Setting Up for Special Environments¶
The following sections describe how to set up HTCondor for use in special environments or configurations.
Using HTCondor with AFS¶
Configuration variables that allow machines to interact with and use a shared file system are given at the Shared File System Configuration File Macros section.
Limitations with AFS occur because HTCondor does not currently have a way to authenticate itself to AFS. This is true of the HTCondor daemons that would like to authenticate as the AFS user condor, and of the condor_shadow which would like to authenticate as the user who submitted the job it is serving. Since neither of these things can happen yet, there are special things to do when interacting with AFS. Some of this must be done by the administrator(s) installing HTCondor. Other things must be done by HTCondor users who submit jobs.
AFS and HTCondor for Administrators¶
The largest result from the lack of authentication with AFS is that the
directory defined by the configuration variable
LOCAL_DIR and its
spool on each machine must be either
writable to unauthenticated users, or must not be on AFS. Making these
directories writable a very bad security hole, so it is not a viable
LOCAL_DIR onto NFS is acceptable. To avoid AFS,
place the directory defined for
LOCAL_DIR on a local partition on
each machine in the pool. This implies running condor_configure to
install the release directory and configure the pool, setting the
LOCAL_DIR variable to a local partition. When that is complete, log
into each machine in the pool, and run condor_init to set up the
local HTCondor directory.
The directory defined by
RELEASE_DIR, which holds all the HTCondor
binaries, libraries, and scripts, can be on AFS. None of the HTCondor
daemons need to write to these files. They only need to read them. So,
the directory defined by
RELEASE_DIR only needs to be world readable
in order to let HTCondor function. This makes it easier to upgrade the
binaries to a newer version at a later date, and means that users can
find the HTCondor tools in a consistent location on all the machines in
the pool. Also, the HTCondor configuration files may be placed in a
centralized location. This is what we do for the UW-Madison’s CS
department HTCondor pool, and it works quite well.
Finally, consider setting up some targeted AFS groups to help users deal with HTCondor and AFS better. This is discussed in the following manual subsection. In short, create an AFS group that contains all users, authenticated or not, but which is restricted to a given host or subnet. These should be made as host-based ACLs with AFS, but here at UW-Madison, we have had some trouble getting that working. Instead, we have a special group for all machines in our department. The users here are required to make their output directories on AFS writable to any process running on any of our machines, instead of any process on any machine with AFS on the Internet.
AFS and HTCondor for Users¶
The condor_shadow daemon runs on the machine where jobs are
submitted. It performs all file system access on behalf of the jobs.
Because the condor_shadow daemon is not authenticated to AFS as the
user who submitted the job, the condor_shadow daemon will not
normally be able to write any output. Therefore the directories in which
the job will be creating output files will need to be world writable;
they need to be writable by non-authenticated AFS users. In addition,
stderr, log file, and any file the program
explicitly opens will need to be in a directory that is world-writable.
An administrator may be able to set up special AFS groups that can make unauthenticated access to the program’s files less scary. For example, there is supposed to be a way for AFS to grant access to any unauthenticated process on a given host. If set up, write access need only be granted to unauthenticated processes on the submit machine, as opposed to any unauthenticated process on the Internet. Similarly, unauthenticated read access could be granted only to processes running on the submit machine.
A solution to this problem is to not use AFS for output files. If disk space on the submit machine is available in a partition not on AFS, submit the jobs from there. While the condor_shadow daemon is not authenticated to AFS, it does run with the effective UID of the user who submitted the jobs. So, on a local (or NFS) file system, the condor_shadow daemon will be able to access the files, and no special permissions need be granted to anyone other than the job submitter. If the HTCondor daemons are not invoked as root however, the condor_shadow daemon will not be able to run with the submitter’s effective UID, leading to a similar problem as with files on AFS.
