snakemake - Man Page

-h,  --help

show this help message and exit

targets

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 PROFILE

Name of profile to use for configuring Snakemake. Snakemake will search for a corresponding folder in /etc/xdg/snakemake and /builddir/.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. The profile can also be set via the environment variable $SNAKEMAKE_PROFILE. To override this variable and use no profile at all, provide the value 'none' to this argument. [env var: SNAKEMAKE_PROFILE]

--workflow-profile WORKFLOW_PROFILE

Path (relative to current directory) to workflow specific profile folder to use for configuring Snakemake with parameters specific for this workflow (like resources). If this flag is not used, Snakemake will by default use 'profiles/default' if present (searched both relative to current directory and relative to Snakefile, in this order). For skipping any workflow specific profile provide the special value 'none'. Settings made in the workflow profile will override settings made in the general profile (see --profile). 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, '--executor slurm' becomes 'executor: slurm' in the YAML file. It is advisable to use the workflow profile to set or overwrite e.g. workflow specific resources like the amount of threads of a particular rule or the amount of memory needed. Note that in such cases, the arguments may be given as nested YAML mappings in the profile, e.g. 'setthreads: myrule: 4' instead of 'set-threads: myrule=4'.

--cache [RULE ...]

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 FILE

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, -c N

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. In case of cluster/cloud execution, this argument sets the maximum number of cores requested from the cluster or cloud scheduler. (See https://snakemake.readthedocs.io/en/stable/snakef iles/rules.html#resources-remote-execution for more info.) This number is available to rules via workflow.cores.

--jobs, -j N

Use at most N CPU cluster/cloud jobs in parallel. For local execution this is an alias for --cores (it is though recommended to use --cores in that case). Note: Set to 'unlimited' to allow any number of parallel jobs.

--local-cores N

In cluster/cloud 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/cloud mode. (default: <available CPU count>)

--resources, --res NAME=INT [NAME=INT ...]

Define additional resources that shall constrain the scheduling analogously to --cores (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. In cluster/cloud mode, this argument will also constrain the amount of resources requested from the server. (See https://snakemake.readthedocs.io/en/s table/snakefiles/rules.html#resources-remote-execution for more info.) (default: )

--set-threads RULE=THREADS [RULE=THREADS ...]

Overwrite thread usage of rules. This allows to finetune 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: )

--max-threads MAX_THREADS

Define a global maximum number of threads available to any rule. Rules requesting more threads (via the threads keyword) will have their values reduced to the maximum. This can be useful when you want to restrict the maximum number of threads without modifying the workflow definition or overwriting rules individually with --set-threads.

--set-resources RULE:RESOURCE=VALUE [RULE:RESOURCE=VALUE ...]

Overwrite resource usage of rules. This allows to fine-tune workflow resources. In particular, this is helpful to target certain cluster nodes by e.g. defining a certain partition for a rule, or overriding a temporary directory. Thereby, VALUE has to be a positive integer or a string, RULE has to be the name of the rule, and RESOURCE has to be the name of the resource. (default: )

--set-scatter NAME=SCATTERITEMS [NAME=SCATTERITEMS ...]

Overwrite number of scatter items of scattergather processes. This allows to fine-tune workflow parallelization. Thereby, SCATTERITEMS has to be a positive integer, and NAME has to be the name of the scattergather process defined via a scattergather directive in the workflow. (default: )

--set-resource-scopes RESOURCE=[global|local] [RESOURCE=[global|local] ...]

Overwrite resource scopes. A scope determines how a constraint is reckoned in cluster execution. With RESOURCE=local, a constraint applied to RESOURCE using --resources will be considered the limit for each group submission. With RESOURCE=global, the constraint will apply across all groups cumulatively. By default, only `mem_mb` and `disk_mb` are considered local, all other resources are global. This may be modified in the snakefile using the `resource_scopes:` directive. Note that number of threads, specified via --cores, is always considered local. (See https://snakemake.readth edocs.io/en/stable/snakefiles/rules.html#resourcesremote-execution for more info) (default: )

--default-resources, --default-res [NAME=INT ...]

