Package pypiper Documentation
Package Overview
The pypiper package provides a framework for building robust, restartable bioinformatics pipelines. It handles common pipeline tasks like checkpointing, logging, and resource monitoring.
Key Features
- Automatic Checkpointing: Resume pipelines from where they left off
- Resource Monitoring: Track memory and CPU usage
- Result Reporting: Integrate with pipestat for standardized results
- Container Support: Run commands in Docker containers
- Pipeline Management: Built-in logging and status tracking
Installation
pip install pypiper
Quick Example
from pypiper import PipelineManager
# Initialize a pipeline
pm = PipelineManager(
name="my_pipeline",
outfolder="results/"
)
# Run a command
pm.run("echo 'Hello, world!'")
# Stop the pipeline
pm.stop_pipeline()
API Reference
PipelineManager Class
The main class for building and managing pipelines:
PipelineManager(name, outfolder, version=None, args=None, multi=False, dirty=False, recover=False, new_start=False, force_follow=False, cores=1, mem='1000M', config_file=None, output_parent=None, overwrite_checkpoints=False, logger_kwargs=None, pipestat_record_identifier=None, pipestat_schema=None, pipestat_results_file=None, pipestat_config=None, pipestat_pipeline_type=None, pipestat_result_formatter=None, **kwargs)
Bases: object
Base class for instantiating a PipelineManager object, the main class of Pypiper.
:param str name: Choose a name for your pipeline; it's used to name the output files, flags, etc. :param str outfolder: Folder in which to store the results. :param argparse.Namespace args: Optional args object from ArgumentParser; Pypiper will simply record these arguments from your script :param bool multi: Enables running multiple pipelines in one script or for interactive use. It simply disables the tee of the output, so you won't get output logged to a file. :param bool dirty: Overrides the pipeline's clean_add() manual parameters, to never clean up intermediate files automatically. Useful for debugging; all cleanup files are added to manual cleanup script. :param bool recover: Specify recover mode, to overwrite lock files. If pypiper encounters a locked target, it will ignore the lock and recompute this step. Useful to restart a failed pipeline. :param bool new_start: start over and run every command even if output exists :param bool force_follow: Force run all follow functions even if the preceding command is not run. By default, following functions are only run if the preceding command is run. :param int cores: number of processors to use, default 1 :param str mem: amount of memory to use. Default units are megabytes unless specified using the suffix [K|M|G|T]." :param str config_file: path to pipeline configuration file, optional :param str output_parent: path to folder in which output folder will live :param bool overwrite_checkpoints: Whether to override the stage-skipping logic provided by the checkpointing system. This is useful if the calls to this manager's run() method will be coming from a class that implements pypiper.Pipeline, as such a class will handle checkpointing logic automatically, and will set this to True to protect from a case in which a restart begins upstream of a stage for which a checkpoint file already exists, but that depends on the upstream stage and thus should be rerun if it's "parent" is rerun. :param str pipestat_record_identifier: record_identifier to report results via pipestat :param str pipestat_schema: output schema used by pipestat to report results :param str pipestat_results_file: path to file backend for reporting results :param str pipestat_config_file: path to pipestat configuration file :param str pipestat_pipeline_type: Sample or Project level pipeline :param pipestat_result_formatter: function used to style reported results, defaults to result_formatter_markdown :raise TypeError: if start or stop point(s) are provided both directly and via args namespace, or if both stopping types (exclusive/prospective and inclusive/retrospective) are provided.
Source code in pypiper/manager.py
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property
halted
Is the managed pipeline in a paused/halted state? :return bool: Whether the managed pipeline is in a paused/halted state.
property
pipestat
pipestat.PipestatManager object to use for pipeline results reporting and status management
Depending on the object configuration it can report to a YAML-formatted file or PostgreSQL database. Please refer to pipestat documentation for more details: http://pipestat.databio.org/
:return pipestat.PipestatManager: object to use for results reporting
callprint(cmd, shell=None, lock_file=None, nofail=False, container=None)
Prints the command, and then executes it, then prints the memory use and return code of the command.
Uses python's subprocess.Popen() to execute the given command. The shell argument is simply passed along to Popen(). You should use shell=False (default) where possible, because this enables memory profiling. You should use shell=True if you require shell functions like redirects (>) or stars (*), but this will prevent the script from monitoring memory use. The pipes (|) will be used to split the command into subprocesses run within python, so the memory profiling is possible.
cmd can also be a series (a dict object) of multiple commands, which will be run in succession.