Enabling the Transfer of Files Specified by a URL¶
Because staging data on the submit machine is not always efficient, HTCondor permits input files to be transferred from a location specified by a URL; likewise, output files may be transferred to a location specified by a URL. All transfers (both input and output) are accomplished by invoking a file transfer plugin: an executable or shell script that handles the task of file transfer.
For transferring input files, URL specification is limited to jobs
running under the vanilla universe and to a vm universe VM image file.
The execute machine retrieves the files. This differs from the normal
file transfer mechanism, in which transfers are from the machine where
the job is submitted to the machine where the job is executed. Each file
to be transferred by specifying a URL, causing a plug-in to be invoked,
is specified separately in the job submit description file with the
see the Submitting Jobs Without a Shared File System: HTCondor’s File Transfer Mechanism section for details.
For transferring output files, either the entire output sandbox, which
are all files produced or modified by the job as it executes, or a
subset of these files, as specified by the submit description file
are transferred to the directory specified by the URL. The URL itself is
specified in the separate submit description file command
see the Submitting Jobs Without a Shared File System: HTCondor’s File Transfer Mechanism section for details. The plug-in
is invoked once for each output file to be transferred.
Configuration identifies the availability of the one or more plug-in(s). The plug-ins must be installed and available on every execute machine that may run a job which might specify a URL, either for input or for output.
URL transfers are enabled by default in the configuration of execute machines. Disabling URL transfers is accomplished by setting
ENABLE_URL_TRANSFERS = FALSE
A comma separated list giving the absolute path and name of all available plug-ins is specified as in the example:
FILETRANSFER_PLUGINS = /opt/condor/plugins/wget-plugin, \ /opt/condor/plugins/hdfs-plugin, \ /opt/condor/plugins/custom-plugin
The condor_starter invokes all listed plug-ins to determine their
capabilities. Each may handle one or more protocols (scheme names). The
plug-in’s response to invocation identifies which protocols it can
handle. When a URL transfer is specified by a job, the condor_starter
invokes the proper one to do the transfer. If more than one plugin is
capable of handling a particular protocol, then the last one within the
list given by
FILETRANSFER_PLUGINS is used.
HTCondor assumes that all plug-ins will respond in specific ways. To
determine the capabilities of the plug-ins as to which protocols they
handle, the condor_starter daemon invokes each plug-in giving it the
command line argument
-classad. In response to invocation with this
command line argument, the plug-in must respond with an output of four
ClassAd attributes. The first three are fixed:
MultipleFileSupport = true PluginVersion = "0.1" PluginType = "FileTransfer"
The fourth ClassAd attribute is
SupportedMethods. This attribute is a
string containing a comma separated list of the protocols that the
plug-in handles. So, for example
SupportedMethods = "http,ftp,file"
would identify that the three protocols described by http, ftp, and file
are supported. These strings will match the protocol specification as
given within a URL in a
command or within a URL in an
command in a submit description file for a job.
When a job specifies a URL transfer, the plug-in is invoked, without the
command line argument
-classad. It will instead be given two other
command line arguments. For the transfer of input file(s), the first
will be the URL of the file to retrieve and the second will be the
absolute path identifying where to place the transferred file. For the
transfer of output file(s), the first will be the absolute path on the
local machine of the file to transfer, and the second will be the URL of
the directory and file name at the destination.
The plug-in is expected to do the transfer, exiting with status 0 if the
transfer was successful, and a non-zero status if the transfer was not
successful. When not successful, the job is placed on hold, and the job
HoldReason will be set as appropriate for the job.
The job ClassAd attribute
HoldReasonSubCode will be set to the exit
status of the plug-in.