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'). The inputsize is the sum of the sizes of all input files of a rule. By default, Snakemake assumes a default for mem_mb, disk_mb, and tmpdir (see below). This option allows to add further defaults (e.g. account and partition for slurm) or to overwrite these default values. The defaults are 'mem_mb=min(max(2*input.size_mb, 1000), 8000)', '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), and the system temporary directory (as given by $TMPDIR, $TEMP, or $TMP) is used for the tmpdir resource. The tmpdir resource is automatically used by shell commands, scripts and wrappers to store temporary data (as it is mirrored into $TMPDIR, $TEMP, and $TMP for the executed subprocesses). If this argument is not specified at all, Snakemake just uses the tmpdir resource as outlined above.

--preemptible-rules [PREEMPTIBLE_RULES ...]

Define which rules shall use a preemptible machine which can be prematurely killed by e.g. a cloud provider (also called spot instances). This is currently only supported by the Google Life Sciences executor and ignored by all other executors. If no rule names are provided, all rules are considered to be preemptible.

--preemptible-retries PREEMPTIBLE_RETRIES

Number of retries that shall be made in order to finish a job from of rule that has been marked as preemptible via the --preemptible-rules setting.

--configfile, --configfiles FILE [FILE ...]

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. Thereby missing keys in previous config files are extended by following configfiles. Note that this order also includes a config file defined in the workflow definition itself (which will come first). (default: )

--config, -C [KEY=VALUE ...]

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 YAML JSON file (see --configfile and Documentation).

--envvars VARNAME [VARNAME ...]

Environment variables to pass to cloud jobs.

--directory, -d DIR

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 lose the provenance information when the files have been created in reality. 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)

--rerun-triggers {code,input,mtime,params,software-env} [{code,input,mtime,params,software-env} ...]

Define what triggers the rerunning of a job. By default, all triggers are used, which guarantees that results are consistent with the workflow code and configuration. If you rather prefer the traditional way of just considering file modification dates, use ' --rerun-trigger mtime'. (default: code input mtime params software-env)

--force, -f

Force the execution of the selected target or the first rule regardless of already created output. (default: False)

--executor, -e {cluster-generic,local,dryrun,touch}

Specify a custom executor, available via an executor plugin: snakemake_executor_<name>

--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 [TARGET ...]

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.

--consider-ancient RULE=INPUTITEMS [RULE=INPUTITEMS ...]

Consider given input items of given rules as ancient, i.e. not triggering re-runs if they are newer than the output files. Putting this into a workflow specific profile (or specifying as argument) allows to overrule rerun triggers caused by file modification dates where the user knows better. RULE is the name of the rule, INPUTITEMS is a comma separated list of input items of the rule (given as name or index (0-based)). (default: )

--prioritize, -P TARGET [TARGET ...]

Tell the scheduler to assign creation of given targets (and all their dependencies) highest priority.

--batch RULE=BATCH/BATCHES

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 TARGET [TARGET ...]

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 TARGET [TARGET ...]

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 DIR

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.

--scheduler [{ilp,greedy}]

Specifies if jobs are selected by a greedy algorithm or by solving an ilp. The ilp scheduler aims to reduce runtime and hdd usage by best possible use of resources. (default: ilp)

--wms-monitor [WMS_MONITOR]

IP and port of workflow management system to monitor the execution of snakemake (e.g. http://127.0.0.1:5000) Note that if your service requires an authorization token, you must export WMS_MONITOR_TOKEN in the environment.

--wms-monitor-arg [NAME=VALUE ...]

