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May 27, 2025
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Update DocStrings #662

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Update DocStrings
jan-janssen May 27, 2025
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Merge remote-tracking branch 'origin/main' into docstring
jan-janssen May 27, 2025
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4 changes: 2 additions & 2 deletions docs/installation.md
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Expand Up @@ -120,8 +120,8 @@ For the version 5 of openmpi the backend changed to `pmix`, this requires the ad
```
conda install -c conda-forge flux-core flux-sched flux-pmix openmpi>=5 executorlib
```
In addition, the `flux_executor_pmi_mode="pmix"` parameter has to be set for the `executorlib.Executor` to switch to
`pmix` as backend.
In addition, the `flux_executor_pmi_mode="pmix"` parameter has to be set for the `FluxJobExecutor` or the
`FluxClusterExecutor` to switch to `pmix` as backend.

### Test Flux Framework
To validate the installation of flux and confirm the GPUs are correctly recognized, you can start a flux session on the
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44 changes: 21 additions & 23 deletions executorlib/executor/flux.py
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Expand Up @@ -19,11 +19,11 @@

class FluxJobExecutor(BaseExecutor):
"""
The executorlib.Executor leverages either the message passing interface (MPI), the SLURM workload manager or
The executorlib.FluxJobExecutor leverages either the message passing interface (MPI), the SLURM workload manager or
preferable the flux framework for distributing python functions within a given resource allocation. In contrast to
the mpi4py.futures.MPIPoolExecutor the executorlib.Executor can be executed in a serial python process and does not
require the python script to be executed with MPI. It is even possible to execute the executorlib.Executor directly
in an interactive Jupyter notebook.
the mpi4py.futures.MPIPoolExecutor the executorlib.FluxJobExecutor can be executed in a serial python process and
does not require the python script to be executed with MPI. It is even possible to execute the
executorlib.FluxJobExecutor directly in an interactive Jupyter notebook.

Args:
max_workers (int): for backwards compatibility with the standard library, max_workers also defines the number of
Expand Down Expand Up @@ -65,7 +65,7 @@ class FluxJobExecutor(BaseExecutor):
Examples:
```
>>> import numpy as np
>>> from executorlib.executor.flux import FluxJobExecutor
>>> from executorlib import FluxJobExecutor
>>>
>>> def calc(i, j, k):
>>> from mpi4py import MPI
Expand Down Expand Up @@ -102,12 +102,11 @@ def __init__(
plot_dependency_graph_filename: Optional[str] = None,
):
"""
Instead of returning a executorlib.Executor object this function returns either a executorlib.mpi.PyMPIExecutor,
executorlib.slurm.PySlurmExecutor or executorlib.flux.PyFluxExecutor depending on which backend is available. The
executorlib.flux.PyFluxExecutor is the preferred choice while the executorlib.mpi.PyMPIExecutor is primarily used
for development and testing. The executorlib.flux.PyFluxExecutor requires flux-core from the flux-framework to be
installed and in addition flux-sched to enable GPU scheduling. Finally, the executorlib.slurm.PySlurmExecutor
requires the SLURM workload manager to be installed on the system.
The executorlib.FluxJobExecutor leverages either the message passing interface (MPI), the SLURM workload manager
or preferable the flux framework for distributing python functions within a given resource allocation. In
contrast to the mpi4py.futures.MPIPoolExecutor the executorlib.FluxJobExecutor can be executed in a serial
python process and does not require the python script to be executed with MPI. It is even possible to execute
the executorlib.FluxJobExecutor directly in an interactive Jupyter notebook.

Args:
max_workers (int): for backwards compatibility with the standard library, max_workers also defines the
Expand Down Expand Up @@ -204,11 +203,11 @@ def __init__(

class FluxClusterExecutor(BaseExecutor):
"""
The executorlib.Executor leverages either the message passing interface (MPI), the SLURM workload manager or
preferable the flux framework for distributing python functions within a given resource allocation. In contrast to
the mpi4py.futures.MPIPoolExecutor the executorlib.Executor can be executed in a serial python process and does not
require the python script to be executed with MPI. It is even possible to execute the executorlib.Executor directly
in an interactive Jupyter notebook.
The executorlib.FluxClusterExecutor leverages either the message passing interface (MPI), the SLURM workload manager
or preferable the flux framework for distributing python functions within a given resource allocation. In contrast
to the mpi4py.futures.MPIPoolExecutor the executorlib.FluxClusterExecutor can be executed in a serial python process
and does not require the python script to be executed with MPI. It is even possible to execute the
executorlib.FluxClusterExecutor directly in an interactive Jupyter notebook.

Args:
max_workers (int): for backwards compatibility with the standard library, max_workers also defines the number of
Expand Down Expand Up @@ -246,7 +245,7 @@ class FluxClusterExecutor(BaseExecutor):
Examples:
```
>>> import numpy as np
>>> from executorlib.executor.flux import FluxClusterExecutor
>>> from executorlib import FluxClusterExecutor
>>>
>>> def calc(i, j, k):
>>> from mpi4py import MPI
Expand Down Expand Up @@ -280,12 +279,11 @@ def __init__(
plot_dependency_graph_filename: Optional[str] = None,
):
"""
Instead of returning a executorlib.Executor object this function returns either a executorlib.mpi.PyMPIExecutor,
executorlib.slurm.PySlurmExecutor or executorlib.flux.PyFluxExecutor depending on which backend is available. The
executorlib.flux.PyFluxExecutor is the preferred choice while the executorlib.mpi.PyMPIExecutor is primarily used
for development and testing. The executorlib.flux.PyFluxExecutor requires flux-core from the flux-framework to be
installed and in addition flux-sched to enable GPU scheduling. Finally, the executorlib.slurm.PySlurmExecutor
requires the SLURM workload manager to be installed on the system.
The executorlib.FluxClusterExecutor leverages either the message passing interface (MPI), the SLURM workload
manager or preferable the flux framework for distributing python functions within a given resource allocation.
In contrast to the mpi4py.futures.MPIPoolExecutor the executorlib.FluxClusterExecutor can be executed in a
serial python process and does not require the python script to be executed with MPI. It is even possible to
execute the executorlib.FluxClusterExecutor directly in an interactive Jupyter notebook.

