.. _bps-preqs: Prerequisites ------------- #. LSST software stack. #. Shared filesystem for data. #. Shared database. `SQLite3`_ is fine for small runs like **ci_hsc_gen3** if have POSIX filesystem. For larger runs, use `PostgreSQL`_. #. A workflow management service. Currently, two workflow management services are supported HTCondor's `DAGMan`_ and `Pegasus WMS`_. Both of them requires an HTCondor cluster. NCSA hosts a few of such clusters, see `this`_ page for details. #. HTCondor's Python `bindings`_ (if using `HTCondor`_) or `Pegasus WMS`_. .. _SQLite3: https://www.sqlite.org/index.html .. _PostgreSQL: https://www.postgresql.org .. _DAGMan: https://htcondor.readthedocs.io/en/latest/users-manual/dagman-workflows.html#dagman-workflows .. _Pegasus WMS: https://pegasus.isi.edu .. _bindings: https://htcondor.readthedocs.io/en/latest/apis/python-bindings/index.html .. _this: https://developer.lsst.io/services/batch.html .. _bps-installation: Installing Batch Processing Service ----------------------------------- Starting from LSST Stack version ``w_2020_45``, the package providing Batch Processing Service, `ctrl_bps`_, comes with ``lsst_distrib``. However, if you'd like to try out its latest features, you may install a bleeding edge version similarly to any other LSST package: .. code-block:: bash git clone https://github.com/lsst-dm/ctrl_bps cd ctrl_bps setup -k -r . scons .. _bps-data-repository: .. _ctrl_bps: https://github.com/lsst/ctrl_bps Creating Butler repository -------------------------- You’ll need a pre-existing Butler dataset repository containing all the input files needed for your run. This repository needs to be on the filesystem shared among all compute resources (e.g. submit and compute nodes) you use during your run. .. note:: Keep in mind though, that you don't need to bootstrap a dataset repository for every BPS run. You only need to do it when Gen3 data definition language (DDL) changes, you want to to start a repository from scratch, and possibly if want to add/change inputs in repo (depending on the inputs and flexibility of the bootstrap scripts). For testing purposes, you can use `pipelines_check`_ package to set up your own Butler dataset repository. To make that repository, follow the usual steps when installing an LSST package: .. code-block:: bash git clone https://github.com/lsst/pipelines_check cd pipelines_check git checkout w_2020_45 # checkout the branch matching the software branch you are using setup -k -r . scons .. _pipelines_check: https://github.com/lsst/pipelines_check .. _bps-submission: Defining a submission --------------------- BPS configuration files are YAML files with some reserved keywords and some special features. They are meant to be syntactically flexible to allow users figure out what works best for them. The syntax and features of a BPS configuration file are described in greater detail in :ref:`bps-configuration-file`. Below is just a minimal example to keep you going. There are groups of information needed to define a submission to the Batch Production Service. They include the pipeline definition, the payload (information about the data in the run), submission and runtime configuration. Describe a pipeline to BPS by telling it where to find either the pipeline YAML file (recommended) .. code-block:: YAML pipelineYaml: "${OBS_SUBARU_DIR}/pipelines/DRP.yaml#processCcd" or a pre-made file containing a serialized QuantumGraph, for example .. code-block:: YAML qgraphFile: pipelines_check_w_2020_45.qgraph .. warning:: The file with a serialized QuantumGraph is not portable. The file must be crated by the same stack being used when running BPS *and* it can be only used on the machine with the same environment. The payload information should be familiar too as it is mostly the information normally used on the pipetask command line (input collections, output collections, etc). The remaining information tells BPS which workflow management system is being used, how to convert Datasets and Pipetasks into compute jobs and what resources those compute jobs need. .. literalinclude:: pipelines_check.yaml :language: YAML :caption: ${CTRL_BPS_DIR}/doc/lsst.ctrl.bps/pipelines_check.yaml .. _bps-submit: Submitting a run ---------------- Submit a run for execution with .. code-block:: bash bps submit example.yaml If submission was successfully, it will output something like this: .. code-block:: bash Submit dir: /home/jdoe/tmp/bps/submit/shared/pipecheck/20201111T13h34m08s Run Id: 176261 Adding ``--log-level INFO`` option to the command line outputs more information especially for those wanting to watch how long the various submission stages take.  .. _bps-report: Checking status --------------- To check the status of the submitted run, you can use tools provided by HTCondor or Pegasus, for example, ``condor_status`` or ``pegasus-status``. To get a more pipeline oriented information use .. code-block:: bash bps report which should display run summary similar to the one below :: X STATE %S ID OPERATOR PRJ CMPGN PAYLOAD RUN ----------------------------------------------------------------------------------------------------------------------- RUNNING 0 176270 jdoe dev quick pcheck shared_pipecheck_20201111T14h59m26s To see results regarding past submissions, use ``bps report --hist X`` where ``X`` is the number of days past day to look at (can be a fraction). For example :: $ bps report --hist 1 STATE %S ID OPERATOR PRJ CMPGN PAYLOAD RUN ----------------------------------------------------------------------------------------------------------------------- FAILED 0 176263 jdoe dev quick pcheck shared_pipecheck_20201111T13h51m59s SUCCEEDED 100 176265 jdoe dev quick pcheck shared_pipecheck_20201111T13h59m26s Use ``bps report --help`` to see all currently supported options. .. _bps-terminate: Canceling submitted jobs -------------------------- The bps command to cancel bps-submitted jobs is .. code-block:: bash bps cancel --id or to cancel all of your runs use .. code-block:: bash bps cancel --user For example :: $ bps submit pipelines_check.yaml Submit dir: /scratch/mgower/submit/u/mgower/pipelines_check/20210414T190212Z Run Id: 369 $ bps report X STATE %S ID OPERATOR PRJ CMPGN PAYLOAD RUN ------------------------------------------------------------------------------------------------------------------------------------------------------------ RUNNING 0 369 mgower dev quick pcheck u_mgower_pipelines_check_20210414T190212Z $ bps cancel --id 369 Successfully canceled: 369.0 $ bps report X STATE %S ID OPERATOR PRJ CMPGN PAYLOAD RUN ------------------------------------------------------------------------------------------------------------------------------------------------------------ .. note:: Sometimes there may be a small delay between executing cancel and jobs disappearing from the WMS queue. Under normal conditions this delay is less than a minute. This command tries to prevent someone using it to cancel non-bps jobs. It can be forced to skip this check by including the option ``--skip-require-bps``. Use this at your own risk. If ``bps cancel`` says "0 jobs found matching arguments", first double check the id for typos. If you believe there is a problem with the "is it a bps job" check, add ``--skip-require-bps``. If jobs are hanging around in the queue with an X status in condor_q, you can add the following to force delete those jobs from the queue :: --pass-thru "-forcex" If ``bps cancel`` fails to delete the jobs, you can use direct WMS executables like `condor_rm`__ or `pegasus-remove`__.  .. note:: Using the WMS commands directly under normal circumstances is not advised as bps may someday include additional code. Both take the ``runId`` printed by ``bps submit``. For example .. code-block:: bash condor_rm 176270 # HTCondor pegasus-remove 176270 # Pegasus WMS ``bps report`` also prints the ``runId`` usable by ``condor_rm``.   If you want to just clobber all of the runs that you have currently submitted, you can just do the following no matter if using HTCondor or Pegasus WMS plugin: .. code-block:: bash condor_rm .. __: https://htcondor.readthedocs.io/en/latest/man-pages/condor_rm.html .. __: https://pegasus.isi.edu/documentation/cli-pegasus-remove.php .. _bps-configuration-file: BPS configuration file ---------------------- The configuration file is in YAML format. One particular YAML syntax to be mindful about is that boolean values must be all lowercase. Configuration file can include other configuration files using ``includeConfigs`` with YAML array syntax. For example .. code-block:: YAML includeConfigs: - bps-operator.yaml - bps-site-htcondor.yaml Values in the configuration file can be defined in terms of other values using ``{key}`` syntax, for example .. code-block:: YAML patch: 69 dataQuery: patch = {patch} Environment variables can be used as well with ``${var}`` syntax, for example .. code-block:: YAML submitRoot: ${PWD}/submit runQuantumExec: ${CTRL_MPEXEC_DIR}/bin/pipetask .. note:: Note the difference, ``$`` (dollar sign), when using an environmental variable, e.g. ``${foo}``, and plain config variable ``{foo}``. Section names can be used to store default settings at that concept level which can be overridden by settings at more specific concept levels.  