUDA Toolkit can be found here: [http://docs.nvidia.com/cuda/index.html http://docs.nvidia.com/cuda/index.html]
  
−
==== OpenMPI ====
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=== OpenMPI ===
  
 
Currently OpenMPI has been setup on the 14 nodes connected over EDR Infiniband.
 
Currently OpenMPI has been setup on the 14 nodes connected over EDR Infiniband.
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TIP: If you plan to use Tensorflow within Anaconda, download the Python 2.7 version of Anaconda
 
TIP: If you plan to use Tensorflow within Anaconda, download the Python 2.7 version of Anaconda
 +
 +
=== cuDNN ===
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The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN accelerates widely used deep learning frameworks, including Caffe2, MATLAB, Microsoft Cognitive Toolkit, TensorFlow, Theano, and PyTorch. If a specific version of cuDNN is needed, user can download from https://developer.nvidia.com/cudnn and choose '''"cuDNN [VERSION] Library for Linux (Power8/Power9)"'''.
 +
 +
The default cuDNN installed on the system is version 6 with CUDA-8 from IBM PowerAI. More recent cuDNN versions are installed as modules:
 +
<pre>
 +
cudnn/cuda9.0/7.0.5
 +
</pre>
  
 
=== Keras ===
 
=== Keras ===
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Keras ([https://keras.io/ https://keras.io/]) is a popular high-level deep learning software development framework. It runs on top of other deep-learning frameworks such as TensorFlow.
 
Keras ([https://keras.io/ https://keras.io/]) is a popular high-level deep learning software development framework. It runs on top of other deep-learning frameworks such as TensorFlow.
  
−
The easiest way to install Keras is to install Anaconda first, then install Keras by using using the pip command.
+
*The easiest way to install Keras is to install Anaconda first, then install Keras by using using the pip command. Keras uses TensorFlow underneath to run neural network models. Before running code using Keras, be sure to load the PowerAI TensorFlow module and the cuda module.
  
−
Keras uses TensorFlow underneath to run neural network models. Before running code using Keras, be sure to load the PowerAI TensorFlow module and the cuda module.
+
*Keras can also be installed into a Python virtual environment by using '''pip'''. User can install optimized scipy (built with OpenBLAS) before installing Keras.
 +
In a virtual environment (python2.7 as example):
 +
<pre>
 +
pip install /scinet/sgc/Libraries/scipy/scipy-1.1.0-cp27-cp27mu-linux_ppc64le.whl
 +
pip install keras
 +
</pre>
  
−
=== Numpy/Scipy (OpenBLAS) ===
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=== NumPy/SciPy (built with OpenBLAS) ===
  
−
Optimized Numpy and Scipy are provided as Python wheels located in '''/scinet/sgc/Libraries/numpy''' and '''/scinet/sgc/Libraries/scipy''' and can be installed by '''pip'''.
+
Optimized NumPy and SciPy are provided as Python wheels located in '''/scinet/sgc/Libraries/numpy''' and '''/scinet/sgc/Libraries/scipy''' and can be installed by '''pip'''. Please uninstall old numpy/scipy before installing the new ones.
  
 
=== PyTorch ===
 
=== PyTorch ===
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source tensorflow-1.8-py2/bin/activate
 
source tensorflow-1.8-py2/bin/activate
 
</pre>
 
</pre>
−
* Install TensorFlow into the virtual environment: (A custom Numpy built with optimized OpenBLAS library can be installed)
+
* Install TensorFlow into the virtual environment: (A custom Numpy built with OpenBLAS library can be installed)
 
<pre>
 
<pre>
 
pip install --upgrade --force-reinstall /scinet/sgc/Libraries/numpy/numpy-1.14.3-cp27-cp27mu-linux_ppc64le.whl
 
pip install --upgrade --force-reinstall /scinet/sgc/Libraries/numpy/numpy-1.14.3-cp27-cp27mu-linux_ppc64le.whl
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source tensorflow-1.8-py3/bin/activate
 
source tensorflow-1.8-py3/bin/activate
 
</pre>
 
</pre>
−
* Install TensorFlow into the virtual environment: (A custom Numpy built with optimized OpenBLAS library can be installed)
+
* Install TensorFlow into the virtual environment: (A custom Numpy built with OpenBLAS library can be installed)
 
<pre>
 
<pre>
 
pip3 install --upgrade --force-reinstall /scinet/sgc/Libraries/numpy/numpy-1.14.3-cp35-cp35m-linux_ppc64le.whl
 
pip3 install --upgrade --force-reinstall /scinet/sgc/Libraries/numpy/numpy-1.14.3-cp35-cp35m-linux_ppc64le.whl
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# GPU Cluster Introduction: [[Media:GPU_Training_01.pdf‎|SOSCIP GPU Platform]]
 
# GPU Cluster Introduction: [[Media:GPU_Training_01.pdf‎|SOSCIP GPU Platform]]
 +
-->

Latest revision as of 15:17, 5 October 2018


WARNING: SciNet is in the process of replacing this wiki with a new documentation site. For current information, please go to https://docs.scinet.utoronto.ca

SOSCIP GPU
S882lc.png
Installed September 2017
Operating System Ubuntu 16.04 le
Number of Nodes 14x Power 8 with 4x NVIDIA P100
Interconnect Infiniband EDR
Ram/Node 512 GB
Cores/Node 2 x 10core (20 physical, 160 SMT)
Login/Devel Node sgc01
Vendor Compilers xlc/xlf, nvcc

New Documentation Site

Please visit the new documentation site: https://docs.scinet.utoronto.ca/index.php/SOSCIP_GPU for updated information.

SOSCIP

The SOSCIP GPU Cluster is a Southern Ontario Smart Computing Innovation Platform (SOSCIP) resource located at theUniversity of Toronto's SciNet HPC facility. The SOSCIP multi-university/industry consortium is funded by the Ontario Government and the Federal Economic Development Agency for Southern Ontario [1].

Support Email

Please use <soscip-support@scinet.utoronto.ca> for SOSCIP GPU specific inquiries.


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