Args:
max_workers (int): for backwards compatibility with the standard library, max_workers also defines the
Expand Down
23 changes: 11 additions & 12 deletions executorlib/executor/single.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,11 +19,11 @@

class SingleNodeExecutor(BaseExecutor):
"""
The executorlib.Executor leverages either the message passing interface (MPI), the SLURM workload manager or
preferable the flux framework for distributing python functions within a given resource allocation. In contrast to
the mpi4py.futures.MPIPoolExecutor the executorlib.Executor can be executed in a serial python process and does not
require the python script to be executed with MPI. It is even possible to execute the executorlib.Executor directly
in an interactive Jupyter notebook.
The executorlib.SingleNodeExecutor leverages either the message passing interface (MPI), the SLURM workload manager
or preferable the flux framework for distributing python functions within a given resource allocation. In contrast
to the mpi4py.futures.MPIPoolExecutor the executorlib.SingleNodeExecutor can be executed in a serial python process
and does not require the python script to be executed with MPI. It is even possible to execute the
executorlib.SingleNodeExecutor directly in an interactive Jupyter notebook.

Args:
max_workers (int): for backwards compatibility with the standard library, max_workers also defines the number of
Expand Down Expand Up @@ -60,7 +60,7 @@ class SingleNodeExecutor(BaseExecutor):
Examples:
```
>>> import numpy as np
>>> from executorlib.executor.single import SingleNodeExecutor
>>> from executorlib import SingleNodeExecutor
>>>
>>> def calc(i, j, k):
>>> from mpi4py import MPI
Expand Down Expand Up @@ -93,12 +93,11 @@ def __init__(
plot_dependency_graph_filename: Optional[str] = None,
):
"""
Instead of returning a executorlib.Executor object this function returns either a executorlib.mpi.PyMPIExecutor,
executorlib.slurm.PySlurmExecutor or executorlib.flux.PyFluxExecutor depending on which backend is available. The
executorlib.flux.PyFluxExecutor is the preferred choice while the executorlib.mpi.PyMPIExecutor is primarily used
for development and testing. The executorlib.flux.PyFluxExecutor requires flux-core from the flux-framework to be
installed and in addition flux-sched to enable GPU scheduling. Finally, the executorlib.slurm.PySlurmExecutor
requires the SLURM workload manager to be installed on the system.
The executorlib.SingleNodeExecutor leverages either the message passing interface (MPI), the SLURM workload
manager or preferable the flux framework for distributing python functions within a given resource allocation.
In contrast to the mpi4py.futures.MPIPoolExecutor the executorlib.SingleNodeExecutor can be executed in a serial
python process and does not require the python script to be executed with MPI. It is even possible to execute
the executorlib.SingleNodeExecutor directly in an interactive Jupyter notebook.

Args:
max_workers (int): for backwards compatibility with the standard library, max_workers also defines the
Expand Down
44 changes: 21 additions & 23 deletions executorlib/executor/slurm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,11 +20,11 @@

class SlurmClusterExecutor(BaseExecutor):
"""
The executorlib.Executor leverages either the message passing interface (MPI), the SLURM workload manager or
preferable the flux framework for distributing python functions within a given resource allocation. In contrast to
the mpi4py.futures.MPIPoolExecutor the executorlib.Executor can be executed in a serial python process and does not
require the python script to be executed with MPI. It is even possible to execute the executorlib.Executor directly
in an interactive Jupyter notebook.
The executorlib.SlurmClusterExecutor leverages either the message passing interface (MPI), the SLURM workload
manager or preferable the flux framework for distributing python functions within a given resource allocation. In
contrast to the mpi4py.futures.MPIPoolExecutor the executorlib.SlurmClusterExecutor can be executed in a serial
python process and does not require the python script to be executed with MPI. It is even possible to execute the
executorlib.SlurmClusterExecutor directly in an interactive Jupyter notebook.

Args:
max_workers (int): for backwards compatibility with the standard library, max_workers also defines the number of
Expand Down Expand Up @@ -62,7 +62,7 @@ class SlurmClusterExecutor(BaseExecutor):
Examples:
```
>>> import numpy as np
>>> from executorlib.executor.slurm import SlurmClusterExecutor
>>> from executorlib import SlurmClusterExecutor
>>>
>>> def calc(i, j, k):
>>> from mpi4py import MPI
Expand Down Expand Up @@ -96,12 +96,11 @@ def __init__(
plot_dependency_graph_filename: Optional[str] = None,
):
"""
Instead of returning a executorlib.Executor object this function returns either a executorlib.mpi.PyMPIExecutor,
executorlib.slurm.PySlurmExecutor or executorlib.flux.PyFluxExecutor depending on which backend is available. The
executorlib.flux.PyFluxExecutor is the preferred choice while the executorlib.mpi.PyMPIExecutor is primarily used
for development and testing. The executorlib.flux.PyFluxExecutor requires flux-core from the flux-framework to be
installed and in addition flux-sched to enable GPU scheduling. Finally, the executorlib.slurm.PySlurmExecutor
requires the SLURM workload manager to be installed on the system.
The executorlib.SlurmClusterExecutor leverages either the message passing interface (MPI), the SLURM workload
manager or preferable the flux framework for distributing python functions within a given resource allocation.
In contrast to the mpi4py.futures.MPIPoolExecutor the executorlib.SlurmClusterExecutor can be executed in a
serial python process and does not require the python script to be executed with MPI. It is even possible to
execute the executorlib.SlurmClusterExecutor directly in an interactive Jupyter notebook.

