Command line interface

This part of the documentation describes the snakemake executable. Snakemake is primarily a command-line tool, so the snakemake executable is the primary way to execute, debug, and visualize workflows.

Useful Command Line Arguments

If called without parameters, i.e.

$ snakemake

Snakemake tries to execute the workflow specified in a file called Snakefile in the same directory (instead, the Snakefile can be given via the parameter -s).

By issuing

$ snakemake -n

a dry-run can be performed. This is useful to test if the workflow is defined properly and to estimate the amount of needed computation. Further, the reason for each rule execution can be printed via

$ snakemake -n -r

Importantly, Snakemake can automatically determine which parts of the workflow can be run in parallel. By specifying the number of available cores, i.e.

$ snakemake --cores 4

one can tell Snakemake to use up to 4 cores and solve a binary knapsack problem to optimize the scheduling of jobs. If the number is omitted (i.e., only --cores is given), the number of used cores is determined as the number of available CPU cores in the machine.

Snakemake workflows usually define the number of used threads of certain rules. Sometimes, it makes sense to overwrite the defaults given in the workflow definition. This can be done by using the --set-threads argument, e.g.,

$ snakemake --cores 4 --set-threads myrule=2

would overwrite whatever number of threads has been defined for the rule myrule and use 2 instead. This can be particularly handy when used in combination with cluster execution.

Dealing with very large workflows

If your workflow has a lot of jobs, Snakemake might need some time to infer the dependencies (the job DAG) and which jobs are actually required to run. The major bottleneck involved is the filesystem, which has to be queried for existence and modification dates of files. To overcome this issue, Snakemake allows to run large workflows in batches. This way, fewer files have to be evaluated at once, and therefore the job DAG can be inferred faster. By running

$ snakemake --cores 4 --batch myrule=1/3

you instruct to only compute the first of three batches of the inputs of the rule myrule. To generate the second batch, run

$ snakemake --cores 4 --batch myrule=2/3

Finally, when running

$ snakemake --cores 4 --batch myrule=3/3

Snakemake will process beyond the rule myrule, because all of its input files have been generated, and complete the workflow. Obviously, a good choice of the rule to perform the batching is a rule that has a lot of input files and upstream jobs, for example a central aggregation step within your workflow. We advice all workflow developers to inform potential users of the best suited batching rule.

Profiles

Adapting Snakemake to a particular environment can entail many flags and options. Therefore, since Snakemake 4.1, it is possible to specify a configuration profile to be used to obtain default options:

$ snakemake --profile myprofile

Here, a folder myprofile is searched in per-user and global configuration directories (on Linux, this will be $HOME/.config/snakemake and /etc/xdg/snakemake, you can find the answer for your system via snakemake --help). Alternatively, an absolute or relative path to the folder can be given. The profile folder is expected to contain a file config.yaml that defines default values for the Snakemake command line arguments. For example, the file

cluster: qsub
jobs: 100

would setup Snakemake to always submit to the cluster via the qsub command, and never use more than 100 parallel jobs in total. Under https://github.com/snakemake-profiles/doc, you can find publicly available profiles. Feel free to contribute your own.

The profile folder can additionally contain auxilliary files, e.g., jobscripts, or any kind of wrappers. See https://github.com/snakemake-profiles/doc for examples.

Visualization

To visualize the workflow, one can use the option --dag. This creates a representation of the DAG in the graphviz dot language which has to be postprocessed by the graphviz tool dot. E.g. to visualize the DAG that would be executed, you can issue:

$ snakemake --dag | dot | display

For saving this to a file, you can specify the desired format:

$ snakemake --dag | dot -Tpdf > dag.pdf

To visualize the whole DAG regardless of the eventual presence of files, the forceall option can be used:

$ snakemake --forceall --dag | dot -Tpdf > dag.pdf

Of course the visual appearance can be modified by providing further command line arguments to dot.

Note: The DAG is printed in DOT format straight to the standard output, along with other print statements you may have in your Snakefile. Make sure to comment these other print statements so that dot can build a visual representation of your DAG.

All Options

All command line options can be printed by calling snakemake -h.

