Metadata-Version: 2.4
Name: snakemake-executor-plugin-lsf-sanger
Version: 1.3.1
Summary: A Snakemake executor plugin for submitting jobs to the Sanger LSF cluster.
License: MIT
License-File: LICENSE
Keywords: snakemake,plugin,executor,cluster,lsf,sanger
Author: Filip Makosza
Author-email: fm12@sanger.ac.uk
Requires-Python: >=3.11,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Dist: snakemake (>=9.0.0,<10.0.0)
Requires-Dist: snakemake-interface-common (>=1.17.1,<2.0.0)
Requires-Dist: snakemake-interface-executor-plugins (>=9.0.0,<10.0.0)
Requires-Dist: throttler (>=1.2.2,<2.0.0)
Project-URL: Documentation, https://github.com/FMakosza/snakemake-executor-plugin-lsf-sanger/blob/main/README.md
Project-URL: Repository, https://github.com/FMakosza/snakemake-executor-plugin-lsf-sanger
Description-Content-Type: text/markdown

# Snakemake executor plugin: LSF-Sanger

[LSF](https://www.ibm.com/docs/en/spectrum-lsf/) is common high performance
computing batch system. This is a version of the [generic LSF executor plugin](https://github.com/BEFH/snakemake-executor-plugin-lsf) modified to better integrate with the Sanger compute environment and simplify pipeline execution.

Rule time and memory requirements are used to select an appropriate farm queue automatically, following the same criteria as the [Sanger Nextflow profile](https://nf-co.re/configs/sanger/).

The following readme is largely preserved from the original LSF executor plugin. `lsf-sanger` users do not have to specify queues for rules which specify the resources they need.

## Installation

To use the `lsf-sanger` executor, install the latest release into the same Python environment as Snakemake.

```
$ source venv/bin/activate
(venv) $ pip install https://github.com/FMakosza/snakemake-executor-plugin-lsf-sanger/archive/refs/tags/[VERSION].tar.gz
```

## Specifying Project and Queue

LSF clusters can have mandatory resource indicators for accounting and scheduling, Project and Queue, respectively. These resources are usually omitted from Snakemake workflows in order to keep the workflow definition independent from the platform. However, it is also possible to specify them inside of the workflow as resources in the rule definition (see the [Resources](https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#resources) document).

To specify them at the command line, define them as default resources:

``` console
$ snakemake --executor lsf-sanger --default-resources lsf_project=<your LSF project> lsf_queue=<your LSF queue>
```

If individual rules require e.g. a different queue, you can override
the default per rule:

``` console
$ snakemake --executor lsf-sanger --default-resources lsf_project=<your LSF project> lsf_queue=<your LSF queue> --set-resources <somerule>:lsf_queue=<some other queue>
```

Usually, it is advisable to persist such settings via a
[configuration profile](https://snakemake.readthedocs.io/en/latest/executing/cli.html#profiles), which
can be provided system-wide, per user, and in addition per workflow.

This is an example of the relevant profile settings:

```yaml
jobs: '<max concurrent jobs>'
executor: lsf-sanger
default-resources:
  - 'lsf_project=<your LSF project>'
  - 'lsf_queue=<your LSF queue>'
```

## Ordinary SMP jobs

Most jobs will be carried out by programs which are either single core
scripts or threaded programs, hence SMP ([shared memory
programs](https://en.wikipedia.org/wiki/Shared_memory)) in nature. Any
given threads and `mem_mb` requirements will be passed to LSF:

``` python
rule a:
    input: ...
    output: ...
    threads: 8
    resources:
        mem_mb=14000
```

This will give jobs from this rule 14GB of memory and 8 CPU cores. It is
advisable to use resonable default resources, such that you don\'t need
to specify them for every rule. Snakemake already has reasonable
defaults built in, which are automatically activated when using any non-local executor
(hence also with lsf). Use mem_mb_per_cpu to give the standard LSF type memory per CPU

## MPI jobs

Snakemake\'s LSF backend also supports MPI jobs, see
the [MPI support document](https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#mpi-support) for details.

``` python
rule calc_pi:
  output:
      "pi.calc",
  log:
      "logs/calc_pi.log",
  threads: 40
  resources:
      tasks=10,
      mpi='mpirun',
  shell:
      "{resources.mpi} -np {resources.tasks} calc-pi-mpi > {output} 2> {log}"
```

``` console
$ snakemake --set-resources calc_pi:mpi="mpiexec" ...
```

## Advanced Resource Specifications

A workflow rule may support a number of
[resource specifications](https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#resources).
For a LSF cluster, a mapping between Snakemake and LSF needs to be performed.

You can use the following specifications:

| LSF                                 | Snakemake        | Description                            |
|-------------------------------------|------------------|----------------------------------------|
| `-q`                                | `lsf_queue`      | the queue a rule/job is to use         |
| `--W`                               | `walltime`       | the walltime per job in minutes        |
| `-R "rusage[mem=<memory_amount>]"`  | `mem`, `mem_mb`  | memory a cluster node must provide     |
|                                     |                  | (`mem`: string with unit, `mem_mb`: i) |
| `-R "rusage[mem=<memory_amount>]"`  | `mem_mb_per_cpu` | memory per reserved CPU                |
| `-R "rusage[ngpus_physical=<gpus>"]`| `gpu`            | GPUs to request for the job            |
| omit `-R span[hosts=1]`             | `mpi`            | Allow splitting across nodes for MPI   |
| `-R span[ptile=<ptile>]`            | `ptile`          | Processors per host. Reqires `mpi`     |
| Other `bsub` arguments              | `lsf_extra`      | Other args to pass to `bsub` (str)     |


Each of these can be part of a rule, e.g.:

``` python
rule:
    input: ...
    output: ...
    resources:
        lsf_queue: <queue name>
        walltime: <some number>
```

`walltime` and `runtime` are synonyms.

Please note: as `--mem` and `--mem-per-cpu` are mutually exclusive,
their corresponding resource flags `mem`/`mem_mb` and
`mem_mb_per_cpu` are mutually exclusive, too. You can only reserve
memory a compute node has to provide or the memory required per CPU
(LSF does not make any distintion between real CPU cores and those
provided by hyperthreads). The executor will convert the provided options
based on cluster config.

## Additional custom job configuration

There are various `bsub` options not directly supported via the resource
definitions shown above. You may use the `lsf_extra` resource to specify
additional flags to `bsub`:

``` python
rule myrule:
    input: ...
    output: ...
    resources:
        lsf_extra="-R a100 -gpu num=2"
```

Again, rather use a [profile](https://snakemake.readthedocs.io/en/latest/executing/cli.html#profiles) to specify such resources.

## Per-job vs per-core

By default, this plugin keeps the specified memory request as a per-job, as expected by the Sanger LSF cluster.
If for some reason you want the request to be per-CPU core (i.e. `-R rusage[mem=<mem_mb/threads>]`) then set the
environment variable `SNAKEMAKE_LSF_MEMFMT` to `percpu`.

The executor automatically detects the request unit from cluster configuration, so if your cluster does not use MB,
you do not need to do anything.