Args:
max_workers (int): for backwards compatibility with the standard library, max_workers also defines the
Expand Down Expand Up @@ -196,11 +195,11 @@ def __init__(

class SlurmJobExecutor(BaseExecutor):
"""
The executorlib.Executor leverages either the message passing interface (MPI), the SLURM workload manager or
The executorlib.SlurmJobExecutor leverages either the message passing interface (MPI), the SLURM workload manager or
preferable the flux framework for distributing python functions within a given resource allocation. In contrast to
the mpi4py.futures.MPIPoolExecutor the executorlib.Executor can be executed in a serial python process and does not
require the python script to be executed with MPI. It is even possible to execute the executorlib.Executor directly
in an interactive Jupyter notebook.
the mpi4py.futures.MPIPoolExecutor the executorlib.SlurmJobExecutor can be executed in a serial python process and
does not require the python script to be executed with MPI. It is even possible to execute the
executorlib.SlurmJobExecutor directly in an interactive Jupyter notebook.

Args:
max_workers (int): for backwards compatibility with the standard library, max_workers also defines the number of
Expand Down Expand Up @@ -241,7 +240,7 @@ class SlurmJobExecutor(BaseExecutor):
Examples:
```
>>> import numpy as np
>>> from executorlib.executor.slurm import SlurmJobExecutor
>>> from executorlib import SlurmJobExecutor
>>>
>>> def calc(i, j, k):
>>> from mpi4py import MPI
Expand Down Expand Up @@ -274,12 +273,11 @@ def __init__(
plot_dependency_graph_filename: Optional[str] = None,
):
"""
Instead of returning a executorlib.Executor object this function returns either a executorlib.mpi.PyMPIExecutor,
executorlib.slurm.PySlurmExecutor or executorlib.flux.PyFluxExecutor depending on which backend is available. The
executorlib.flux.PyFluxExecutor is the preferred choice while the executorlib.mpi.PyMPIExecutor is primarily used
for development and testing. The executorlib.flux.PyFluxExecutor requires flux-core from the flux-framework to be
installed and in addition flux-sched to enable GPU scheduling. Finally, the executorlib.slurm.PySlurmExecutor
requires the SLURM workload manager to be installed on the system.
The executorlib.SlurmJobExecutor leverages either the message passing interface (MPI), the SLURM workload
manager or preferable the flux framework for distributing python functions within a given resource allocation.
In contrast to the mpi4py.futures.MPIPoolExecutor the executorlib.SlurmJobExecutor can be executed in a serial
python process and does not require the python script to be executed with MPI. It is even possible to execute
the executorlib.SlurmJobExecutor directly in an interactive Jupyter notebook.

Args:
max_workers (int): for backwards compatibility with the standard library, max_workers also defines the
Expand Down
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