Overview¶

The package provides LSST Batch Processing Service (BPS). BPS allow large-scale workflows to execute in well-managed fashion, potentially in multiple environments.

Specifying WMS plugin¶

Many ctrl_bps subcommands described in this document delegate responsibility to perform actual operations to the specific WMS plugin and thus need to know how to find it.

The location of the plugin can be specified as listed below (in the increasing order of priority):

by setting BPS_WMS_SERVICE_CLASS environment variable,
in the config file via wmsServiceClass setting,
using command-line option --wms-service-class.

If plugin location is not specified explicitly using one of the methods above, a default value, lsst.ctrl.bps.htcondor.HTCondorService, will be used.

Checking status of WMS services¶

Run bps ping to check the status of the WMS services. This subcommand requires specifying the WMS plugin (see Specifying WMS plugin). If the plugin provides such functionality, it will check whether the WMS services necessary for workflow management (submission, reporting, canceling, etc) are usable. If the WMS services require authentication, that will also be tested.

If services are ready for use, then bps ping will log an INFO success message and exit with 0. If not, it will log ERROR messages and exit with a non-0 exit code. If the WMS plugin did not implement the ping functionality, a NotImplementedError will be thrown.

Note

bps ping does not test whether compute resources are available or that jobs will run.

Specifying the Compute site¶

A number of ctrl_bps subcommands described in this document require the specification of a compute site. This denotes the site where the workflow will be run and determines which site settings to use (e.g., in bps prepare).

The compute site must be specified (in increasing order of priority) via either the computeSite setting in the config file or by using the --compute-site command-line option.

Note

Some plugins save the compute site in files produced via prepare. In this case, an override of the compute site may not be picked up when performing a restart. Consult WMS plugin documentation to see if the plugin fully supports setting computeSite for a restart.

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 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)

pipelineYaml: "${OBS_SUBARU_DIR}/pipelines/DRP.yaml#processCcd"

or a pre-made file containing a serialized QuantumGraph, for example

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.

Listing 1 ${CTRL_BPS_DIR}/doc/lsst.ctrl.bps/pipelines_check.yaml¶

pipelineYaml: "${DRP_PIPE_DIR}/pipelines/HSC/pipelines_check.yaml#processCcd"

project: dev
campaign: quick
computeSite: ncsapool

# Make sure these values correspond to ones in the bin/run_demo.sh's
# pipetask command line.
payload:
  payloadName: pipelines_check
  butlerConfig: ${PIPELINES_CHECK_DIR}/DATA_REPO/butler.yaml
  inCollection: HSC/calib,HSC/raw/all,refcats
  dataQuery: exposure=903342 AND detector=10

Submitting a run¶

Submit a run for execution with

bps submit example.yaml

If submission was successfully, it will output something like this:

Submit dir: /home/jdoe/tmp/bps/submit/shared/pipecheck/20211117T155008Z
Run Id: 176261
Run Name: u_jdoe_pipelines_check_20211117T155008Z

Additional Submit Options¶

See bps submit --help for more detailed information. Command-line values override values in the YAML file. (You can find more about BPS precedence order in this section)

Pass-thru Arguments

The following options allow additions to pipetask command lines via variables.

--extra-qgraph-options String to pass through to QuantumGraph builder. Replaces variable extraQgraphOptions in createQuantumGraph.
--extra-update-qgraph-options String to pass through to QuantumGraph updater. Replaces variable extraUpdateQgraphOptions in updateQuantumGraph.
--extra-init-options String to pass through to pipetaskInit execution. Replaces variable extraInitOptions in pipetaskInit’s runQuantumCommand.
--extra-run-quantum-options String to pass through to Quantum execution. For example this can be used to pass “–no-versions” to pipetask. Replaces variable extraRunQuantumOptions in runQuantumCommand.

Payload Options

The following subset of pipetask options are also usable on bps submit command lines.

-b, --butler-config
-i, --input COLLECTION
-o, --output COLLECTION
--output-run COLLECTION
-d, --data-query QUERY
-p, --pipeline FILE
-g, --qgraph FILENAME

Checking status¶

To check the status of the submitted run, use

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

For more detailed information on a given submission, use bps report --id ID.

Use bps report --help to see all currently supported options.

