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layout: default
navsection: installguide
title: Install the LSF dispatcher
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{% comment %}
Copyright (C) The Arvados Authors. All rights reserved.
SPDX-License-Identifier: CC-BY-SA-3.0
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{% include 'notebox_begin_warning' %}
@arvados-dispatch-lsf@ is only relevant for on premises clusters that will spool jobs to LSF. Skip this section if you use Slurm or if you are installing a cloud cluster.
{% include 'notebox_end' %}
h2(#overview). Overview
Containers can be dispatched to an LSF cluster. The dispatcher sends work to the cluster using LSF's @bsub@ command, so it works in a variety of LSF configurations.
In order to run containers, you must choose a user that has permission to set up FUSE mounts and run Singularity/Docker containers on each compute node. This install guide refers to this user as the @crunch@ user. We recommend you create this user on each compute node with the same UID and GID, and add it to the @fuse@ and @docker@ system groups to grant it the necessary permissions. However, you can run the dispatcher under any account with sufficient permissions across the cluster.
Set up all of your compute nodes with "Docker":../crunch2/install-compute-node-singularity.html or "Singularity":../crunch2/install-compute-node-docker.html.
*Current limitations*:
* Arvados container priority is not propagated to LSF job priority. This can cause inefficient use of compute resources, and even deadlock if there are fewer compute nodes than concurrent Arvados workflows.
* Combining LSF with docker may not work, depending on LSF configuration and user/group IDs (if LSF only sets up the configured user's primary group ID when executing the crunch-run process on a compute node, it may not have permission to connect to the docker daemon).
h2(#update-config). Update config.yml
Arvados-dispatch-lsf reads the common configuration file at @/etc/arvados/config.yml@.
Add a DispatchLSF entry to the Services section, using the hostname where @arvados-dispatch-lsf@ will run, and an available port:
Services:
DispatchLSF:
InternalURLs:
"http://hostname.zzzzz.arvadosapi.com:9007": {}
Review the following configuration parameters and adjust as needed.
h3(#BsubSudoUser). Containers.LSF.BsubSudoUser
arvados-dispatch-lsf uses @sudo@ to execute @bsub@, for example @sudo -E -u crunch bsub [...]@. This means the @crunch@ account must exist on the hosts where LSF jobs run ("execution hosts"), as well as on the host where you are installing the Arvados LSF dispatcher (the "submission host"). To use a user account other than @crunch@, configure @BsubSudoUser@:
Containers:
LSF:
BsubSudoUser: lsfuser
Alternatively, you can arrange for the arvados-dispatch-lsf process to run as an unprivileged user that has a corresponding account on all compute nodes, and disable the use of @sudo@ by specifying an empty string:
Containers:
LSF:
# Don't use sudo
BsubSudoUser: ""
h3(#BsubArgumentsList). Containers.LSF.BsubArgumentsList
When arvados-dispatch-lsf invokes @bsub@, you can add arguments to the command by specifying @BsubArgumentsList@. You can use this to send the jobs to specific cluster partitions or add resource requests. Set @BsubArgumentsList@ to an array of strings.
Template variables starting with % will be substituted as follows:
%U uuid
%C number of VCPUs
%M memory in MB
%T tmp in MB
%G number of GPU devices (@runtime_constraints.cuda.device_count@)
Use %% to express a literal %. The %%J in the default will be changed to %J, which is interpreted by @bsub@ itself.
For example:
Containers:
LSF:
BsubArgumentsList: ["-o", "/tmp/crunch-run.%%J.out", "-e", "/tmp/crunch-run.%%J.err", "-J", "%U", "-n", "%C", "-D", "%MMB", "-R", "rusage[mem=%MMB:tmp=%TMB] span[hosts=1]", "-R", "select[mem>=%MMB]", "-R", "select[tmp>=%TMB]", "-R", "select[ncpus>=%C]"]
Note that the default value for @BsubArgumentsList@ uses the @-o@ and @-e@ arguments to write stdout/stderr data to files in @/tmp@ on the compute nodes, which is helpful for troubleshooting installation/configuration problems. Ensure you have something in place to delete old files from @/tmp@, or adjust these arguments accordingly.