Currently the order from most specific to general is: ``payload``, ``pipetask``, and ``site``. **payload** description of the submission including definition of inputs **pipetask** subsections are pipetask labels where can override/set runtime settings for particular pipetasks (currently no Quantum-specific settings). **site** settings for specific sites can be set here. Subsections are site names which are matched to ``computeSite``. The following are examples for specifying values needed to match jobs to glideins. HTCondor plugin example: .. code-block:: YAML site: acsws02: profile: condor: requirements: "(GLIDEIN_NAME == "test_gname")" +GLIDEIN_NAME: "test_gname" Pegasus plugin example: .. code-block:: YAML site: acsws02: arch: x86_64 os: LINUX directory: shared-scratch: path: /work/shared-scratch/${USER} file-server: operation: all url: file:///work/shared-scratch/${USER} profile: pegasus: style: condor auxillary.local: true condor: universe: vanilla getenv: true requirements: '(ALLOCATED_NODE_SET == "${NODESET}")' +JOB_NODE_SET: '"${NODESET}"' dagman: retry: 0 env: PEGASUS_HOME: /usr/local/pegasus/current Supported settings ^^^^^^^^^^^^^^^^^^ **butlerConfig** Location of the Butler configuration file needed by BPS to create run collection entry in Butler dataset repository **campaign** A label used to group submissions together. May be used for grouping submissions for particular deliverable (e.g., a JIRA issue number, a milestone, etc.). Can be used as variable in output collection name. Displayed in ``bps report`` output. **clusterAlgorithm** Algorithm to use to group Quanta into single Python executions that can share in-memory datastore. Currently, just uses single quanta executions, but this is here for future growth. **computeSite** Specification of the compute site where to run the workflow and which site settings to use in ``bps prepare``). **createQuantumGraph** The command line specification for generating QuantumGraphs. **operator** Name of the Operator who made a submission. Displayed in ``bps report`` output. Defaults to the Operator's username. **pipelineYaml** Location of the YAML file describing the science pipeline. **project** Another label for groups of submissions. May be used to differentiate between test submissions from production submissions. Can be used as a variable in the output collection name. Displayed in ``bps report`` output. **requestMemory**, optional Amount of memory, in MB, a single Quantum execution of a particular pipetask will need (e.g., 2048). **requestCpus**, optional Number of cpus that a single Quantum execution of a particular pipetask will need (e.g., 1). **uniqProcName** Used when giving names to graphs, default names to output files, etc.  If not specified by user, BPS tries to use ``outCollection`` with '/' replaced with '_'. **submitPath** Directory where the output files of ``bps prepare`` go. **runQuantumCommand** The command line specification for running a Quantum. Must start with executable name (a full path if using HTCondor plugin) followed by options and arguments. May contain other variables defined in the configuration file. **runInit** Whether to add a ``pipetask --init-only`` to the workflow or not. If true, expects there to be a **pipetask** section called **pipetaskInit** which contains the ``runQuantumCommand`` for the ``pipetask --init-only``. For example .. code-block:: YAML payload: runInit: true pipetask: pipetask_init: runQuantumCommand: "${CTRL_MPEXEC_DIR}/bin/pipetask --long-log run -b {butlerConfig} -i {inCollection} --output {output} --output-run {outCollection} --init-only --register-dataset-types --qgraph {qgraphFile} --clobber-partial-outputs --no-versions" requestMemory: 2048 The above example command uses both ``--output`` and ``--output-run``. The ``--output`` option creates the chained collection if necessary and defines it to include both the ``--input`` and ``--output-run`` collections. The ``--output-run`` option saves the unique run collection that is also passed to all other compute jobs (i.e., one run collection per submission). If using both here, must include both ``--output`` and ``--output-run`` in the other ``runQuantumCommand``. **templateDataId** Template to use when creating job names (and HTCondor plugin then uses for job output filenames). **wmsServiceClass** Workload Management Service plugin to use. For example .. code-block:: YAML wmsServiceClass: lsst.ctrl.bps.wms.htcondor.htcondor_service.HTCondorService # HTCondor **bpsUseShared** Whether to put full submit-time path to QuantumGraph file in command line because the WMS plugin requires shared filesystem. Defaults to False. HTCondor and Pegasus plugins do not need this value. **whenSaveJobQgraph** When to output job QuantumGraph files (default = TRANSFORM). * NEVER = all jobs will use full QuantumGraph file. (Warning: make sure runQuantumCommand has ``--qgraph-id {qgraphId} --qgraph-node-id {qgraphNodeId}``.) * TRANSFORM = Output QuantumGraph files after creating GenericWorkflow. * PREPARE = QuantumGraph files are output after creating WMS submission. **saveClusteredQgraph** A boolean flag. If set to true, BPS will save serialized clustered quantum graph to a file called ``bps_clustered_qgraph.pickle`` using Python's `pickle`_ module. The file will be located in the submit directory. By