Canceling submitted jobs¶

The bps command to cancel bps-submitted jobs is

bps cancel --id <id>

or to cancel all of your runs use

bps cancel --user <username>

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 bps cancel fails to delete the jobs, you can use WMS specific executables.

Note

Using the WMS commands directly under normal circumstances is not advised as bps may someday include additional code.

Restarting a failed run¶

Restart a failed run with

bps restart --id <id>

where <id> is the id of the run that need to be restarted. What the id is depends on the workflow management system the BPS is configured to use. Consult plugin-specific documentation to see what options are available.

If the restart completed successfully, the command will output something similar to:

Run Id: 21054.0
Run Name: u_jdoe_pipelines_check_20211117T155008Z

At the moment a workflow will be restarted as it is, no configuration changes are possible.

BPS precedence order¶

Some settings can be specified simultaneously in multiple places (e.g. with command-line option and in the config file). The value of a setting is determined by following order:

command-line option,
config file (if used by a subcommand),
environment variable,
package default.

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.

${CTRL_BPS_DIR}/python/lsst/ctrl/bps/etc/bps_defaults.yaml contains default values used by every bps submission and is automatically included.

Configuration file can include other configuration files using includeConfigs with YAML array syntax. For example

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

patch: 69
dataQuery: patch = {patch}

Environment variables can be used as well with ${var} syntax, for example

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, site, and cloud.

payload

description of the submission including definition of inputs. These values are mostly those used in the pipetask/butler command lines, so their names must match those used in those commands.

For the default pipetask/butler commands as seen in ${CTRL_BPS_DIR}/python/lsst/ctrl/bps/etc/bps_defaults.yaml, payload values are:

Defaults provided by bps (Should rarely need to be changed):

output: Output collection, passed as -o to pipetask commands. Defaults to “u/{operator}/{payloadName}” where operator is defaulted to username and payloadName must be specified in YAML.
outputRun: Output run collection, passed as --output-run to pipetask commands. Defaults to “{output}/{timestamp}” where timestamp is automatically generated by bps.
runInit: true/false, whether to run pipetask --init-only job. Defaults to true.

Submit YAML must specify:

butlerConfig: Butler config, passed as -b to pipetask commands.
inCollection: Input collections, passed as -i to pipetask commands.
dataQuery: Data query, passed as -d to pipetask qgraph command.
payloadName: Name to describe submission. Used in bps report, and default output collection

pipetask

subsections are pipetask labels where can override/set runtime settings for particular pipetasks (currently no Quantum-specific settings).

A value most commonly used in a subsection is:

requestMemory: Maximum memory (MB) needed to run a Quantum of this PipelineTask.

site

settings for specific sites can be set here. Subsections are site names which are matched to computeSite. See the documentation of the WMS plugin in use for examples of site specifications.

cloud

settings for a particular cloud (group of sites) can be set here. Subsections cloud names which are matched to computeCloud. See the documentation of the WMS plugin in use for examples of cloud specifications.

Supported settings¶

Warning

A plugin may not support all options listed below. See plugin’s documentation for which ones are supported.

accountingGroup

The name of the group to use by the batch system for accounting purposes (if applicable).

accountingUser

The username the batch system should use for accounting purposes (if applicable). Usually, this is the operating system username. However, this setting allows one to use a custom value instead.

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).

computeCloud

Specification of the compute cloud where to run the workflow and which cloud settings to use in bps prepare).

createQuantumGraph

The command line specification for generating QuantumGraphs.

executeMachinesPattern, optional

A regular expression used for looking up available computational resources. By default it is set to .*worker.*.

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).

See Automatic memory scaling for further information and examples.

numberOfRetries, optional

The maximum number of retries allowed for a job (must be non-negative). The default value is None meaning that the job will be run only once.

memoryMultiplier, optional

A positive number greater than 1.0 controlling how fast memory increases between consecutive runs for jobs which failed due to insufficient memory.

If the memoryMultiplier is equal or less than 1.0, the automatic memory scaling will be disabled and memory requirements will not change between retries.

If memoryMultiplier is set, the default value 5 will be used if numberOfRetries was not set explicitly.

See Automatic memory scaling for further information and examples.

memoryLimit, optional

The compute resource’s memory limit, in MB, to control the memory scaling.

requestMemoryMax will be automatically set to this value if not defined or exceeds it.

It has no effect if memoryMultiplier is not set.