:param str | Iterable[str] cmd: Bash command(s) to be run. :param str lock_file: a lock file name :param bool nofail: Should the pipeline bail on a nonzero return from a process? Default: False Nofail can be used to implement non-essential parts of the pipeline; if these processes fail, they will not cause the pipeline to bail out. :param bool shell: if the command should be run it its own shell, default: None (will try to determine based on the command) :param container: Named Docker container in which to execute. :param container: str
Source code in pypiper/manager.py
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checkprint(cmd, shell=None, nofail=False)
Just like callprint, but checks output -- so you can get a variable
in python corresponding to the return value of the command you call.
This is equivalent to running subprocess.check_output()
instead of subprocess.call().
:param str | Iterable[str] cmd: Bash command(s) to be run.
:param bool | str shell: If command requires should be run in its own shell. Optional.
Default: "guess" -- run() will try to guess if the command should be run in a
shell (based on the presence of a pipe (|) or redirect (>), To force a process to
run as a direct subprocess, set shell to False; to force a shell, set True.
:param bool nofail: Should the pipeline bail on a nonzero return from a process? Default: False
Nofail can be used to implement non-essential parts of the pipeline; if these processes fail,
they will not cause the pipeline to bail out.
:return str: text output by the executed subprocess (check_output)
Source code in pypiper/manager.py
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clean_add(regex, conditional=False, manual=False)
Add files (or regexs) to a cleanup list, to delete when this pipeline completes successfully. When making a call with run that produces intermediate files that should be deleted after the pipeline completes, you flag these files for deletion with this command. Files added with clean_add will only be deleted upon success of the pipeline.
:param str regex: A unix-style regular expression that matches files to delete (can also be a file name). :param bool conditional: True means the files will only be deleted if no other pipelines are currently running; otherwise they are added to a manual cleanup script called {pipeline_name}_cleanup.sh :param bool manual: True means the files will just be added to a manual cleanup script.
Source code in pypiper/manager.py
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complete()
Stop a completely finished pipeline.
Source code in pypiper/manager.py
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fail_pipeline(exc, dynamic_recover=False)
If the pipeline does not complete, this function will stop the pipeline gracefully. It sets the status flag to failed and skips the normal success completion procedure.
:param Exception exc: Exception to raise. :param bool dynamic_recover: Whether to recover e.g. for job termination.
Source code in pypiper/manager.py
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get_elapsed_time()
Parse the pipeline profile file, collect the unique and last duplicated runtimes and sum them up. In case the profile is not found, an estimate is calculated (which is correct only in case the pipeline was not rerun)
:return int: sum of runtimes in seconds
Source code in pypiper/manager.py
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get_stat(key)
Returns a stat that was previously reported. This is necessary for reporting new stats that are derived from two stats, one of which may have been reported by an earlier run. For example, if you first use report_result to report (number of trimmed reads), and then in a later stage want to report alignment rate, then this second stat (alignment rate) will require knowing the first stat (number of trimmed reads); however, that may not have been calculated in the current pipeline run, so we must retrieve it from the stats.yaml output file. This command will retrieve such previously reported stats if they were not already calculated in the current pipeline run. :param key: key of stat to retrieve
Source code in pypiper/manager.py
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halt(checkpoint=None, finished=False, raise_error=True)
Stop the pipeline before completion point.
:param str checkpoint: Name of stage just reached or just completed. :param bool finished: Whether the indicated stage was just finished (True), or just reached (False) :param bool raise_error: Whether to raise an exception to truly halt execution.
Source code in pypiper/manager.py
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staticmethod
make_sure_path_exists(path)
Creates all directories in a path if it does not exist.
:param str path: Path to create. :raises Exception: if the path creation attempt hits an error with a code indicating a cause other than pre-existence.
Source code in pypiper/manager.py
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process_counter()
Increments process counter with regard to the follow state: if currently executed command is a follow function of another one, the counter is not incremented.
:return str | int: current counter state, a number if the counter has beed incremented or a number of the previous process plus "f" otherwise
Source code in pypiper/manager.py
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report_object(key, filename, anchor_text=None, anchor_image=None, annotation=None, nolog=False, result_formatter=None, force_overwrite=True)
Writes a key:value pair to self.pipeline_stats_file. Note: this function will be deprecated. Using report_result is recommended.