If the workflow management service accepts extra arguments, provide. them in key value pairs with --wms-monitor-arg. For example, to run an existing workflow using a wms monitor, you can provide the pair id=12345 and the arguments will be provided to the endpoint to first interact with the workflow

--scheduler-ilp-solver {COIN_CMD}

Specifies solver to be utilized when selecting ilpscheduler. (default: COIN_CMD)

--conda-base-path CONDA_BASE_PATH

Path of conda base installation (home of conda, mamba, activate) (internal use only).

--no-subworkflows, --nosw

Do not evaluate or execute subworkflows. (default: False)

--precommand PRECOMMAND

Only used in case of remote execution. Command to be executed before Snakemake executes each job on the remote compute node.

--groups GROUPS [GROUPS ...]

Assign rules to groups (this overwrites any group definitions from the workflow). (default: )

--group-components GROUP_COMPONENTS [GROUP_COMPONENTS ...]

Set the number of connected components a group is allowed to span. By default, this is 1, but this flag allows to extend this. This can be used to run e.g. 3 jobs of the same rule in the same group, although they are not connected. It can be helpful for putting together many small jobs or benefitting of shared memory setups. (default: )

--report [FILE]

Create a self-contained HTML report with default statistics, provenance information and user-specified results. For smaller datasets with a limited report complexity, you can specify an '.html' file and all results will be embedded directly into this file. For customized reports on larger sample sizes, it makes more sense to specify a '.zip' file. The resulting archive will spread the contents across a folder structure, for a quicker loading of individual results. You can unpack this archive anywhere and open the 'report.html` file in its main folder to view the report in any web browser. If no filename is given, an embedded report.html is the default.

--report-stylesheet CSSFILE

Custom stylesheet to use for report. In particular, this can be used for branding the report with e.g. a custom logo, see docs.

--reporter PLUGIN

Specify a custom report plugin. By default, Snakemake's builtin html reporter will be used. For custom reporters, check out their command line options starting with --report-.

--draft-notebook TARGET

Draft a skeleton notebook for the rule used to generate the given target file. This notebook can then be opened in a jupyter server, executed and implemented until ready. After saving, it will automatically be reused in non-interactive mode by Snakemake for subsequent jobs.

--edit-notebook TARGET

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 IP:PORT

The IP address and PORT the notebook server used for editing the notebook (--edit-notebook) will listen on. (default: localhost:8888)

--lint [{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.

--generate-unit-tests [TESTPATH]

Automatically generate unit tests for each workflow rule. This assumes that all input files of each job are already present. Rules without a job with present input files will be skipped (a warning will be issued). For each rule, one test case will be created in the specified test folder (.tests/unit by default). After successful execution, tests can be run with 'pytest TESTPATH'.

--containerize

Print a Dockerfile that provides an execution environment for the workflow, including all conda environments. (default: False)

--export-cwl FILE

Compile workflow to CWL and store it in given FILE.

--list-rules,  --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 | display. Note 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 | display. Note 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 | display. Note 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 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. 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 FILE

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 FILE [FILE ...]

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-changes,  --lc {params,code,input}

List all output files for which the given items (code, input, params) have changed since creation.

--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)

--keep-incomplete

Do not remove incomplete output files by failed jobs. (default: False)

--drop-metadata

Drop metadata file tracking information after job finishes. Provenance-information based reports (e.g. --report and the --list_x_changes functions) will be empty or incomplete. (default: False)

--version,  -v

show program's version number and exit

--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)

--nocolor

Do not use a colored output. (default: False)

--quiet,  -q [{all,host,progress,rules} ...]

Do not output certain information. If used without arguments, do not output any progress or rule information. Defining 'all' results in no information being printed at all.

--print-compilation

Print the python representation of the workflow. (default: False)

--verbose

Print debugging output. (default: False)

--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)

--max-inventory-time SECONDS

Spend at most SECONDS seconds to create a file inventory for the working directory. The inventory vastly speeds up file modification and existence checks when computing which jobs need to be executed. However, creating the inventory itself can be slow, e.g. on network file systems. Hence, we do not spend more than a given amount of time and fall back to individual checks for the rest. (default: 20)

--latency-wait,  --output-wait,  -w SECONDS

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 [FILE ...]