If not set, BPS will try to determine it automatically by querying available computational resources (e.g. execute machines in an HTCondor pool) which match the pattern defined by executeMachinesPattern.

When set explicitly, its value should reflect actual limitations of the compute resources on which the job will be run. For example, it should be set to the largest value that the batch system would give to a single job. If it is larger than the batch system permits, the job may stay in the job queue indefinitely.

See Automatic memory scaling for further information and examples.

requestMemoryMax, optional

Maximal amount of memory, in MB, a single Quantum execution should ever use. By default, it is equal to the memoryLimit.

It has no effect if memoryMultiplier is not set.

If it is set, but its value exceeds the memoryLimit, the value provided by the memoryLimit will be used instead.

See Automatic memory scaling for further information and examples.

requestCpus, optional

Number of cpus that a single Quantum execution of a particular pipetask will need (e.g., 1).

preemptible, optional

A flag indicating whether a job can be safely preempted. Defaults to true which means that unless indicated otherwise any job in the workflow can be safely preempted.

uniqProcName

Used when giving names to graphs, default names to output files, etc. If not specified by user, BPS tries to use outputRun 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

payload:
  runInit: true

pipetask:
  pipetaskInit:
    runQuantumCommand: "${CTRL_MPEXEC_DIR}/bin/pipetask --long-log run -b {butlerConfig} -i {inCollection} --output {output} --output-run {outputRun} --init-only --register-dataset-types --qgraph {qgraphFile} --clobber-outputs"
    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).

subdirTemplate

Template used by bps and plugins when creating input and job subdirectories.

qgraphFileTemplate

Template used when creating QuantumGraph filename.

wmsServiceClass

Workload Management Service plugin to use.

bpsUseShared

Whether to put full submit-time path to QuantumGraph file in command line because the WMS plugin requires shared filesystem. Defaults to True.

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.

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.

Managing job memory requirements¶

The primary parameter controlling job memory requirements is called requestMemory. It specifies the amount of memory (in MiB) each job will have at its disposal during the execution of the workflow. Its default value is 2048 (2 GiB).

The default value can be overridden globally for all the jobs as well as for jobs running specific tasks. For example, including the lines below in your BPS config will result in all jobs having 4 GiB of memory during their execution except jobs running skyCorr task will be able to use up to 8 GiB of RAM as task-specific setting takes precedence over the global one if both are present in your BPS config.

requestMemory: 4096

pipetasks:
  skyCorr:
    requestMemory: 8192

Specifying memory requirements for jobs when using clustering works in a similar manner, see User-defined Dimension Clustering for more details and examples.

Automatic memory scaling¶

Beyond the simple specification of the amount of memory a job needs, selected BPS plugins (ctrl_bps_htcondor and ctrl_bps_panda) support automatic memory scaling (i.e. automatic retries with increased memory) for jobs that are failing due to the out of memory (OOM) error.

The parameter controlling this scaling mechanism is memoryMultiplier. If set to a number greater than 1.0, BPS will instruct the WMS to increase the amount of memory a job has at its disposal by the factor specified by this parameter each time the job fails due to the OOM error.

Similar to requestMemory it can be specified either globally and/or for specific jobs only. It is also subject to the same precedence rule as requestMemory is (i.e. task specific value takes precedence over the global one). For example, having the lines below in your BPS config

memoryMultiplier: 2.0
requestMemory: 4096
# requestMemoryMax: 16384

pipetasks:
  skyCorr:
    memoryMultiplier: 3.0
    requestMemory: 8192
    # requestMemoryMax: 131072

will make BPS instruct the WMS to retry all jobs failing due to the OOM error by doubling the amount of memory between each failed attempts with an exception of jobs running skyCorr. For these jobs the amount of memory will be tripled between attempts when they keep failing due to the OOM error.

The numberOfRetries default is 5 when memoryMultiplier is set, so the WMS will retry the job 5 times no matter what the failure, but the requested memory for the job is only increased when failing due to OOM error.

Note

If the memoryMultiplier is equal or less than 1.0, the automatic memory scaling will be disabled and memory requirements will not change between retries.

The optional parameter requestMemoryMax (commented out in the example above) puts a cap on how much memory a job can ask while trying to recover from the OOM error. You can use it to remove jobs failing due to the OOM error from the job queue before the number of retries reaches its limit (for example, when you know that the job should never need more than 32 GiB of memory). However, to explain how this cap is enforced we need to describe the scaling mechanism in a bit greater detail.