Snakemake is a Python based language and execution environment for GNU Make-like workflows.

usage: snakemake [-h] [--dry-run] [--profile PROFILE]
                 [--cache [RULE [RULE ...]]] [--snakefile FILE] [--cores [N]]
                 [--local-cores N] [--resources [NAME=INT [NAME=INT ...]]]
                 [--set-threads [RULE=THREADS [RULE=THREADS ...]]]
                 [--default-resources [NAME=INT [NAME=INT ...]]]
                 [--config [KEY=VALUE [KEY=VALUE ...]]]
                 [--configfile FILE [FILE ...]]
                 [--envvars VARNAME [VARNAME ...]] [--directory DIR] [--touch]
                 [--keep-going] [--force] [--forceall]
                 [--forcerun [TARGET [TARGET ...]]]
                 [--prioritize TARGET [TARGET ...]]
                 [--batch RULE=BATCH/BATCHES] [--until TARGET [TARGET ...]]
                 [--omit-from TARGET [TARGET ...]] [--rerun-incomplete]
                 [--shadow-prefix DIR] [--report [FILE]]
                 [--report-stylesheet CSSFILE] [--edit-notebook TARGET]
                 [--notebook-listen IP:PORT] [--lint [{text,json}]]
                 [--export-cwl FILE] [--list] [--list-target-rules] [--dag]
                 [--rulegraph] [--filegraph] [--d3dag] [--summary]
                 [--detailed-summary] [--archive FILE]
                 [--cleanup-metadata FILE [FILE ...]] [--cleanup-shadow]
                 [--skip-script-cleanup] [--unlock] [--list-version-changes]
                 [--list-code-changes] [--list-input-changes]
                 [--list-params-changes] [--list-untracked]
                 [--delete-all-output] [--delete-temp-output]
                 [--bash-completion] [--keep-incomplete] [--version]
                 [--reason] [--gui [PORT]] [--printshellcmds] [--debug-dag]
                 [--stats FILE] [--nocolor] [--quiet] [--print-compilation]
                 [--verbose] [--force-use-threads] [--allow-ambiguity]
                 [--nolock] [--ignore-incomplete] [--latency-wait SECONDS]
                 [--wait-for-files [FILE [FILE ...]]] [--notemp]
                 [--keep-remote] [--keep-target-files]
                 [--allowed-rules ALLOWED_RULES [ALLOWED_RULES ...]]
                 [--max-jobs-per-second MAX_JOBS_PER_SECOND]
                 [--max-status-checks-per-second MAX_STATUS_CHECKS_PER_SECOND]
                 [--restart-times RESTART_TIMES] [--attempt ATTEMPT]
                 [--wrapper-prefix WRAPPER_PREFIX]
                 [--default-remote-provider {S3,GS,FTP,SFTP,S3Mocked,gfal,gridftp,iRODS}]
                 [--default-remote-prefix DEFAULT_REMOTE_PREFIX]
                 [--no-shared-fs] [--greediness GREEDINESS] [--no-hooks]
                 [--overwrite-shellcmd OVERWRITE_SHELLCMD] [--debug]
                 [--runtime-profile FILE] [--mode {0,1,2}]
                 [--show-failed-logs] [--log-handler-script FILE]
                 [--log-service {none,slack}]
                 [--cluster CMD | --cluster-sync CMD | --drmaa [ARGS]]
                 [--cluster-config FILE] [--immediate-submit]
                 [--jobscript SCRIPT] [--jobname NAME]
                 [--cluster-status CLUSTER_STATUS] [--drmaa-log-dir DIR]
                 [--kubernetes [NAMESPACE]] [--container-image IMAGE]
                 [--tibanna] [--tibanna-sfn TIBANNA_SFN]
                 [--precommand PRECOMMAND]
                 [--tibanna-config TIBANNA_CONFIG [TIBANNA_CONFIG ...]]
                 [--google-lifesciences]
                 [--google-lifesciences-regions GOOGLE_LIFESCIENCES_REGIONS [GOOGLE_LIFESCIENCES_REGIONS ...]]
                 [--google-lifesciences-location GOOGLE_LIFESCIENCES_LOCATION]
                 [--google-lifesciences-keep-cache] [--use-conda]
                 [--list-conda-envs] [--conda-prefix DIR]
                 [--conda-cleanup-envs]
                 [--conda-cleanup-pkgs [{tarballs,cache}]]
                 [--conda-create-envs-only] [--conda-frontend {conda,mamba}]
                 [--use-singularity] [--singularity-prefix DIR]
                 [--singularity-args ARGS] [--use-envmodules]
                 [target [target ...]]