:param str key: name (key) of the object :param str filename: relative path to the file (relative to parent output dir) :param str anchor_text: text used as the link anchor test or caption to refer to the object. If not provided, defaults to the key. :param str anchor_image: a path to an HTML-displayable image thumbnail (so, .png or .jpg, for example). If a path, the path should be relative to the parent output dir. :param str annotation: By default, the figures will be annotated with the pipeline name, so you can tell which pipeline records which figures. If you want, you can change this. :param bool nolog: Turn on this flag to NOT print this result in the logfile. Use sparingly in case you will be printing the result in a different format. :param str result_formatter: function for formatting via pipestat backend :param bool force_overwrite: overwrite results if they already exist? :return str reported_result: the reported result is returned as a list of formatted strings.
Source code in pypiper/manager.py
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report_result(key, value, nolog=False, result_formatter=None, force_overwrite=True)
Writes a key:value pair to self.pipeline_stats_file.
:param str key: name (key) of the stat :param dict value: value of the stat to report. :param bool nolog: Turn on this flag to NOT print this result in the logfile. Use sparingly in case you will be printing the result in a different format. :param str result_formatter: function for formatting via pipestat backend :param bool force_overwrite: overwrite results if they already exist? :return str reported_result: the reported result is returned as a list of formatted strings.
Source code in pypiper/manager.py
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run(cmd, target=None, lock_name=None, shell=None, nofail=False, clean=False, follow=None, container=None, default_return_code=0)
The primary workhorse function of PipelineManager, this runs a command.
This is the command execution function, which enforces race-free file-locking, enables restartability, and multiple pipelines can produce/use the same files. The function will wait for the file lock if it exists, and not produce new output (by default) if the target output file already exists. If the output is to be created, it will first create a lock file to prevent other calls to run (for example, in parallel pipelines) from touching the file while it is being created. It also records the memory of the process and provides some logging output.
:param str | list[str] cmd: Shell command(s) to be run. :param str | Sequence[str] target: Output file(s) to produce, optional. If all target files exist, the command will not be run. If no target is given, a lock_name must be provided. :param str lock_name: Name of lock file. Optional. :param bool shell: If command requires should be run in its own shell. Optional. Default: None --will try to determine whether the command requires a shell. :param bool nofail: Whether the pipeline proceed past a nonzero return from a process, default False; nofail can be used to implement non-essential parts of the pipeline; if a 'nofail' command fails, the pipeline is free to continue execution. :param bool clean: True means the target file will be automatically added to an auto cleanup list. Optional. :param callable follow: Function to call after executing (each) command. :param str container: Name for Docker container in which to run commands. :param Any default_return_code: Return code to use, might be used to discriminate between runs that did not execute any commands and runs that did. :return int: Return code of process. If a list of commands is passed, this is the maximum of all return codes for all commands.
Source code in pypiper/manager.py
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start_pipeline(args=None, multi=False)
Initialize setup. Do some setup, like tee output, print some diagnostics, create temp files. You provide only the output directory (used for pipeline stats, log, and status flag files).
Source code in pypiper/manager.py
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stop_pipeline(status=COMPLETE_FLAG)
Terminate the pipeline.
This is the "healthy" pipeline completion function. The normal pipeline completion function, to be run by the pipeline at the end of the script. It sets status flag to completed and records some time and memory statistics to the log file.
Source code in pypiper/manager.py
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staticmethod
time_elapsed(time_since)
Returns the number of seconds that have elapsed since the time_since parameter.
:param float time_since: Time as a float given by time.time().
Source code in pypiper/manager.py
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timestamp(message='', checkpoint=None, finished=False, raise_error=True)
Print message, time, and time elapsed, perhaps creating checkpoint.
This prints your given message, along with the current time, and time elapsed since the previous timestamp() call. If you specify a HEADING by beginning the message with "###", it surrounds the message with newlines for easier readability in the log file. If a checkpoint is designated, an empty file is created corresponding to the name given. Depending on how this manager's been configured, the value of the checkpoint, and whether this timestamp indicates initiation or completion of a group of pipeline steps, this call may stop the pipeline's execution.
:param str message: Message to timestamp. :param str checkpoint: Name of checkpoint; this tends to be something that reflects the processing logic about to be or having just been completed. Provision of an argument to this parameter means that a checkpoint file will be created, facilitating arbitrary starting and stopping point for the pipeline as desired. :param bool finished: Whether this call represents the completion of a conceptual unit of a pipeline's processing :param raise_error: Whether to raise exception if checkpoint or current state indicates that a halt should occur.
Source code in pypiper/manager.py
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