When the automatic memory scaling is enabled the job memory requirement, $m_n$, increases in a geometric manner between consecutive executions according to the formula:

(1)¶\[m_{n} = \min(m_0 * M^{n}, m_\mathrm{max})\]

with memoryMultiplier ($M$) playing a role of the common ratio.

During the first attempt ($n = 0$), the job is run with memory limit determined by requestMemory ($m_0$). If it fails due to the insufficient memory, it will be retried with a new memory limit equal to the product of the memoryMultiplier and the memory usage from the previous attempt.

The process will continue until number of retries, $n$, reaches its limit determined by numberOfRetries (5 by default) or the resultant memory request reaches the memory cap determined by requestMemoryMax ($m_\mathrm{max}$). If requestMemoryMax is not set, the value defined by memoryLimit will be used instead (see Supported settings for more information about this parameter).

Note

You should not use requestMemoryMax and memoryLimit exchangeably. The latter should reflect actual physical limitations of the compute resource and rarely needs to be changed.

Once the memory request reaches the cap the job will be run one time allowing to use the amount of memory determined by the cap (providing a retry is still permitted) and removed from the job queue afterwards if it fails due to insufficient memory again (even if more retries are permitted).

For example, with requestMemory = 3072 (3 GB), requestMemoryMax = 20480 (20 GB), and memoryMultiplier = 2.0 the job will be allowed to use 6 GB of memory during the first retry and 12 GB during the second one, and 20 GB during the third one if each earlier run attempt failed due to insufficient memory. If the third retry also fails due to the insufficient memory, the job will be removed from the job queue.

With requestMemory = 32768 (32 GB), requestMemoryMax = 65536 (64 GB), and memoryMultiplier = 2.0 the job will be allowed to use 64 GB of memory during its first retry. If it fails due to insufficient memory, it will be removed from the job queue.

In both examples if the job keeps failing for other reasons, the final number of retries will be determined by numberOfRetries.

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:

whenSaveJobQgraph: "NEVER"
runQuantumCommand: "${CTRL_MPEXEC_DIR}/bin/pipetask --long-log run -b {butlerConfig} --output {output} --output-run {outputRun} --qgraph {qgraphFile} --qgraph-id {qgraphId} --qgraph-node-id {qgraphNodeId} --skip-init-writes --extend-run --clobber-outputs --skip-existing"

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.

Execution Butler¶

Warning

Execution butler is being deprecated. BPS will now use the quantum-backed butler by default. Until ticket DM-40342 is merged you can still use the execution butler for your runs by adding resource://lsst.ctrl.bps/etc/bps_eb.yaml in your existing submit YAML with includeConfigs setting after other includes if any.

Execution Butler is a behind-the-scenes mechanism to lessen the number of simultaneous connections to the Butler database.

Command-line Changes¶

There is no a single configuration option to enable/disable Execution butler. Pipetasks commands need to be modified manually in the BPS submit YAML file (modified for you when doing the above mentioned include of bps_eb.yaml):

runQuantumCommand: >-
  ${CTRL_MPEXEC_DIR}/bin/pipetask {runPreCmdOpts} run
  --butler-config {butlerConfig}
  {pipetaskInput}
  {pipetaskOutput}
  --output-run {outputRun}
  --qgraph {fileDistributionEndPoint}{qgraphFile}
  --qgraph-id {qgraphId}
  --qgraph-node-id {qgraphNodeId}
  --clobber-outputs
  --skip-init-writes
  --extend-run
  {extraRunQuantumOptions}
pipetask:
  pipetaskInit:
    runQuantumCommand: >-
      ${CTRL_MPEXEC_DIR}/bin/pipetask {initPreCmdOpts} run
      --butler-config {butlerConfig}
      {pipetaskInput}
      {pipetaskOutput}
      --output-run {outputRun}
      --qgraph {fileDistributionEndPoint}{qgraphFile}
      --qgraph-id {qgraphId}
      --qgraph-node-id {qgraphNodeId}
      --clobber-outputs
      --init-only
      --extend-run
      {extraInitOptions}