default, it is set to false. Setting it to true will significantly increase memory requirements when submitting large workflows as `pickle`_ constructs a complete copy of the object in memory before it writes it to disk. [`ref`_] **saveGenericWorkflow** A boolean flag. If set to true, BPS will save serialized generic workflow called to a file called ``bps_generic_workflow.pickle`` using Python's `pickle`_ module. The file will be located in the submit directory. By default, it is set to false. **saveDot** A boolean flag. If set to true, BPS will generate graphical representations of both the clustered quantum graph and the generic workflow in DOT format. The files will be located in the submit directory and their names will be ``bps_clustered_qgraph.dot`` and ``bps_generic_workflow.dot``, respectively. By default, it is set to false. It is recommended to use this option only when working with small graphs/workflows. The plots will be practically illegible for graphs which number of nodes exceeds order of tens. .. _pickle: https://docs.python.org/3/library/pickle.html .. _ref: https://stackoverflow.com/a/38971446 Reserved keywords ^^^^^^^^^^^^^^^^^ **gqraphFile** Name of the file with a pre-made, serialized QuantumGraph. Such a file is an alternative way to describe a science pipeline. However, contrary to YAML specification, it is currently not portable. **qgraphId** Internal ID for the full QuantumGraph (passed as ``--qgraph-id`` on pipetask command line). **qgraphNodeId** Comma-separated list of internal QuantumGraph node numbers to be executed from the full QuantumGraph (passed as ``--qgraph-node-id`` on pipetask command line). **timestamp** Created automatically by BPS at submit time that can be used in the user specification of other values (e.g., in output collection names so that one can repeatedly submit the same BPS configuration without changing anything) .. note:: Any values shown in the example configuration file, but not covered in this section are examples of user-defined variables (e.g. ``inCollection``) and are not required by BPS. .. _job-qgraph-files: QuantumGraph Files ------------------ BPS can be configured to either create per-job QuantumGraph files or use the single full QuantumGraph file plus node numbers for each job. The default is using per-job QuantumGraph files. To use full QuantumGraph file, the submit YAML must set `whenSaveJobQgraph` to "NEVER" and the ``pipetask run`` command must include ``--qgraph-id {qgraphId} --qgraph-node-id {qgraphNodeId}``. For example: .. code:: whenSaveJobQgraph: "NEVER" runQuantumCommand: "${CTRL_MPEXEC_DIR}/bin/pipetask --long-log run -b {butlerConfig} -i {inCollection} --output {output} --output-run {outCollection} --extend-run --skip-init-writes --qgraph {qgraphFile} --qgraph-id {qgraphId} --qgraph-node-id {qgraphNodeId} --clobber-partial-outputs --no-versions" -- warning:: Do not modify the QuantumGraph options in pipetaskInit's runQuantumCommand. It needs the entire QuantumGraph. -- note:: If running on a system with a shared filesystem, you'll more than likely want to also set bpsUseShared to true. .. _bps-troubleshooting: Troubleshooting --------------- Where is stdout/stderr from pipeline tasks? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For now, stdout/stderr can be found in files in the submit run directory. HTCondor """""""" The names are of the format: .. code-block:: bash /jobs//__[..[sub|out|err|log] Pegasus WMS """"""""""" Pegasus does its own directory structure and wrapping of ``pipetask`` output. You can dig around in the submit run directory here too, but try `pegasus-analyzer`__ command first. .. __: https://pegasus.isi.edu/documentation/cli-pegasus-analyzer.php Advanced debugging ^^^^^^^^^^^^^^^^^^ Here are some advanced debugging tips: #. If ``bps submit`` is taking a long time, probably it is spending the time during QuantumGraph generation.  The QuantumGraph generation command line and output will be in ``quantumGraphGeneration.out`` in the submit run directory, e.g. ``submit/shared/pipecheck/20200806T00h22m26s/quantumGraphGeneration.out``. #. Check the ``*.dag.dagman.out`` for errors (in particular for ``ERROR: submit attempt failed``). #. The Pegasus ``runId`` is the submit subdirectory where the underlying DAG lives.  If you’ve forgotten the Pegasus ``runId`` needed to use in the Pegasus commands try one of the following: #. It’s the submit directory in which the ``braindump.txt`` file lives.  If you know the submit root directory, use find to give you a list of directories to try.  (Note that many of these directories could be for old runs that are no longer running.)o .. code-block:: bash find submit  -name "braindump.txt" #. Use HTCondor commands to find submit directories for running jobs .. code-block:: bash condor_q -constraint 'pegasus_wf_xformation == "pegasus::dagman"' -l | grep Iwd