As an example of the transfer of a subset of output files, assume that the submit description file contains
output_destination = url://server/some/directory/ transfer_output_files = foo, bar, qux
HTCondor invokes the plug-in that handles the
url protocol with
input classads describing all the files to be transferred and their
destinations. The directory delimiter (/ on Unix, and \ on Windows) is
appended to the destination URL, such that the input will look like the
[ LocalFileName = "/path/to/local/copy/of/foo"; Url = "url://server/some/directory//foo" ] [ LocalFileName = "/path/to/local/copy/of/bar"; Url = "url://server/some/directory//bar" ] [ LocalFileName = "/path/to/local/copy/of/qux"; Url = "url://server/some/directory//qux" ]
HTCondor also expects the plugin to exit with one of the following standardized exit codes:
0: Transfer successful
1: Transfer failed
2: Transfer needs a refreshed authentication token, should be retried (slated for development, not implemented yet)
Custom File Transfer Plugins¶
This functionality is not limited to a predefined set of protocols or plugins.
New ones can be invented. As an invented example, the
transfer type writes random bytes to a file. The plug-in that handles
zkm transfers would respond to invocation with the
line argument with:
MultipleFileSupport = true PluginVersion = "0.1" PluginType = "FileTransfer" SupportedMethods = "zkm"
And, then when a job requested that this plug-in be invoked, for the invented example:
transfer_input_files = zkm://128/r-data
the plug-in will be invoked with a first command line argument of
zkm://128/r-data and a second command line argument giving the full path
along with the file name
r-data as the location for the plug-in to
write 128 bytes of random data.
By default, HTCondor includes plugins for standard file protocols
ftp://.... Additionally, URL plugins exist
for transferring files to/from Box.com accounts (
Google Drive accounts (
and Microsoft OneDrive accounts (
These plugins require users to have obtained OAuth2 credentials
for the relevant service(s) before they can be used.
See Enabling the Fetching and Use of OAuth2 Credentials for how to enable users
to fetch OAuth2 credentials.
An example template for a file transfer plugin is available in our source repository under /src/condor_examples/filetransfer_example_plugin.py. This provides most of the functionality required in the plugin, except for the transfer logic itself, which is clearly indicated in the comments.
Sending File Transfer Plugins With Your Job¶
You can also use custom protocols on machines that do not have the necessary
plugin installed. This is achieved by sending the file transfer plugin along
with your job, using the
transfer_plugins submit attribute described
on the condor_submit man page.
Assume you want to transfer some URLs that use the
protocol, and you also have a plugin script called
custommethod_plugin.py that knows how to handle these URLs. Since this
plugin is not available on any of the execution points in your pool, you can
send it along with your job by including the following in the submit file:
transfer_plugins = custommethod=custommethod_plugin.py transfer_output_files = custommethod://path/to/file1, custommethod://path/to/file2
When the job arrives at an exeuction point, it will know to use the plugin
script provided to transfer these URLs. If your
is already supported at your execution point, the plugin provided in your
submit file will take predence.
Enabling the Transfer of Public Input Files over HTTP¶
Another option for transferring files over HTTP is for users to specify a list of public input files. These are specified in the submit file as follows:
public_input_files = file1,file2,file3
HTCondor will automatically convert these files into URLs and transfer them over HTTP using plug-ins. The advantage to this approach is that system administrators can leverage Squid caches or load-balancing infrastructure, resulting in improved performance. This also allows us to gather statistics about file transfers that were not previously available.
When a user submits a job with public input files, HTCondor generates a hash link for each file in the root directory for the web server. Each of these links points back to the original file on local disk. Next, HTCondor replaces the names of the files in the submit job with web links to their hashes. These get sent to the execute node, which downloads the files using our curl_plugin tool, and are then remapped back to their original names.
In the event of any errors or configuration problems, HTCondor will fall back to a regular (non-HTTP) file transfer.
To enable HTTP public file transfers, a system administrator must perform several steps as described below.
Install a web service for public input files¶
An HTTP service must be installed and configured on the submit node. Any regular web server software such as Apache (https://httpd.apache.org/) or nginx (https://nginx.org) will do. The submit node must be running a Linux system.