New YAML Section¶

executionButlerTemplate: "{submitPath}/EXEC_REPO-{uniqProcName}"
executionButler:
    whenCreate: "SUBMIT"
    #USER executionButlerDir: "/my/exec/butler/dir"  # if user provided, otherwise uses executionButlerTemplate
    createCommand: "${CTRL_MPEXEC_DIR}/bin/pipetask qgraph -b {butlerConfig} --input {inCollection} --output-run {outputRun} --save-execution-butler {executionButlerDir} -g {qgraphFile}"
    whenMerge: "ALWAYS"
    implementation: JOB  # JOB, WORKFLOW
    concurrencyLimit: db_limit
    command1: "${DAF_BUTLER_DIR}/bin/butler --log-level=VERBOSE transfer-datasets  {executionButlerDir} {butlerConfig} --collections {outputRun} --register-dataset-types"
    command2: "${DAF_BUTLER_DIR}/bin/butler collection-chain {butlerConfig} {output} {outputRun} --mode=prepend"

For --replace-run behavior, replace the one collection-chain command with these two:

command2: "${DAF_BUTLER_DIR}/bin/butler collection-chain {butlerConfig} {output} --mode=pop 1"
command3: "${DAF_BUTLER_DIR}/bin/butler collection-chain {butlerConfig} {output} --mode=prepend {outputRun}"

whenCreate

When during the submission process that the Execution Butler is created. whenCreate valid values: “NEVER”, “ACQUIRE”, “TRANSFORM”, “PREPARE”, “SUBMIT”. The recommended setting is “SUBMIT” because the run collection is stored in the Execution Butler and that should be set as late as possible in the submission process.

NEVER = Execution Butler is never created and the provided pipetask commands must be appropriate for not using a Execution Butler.
ACQUIRE = Execution Butler is created in the ACQUIRE submission stage right after creating or reading the QuantumGraph.
TRANSFORM = Execution Butler is created in the TRANSFORM submission stage right before creating the Generic Workflow.
PREPARE = Execution Butler is created in the PREPARE submission stage right before calling the WMS plugin’s prepare method.
SUBMIT = Execution Butler is created in the SUBMIT stage right before calling the WMS plugin’s submit method.

whenMerge

When the Execution Butler should be merged back to the central repository. whenMerge valid values: “ALWAYS”, “SUCCESS”, “NEVER”. The recommended setting is “ALWAYS” especially when jobs are writing to the central Datastore.

ALWAYS = Merge even if entire workflow was not executed successfully or run was cancelled.
SUCCESS = Only merge if entire workflow was executed successfully.
NEVER = bps is not responsible for merging the Execution Butler back to the central repository.

createCommand

Command to create the Execution Butler.

implementation

How to implement the mergeExecutionButler steps.

JOB = Single bash script is written with sequence of commands and is represented in the GenericWorkflow as a GenericWorkflowJob.
WORKFLOW = (Not implemented yet) Instead of a bash script, make a little workflow representing the sequence of commands.

concurrency_limit

Name of the concurrency limit. For butler repositories that need to limit the number of simultaneous merges, this name tells the plugin to limit the number of mergeExecutionButler jobs via some mechanism, e.g., a special queue.

db_limit = special concurrency limit to be used when limiting number of simultaneous butler database connections.

command1, command2, …

Commands executed in numerical order as part of the mergeExecutionButler job.

executionButlerTemplate

Template for Execution Butler directory name.

You can include other job specific requirements in executionButler section as well. For example, to ensure that the job running the Execution Butler will have 4 GB of memory at its disposal, use requestMemory option:

executionButler:
  requestMemory: 4096
  ...

Automatic memory scaling (for a WMS plugin that supports it) can be enabled in a similar way, for example

executionButler:
  requestMemory: 4096
  requestMemoryMax: 16384
  memoryMultiplier: 2.0
  ...

User-visible Changes¶

The major differences visible to users are:

bps report shows new job called mergeExecutionButler in detailed view. This is what saves the run info into the central butler repository. As with any job, it can succeed or fail. Different from other jobs, it will execute at the end of a run regardless of whether a job failed or not. It will even execute if the run is cancelled unless the cancellation is while the merge is running. Its output will go where other jobs go (at NCSA in jobs/mergeExecutionButler directory).

Extra files in submit directory:

EXEC_REPO-<run>: Execution Butler (YAML + initial SQLite file)

execution_butler_creation.out: Output of command to create execution butler.

final_job.bash: Script that is executed to do the merging of the run info into the central repo.

final_post_mergeExecutionButler.out: An internal file for debugging incorrect reporting of final run status.

