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Copy file name to clipboardExpand all lines: docs/user/_sources/compute_config.txt
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#. **FSL Path - [path]:** Full path to the FSL version to be used by CPAC. If you have specified an FSL path in your .bashrc file, this path will be set automatically.
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#. **Job Scheduler / Resource Manager - [SGE, PBS]:** Sun Grid Engine (SGE) or Portable Batch System (PBS). Only applies if you are running on a grid or compute cluster. See the section below entitled `SGE Configuration` for more information on how to set up a SGE.
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#. **Job Scheduler / Resource Manager - [SGE, PBS]:** Sun Grid Engine (SGE) or Portable Batch System (PBS). Only applies if you are running on a grid or compute cluster. See the section below entitled `SGE Configuration` for more information on how to set up SGE.
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#. **SGE Parallel Environment - [text]:** SGE Parallel Environment to use when running CPAC. Only applies when you are running on a grid or compute cluster using SGE. See the section below entitled `SGE Configuration` for more information on how to set up a SGE.
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#. **SGE Parallel Environment - [text]:** SGE Parallel Environment to use when running CPAC. Only applies when you are running on a grid or compute cluster using SGE. See the section below entitled `SGE Configuration` for more information on how to set up SGE.
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#. **SGE Queue - [text]:** SGE Queue to use when running CPAC. Only applies when you are running on a grid or compute cluster using SGE. See the section below entitled `SGE Configuration` for more information on how to set up a SGE.
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#. **SGE Queue - [text]:** SGE Queue to use when running CPAC. Only applies when you are running on a grid or compute cluster using SGE. See the section below entitled `SGE Configuration` for more information on how to set up SGE.
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#. **Number of Cores Per Subject - [integer]:** Number of cores (on a single machine) or slots on a node (cluster/grid) per subject. Slots are cores on a cluster/grid node. 'Number of Cores Per Subject' multiplied by 'Number of Subjects to Run Simultaneously' multiplied by 'Number of Cores for Anatomical Registration (ANTS)' must not be greater than the total number of cores.
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Before you configure Sun Grid Engine so that it works with C-PAC, you should understand the following concepts:
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* **Job Scheduler** - A program that can allocate computational resources in an HPC cluster to jobs based on availability and distribute jobs across nodes. C-PAC can use Sun Grid Engine (SGE) as its job scheduler (and SGE is comes pre-configured with C-PAC's :doc:`cloud image <cloud>`).
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* **Job Scheduler** - A program that can allocate computational resources in an HPC cluster to jobs based on availability and distribute jobs across nodes. C-PAC can use Sun Grid Engine (SGE) as its job scheduler (and SGE comes pre-configured with C-PAC's :doc:`cloud image <cloud>`).
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* **Parallel Environment** - A specification for how SGE parallelizes work. Parallel environments can have limits on the number of CPUs used, whitelists and blacklists that dictate who can use resources, and specific methods for balancing server load during distributed tasks.
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Configuring A Parallel Environment
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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The specifics of configuring a parallel environment in SGE are beyond the scope of this guide (see `Oracle's blog <https://blogs.oracle.com/templedf/entry/configuring_a_new_parallel_environment>`_ for a good primer on how to prepare a parallel environment). Nevertheless, we will discuss how to configure a simple example (the `mpi_conf` environment used by the C-PAC cloud image). To do this, we will first create a file named mpi_smp.conf that will appear as follows:
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The specifics of configuring a parallel environment in SGE more broadly are beyond the scope of this guide (see `Oracle's blog <https://blogs.oracle.com/templedf/entry/configuring_a_new_parallel_environment>`_ for a good primer). Nevertheless, we will discuss how to configure an environment that is compatible with C-PAC. To do this, we will first create a file named *mpi_smp.conf* that will appear as follows:
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.. code-block:: bash
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pe_name mpi_smp
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slots 999
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user_lists NONE
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accounting_summary TRUE
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This configuration ensures that:
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* Up to 999 slots will be used.
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* All of the cores will be used (assuming your system has fewer than 999 cores; if you are lucky enough to have more than this, the maximum value for this field is 9999999).
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* No users are whitelisted or blacklisted and no special hooks or cleanup tasks occur before or after a job.
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* All job slots that a C-PAC job submission requests are on the same machine.
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* All job slots that a C-PAC job submission requests are on the same machine (this ensures that each unique subject's computations are taken care of by the same node and the cores allocated for one of C-PAC's steps are not distributed across different machines).