EXECUTION

target Targets to build. May be rules or files.
--dry-run, --dryrun, -n
 

Do not execute anything, and display what would be done. If you have a very large workflow, use –dry-run –quiet to just print a summary of the DAG of jobs.

Default: False

--profile
Name of profile to use for configuring Snakemake. Snakemake will search for a corresponding folder in /etc/xdg/snakemake and /home/docs/.config/snakemake. Alternatively, this can be an absolute or relative path. The profile folder has to contain a file ‘config.yaml’. This file can be used to set default values for command line options in YAML format. For example, ‘–cluster qsub’ becomes ‘cluster: qsub’ in the YAML file. Profiles can be obtained from https://github.com/snakemake-profiles.
--cache Store output files of given rules in a central cache given by the environment variable $SNAKEMAKE_OUTPUT_CACHE. Likewise, retrieve output files of the given rules from this cache if they have been created before (by anybody writing to the same cache), instead of actually executing the rules. Output files are identified by hashing all steps, parameters and software stack (conda envs or containers) needed to create them.
--snakefile, -s
 The workflow definition in form of a snakefile.Usually, you should not need to specify this. By default, Snakemake will search for ‘Snakefile’, ‘snakefile’, ‘workflow/Snakefile’, ‘workflow/snakefile’ beneath the current working directory, in this order. Only if you definitely want a different layout, you need to use this parameter.
--cores, --jobs, -j
 Use at most N CPU cores/jobs in parallel. If N is omitted or ‘all’, the limit is set to the number of available CPU cores.
--local-cores

In cluster mode, use at most N cores of the host machine in parallel (default: number of CPU cores of the host). The cores are used to execute local rules. This option is ignored when not in cluster mode.

Default: 1

--resources, --res
 Define additional resources that shall constrain the scheduling analogously to threads (see above). A resource is defined as a name and an integer value. E.g. –resources mem_mb=1000. Rules can use resources by defining the resource keyword, e.g. resources: mem_mb=600. If now two rules require 600 of the resource ‘mem_mb’ they won’t be run in parallel by the scheduler.
--set-threads Overwrite thread usage of rules. This allows to fine-tune workflow parallelization. In particular, this is helpful to target certain cluster nodes by e.g. shifting a rule to use more, or less threads than defined in the workflow. Thereby, THREADS has to be a positive integer, and RULE has to be the name of the rule.
--default-resources, --default-res
 Define default values of resources for rules that do not define their own values. In addition to plain integers, python expressions over inputsize are allowed (e.g. ‘2*input.size_mb’).When specifying this without any arguments (–default-resources), it defines ‘mem_mb=max(2*input.size_mb, 1000)’ ‘disk_mb=max(2*input.size_mb, 1000)’, i.e., default disk and mem usage is twice the input file size but at least 1GB.
--config, -C Set or overwrite values in the workflow config object. The workflow config object is accessible as variable config inside the workflow. Default values can be set by providing a JSON file (see Documentation).
--configfile, --configfiles
 Specify or overwrite the config file of the workflow (see the docs). Values specified in JSON or YAML format are available in the global config dictionary inside the workflow. Multiple files overwrite each other in the given order.
--envvars Environment variables to pass to cloud jobs.
--directory, -d
 Specify working directory (relative paths in the snakefile will use this as their origin).
--touch, -t

Touch output files (mark them up to date without really changing them) instead of running their commands. This is used to pretend that the rules were executed, in order to fool future invocations of snakemake. Fails if a file does not yet exist. Note that this will only touch files that would otherwise be recreated by Snakemake (e.g. because their input files are newer). For enforcing a touch, combine this with –force, –forceall, or –forcerun. Note however that you loose the provenance information when the files have been created in realitiy. Hence, this should be used only as a last resort.