Quantum-backed Butler¶

Warning

BPS now uses the quantum-backed butler by default and resource://lsst.ctrl.bps/etc/bps_qbb.yaml was removed from the package. If the include list in your config still contains it, please remove it.

Similarly to the execution butler, the quantum-backed butler is a mechanism for reducing access to the central butler registry when running LSST pipelines at scale. See DMTN-177 for more details.

Command-line Changes¶

At the moment there is no a single configuration option to enable/disable the quantum-backed Butler. Pipetasks commands need to be modified manually in the BPS config file:

runQuantumCommand: >-
  ${CTRL_MPEXEC_DIR}/bin/pipetask run-qbb
  {butlerConfig}
  {fileDistributionEndPoint}{qgraphFile}
  --qgraph-node-id {qgraphNodeId}
  {extraRunQuantumOptions}
pipetask:
  pipetaskInit:
    runQuantumCommand: >-
      ${CTRL_MPEXEC_DIR}/bin/pipetask pre-exec-init-qbb
      {butlerConfig}
      {fileDistributionEndPoint}{qgraphFile}
      {extraInitOptions}

To be able to reuse pre-existing quantum graphs a new command, updateQuantumGraph needs to be included as well:

updateQuantumGraph: >-
  ${CTRL_MPEXEC_DIR}/bin/pipetask update-graph-run
  {qgraphFile}
  {outputRun}
  {outputQgraphFile}

as the outputRun value embedded in the existing quantum graph must be updated for each run.

New YAML Section¶

finalJob:
  whenSetup: "NEVER"
  whenRun: "ALWAYS"
  # Added for future flexibility, e.g., if prefer workflow instead of shell
  # script.
  implementation: JOB
  concurrencyLimit: db_limit
  command1: >-
    ${DAF_BUTLER_DIR}/bin/butler transfer-from-graph
    {fileDistributionEndPoint}{qgraphFile}
    {butlerConfig}
    --register-dataset-types
    --update-output-chain

whenSetup

When during the submission process set up the final job. Provided for future use, has no effect when using quantum-backed Butler yet.

whenRun

Determines when the final job will be executed:

ALWAYS: Execute the final job even if entire workflow was not executed successfully or run was cancelled.
SUCCESS: Only execute the final job if entire workflow was executed successfully.
NEVER: BPS is not responsible for performing any additional actions after the execution of the workflow is finished.

implementation

How to implement the steps to be executed as the final job:

JOB: Single bash script is written with sequence of commands and is represented in the GenericWorkflow as a GenericWorkflowJob.
WORKFLOW: (Not implemented yet): Instead of a bash script, make a little workflow representing the sequence of commands.

concurrency_limit

Name of the concurrency limit. For butler repositories that need to limit the number of simultaneous merges, this name tells the plugin to limit the number of finalJob jobs via some mechanism, e.g., a special queue.

db_limit: special concurrency limit to be used when limiting number of simultaneous butler database connections.

command1, command2, …

Commands executed in numerical order as part of the finalJob job.

You can include other job specific requirements in finalJob section as well. For example, to ensure that the job running the quantum-backed Butler will have 4 GB of memory at its disposal, use requestMemory option:

finalJob:
  requestMemory: 4096
  ...

Automatic memory scaling (for a WMS plugin that supports it) can be enabled in a similar way, for example

finalJob:
  requestMemory: 4096
  requestMemoryMax: 16384
  memoryMultiplier: 2.0
  ...

User-visible Changes¶

The major differences to users are:

Quantum-backed Butler takes precedence over the execution Butler. If the BPS configuration file contains both the executionButler and the finalJob sections, the quantum-backed Butler will be used during the workflow execution.
bps report shows a new job called finalJob in the detailed view. This job is responsible for transferring datasets from the quantum graph back to the central Butler. Similarly to other jobs, it can succeed or fail.
Extra files in the submit directory:
- final_job.bash: Script that is executed to transfer the datasets back to the central repo.
- quantumGraphUpdate.out: Output of the command responsible for updating the output run in the provided pre-existing quantum graph.
- final_post_finalJob.out: An internal file for debugging incorrect reporting of final run status.
- <qgraph_filename>_orig.qgraph: A backup copy of the original pre-existing quantum graph file that was used for submitting the run. Note that this file will not be present in the submit directory if the pipeline YAML specification was used during the submission instead.