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* SGE has full control over the jobs submitted (in terms of resource scheduling).
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* The C-PAC run is not part of a parallel job that would require an awareness of which task was performed first.
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* The C-PAC run is not part of a parallel job that would require an awareness of which task was performed first (the subjects can be assigned to nodes in any order).
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* An accounting record is written concerning how the job used resources.
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To activate this parallel environment and tie it to a job queue named 'all.q', use the following commands:
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To activate this parallel environment and tie it to a job queue named *all.q*, execute the following commands on your cluster's master node:
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.. code-block:: bash
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qconf -Ap /path/to/mpi_smp.conf
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qconf -mattr queue pe_list "mpi_smp" all.q
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You would then set the SGE Parallel Environment to "mpi_smp" and the SGE queue to "all.q" in your pipeline configuration file before starting your C-PAC run.
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You would then set the SGE Parallel Environment to *mpi_smp* and the SGE queue to *all.q* in your pipeline configuration file before starting your C-PAC run.
Copy file name to clipboardExpand all lines: docs/user/_sources/install.txt
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Installation Overview
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^^^^^^^^^^^^^^^^^^^^^
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If you are using Ubuntu (version 12.04 or newer) you can download and run `cpac_install.sh <https://github.com/FCP-INDI/C-PAC/blob/master/scripts/cpac_install.sh?raw=true>`_ with the following terminal commands:
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If you are using Ubuntu (version 12/14) you can download and run `cpac_install.sh <https://github.com/FCP-INDI/C-PAC/blob/master/scripts/cpac_install.sh?raw=true>`_ with the following terminal commands:
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.. code-block:: bash
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This will install all of the prerequisites and resources listed below. If you have previously installed versions of these, they will not be reinstalled, and the script will not check the release version to match the requirements of C-PAC.
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**Note**: This script is intended for Ubuntu Linux 12.04, but it may work for older releases of Ubuntu.
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**Note**: This script is intended for Ubuntu Linux 12/14, but it may work for older releases of Ubuntu.
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**For other operating systems**, this page will guide you through the following C-PAC installation steps:
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* zlib and its developer libraries
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* `Graphviz <http://www.graphviz.org/>`_ (optional; required for workflow graphs)
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The command to install all of these on Ubuntu is:
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The command to install all of these on Ubuntu 12/14 is:
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.. code-block:: bash
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* Installing system-level and Python dependencies will be made much easier if you download the `Xcode Command Line Tools from Apple <https://developer.apple.com/downloads/index.action>`_ (requires a free Apple account), which includes (among other things) Git and gcc.
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* You may want to consider using a package manager such as `Homebrew <http://brew.sh/>`_, `Macports <https://www.macports.org/>`_, or `Fink <http://www.finkproject.org/>`_.
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**Ubuntu 16.04:**
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* The apt-get command used for Ubuntu 12/14 works with *libxp6* omitted. Unfortunately, libxp is no longer included in the Ubuntu repositories and will need to be compiled from source. The commands to accomplish this are as follows (note that these commands must be executed as the root user).
Please ensure that you are using Python 2.7 and above, but not Python 3 (since it is not backwards-compatible). Though many computers come with Python pre-installed, C-PAC relies on a number of special-purpose packages, which are listed below. Packages with an asterisk can be installed through `easy_install <https://pythonhosted.org/setuptools/easy_install.html>`_ or pip. Installing `Anaconda <https://store.continuum.io/cshop/anaconda/>`_ (**64-bit version only**), `Miniconda <http://conda.pydata.org/miniconda.html>`_ or `Enthought Canopy <https://www.enthought.com/products/canopy/>`_ and using a package manager can simplify installation greatly. Instructions for Miniconda are given below.
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* `SciPy and NumPy <http://www.scipy.org/install.html>`_
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* Nipype (version 0.10 for versions of C-PAC after 0.3.7, `version 0.9.2 <https://github.com/FCP-INDI/nipype/releases/tag/v0.9.2_cpac_tested>`_ for C-PAC versions 0.3.4b, 0.3.5, and 0.3.6)
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* Nipype (version 0.11 is compatible with C-PAC 0.3.9; version 0.10 is compatible with versions of C-PAC after 0.3.7, `version 0.9.2 <https://github.com/FCP-INDI/nipype/releases/tag/v0.9.2_cpac_tested>`_ for C-PAC versions 0.3.4b, 0.3.5, and 0.3.6; version 0.12 of nipype is not compatible with the currently release versions of C-PAC)
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