Configuration knobs for public input files¶
Several knobs must be set and configured correctly for this functionality to work:
ENABLE_HTTP_PUBLIC_FILES: Must be set to true (default: false)
HTTP_PUBLIC_FILES_ADDRESS: The full web address (hostname + port) where your web server is serving files (default: 127.0.0.1:8080)
HTTP_PUBLIC_FILES_ROOT_DIR: Absolute path to the local directory where the web service is serving files from.
HTTP_PUBLIC_FILES_USER: User security level used to write links to the directory specified by HTTP_PUBLIC_FILES_ROOT_DIR. There are three valid options for this knob:
<user>: Links will be written as user who submitted the job.
<condor>: Links will be written as user running condor daemons. By default this is the user condor unless you have changed this by setting the configuration parameter CONDOR_IDS.
<%username%>: Links will be written as the user %username% (ie. httpd, nobody) If using this option, make sure the directory is writable by this particular user.
The default setting is <condor>.
Additional HTTP infrastructure for public input files¶
The main advantage of using HTTP for file transfers is that system administrators can use additional infrastructure (such as Squid caching) to improve file transfer performance. This is outside the scope of the HTCondor configuration but is still worth mentioning here. When curl_plugin is invoked, it checks the environment variable http_proxy for a proxy server address; by setting this appropriately on execute nodes, a system can dramatically improve transfer speeds for commonly used files.
Enabling the Fetching and Use of OAuth2 Credentials¶
HTCondor supports two distinct methods for using OAuth2 credentials. One uses its own native OAuth client or issuer, and one uses a separate Hashicorp Vault server as the OAuth client and secure refresh token storage. Each method uses a separate credmon implementation and rpm and have their own advantages and disadvantages.
If the native OAuth client is used with a remote token issuer, then each time a new refresh token is needed the user has to reauthorize it through a web browser. An hour after all jobs of a user are stopped (by default), the refresh tokens are deleted. If the client is used with the native token issuer is used, then no web browser authorizations are needed but the public keys of every token issuer have to be managed by all the resource providers. In both cases, the tokens are only available inside HTCondor jobs.
If on the other hand a Vault server is used as the OAuth client, it stores the refresh token long term (typically about a month since last use) for multiple use cases. It can be used both by multiple HTCondor submit machines and by other client commands that need access tokens. Submit machines keep a medium term vault token (typically about a week) so at most users have to authorize in their web browser once a week. If kerberos is also available, new vault tokens can be obtained automatically without any user intervention. The HTCondor vault credmon also stores a longer lived vault token for use as long as jobs might run.
Using the native OAuth client and/or issuer¶
HTCondor can be configured to allow users to request and securely store credentials from most OAuth2 service providers. Users’ jobs can then request these credentials to be securely transferred to job sandboxes, where they can be used by file transfer plugins or be accessed by the users’ executable(s).
There are three steps to fully setting up HTCondor to enable users to be able to request credentials from OAuth2 services:
Enabling the condor_credd and condor_credmon_oauth daemons,
Optionally enabling the companion OAuth2 credmon WSGI application, and
Setting up API clients and related configuration.
First, to enable the condor_credd and condor_credmon_oauth daemons,
the easiest way is to install the
condor-credmon-oauth rpm. This
installs the condor_credmon_oauth daemon and enables both it and
condor_credd with reasonable defaults via the
use feature: oauth
Second, a token issuer, an HTTPS-enabled web server running on the submit
machine needs to be configured to execute its wsgi script as the user
condor. An example configuration is available at the path found with
rpm -ql condor-credmon-oauth|grep "condor_credmon_oauth\.conf" which
you can copy to an apache webserver’s configuration directory.
Third, for each OAuth2 service that one wishes to configure, an OAuth2 client application should be registered for each submit machine on each service’s API console. For example, for Box.com, a client can be registered by logging in to https://app.box.com/developers/console, creating a new “Custom App”, and selecting “Standard OAuth 2.0 (User Authentication).”