Clustering¶

The description of all the Quanta to be executed by a submission exists in the full QuantumGraph for the run. bps breaks that work up into compute jobs where each compute job is assigned a subgraph of the full QuantumGraph. This subgraph of Quanta is called a “cluster”. bps can be configured to use different clustering algorithms by setting clusterAlgorithm. The default is single Quantum per Job.

Single Quantum per Job¶

This is the default clustering algorithm. Each job gets a cluster containing a single Quantum.

Compute job names are based upon the Quantum dataId + templateDataId. The PipelineTask label is used for grouping jobs in bps report output.

Config Entries (not currently needed as it is the default):

clusterAlgorithm: lsst.ctrl.bps.quantum_clustering_funcs.single_quantum_clustering

User-defined Dimension Clustering¶

This algorithm creates clusters based upon user-definitions that specify which PipelineTask labels go in the cluster and what dimensions to use to divide them into compute jobs. Requested job resources (and other job-based values) can be set within the each cluster definition. If a particular resource value is not defined there, bps will try to determine the value from the pipetask definitions for the Quanta in the cluster. For example, request_memory would first come from the cluster config, then the max of all the request_memory in the cluster, or finally any global default.

Compute job names are based upon the dimensions used for clustering. The cluster label is used for grouping jobs in bps report output.

The minimum configuration information is a label, a list of PipelineTask labels, and a list of dimensions. Sometimes a submission may want to treat two dimensions as the same thing (e.g., visit and exposure) in terms of putting Quanta in the same cluster. That is handled in the config via equalDimensions (a comma-separated list of dimA:dimB pairs).

Job dependencies are created based upon the Quanta dependencies. This means that the naming and order of the clusters in the submission YAML does not matter. The algorithm will fail if a circular dependency is created. It will also fail if a PipelineTask label is included in more than one cluster section.

Any Quanta not covered in the cluster config will fall back to the single Quanta per Job algorithm.

Relevant Config Entries:

clusterAlgorithm: lsst.ctrl.bps.quantum_clustering_funcs.dimension_clustering
cluster:
  # Repeat cluster subsection for however many clusters there are
  clusterLabel1:
    pipetasks: label1, label2     # comma-separated list of labels
    dimensions: dim1, dim2        # comma-separated list of dimensions
    equalDimensions: dim1:dim1a   # e.g., visit:exposure
    # requestCpus: N              # Overrides for jobs in this cluster
    # requestMemory: NNNN         # MB, Overrides for jobs in this cluster

Troubleshooting¶

Where is stdout/stderr from pipeline tasks?¶

See the documentation on the plugin in use to find out where stdout/stderr are.

Why is my submission taking so long?¶

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/20220407T184331Z/quantumGraphGeneration.out.

Why is my running job taking so long?¶

If the submission seems to be stuck in RUNNING state, some jobs may be held due to running out of memory. Check using bps report --id ID.

If that’s the case, the jobs can often be edited and released in a plugin-specific way.

Appendix A¶

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 and its dependencies if any.

For currently supported WMS plugins see lsst_bps_plugins.

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:

git clone https://github.com/lsst-dm/ctrl_bps
cd ctrl_bps
setup -k -r .
scons

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:

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

Navigation

Overview¶

Specifying WMS plugin¶

Checking status of WMS services¶

Specifying the Compute site¶

Defining a submission¶

Submitting a run¶

Additional Submit Options¶

Checking status¶

Canceling submitted jobs¶

Restarting a failed run¶

BPS precedence order¶

BPS configuration file¶

Supported settings¶

Reserved keywords¶

Managing job memory requirements¶

Automatic memory scaling¶

QuantumGraph Files¶

Execution Butler¶

Command-line Changes¶

New YAML Section¶

User-visible Changes¶

Quantum-backed Butler¶

Command-line Changes¶

New YAML Section¶

User-visible Changes¶

Clustering¶

Single Quantum per Job¶

User-defined Dimension Clustering¶

Troubleshooting¶

Where is stdout/stderr from pipeline tasks?¶

Why is my submission taking so long?¶

Why is my running job taking so long?¶

Appendix A¶

Prerequisites¶

Installing Batch Processing Service¶

Creating Butler repository¶