Feedstock license: BSD-3-Clause
Home: https://github.com/basnijholt/adaptive-scheduler
Package license: BSD-3-Clause
Summary: An asynchronous scheduler for Adaptive
Development: https://github.com/basnijholt/adaptive-scheduler
Documentation: http://adaptive-scheduler.readthedocs.io
The Adaptive scheduler solves the following problem, you need to run a few 100
learners and can use >1k cores. ipyparallel and dask.distributed provide
very powerful engines for interactive sessions. However, when you want to
connect to >1k cores it starts to struggle. Besides that, on a shared cluster
there is often the problem of starting an interactive session with ample space
available. Our approach is to schedule a different job for each adaptive.Learner. The creation and running of these jobs are managed by adaptive-scheduler. This means that your calculation will definitely run, even
though the cluster might be fully occupied at the moment. Because of this
approach, there is almost no limit to how many cores you want to use. You can
either use 10 nodes for 1 job (learner) or 1 core for 1 job (learner) while
scheduling hundreds of jobs. Everything is written such that the computation is
maximally local. This means that is one of the jobs crashes, there is no
problem and it will automatically schedule a new one and continue the
calculation where it left off (because of Adaptive's periodic saving
functionality). Even if the central "job manager" dies, the jobs will continue
to run (although no new jobs will be scheduled.)
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Installing adaptive-scheduler from the conda-forge channel can be achieved by adding conda-forge to your channels with:
conda config --add channels conda-forge
conda config --set channel_priority strict
Once the conda-forge channel has been enabled, adaptive-scheduler can be installed with conda:
conda install adaptive-scheduler
or with mamba:
mamba install adaptive-scheduler
It is possible to list all of the versions of adaptive-scheduler available on your platform with conda:
conda search adaptive-scheduler --channel conda-forge
or with mamba:
mamba search adaptive-scheduler --channel conda-forge
Alternatively, mamba repoquery may provide more information:
# Search all versions available on your platform:
mamba repoquery search adaptive-scheduler --channel conda-forge
# List packages depending on `adaptive-scheduler`:
mamba repoquery whoneeds adaptive-scheduler --channel conda-forge
# List dependencies of `adaptive-scheduler`:
mamba repoquery depends adaptive-scheduler --channel conda-forge
conda-forge is a community-led conda channel of installable packages. In order to provide high-quality builds, the process has been automated into the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages. Such a repository is known as a feedstock.
A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configurations for automatic building using freely available continuous integration services. Thanks to the awesome service provided by Azure, GitHub, CircleCI, AppVeyor, Drone, and TravisCI it is possible to build and upload installable packages to the conda-forge Anaconda-Cloud channel for Linux, Windows and OSX respectively.
To manage the continuous integration and simplify feedstock maintenance
conda-smithy has been developed.
Using the conda-forge.yml within this repository, it is possible to re-render all of
this feedstock's supporting files (e.g. the CI configuration files) with conda smithy rerender.
For more information please check the conda-forge documentation.
feedstock - the conda recipe (raw material), supporting scripts and CI configuration.
conda-smithy - the tool which helps orchestrate the feedstock.
Its primary use is in the construction of the CI .yml files
and simplify the management of many feedstocks.
conda-forge - the place where the feedstock and smithy live and work to produce the finished article (built conda distributions)
If you would like to improve the adaptive-scheduler recipe or build a new
package version, please fork this repository and submit a PR. Upon submission,
your changes will be run on the appropriate platforms to give the reviewer an
opportunity to confirm that the changes result in a successful build. Once
merged, the recipe will be re-built and uploaded automatically to the
conda-forge channel, whereupon the built conda packages will be available for
everybody to install and use from the conda-forge channel.
Note that all branches in the conda-forge/adaptive-scheduler-feedstock are
immediately built and any created packages are uploaded, so PRs should be based
on branches in forks and branches in the main repository should only be used to
build distinct package versions.
In order to produce a uniquely identifiable distribution:
- If the version of a package is not being increased, please add or increase
the
build/number. - If the version of a package is being increased, please remember to return
the
build/numberback to 0.