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|
%global _empty_manifest_terminate_build 0
Name: python-dispy
Version: 4.15.2
Release: 1
Summary: Distributed and Parallel Computing with/for Python.
License: Apache 2.0
URL: https://dispy.org
Source0: https://mirrors.aliyun.com/pypi/web/packages/59/88/b2bd984a81db9ba0d73a47645ded9da8e0bcfddc231644f9017668aeabcc/dispy-4.15.2.tar.gz
BuildArch: noarch
%description
* dispy is implemented with `pycos <https://pycos.org>`_,
an independent framework for asynchronous, concurrent, distributed, network
programming with tasks (without threads). pycos uses non-blocking sockets with
I/O notification mechanisms epoll, kqueue and poll, and Windows I/O Completion
Ports (IOCP) for high performance and scalability, so dispy works efficiently
with a single node or large cluster(s) of nodes. pycos itself has support for
distributed/parallel computing, including transferring computations, files
etc., and message passing (for communicating with client and other computation
tasks). While dispy can be used to schedule jobs of a computation to get the
results, pycos can be used to create `distributed communicating processes
<https://pycos.org/dispycos.html>`_, for broad range of use cases.
* Computations (Python functions or standalone programs) and their
dependencies (files, Python functions, classes, modules) are
distributed automatically.
* Computation nodes can be anywhere on the network (local or
remote). For security, either simple hash based authentication or
SSL encryption can be used.
* After each execution is finished, the results of execution, output,
errors and exception trace are made available for further
processing.
* Nodes may become available dynamically: dispy will schedule jobs
whenever a node is available and computations can use that node.
* If callback function is provided, dispy executes that function
when a job is finished; this can be used for processing job
results as they become available.
* Client-side and server-side fault recovery are supported:
If user program (client) terminates unexpectedly (e.g., due to
uncaught exception), the nodes continue to execute scheduled
jobs. If client-side fault recover option is used when creating a
cluster, the results of the scheduled (but unfinished at the time of
crash) jobs for that cluster can be retrieved later.
If a computation is marked reentrant when a cluster is created and a
node (server) executing jobs for that computation fails, dispy
automatically resubmits those jobs to other available nodes.
* dispy can be used in a single process to use all the nodes
exclusively (with ``JobCluster`` - simpler to use) or in multiple
processes simultaneously sharing the nodes (with
``SharedJobCluster`` and *dispyscheduler* program).
* Cluster can be `monitored and managed
<https:/dispy.org/httpd.html>`_ with web browser.
%package -n python3-dispy
Summary: Distributed and Parallel Computing with/for Python.
Provides: python-dispy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-dispy
* dispy is implemented with `pycos <https://pycos.org>`_,
an independent framework for asynchronous, concurrent, distributed, network
programming with tasks (without threads). pycos uses non-blocking sockets with
I/O notification mechanisms epoll, kqueue and poll, and Windows I/O Completion
Ports (IOCP) for high performance and scalability, so dispy works efficiently
with a single node or large cluster(s) of nodes. pycos itself has support for
distributed/parallel computing, including transferring computations, files
etc., and message passing (for communicating with client and other computation
tasks). While dispy can be used to schedule jobs of a computation to get the
results, pycos can be used to create `distributed communicating processes
<https://pycos.org/dispycos.html>`_, for broad range of use cases.
* Computations (Python functions or standalone programs) and their
dependencies (files, Python functions, classes, modules) are
distributed automatically.
* Computation nodes can be anywhere on the network (local or
remote). For security, either simple hash based authentication or
SSL encryption can be used.
* After each execution is finished, the results of execution, output,
errors and exception trace are made available for further
processing.
* Nodes may become available dynamically: dispy will schedule jobs
whenever a node is available and computations can use that node.
* If callback function is provided, dispy executes that function
when a job is finished; this can be used for processing job
results as they become available.
* Client-side and server-side fault recovery are supported:
If user program (client) terminates unexpectedly (e.g., due to
uncaught exception), the nodes continue to execute scheduled
jobs. If client-side fault recover option is used when creating a
cluster, the results of the scheduled (but unfinished at the time of
crash) jobs for that cluster can be retrieved later.
If a computation is marked reentrant when a cluster is created and a
node (server) executing jobs for that computation fails, dispy
automatically resubmits those jobs to other available nodes.
* dispy can be used in a single process to use all the nodes
exclusively (with ``JobCluster`` - simpler to use) or in multiple
processes simultaneously sharing the nodes (with
``SharedJobCluster`` and *dispyscheduler* program).
* Cluster can be `monitored and managed
<https:/dispy.org/httpd.html>`_ with web browser.
%package help
Summary: Development documents and examples for dispy
Provides: python3-dispy-doc
%description help
* dispy is implemented with `pycos <https://pycos.org>`_,
an independent framework for asynchronous, concurrent, distributed, network
programming with tasks (without threads). pycos uses non-blocking sockets with
I/O notification mechanisms epoll, kqueue and poll, and Windows I/O Completion
Ports (IOCP) for high performance and scalability, so dispy works efficiently
with a single node or large cluster(s) of nodes. pycos itself has support for
distributed/parallel computing, including transferring computations, files
etc., and message passing (for communicating with client and other computation
tasks). While dispy can be used to schedule jobs of a computation to get the
results, pycos can be used to create `distributed communicating processes
<https://pycos.org/dispycos.html>`_, for broad range of use cases.
* Computations (Python functions or standalone programs) and their
dependencies (files, Python functions, classes, modules) are
distributed automatically.
* Computation nodes can be anywhere on the network (local or
remote). For security, either simple hash based authentication or
SSL encryption can be used.
* After each execution is finished, the results of execution, output,
errors and exception trace are made available for further
processing.
* Nodes may become available dynamically: dispy will schedule jobs
whenever a node is available and computations can use that node.
* If callback function is provided, dispy executes that function
when a job is finished; this can be used for processing job
results as they become available.
* Client-side and server-side fault recovery are supported:
If user program (client) terminates unexpectedly (e.g., due to
uncaught exception), the nodes continue to execute scheduled
jobs. If client-side fault recover option is used when creating a
cluster, the results of the scheduled (but unfinished at the time of
crash) jobs for that cluster can be retrieved later.
If a computation is marked reentrant when a cluster is created and a
node (server) executing jobs for that computation fails, dispy
automatically resubmits those jobs to other available nodes.
* dispy can be used in a single process to use all the nodes
exclusively (with ``JobCluster`` - simpler to use) or in multiple
processes simultaneously sharing the nodes (with
``SharedJobCluster`` and *dispyscheduler* program).
* Cluster can be `monitored and managed
<https:/dispy.org/httpd.html>`_ with web browser.
%prep
%autosetup -n dispy-4.15.2
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-dispy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 4.15.2-1
- Package Spec generated
|