Default: False

--keep-going, -k
 

Go on with independent jobs if a job fails.

Default: False

--force, -f

Force the execution of the selected target or the first rule regardless of already created output.

Default: False

--forceall, -F

Force the execution of the selected (or the first) rule and all rules it is dependent on regardless of already created output.

Default: False

--forcerun, -R Force the re-execution or creation of the given rules or files. Use this option if you changed a rule and want to have all its output in your workflow updated.
--prioritize, -P
 Tell the scheduler to assign creation of given targets (and all their dependencies) highest priority. (EXPERIMENTAL)
--batch Only create the given BATCH of the input files of the given RULE. This can be used to iteratively run parts of very large workflows. Only the execution plan of the relevant part of the workflow has to be calculated, thereby speeding up DAG computation. It is recommended to provide the most suitable rule for batching when documenting a workflow. It should be some aggregating rule that would be executed only once, and has a large number of input files. For example, it can be a rule that aggregates over samples.
--until, -U Runs the pipeline until it reaches the specified rules or files. Only runs jobs that are dependencies of the specified rule or files, does not run sibling DAGs.
--omit-from, -O
 Prevent the execution or creation of the given rules or files as well as any rules or files that are downstream of these targets in the DAG. Also runs jobs in sibling DAGs that are independent of the rules or files specified here.
--rerun-incomplete, --ri
 

Re-run all jobs the output of which is recognized as incomplete.

Default: False

--shadow-prefix
 Specify a directory in which the ‘shadow’ directory is created. If not supplied, the value is set to the ‘.snakemake’ directory relative to the working directory.

REPORTS

--report Create an HTML report with results and statistics. This can be either a .html file or a .zip file. In the former case, all results are embedded into the .html (this only works for small data). In the latter case, results are stored along with a file report.html in the zip archive. If no filename is given, an embedded report.html is the default.
--report-stylesheet
 Custom stylesheet to use for report. In particular, this can be used for branding the report with e.g. a custom logo, see docs.

NOTEBOOKS

--edit-notebook
 Interactively edit the notebook associated with the rule used to generate the given target file. This will start a local jupyter notebook server. Any changes to the notebook should be saved, and the server has to be stopped by closing the notebook and hitting the ‘Quit’ button on the jupyter dashboard. Afterwards, the updated notebook will be automatically stored in the path defined in the rule. If the notebook is not yet present, this will create an empty draft.
--notebook-listen
 

The IP address and PORT the notebook server used for editing the notebook (–edit-notebook) will listen on.

Default: “localhost:8888”

UTILITIES

--lint

Possible choices: text, json

Perform linting on the given workflow. This will print snakemake specific suggestions to improve code quality (work in progress, more lints to be added in the future). If no argument is provided, plain text output is used.

--export-cwl Compile workflow to CWL and store it in given FILE.
--list, -l

Show available rules in given Snakefile.

Default: False

--list-target-rules, --lt
 

Show available target rules in given Snakefile.

Default: False

--dag

Do not execute anything and print the directed acyclic graph of jobs in the dot language. Recommended use on Unix systems: snakemake –dag | dot | displayNote print statements in your Snakefile may interfere with visualization.

Default: False

--rulegraph

Do not execute anything and print the dependency graph of rules in the dot language. This will be less crowded than above DAG of jobs, but also show less information. Note that each rule is displayed once, hence the displayed graph will be cyclic if a rule appears in several steps of the workflow. Use this if above option leads to a DAG that is too large. Recommended use on Unix systems: snakemake –rulegraph | dot | displayNote print statements in your Snakefile may interfere with visualization.

Default: False

--filegraph

Do not execute anything and print the dependency graph of rules with their input and output files in the dot language. This is an intermediate solution between above DAG of jobs and the rule graph. Note that each rule is displayed once, hence the displayed graph will be cyclic if a rule appears in several steps of the workflow. Use this if above option leads to a DAG that is too large. Recommended use on Unix systems: snakemake –filegraph | dot | displayNote print statements in your Snakefile may interfere with visualization.