h3(#BsubCUDAArguments). Containers.LSF.BsubCUDAArguments
If the container requests access to GPUs (@runtime_constraints.cuda.device_count@ of the container request is greater than zero), the command line arguments in @BsubCUDAArguments@ will be added to the command line _after_ @BsubArgumentsList@. This should consist of the additional @bsub@ flags your site requires to schedule the job on a node with GPU support. Set @BsubCUDAArguments@ to an array of strings. For example:
Containers:
LSF:
BsubCUDAArguments: ["-gpu", "num=%G"]
h3(#PollInterval). Containers.PollInterval
arvados-dispatch-lsf polls the API server periodically for new containers to run. The @PollInterval@ option controls how often this poll happens. Set this to a string of numbers suffixed with one of the time units @s@, @m@, or @h@. For example:
Containers:
PollInterval: 10s
h3(#ReserveExtraRAM). Containers.ReserveExtraRAM: Extra RAM for jobs
Extra RAM to reserve (in bytes) on each LSF job submitted by Arvados, which is added to the amount specified in the container's @runtime_constraints@. If not provided, the default value is zero.
Supports suffixes @KB@, @KiB@, @MB@, @MiB@, @GB@, @GiB@, @TB@, @TiB@, @PB@, @PiB@, @EB@, @EiB@ (where @KB@ is 10[^3^], @KiB@ is 2[^10^], @MB@ is 10[^6^], @MiB@ is 2[^20^] and so forth).
Containers:
ReserveExtraRAM: 256MiB
h3(#CrunchRunArgumentList). Containers.CrunchRunArgumentList: Using host networking for containers
Older Linux kernels (prior to 3.18) have bugs in network namespace handling which can lead to compute node lockups. This by is indicated by blocked kernel tasks in "Workqueue: netns cleanup_net". If you are experiencing this problem, as a workaround you can disable use of network namespaces by Docker across the cluster. Be aware this reduces container isolation, which may be a security risk.
Containers:
CrunchRunArgumentsList:
- "-container-enable-networking=always"
- "-container-network-mode=host"
h3(#InstanceTypes). InstanceTypes: Avoid submitting jobs with unsatisfiable resource constraints
LSF does not provide feedback when a submitted job's RAM, CPU, or disk space constraints cannot be satisfied by any node: the job will wait in the queue indefinitely with "pending" status, reported by Arvados as "queued".
As a workaround, you can configure @InstanceTypes@ with your LSF cluster's compute node sizes. Arvados will use these sizes to determine when a container is impossible to run, and cancel it instead of submitting an LSF job.
Apart from detecting non-runnable containers, the configured instance types will not have any effect on scheduling.
InstanceTypes:
most-ram:
VCPUs: 8
RAM: 640GiB
IncludedScratch: 640GB
most-cpus:
VCPUs: 32
RAM: 256GiB
IncludedScratch: 640GB
gpu:
VCPUs: 8
RAM: 256GiB
IncludedScratch: 640GB
CUDA:
DriverVersion: "11.4"
HardwareCapability: "7.5"
DeviceCount: 1
{% assign arvados_component = 'arvados-dispatch-lsf' %}
{% include 'install_packages' %}
{% include 'start_service' %}
{% include 'restart_api' %}
h2(#confirm-working). Confirm working installation
On the dispatch node, start monitoring the arvados-dispatch-lsf logs:
# journalctl -o cat -fu arvados-dispatch-lsf.service
In another terminal window, use the diagnostics tool to run a simple container.
# arvados-client sudo diagnostics
INFO 5: running health check (same as `arvados-server check`)
INFO 10: getting discovery document from https://zzzzz.arvadosapi.com/discovery/v1/apis/arvados/v1/rest
...
INFO 160: running a container
INFO ... container request submitted, waiting up to 10m for container to run
After performing a number of other quick tests, this will submit a new container request and wait for it to finish.
While the diagnostics tool is waiting, the @arvados-dispatch-lsf@ logs will show details about submitting an LSF job to run the container.