For each client, store the client ID in the HTCondor configuration
Store the client secret in a file only readable by root,
then point to it using
For our Box.com example, this might look like:
BOX_CLIENT_ID = ex4mpl3cl13nt1d BOX_CLIENT_SECRET_FILE = /etc/condor/.secrets/box_client_secret
# ls -l /etc/condor/.secrets/box_client_secret -r-------- 1 root root 33 Jan 1 10:10 /etc/condor/.secrets/box_client_secret # cat /etc/condor/.secrets/box_client_secret EXAmpL3ClI3NtS3cREt
Each service will need to redirect users back
to a known URL on the submit machine
after each user has approved access to their credentials.
For example, Box.com asks for the “OAuth 2.0 Redirect URI.”
This should be set to match
<OAuth2ServiceName>_RETURN_URL_SUFFIX such that
the user is returned to
The return URL suffix should be composed using the directory where the WSGI application is running,
and then the name of the OAuth2 service.
For our Box.com example, if running the WSGI application at the root of the webserver (
this should be
BOX_RETURN_URL_SUFFIX = /return/box.
The condor_credmon_oauth and its companion WSGI application
need to know where to send users to fetch their initial credentials
and where to send API requests to refresh these credentials.
Some well known service providers (
condor_config_val -dump TOKEN_URL)
already have their authorization and token URLs predefined in the default HTCondor config.
Other service providers will require searching through API documentation to find these URLs,
which then must be added to the HTCondor configuration.
For example, if you search the Box.com API documentation,
you should find the following authorization and token URLs,
and these URLs could be added them to the HTCondor config as below:
BOX_AUTHORIZATION_URL = https://account.box.com/api/oauth2/authorize BOX_TOKEN_URL = https://api.box.com/oauth2/token
After configuring OAuth2 clients,
make sure users know which names (
<OAuth2ServiceName>s) have been configured
so that they know what they should put under
in their job submit files.
Using Vault as the OAuth client¶
To instead configure HTCondor to use Vault as the OAuth client,
condor-credmon-vault rpm. Also install the htgettoken
rpm on the submit machine. Additionally, on the submit machine
SEC_CREDENTIAL_GETTOKEN_OPTS configuration option to
-a <vault.name> where <vault.name> is the fully qualified domain name
of the Vault machine. condor_submit users will then be able to select
the oauth services that are defined on the Vault server. See the
documentation to see how to set up and configure the Vault server.
Configuring HTCondor for Multiple Platforms¶
A single, initial configuration file may be used for all platforms in an
HTCondor pool, with platform-specific settings placed in separate files.
This greatly simplifies administration of a heterogeneous pool by
allowing specification of platform-independent, global settings in one
place, instead of separately for each platform. This is made possible by
configuration variable as a list of files, instead of a single file. Of
course, this only helps when using a shared file system for the machines
in the pool, so that multiple machines can actually share a single set
of configuration files.
With multiple platforms, put all platform-independent settings (the vast
majority) into the single initial configuration file, which will be
shared by all platforms. Then, set the
configuration variable from that global configuration file to specify
both a platform-specific configuration file and optionally, a local,
machine-specific configuration file.
The name of platform-specific configuration files may be specified by
$(OPSYS), as defined automatically by
HTCondor. For example, for 32-bit Intel Windows 7 machines and 64-bit
Intel Linux machines, the files ought to be named:
$ condor_config.INTEL.WINDOWS condor_config.X86_64.LINUX
Then, assuming these files are in the directory defined by the
configuration variable, and machine-specific configuration files are in
the same directory, named by each machine’s host name,
LOCAL_CONFIG_FILE = $(ETC)/condor_config.$(ARCH).$(OPSYS), \ $(ETC)/$(HOSTNAME).local
Alternatively, when using AFS, an
@sys link may be used to specify
the platform-specific configuration file, which lets AFS resolve this
link based on platform name. For example, consider a soft link named
condor_config.platform that points to
this case, the files might be named:
$ condor_config.i386_linux2 condor_config.platform -> condor_config.@sys
LOCAL_CONFIG_FILE configuration variable would be set to
LOCAL_CONFIG_FILE = $(ETC)/condor_config.platform, \ $(ETC)/$(HOSTNAME).local
Platform-Specific Configuration File Settings¶
The configuration variables that are truly platform-specific are:
Full path to to the installed HTCondor binaries. While the configuration files may be shared among different platforms, the binaries certainly cannot. Therefore, maintain separate release directories for each platform in the pool.