Default: False

--d3dag

Print the DAG in D3.js compatible JSON format.

Default: False

--summary, -S

Print a summary of all files created by the workflow. The has the following columns: filename, modification time, rule version, status, plan. Thereby rule version contains the versionthe file was created with (see the version keyword of rules), and status denotes whether the file is missing, its input files are newer or if version or implementation of the rule changed since file creation. Finally the last column denotes whether the file will be updated or created during the next workflow execution.

Default: False

--detailed-summary, -D
 

Print a summary of all files created by the workflow. The has the following columns: filename, modification time, rule version, input file(s), shell command, status, plan. Thereby rule version contains the version the file was created with (see the version keyword of rules), and status denotes whether the file is missing, its input files are newer or if version or implementation of the rule changed since file creation. The input file and shell command columns are self explanatory. Finally the last column denotes whether the file will be updated or created during the next workflow execution.

Default: False

--archive Archive the workflow into the given tar archive FILE. The archive will be created such that the workflow can be re-executed on a vanilla system. The function needs conda and git to be installed. It will archive every file that is under git version control. Note that it is best practice to have the Snakefile, config files, and scripts under version control. Hence, they will be included in the archive. Further, it will add input files that are not generated by by the workflow itself and conda environments. Note that symlinks are dereferenced. Supported formats are .tar, .tar.gz, .tar.bz2 and .tar.xz.
--cleanup-metadata, --cm
 Cleanup the metadata of given files. That means that snakemake removes any tracked version info, and any marks that files are incomplete.
--cleanup-shadow
 

Cleanup old shadow directories which have not been deleted due to failures or power loss.

Default: False

--skip-script-cleanup
 

Don’t delete wrapper scripts used for execution

Default: False

--unlock

Remove a lock on the working directory.

Default: False

--list-version-changes, --lv
 

List all output files that have been created with a different version (as determined by the version keyword).

Default: False

--list-code-changes, --lc
 

List all output files for which the rule body (run or shell) have changed in the Snakefile.

Default: False

--list-input-changes, --li
 

List all output files for which the defined input files have changed in the Snakefile (e.g. new input files were added in the rule definition or files were renamed). For listing input file modification in the filesystem, use –summary.

Default: False

--list-params-changes, --lp
 

List all output files for which the defined params have changed in the Snakefile.

Default: False

--list-untracked, --lu
 

List all files in the working directory that are not used in the workflow. This can be used e.g. for identifying leftover files. Hidden files and directories are ignored.

Default: False

--delete-all-output
 

Remove all files generated by the workflow. Use together with –dry-run to list files without actually deleting anything. Note that this will not recurse into subworkflows. Write-protected files are not removed. Nevertheless, use with care!

Default: False

--delete-temp-output
 

Remove all temporary files generated by the workflow. Use together with –dry-run to list files without actually deleting anything. Note that this will not recurse into subworkflows.

Default: False

--bash-completion
 

Output code to register bash completion for snakemake. Put the following in your .bashrc (including the accents): snakemake –bash-completion or issue it in an open terminal session.

Default: False

--keep-incomplete
 

Do not remove incomplete output files by failed jobs.

Default: False

--version, -v show program’s version number and exit

OUTPUT

--reason, -r

Print the reason for each executed rule.

Default: False

--gui Serve an HTML based user interface to the given network and port e.g. 168.129.10.15:8000. By default Snakemake is only available in the local network (default port: 8000). To make Snakemake listen to all ip addresses add the special host address 0.0.0.0 to the url (0.0.0.0:8000). This is important if Snakemake is used in a virtualised environment like Docker. If possible, a browser window is opened.
--printshellcmds, -p
 

Print out the shell commands that will be executed.

Default: False

--debug-dag

Print candidate and selected jobs (including their wildcards) while inferring DAG. This can help to debug unexpected DAG topology or errors.

Default: False

--stats Write stats about Snakefile execution in JSON format to the given file.
--nocolor

Do not use a colored output.

Default: False

--quiet, -q

Do not output any progress or rule information.

Default: False

--print-compilation
 

Print the python representation of the workflow.