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.

--wait-for-files-file FILE

Same behaviour as --wait-for-files, but file list is stored in file instead of being passed on the commandline. This is useful when the list of files is too long to be passed on the commandline.

--queue-input-wait-time SECONDS

Set the interval in seconds to check for new input in rules that use from_queue to obtain input files. (default: 10)

--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)

--all-temp

Mark all output files as temp files. This can be useful for CI testing, in order to save space. (default: False)

--unneeded-temp-files FILE [FILE ...]

Given files will not be uploaded to storage and immediately deleted after job or group job completion.

--keep-storage-local-copies

Keep local copies of remote input files. (default: False)

--target-files-omit-workdir-adjustment

Do not adjust the paths of given target files relative to the working directory. (default: False)

--allowed-rules ALLOWED_RULES [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-timespan MAX_JOBS_PER_TIMESPAN

Maximal number of job submissions/executions per timespan. Format: <number><timespan>, e.g. 50/1m or 0.5/1s.

--max-jobs-per-second MAX_JOBS_PER_SECOND

Maximal number of job submissions/executions per second. Deprecated in favor of --max-jobs-pertimespan.

--max-status-checks-per-second MAX_STATUS_CHECKS_PER_SECOND

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

--seconds-between-status-checks SECONDS_BETWEEN_STATUS_CHECKS

Number of seconds to wait between two rounds of status checks. (default: 10)

--retries,  --restart-times,  -T RETRIES

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

--wrapper-prefix WRAPPER_PREFIX

Prefix for URL created from wrapper directive (default: https://github.com/snakemake/snakemakewrappers/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-storage-provider DEFAULT_STORAGE_PROVIDER

Specify default storage provider to be used for all input and output files that don't yet specify one (e.g. 's3'). See https://snakemake.github.io/snakemake-plugin-catalog for available storage provider plugins. If not set or explicitly 'none', no default storage provider will be used.

--default-storage-prefix DEFAULT_STORAGE_PREFIX

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

--local-storage-prefix LOCAL_STORAGE_PREFIX

Specify prefix for storing local copies of storage files and folders. By default, this is a hidden subfolder in the workdir. It can however be freely chosen, e.g. in order to store those files on a local scratch disk. Environment variables will be expanded. (default: .snakemake/storage)

--remote-job-local-storage-prefix REMOTE_JOB_LOCAL_STORAGE_PREFIX

Specify prefix for storing local copies of storage files and folders in case of remote jobs (e.g. cluster or cloud jobs). This may differ from --local-storageprefix. If not set, uses value of --local-storageprefix. By default, this is a hidden subfolder in the workdir. It can however be freely chosen, e.g. in order to store those files on a local scratch disk. Environment variables will be expanded within the remote job.

--shared-fs-usage {input-output,persistence,software-deployment,source-cache,sources,storage-local-copies,none} [{input-output,persistence,software-deployment,source-cache,sources,storage-local-copies,none} ...]

Set assumptions on shared filesystem for non-local workflow execution. To disable any sharing via the filesystem, specify 'none'. Usually, the executor plugin sets this to a correct default. However, sometimes it is worth tuning this setting, e.g. for optimizing cluster performance. For example, when using '--default-storage-provider fs' together with a cluster executor like slurm, you might want to set '-- shared-fs-usage persistence software-deployment sources source-cache', such that software deployment and data provenance will be handled by NFS but input and output files will be handled exclusively by the storage provider. (default: input-output persistence software-deployment source-cache sources storagelocal-copies)

--scheduler-greediness,  --greediness SCHEDULER_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.

--scheduler-subsample SCHEDULER_SUBSAMPLE

Set the number of jobs to be considered for scheduling. If number of ready jobs is greater than this value, this number of jobs is randomly chosen for scheduling; if number of ready jobs is lower, this option has no effect. This can be useful on very large DAGs, where the scheduler can take some time selecting which jobs to run.

