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.yaml"

The config file can be used to define a dictionary of configuration parameters and their values. In case of YAML, the file can optionally be processed with YTE. To activate this, you have to add the top-level key __use_yte__ = true to the YAML file.

In the workflow, the configuration is accessible via the global variable config, e.g.

rule all:
        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 api_reference_snakemake, 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:

    "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.

Environment variables

Sometimes, it is not desirable to put configuration information into text files. For example, this holds for secrets like access tokens or passwords. Here, environment variables are the method of choice. Snakemake allows to assert the existence of environment variables by adding a statement like:


When executing, Snakemake will fail with a reasonable error message if the variables SOME_VARIABLE and SOME_OTHER_VARIABLE are undefined. Otherwise, it will take care of passing them to cluster and cloud environments. However, note that this does not mean that Snakemake makes them available e.g. in the jobs shell command. Instead, for data provenance and reproducibility reasons, you are required to pass them explicitly to your job via the params directive, e.g. like this:


rule do_something:
        "echo {params.x} > {output}"


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. Also, by using the defaults property it is possible to populate entries with default values. See jsonschema FAQ on setting default values for details. 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:
        expand("test.{sample}.txt", sample=samples.index)

rule a:
        "touch {output}"

the schema for validating the samples data frame looks like this:

$schema: ""
description: an entry in the sample sheet
    type: string
    description: sample name/identifier
    type: string
    description: sample condition that will be compared during differential expression analysis (e.g. a treatment, a tissue time, a disease)
    type: boolean
    default: true
    description: boolean that indicates if sample is case or control

  - sample
  - condition

Here, in case the case column is missing, the validate function will populate it with True for all entries.

Configuring scientific experiments via PEPs

Often scientific experiments consist of a set of samples (with optional subsamples), for which raw data and metainformation is known. Instead of writing custom sample sheets as shown above, Snakemake allows to use portable encapsulated project (PEP) definitions to configure a workflow. This is done via a special directive pepfile, that can optionally complemented by a schema for validation (which is recommended for production workflows):

pepfile: "pep/config.yaml"
pepschema: "schemas/pep.yaml"

rule all:
        expand("{sample}.txt", sample=pep.sample_table["sample_name"])

rule a:
        "touch {output}"

Using the pepfile directive leads to parsing of the provided PEP with peppy. The resulting project object is made globally available under the name pep. Here, we use it to aggregate over the set of sample names that is defined in the corresponding PEP.

Importantly, note that PEPs are meant to contain sample metadata and any global information about a project or experiment. They should not be used to encode workflow specific configuration options. For those, one should always complement the pepfile with an ordinary config file. The rationale is that PEPs should be portable between different data analysis workflows (that could be applied to the same data) and even between workflow management systems. In other words, a PEP should describe everything needed about the data, while a workflow and its configuration should describe everything needed about the analysis that is applied to it.

Validating PEPs

Using the pepschema directive leads to an automatic parsing of the provided schema and PEP validation with the PEP validation tool – eido. Eido schemas extend JSON Schema vocabulary to accommodate the powerful PEP features. Follow the How to write a PEP schema guide to learn more.

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.

Cluster Configuration (not supported anymore)

The previously supported cluster configuration has been replaced by configuration profiles (see Profiles).