Default: False

--verbose

Print debugging output.

Default: False

BEHAVIOR

--force-use-threads
 

Force threads rather than processes. Helpful if shared memory (/dev/shm) is full or unavailable.

Default: False

--allow-ambiguity, -a
 

Don’t check for ambiguous rules and simply use the first if several can produce the same file. This allows the user to prioritize rules by their order in the snakefile.

Default: False

--nolock

Do not lock the working directory

Default: False

--ignore-incomplete, --ii
 

Do not check for incomplete output files.

Default: False

--latency-wait, --output-wait, -w
 

Wait given seconds if an output file of a job is not present after the job finished. This helps if your filesystem suffers from latency (default 5).

Default: 5

--wait-for-files
 Wait –latency-wait seconds for these files to be present before executing the workflow. This option is used internally to handle filesystem latency in cluster environments.
--notemp, --nt

Ignore temp() declarations. This is useful when running only a part of the workflow, since temp() would lead to deletion of probably needed files by other parts of the workflow.

Default: False

--keep-remote

Keep local copies of remote input files.

Default: False

--keep-target-files
 

Do not adjust the paths of given target files relative to the working directory.

Default: False

--allowed-rules
 Only consider given rules. If omitted, all rules in Snakefile are used. Note that this is intended primarily for internal use and may lead to unexpected results otherwise.
--max-jobs-per-second
 

Maximal number of cluster/drmaa jobs per second, default is 10, fractions allowed.

Default: 10

--max-status-checks-per-second
 

Maximal number of job status checks per second, default is 10, fractions allowed.

Default: 10

--restart-times
 

Number of times to restart failing jobs (defaults to 0).

Default: 0

--attempt

Internal use only: define the initial value of the attempt parameter (default: 1).

Default: 1

--wrapper-prefix
 

Prefix for URL created from wrapper directive (default: https://github.com/snakemake/snakemake-wrappers/raw/). Set this to a different URL to use your fork or a local clone of the repository, e.g., use a git URL like ‘git+file://path/to/your/local/clone@’.

Default: “https://github.com/snakemake/snakemake-wrappers/raw/

--default-remote-provider
 

Possible choices: S3, GS, FTP, SFTP, S3Mocked, gfal, gridftp, iRODS

Specify default remote provider to be used for all input and output files that don’t yet specify one.

--default-remote-prefix
 

Specify prefix for default remote provider. E.g. a bucket name.

Default: “”

--no-shared-fs

Do not assume that jobs share a common file system. When this flag is activated, Snakemake will assume that the filesystem on a cluster node is not shared with other nodes. For example, this will lead to downloading remote files on each cluster node separately. Further, it won’t take special measures to deal with filesystem latency issues. This option will in most cases only make sense in combination with –default-remote-provider. Further, when using –cluster you will have to also provide –cluster-status. Only activate this if you know what you are doing.

Default: False

--greediness Set the greediness of scheduling. This value between 0 and 1 determines how careful jobs are selected for execution. The default value (1.0) provides the best speed and still acceptable scheduling quality.
--no-hooks

Do not invoke onstart, onsuccess or onerror hooks after execution.

Default: False

--overwrite-shellcmd
 Provide a shell command that shall be executed instead of those given in the workflow. This is for debugging purposes only.
--debug

Allow to debug rules with e.g. PDB. This flag allows to set breakpoints in run blocks.

Default: False

--runtime-profile
 Profile Snakemake and write the output to FILE. This requires yappi to be installed.
--mode

Possible choices: 0, 1, 2

Set execution mode of Snakemake (internal use only).

Default: 0

--show-failed-logs
 

Automatically display logs of failed jobs.

Default: False

--log-handler-script
 Provide a custom script containing a function ‘def log_handler(msg):’. Snakemake will call this function for every logging output (given as a dictionary msg)allowing to e.g. send notifications in the form of e.g. slack messages or emails.
--log-service

Possible choices: none, slack

Set a specific messaging service for logging output.Snakemake will notify the service on errors and completed execution.Currently only slack is supported.