The full path to the mail program.
Which devices in
/devshould be treated as console devices.
Which daemons the condor_master should start up. The reason this setting is platform-specific is to distinguish the condor_kbdd. It is needed on many Linux and Windows machines, and it is not needed on other platforms.
Reasonable defaults for all of these configuration variables will be
found in the default configuration files inside a given platform’s
binary distribution (except the
RELEASE_DIR, since the location of
the HTCondor binaries and libraries is installation specific). With
multiple platforms, use one of the
condor_config files from either
running condor_configure or from the
$(RELEASE_DIR)/etc/examples/condor_config.generic file, take these
settings out, save them into a platform-specific file, and install the
resulting platform-independent file as the global configuration file.
Then, find the same settings from the configuration files for any other
platforms to be set up, and put them in their own platform-specific
files. Finally, set the
LOCAL_CONFIG_FILE configuration variable to
point to the appropriate platform-specific file, as described above.
Not even all of these configuration variables are necessarily going to
be different. For example, if an installed mail program understands the
-s option in
/usr/local/bin/mail on all platforms, the
DAEMON_LIST will be the same for each, so there is no reason not
to put that in the global configuration file.
Other Uses for Platform-Specific Configuration Files¶
It is certainly possible that an installation may want other configuration variables to be platform-specific as well. Perhaps a different policy is desired for one of the platforms. Perhaps different people should get the e-mail about problems with the different platforms. There is nothing hard-coded about any of this. What is shared and what should not shared is entirely configurable.
can be an arbitrary list of files, an installation can even break up the
global, platform-independent settings into separate files. In fact, the
global configuration file might only contain a definition for
LOCAL_CONFIG_FILE, and all other configuration variables would be
placed in separate files.
Different people may be given different permissions to change different
HTCondor settings. For example, if a user is to be able to change
certain settings, but nothing else, those settings may be placed in a
file which was early in the
LOCAL_CONFIG_FILE list, to give that
user write permission on that file. Then, include all the other files
after that one. In this way, if the user was attempting to change
settings that the user should not be permitted to change, the settings
would be overridden.
This mechanism is quite flexible and powerful. For very specific
configuration needs, they can probably be met by using file permissions,
LOCAL_CONFIG_FILE configuration variable, and imagination.
The HTCondor keyboard daemon, condor_kbdd, monitors X events on machines where the operating system does not provide a way of monitoring the idle time of the keyboard or mouse. On Linux platforms, it is needed to detect USB keyboard activity. Otherwise, it is not needed. On Windows platforms, the condor_kbdd is the primary way of monitoring the idle time of both the keyboard and mouse.
The condor_kbdd on Windows Platforms¶
Windows platforms need to use the condor_kbdd to monitor the idle
time of both the keyboard and mouse. By adding
KBDD to configuration
DAEMON_LIST, the condor_master daemon invokes the
condor_kbdd, which then does the right thing to monitor activity
given the version of Windows running.
With Windows Vista and more recent version of Windows, user sessions are moved out of session 0. Therefore, the condor_startd service is no longer able to listen to keyboard and mouse events. The condor_kbdd will run in an invisible window and should not be noticeable by the user, except for a listing in the task manager. When the user logs out, the program is terminated by Windows. This implementation also appears in versions of Windows that predate Vista, because it adds the capability of monitoring keyboard activity from multiple users.
