# Configuration¶

Snakemake allows you to use configuration files for making your workflows more flexible and also for abstracting away direct dependencies to a fixed HPC cluster scheduler.

## Standard Configuration¶

Snakemake directly supports the configuration of your workflow. A configuration is provided as a JSON or YAML file and can be loaded with:

configfile: "path/to/config.json"


The config file can be used to define a dictionary of configuration parameters and their values. In the workflow, the configuration is accessible via the global variable config, e.g.

rule all:
input:
expand("{sample}.{param}.output.pdf", sample=config["samples"], param=config["yourparam"])


If the configfile statement is not used, the config variable provides an empty array. In addition to the configfile statement, config values can be overwritten via the command line or the The Snakemake API, e.g.:

$snakemake --config yourparam=1.5  Further, you can manually alter the config dictionary using any Python code outside of your rules. Changes made from within a rule won’t be seen from other rules. Finally, you can use the –configfile command line argument to overwrite values from the configfile statement. Note that any values parsed into the config dictionary with any of above mechanisms are merged, i.e., all keys defined via a configfile statement, or the –configfile and –config command line arguments will end up in the final config dictionary, but if two methods define the same key, command line overwrites the configfile statement. For adding config placeholders into a shell command, Python string formatting syntax requires you to leave out the quotes around the key name, like so: shell: "mycommand {config[foo]} ..."  ## Tabular configuration¶ It is usually advisable to complement YAML based configuration (see above) by a sheet based approach for meta-data that is of tabular form. For example, such a sheet can contain per-sample information. With the Pandas library such data can be read and used with minimal overhead, e.g., import pandas as pd samples = pd.read_table("samples.tsv").set_index("samples", drop=False)  reads in a table samples.tsv in TSV format and makes every record accessible by the sample name. For details, see the Pandas documentation. A fully working real-world example containing both types of configuration can be found here. ## Validation¶ With Snakemake 5.1, it is possible to validate both types of configuration via JSON schemas. The function snakemake.utils.validate takes a loaded configuration (a config dictionary or a Pandas data frame) and validates it with a given JSON schema. Thereby, the schema can be provided in JSON or YAML format. In case of the data frame, the schema should model the record that is expected in each row of the data frame. In the following example, import pandas as pd from snakemake.utils import validate configfile: "config.yaml" validate(config, "config.schema.yaml") samples = pd.read_table(config["samples"]).set_index("sample", drop=False) validate(samples, "samples.schema.yaml") rule all: input: expand("test.{sample}.txt", sample=samples.index) rule a: output: "test.{sample}.txt" shell: "touch {output}"  the schema for validating the samples data frame looks like this: $schema: "http://json-schema.org/draft-06/schema#"
description: an entry in the sample sheet
properties:
sample:
type: string
description: sample name/identifier
condition:
type: string
description: sample condition that will be compared during differential expression analysis (e.g. a treatment, a tissue time, a disease)

required:
- sample
- condition


## Cluster Configuration¶

Snakemake supports a separate configuration file for execution on a cluster. A cluster config file allows you to specify cluster submission parameters outside the Snakefile. The cluster config is a JSON- or YAML-formatted file that contains objects that match names of rules in the Snakefile. The parameters in the cluster config are then accessed by the cluster.* wildcard when you are submitting jobs. Note that a workflow shall never depend on a cluster configuration, because this would limit its portability. Therefore, it is also not intended to access the cluster configuration from within the workflow.

For example, say that you have the following Snakefile:

rule all:
input: "input1.txt", "input2.txt"

rule compute1:
output: "input1.txt"
shell: "touch input1.txt"

rule compute2:
output: "input2.txt"
shell: "touch input2.txt"


This Snakefile can then be configured by a corresponding cluster config, say “cluster.json”:

{
"__default__" :
{
"account" : "my account",
"time" : "00:15:00",
"n" : 1,
"partition" : "core"
},
"compute1" :
{
"time" : "00:20:00"
}
}


Any string in the cluster configuration can be formatted in the same way as shell commands, e.g. {rule}.{wildcards.sample} is formatted to a.xy if the rulename is a and the wildcard value is xy. Here __default__ is a special object that specifies default parameters, these will be inherited by the other configuration objects. The compute1 object here changes the time parameter, but keeps the other parameters from __default__. The rule compute2 does not have any configuration, and will therefore use the default configuration. You can then run the Snakefile with the following command on a SLURM system.

\$ snakemake -j 999 --cluster-config cluster.json --cluster "sbatch -A {cluster.account} -p {cluster.partition} -n {cluster.n}  -t {cluster.time}"


For cluster systems using LSF/BSUB, a cluster config may look like this:

{
"__default__" :
{
"queue"     : "medium_priority",
"nCPUs"     : "16",
"memory"    : 20000,
"resources" : "\"select[mem>20000] rusage[mem=20000] span[hosts=1]\"",
"name"      : "JOBNAME.{rule}.{wildcards}",
"output"    : "logs/cluster/{rule}.{wildcards}.out",
"error"     : "logs/cluster/{rule}.{wildcards}.err"
},

"trimming_PE" :
{
"memory"    : 30000,
"resources" : "\"select[mem>30000] rusage[mem=30000] span[hosts=1]\"",
}
}


The advantage of this setup is that it is already pretty general by exploiting the wildcard possibilities that Snakemake provides via {rule} and {wildcards}. So job names, output and error files all have reasonable and trackable default names, only the directies (logs/cluster) and job names (JOBNAME) have to adjusted accordingly. If a rule named bamCoverage is executed with the wildcard basename = sample1, for example, the output and error files will be bamCoverage.basename=sample1.out and bamCoverage.basename=sample1.err, respectively.

## Configure Working Directory¶

All paths in the snakefile are interpreted relative to the directory snakemake is executed in. This behaviour can be overridden by specifying a workdir in the snakefile:

workdir: "path/to/workdir"


Usually, it is preferred to only set the working directory via the command line, because above directive limits the portability of Snakemake workflows.