CLUSTER

--cluster, -c Execute snakemake rules with the given submit command, e.g. qsub. Snakemake compiles jobs into scripts that are submitted to the cluster with the given command, once all input files for a particular job are present. The submit command can be decorated to make it aware of certain job properties (name, rulename, input, output, params, wildcards, log, threads and dependencies (see the argument below)), e.g.: $ snakemake –cluster ‘qsub -pe threaded {threads}’.
--cluster-sync cluster submission command will block, returning the remote exitstatus upon remote termination (for example, this should be usedif the cluster command is ‘qsub -sync y’ (SGE)
--drmaa Execute snakemake on a cluster accessed via DRMAA, Snakemake compiles jobs into scripts that are submitted to the cluster with the given command, once all input files for a particular job are present. ARGS can be used to specify options of the underlying cluster system, thereby using the job properties name, rulename, input, output, params, wildcards, log, threads and dependencies, e.g.: –drmaa ‘ -pe threaded {threads}’. Note that ARGS must be given in quotes and with a leading whitespace.
--cluster-config, -u
 

A JSON or YAML file that defines the wildcards used in ‘cluster’for specific rules, instead of having them specified in the Snakefile. For example, for rule ‘job’ you may define: { ‘job’ : { ‘time’ : ‘24:00:00’ } } to specify the time for rule ‘job’. You can specify more than one file. The configuration files are merged with later values overriding earlier ones. This option is deprecated in favor of using –profile, see docs.

Default: []

--immediate-submit, --is
 

Immediately submit all jobs to the cluster instead of waiting for present input files. This will fail, unless you make the cluster aware of job dependencies, e.g. via: $ snakemake –cluster ‘sbatch –dependency {dependencies}. Assuming that your submit script (here sbatch) outputs the generated job id to the first stdout line, {dependencies} will be filled with space separated job ids this job depends on.

Default: False

--jobscript, --js
 Provide a custom job script for submission to the cluster. The default script resides as ‘jobscript.sh’ in the installation directory.
--jobname, --jn
 

Provide a custom name for the jobscript that is submitted to the cluster (see –cluster). NAME is “snakejob.{name}.{jobid}.sh” per default. The wildcard {jobid} has to be present in the name.

Default: “snakejob.{name}.{jobid}.sh”

--cluster-status
 Status command for cluster execution. This is only considered in combination with the –cluster flag. If provided, Snakemake will use the status command to determine if a job has finished successfully or failed. For this it is necessary that the submit command provided to –cluster returns the cluster job id. Then, the status command will be invoked with the job id. Snakemake expects it to return ‘success’ if the job was successfull, ‘failed’ if the job failed and ‘running’ if the job still runs.
--drmaa-log-dir
 Specify a directory in which stdout and stderr files of DRMAA jobs will be written. The value may be given as a relative path, in which case Snakemake will use the current invocation directory as the origin. If given, this will override any given ‘-o’ and/or ‘-e’ native specification. If not given, all DRMAA stdout and stderr files are written to the current working directory.

KUBERNETES

--kubernetes Execute workflow in a kubernetes cluster (in the cloud). NAMESPACE is the namespace you want to use for your job (if nothing specified: ‘default’). Usually, this requires –default-remote-provider and –default-remote-prefix to be set to a S3 or GS bucket where your . data shall be stored. It is further advisable to activate conda integration via –use-conda.
--container-image
 Docker image to use, e.g., when submitting jobs to kubernetes Defaults to ‘https://hub.docker.com/r/snakemake/snakemake’, tagged with the same version as the currently running Snakemake instance. Note that overwriting this value is up to your responsibility. Any used image has to contain a working snakemake installation that is compatible with (or ideally the same as) the currently running version.

TIBANNA

--tibanna

Execute workflow on AWS cloud using Tibanna. This requires –default-remote-prefix to be set to S3 bucket name and prefix (e.g. ‘bucketname/subdirectory’) where input is already stored and output will be sent to. Using –tibanna implies –default-resources is set as default. Optionally, use –precommand to specify any preparation command to run before snakemake command on the cloud (inside snakemake container on Tibanna VM). Also, –use-conda, –use-singularity, –config, –configfile are supported and will be carried over.