--no-hooks

Do not invoke onstart, onsuccess or onerror hooks after execution. (default: False)

--debug

Allow to debug rules with e.g. PDB. This flag allows to set breakpoints in run blocks. (default: False)

--runtime-profile FILE

Profile Snakemake and write the output to FILE. This requires yappi to be installed.

--local-groupid LOCAL_GROUPID

Internal use only: Name for local groupid. (default: local)

--attempt ATTEMPT

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

--show-failed-logs

Automatically display logs of failed jobs. (default: False)

--log-handler-script FILE

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 {none,slack,wms}

Set a specific messaging service for logging output. Snakemake will notify the service on errors and completed execution. Currently slack and workflow management system (wms) are supported.

--job-deploy-sources

Whether the workflow sources shall be deployed before a remote job is started. Only applies if --no-sharedfs is set or executors are used that imply no shared FS (e.g. the kubernetes executor). (default: False)

--benchmark-extended

Write extended benchmarking metrics. (default: False)

--container-image 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. (default: snakemake/snakemake:v8.30.0)

--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. Does not work for workflows that contain checkpoint rules. (default: False)

--jobscript,  --js SCRIPT

Provide a custom job script for submission to the cluster. The default script resides as 'jobscript.sh' in the installation directory.

--jobname,  --jn NAME

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)

--flux

Execute your workflow on a flux cluster. Flux can work with both a shared network filesystem (like NFS) or without. If you don't have a shared filesystem, additionally specify --no-shared-fs. (default: False)

--software-deployment-method,  --deployment-method,  --deployment,  --sdm {apptainer,conda,env-modules} [{apptainer,conda,env-modules} ...]

Specify software environment deployment method.

--container-cleanup-images

Remove unused containers (default: False)

--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)

--conda-not-block-search-path-envvars

Do not block environment variables that modify the search path (R_LIBS, PYTHONPATH, PERL5LIB, PERLLIB) when using conda environments. (default: False)

--list-conda-envs

List all conda environments and their location on disk. (default: False)

--conda-prefix DIR

Specify a directory in which the 'conda' and 'condaarchive' 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. The value can also be provided via the environment variable $SNAKEMAKE_CONDA_PREFIX. In any case, the prefix may contain environment variables which will be properly expanded. Note that if you use remote execution e.g. on a cluster and you have node specific values for this, you should disable assuming shared fs for software-deployment (see --shared-fs-usage).

--conda-cleanup-envs

Cleanup unused conda environments. (default: False)

--conda-cleanup-pkgs [{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 {conda,mamba}

Choose the conda frontend for installing environments. Mamba is much faster and highly recommended. (default: conda)

--use-apptainer,  --use-singularity

If defined in the rule, run job within a apptainer/singularity container. If this flag is not set, the singularity directive is ignored. (default: False)

--apptainer-prefix,  --singularity-prefix DIR

Specify a directory in which apptainer/singularity images will be stored.If not supplied, the value is set to the '.snakemake' directory relative to the invocation directory. If supplied, the `--useapptainer` 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. If not supplied, APPTAINER_CACHEDIR is used. In any case, the prefix may contain environment variables which will be properly expanded. Note that if you use remote execution e.g. on a cluster and you have node specific values for this, you should disable assuming shared fs for software-deployment (see --shared-fsusage).

--apptainer-args,  --singularity-args ARGS

Pass additional args to apptainer/singularity.

--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 --usesingularity, which will then be only used as a fallback for rules which don't define environment modules. (default: False)

--scheduler-solver-path SCHEDULER_SOLVER_PATH

Internal use only: Set the PATH to search for scheduler solver binaries.

--deploy-sources QUERY CHECKSUM

Internal use only: Deploy sources archive from given storage provider query to the current working subdirectory and control for archive checksum to proceed.

