dispy version 4.6.0 has been released. The changes since last release are:
dispy version 4.5.5 has been released. This version fixes Issue #21 - Using same cluster more than once.
dispy version 4.5.4 has been released.
This version supports setup and cleanup parameters under Windows with some limitations (compared to Linux, OS X and other Posix systems, where there are no limitations); these should be Python functions or partial functions. See JobCluster and
node_shvars.py examples for more details on limitations with Windows.
dispy version 4.5.3 has been released. This version supports passing class instances in client to servers with Python 3 under Windows. Processes started with multiprocessing in Python 3 under Windows use
__mp_main__ namespace, so user defined code is executed in that namespace so unpickling/deserialization of objects works.
dispy version 4.5.2 has been released.
Version 4.5 introduced a feature to keep a computation's global variables in a (dictionary) variable in that computation. This feature works with Linux, OS X and other Unix variants, but not with Windows. The implementation of this feature broke Windows, as the variable couldn't be sent as argument to multiprocessing.Process. Version 4.5.2 fixes this issue so dispynode works with Windows again.
dispy version 4.5.1 release fixes issue in 4.5 version with loading httpd module in dispyscheduler.
Short summary of changes in version 4.5:
Short summary of changes in version 4.4:
dispy version 4.3 has been released. Most of the changes since last release are fixes. The one change is simplification of fault recovery. Now the clients store recovery information always and in case of crash, function recover_jobs in dispy module can be used to retrieve the results of jobs and release the nodes. See Fualt Recovery for more details.
dispy version 4.2 has been released. The changes are:
dispy version 4.1 has been released with following changes from version 4.0:
dispy version 4.0 has been released. In this release,
dispy version 3.22 has been released. With this release, dependencies can be distributed for each job (in addition to distributing dependencies for the whole computation). Job's dependencies (functions, classes, modules and files if computations are Python functions, and modules and files if computations are standalone programs) are sent with the job to the node and removed after the job is done. Job's dependencies should be given as a list to 'cluster.submit()' when creating a job. See Cluster methods for details.... read more
dispy version 3.21 has been released. This version adds support for computations to transfer files to client.
dispy version 3.20 has been released. This version fixes SSL setup so dispy, dispynode and dispyscheduler can be configured to use SSL certificates as documented.
dispy version 3.19 has been released. This release
__future__has been imported, which made any user program using standard print statement to be invalid),
dispy version 3.18 has been released. In this version dispyscheduler.py (used with SharedJobCluster) has been fixed to close the socket used in transferring files.
dispy version 3.17 has been released. This release adds 'setup' parameter to JobCluster and SharedJobCluster. If given, this must be a Python function which is run on a node before running any jobs. The parameter 'cleanup' can also be a function, which is run at a node after the computation is done. See dispy and Examples for details.
dispy version 3.16 has been released. This release fixes following issues:
dispy version 3.15 has been released. This version fixes crash with dispyscheduler in Windows. Files are now copied to tempfile.gettempdir() instead of '/tmp'.
dispy version 3.14 has been released. This version fixes socket timeout error processing in Windows (in asyncoro module).
dispy version 3.13 has been released. This version fixes an issue with processing provisional results. asyncoro has also been updated to latest version.
Note that asyncoro project now supports distributed / parallel computing (among other features) where computations are distributed to nodes, as done by dispy. With asyncoro the computation tasks and client(s) can communicate using message passing, which is not possible with dispy (other than sending provisional results). However, asyncoro doesn't provide job scheduling. See 'discoro_client.py' in asyncoro's files for an example.
dispy version 3.12 has been released. The major changes since version 3.11 are:
dispy version 3.11 has been released. Summary of changes since version 3.10:
dispy version 3.10 has been released. In this release