Default: False

--tibanna-sfn Name of Tibanna Unicorn step function (e.g. tibanna_unicorn_monty).This works as serverless scheduler/resource allocator and must be deployed first using tibanna cli. (e.g. tibanna deploy_unicorn –usergroup=monty –buckets=bucketname)
--precommand Any command to execute before snakemake command on AWS cloud such as wget, git clone, unzip, etc. This is used with –tibanna.Do not include input/output download/upload commands - file transfer between S3 bucket and the run environment (container) is automatically handled by Tibanna.
--tibanna-config
 Additional tibanan config e.g. –tibanna-config spot_instance=true subnet=<subnet_id> security group=<security_group_id>

GOOGLE_LIFE_SCIENCE

--google-lifesciences
 

Execute workflow on Google Cloud cloud using the Google Life. Science API. This requires default application credentials (json) to be created and export to the environment to use Google Cloud Storage, Compute Engine, and Life Sciences. The credential file should be exported as GOOGLE_APPLICATION_CREDENTIALS for snakemake to discover. Also, –use-conda, –use-singularity, –config, –configfile are supported and will be carried over.

Default: False

--google-lifesciences-regions
 

Specify one or more valid instance regions (defaults to US)

Default: [‘us-east1’, ‘us-west1’, ‘us-central1’]

--google-lifesciences-location
 The Life Sciences API service used to schedule the jobs. E.g., us-centra1 (Iowa) and europe-west2 (London) Watch the terminal output to see all options found to be available. If not specified, defaults to the first found with a matching prefix from regions specified with –google-lifesciences-regions.
--google-lifesciences-keep-cache
 

Cache workflows in your Google Cloud Storage Bucket specified by –default-remote-prefix/{source}/{cache}. Each workflow working directory is compressed to a .tar.gz, named by the hash of the contents, and kept in Google Cloud Storage. By default, the caches are deleted at the shutdown step of the workflow.

Default: False

CONDA

--use-conda

If defined in the rule, run job in a conda environment. If this flag is not set, the conda directive is ignored.

Default: False

--list-conda-envs
 

List all conda environments and their location on disk.

Default: False

--conda-prefix Specify a directory in which the ‘conda’ and ‘conda-archive’ directories are created. These are used to store conda environments and their archives, respectively. If not supplied, the value is set to the ‘.snakemake’ directory relative to the invocation directory. If supplied, the –use-conda flag must also be set. The value may be given as a relative path, which will be extrapolated to the invocation directory, or as an absolute path.
--conda-cleanup-envs
 

Cleanup unused conda environments.

Default: False

--conda-cleanup-pkgs
 

Possible choices: tarballs, cache

Cleanup conda packages after creating environments. In case of ‘tarballs’ mode, will clean up all downloaded package tarballs. In case of ‘cache’ mode, will additionally clean up unused package caches. If mode is omitted, will default to only cleaning up the tarballs.

--conda-create-envs-only
 

If specified, only creates the job-specific conda environments then exits. The –use-conda flag must also be set.

Default: False

--conda-frontend
 

Possible choices: conda, mamba

Choose the conda frontend for installing environments. Caution: mamba is much faster, but still in beta test.

Default: “conda”

SINGULARITY

--use-singularity
 

If defined in the rule, run job within a singularity container. If this flag is not set, the singularity directive is ignored.

Default: False

--singularity-prefix
 Specify a directory in which singularity images will be stored.If not supplied, the value is set to the ‘.snakemake’ directory relative to the invocation directory. If supplied, the –use-singularity flag must also be set. The value may be given as a relative path, which will be extrapolated to the invocation directory, or as an absolute path.
--singularity-args
 

Pass additional args to singularity.

Default: “”

ENVIRONMENT MODULES

--use-envmodules
 

If defined in the rule, run job within the given environment modules, loaded in the given order. This can be combined with –use-conda and –use-singularity, which will then be only used as a fallback for rules which don’t define environment modules.

Default: False

Bash Completion

Snakemake supports bash completion for filenames, rulenames and arguments. To enable it globally, just append

`snakemake --bash-completion`

including the accents to your .bashrc. This only works if the snakemake command is in your path.