--target-jobs TARGET_JOBS [TARGET_JOBS ...]

Internal use only: Target particular jobs by RULE:WILDCARD1=VALUE,WILDCARD2=VALUE,...

--mode {subprocess,remote,default}

Internal use only: Set execution mode of Snakemake. (default: default)

--cluster-generic-submit-cmd VALUE

Command for submitting jobs

--cluster-generic-status-cmd VALUE

Command for retrieving job status

--cluster-generic-cancel-cmd VALUE

Command for cancelling jobs. Expected to take one or more jobids as arguments.

--cluster-generic-cancel-nargs VALUE

Number of jobids to pass to cancel_cmd. If more are given, cancel_cmd will be called multiple times.

--cluster-generic-sidecar-cmd VALUE

Command for sidecar process.

--storage-http-max-requests-per-second [TAG::]VALUE [[TAG::]VALUE ...]

Maximum number of requests per second for this storage provider. If nothing is specified, the default implemented by the storage plugin is used. Can be specified multiple times to set different values for different tags.

--storage-http-auth [TAG::]AUTH_TYPE=ARG1,ARG2,... [[TAG::]AUTH_TYPE=ARG1,ARG2,... ...]

HTTP(S) authentication. AUTH_TYPE is the class name of requests.auth (e.g. HTTPBasicAuth), ARG1,ARG2,... are the arguments required by the specified type. Can be specified multiple times to set different values for different tags. [env var: SNAKEMAKE_STORAGE_HTTP_AUTH]

--storage-http-allow-redirects [TAG::]VALUE [[TAG::]VALUE ...]

Allow redirects when retrieving files. Can be specified multiple times to set different values for different tags.

--storage-s3-max-requests-per-second [TAG::]VALUE [[TAG::]VALUE ...]

Maximum number of requests per second for this storage provider. If nothing is specified, the default implemented by the storage plugin is used. Can be specified multiple times to set different values for different tags.

--storage-s3-endpoint-url [TAG::]VALUE [[TAG::]VALUE ...]

S3 endpoint URL (if omitted, AWS S3 is used) Can be specified multiple times to set different values for different tags. [env var: SNAKEMAKE_STORAGE_S3_ENDPOINT_URL]

--storage-s3-region [TAG::]VALUE [[TAG::]VALUE ...]

region constraint for the S3 storage Can be specified multiple times to set different values for different tags. [env var: SNAKEMAKE_STORAGE_S3_REGION]

--storage-s3-access-key [TAG::]VALUE [[TAG::]VALUE ...]

S3 access key (if omitted, credentials are taken from .aws/credentials as e.g. created by aws configure) Can be specified multiple times to set different values for different tags. [env var: SNAKEMAKE_STORAGE_S3_ACCESS_KEY]

--storage-s3-secret-key [TAG::]VALUE [[TAG::]VALUE ...]

S3 secret key (if omitted, credentials are taken from .aws/credentials as e.g. created by aws configure) Can be specified multiple times to set different values for different tags. [env var: SNAKEMAKE_STORAGE_S3_SECRET_KEY]

--storage-s3-token [TAG::]VALUE [[TAG::]VALUE ...]

S3 token (usually not required) Can be specified multiple times to set different values for different tags. [env var: SNAKEMAKE_STORAGE_S3_TOKEN]

--storage-s3-signature-version [TAG::]VALUE [[TAG::]VALUE ...]

S3 signature version Can be specified multiple times to set different values for different tags.

--storage-s3-retries [TAG::]VALUE [[TAG::]VALUE ...]

S3 API retries Can be specified multiple times to set different values for different tags.

--report-html-path VALUE

Path to the report file (either .html or .zip). Use zip if your report contains large results or directories with htmlindex as results.

--report-html-stylesheet-path VALUE

Path to a custom stylesheet for the report.

In general, command-line values override environment variables which override

defaults.