To achieve the auto-start with user login, the HTCondor installer adds a condor_kbdd entry to the registry key at HKLM\Software\Microsoft\Windows\CurrentVersion\Run. On 64-bit versions of Vista and more recent Windows versions, the entry is actually placed in HKLM\Software\Wow6432Node\Microsoft\Windows\CurrentVersion\Run.
In instances where the condor_kbdd is unable to connect to the condor_startd, it is likely because an exception was not properly added to the Windows firewall.
The condor_kbdd on Linux Platforms¶
On Linux platforms, great measures have been taken to make the condor_kbdd as robust as possible, but the X window system was not designed to facilitate such a need, and thus is not as efficient on machines where many users frequently log in and out on the console.
In order to work with X authority, which is the system by which X
authorizes processes to connect to X servers, the condor_kbdd needs
to run with super user privileges. Currently, the condor_kbdd assumes
that X uses the
HOME environment variable in order to locate a file
.Xauthority. This file contains keys necessary to connect to
an X server. The keyboard daemon attempts to set
HOME to various
users’ home directories in order to gain a connection to the X server
and monitor events. This may fail to work if the keyboard daemon is not
allowed to attach to the X server, and the state of a machine may be
incorrectly set to idle when a user is, in fact, using the machine.
In some environments, the condor_kbdd will not be able to connect to
the X server because the user currently logged into the system keeps
their authentication token for using the X server in a place that no
local user on the current machine can get to. This may be the case for
files on AFS, because the user’s
.Xauthority file is in an AFS home
There may also be cases where the condor_kbdd may not be run with
super user privileges because of political reasons, but it is still
desired to be able to monitor X activity. In these cases, change the XDM
configuration in order to start up the condor_kbdd with the
permissions of the logged in user. If running X11R6.3, the files to edit
will probably be in
should start up the condor_kbdd at the end, and the
should shut down the condor_kbdd. The -l option can be used to
write the daemon’s log file to a place where the user running the daemon
has permission to write a file. The file’s recommended location will be
$HOME/.kbdd.log, since this is a place where every user
can write, and the file will not get in the way. The -pidfile and
-k options allow for easy shut down of the condor_kbdd by storing
the process ID in a file. It will be necessary to add lines to the XDM
configuration similar to
$ condor_kbdd -l $HOME/.kbdd.log -pidfile $HOME/.kbdd.pid
This will start the condor_kbdd as the user who is currently logged
in and write the log to a file in the directory
This will also save the process ID of the daemon to
that when the user logs out, XDM can do:
$ condor_kbdd -k $HOME/.kbdd.pid
This will shut down the process recorded in file
To see how well the keyboard daemon is working, review the log for the daemon and look for successful connections to the X server. If there are none, the condor_kbdd is unable to connect to the machine’s X server.
Configuring The HTCondorView Server¶
The HTCondorView server is an alternate use of the condor_collector that logs information on disk, providing a persistent, historical database of pool state. This includes machine state, as well as the state of jobs submitted by users.
An existing condor_collector may act as the HTCondorView collector through configuration. This is the simplest situation, because the only change needed is to turn on the logging of historical information. The alternative of configuring a new condor_collector to act as the HTCondorView collector is slightly more complicated, while it offers the advantage that the same HTCondorView collector may be used for several pools as desired, to aggregate information into one place.
The following sections describe how to configure a machine to run a HTCondorView server and to configure a pool to send updates to it.
Configuring a Machine to be a HTCondorView Server¶
To configure the HTCondorView collector, a few configuration variables are added or modified for the condor_collector chosen to act as the HTCondorView collector. These configuration variables are described in condor_collector Configuration File Entries. Here are brief explanations of the entries that must be customized:
The directory where historical data will be stored. This directory must be writable by whatever user the HTCondorView collector is running as (usually the user condor). There is a configurable limit to the maximum space required for all the files created by the HTCondorView server called (
NOTE: This directory should be separate and different from the
logdirectories already set up for HTCondor. There are a few problems putting these files into either of those directories.
A boolean value that determines if the HTCondorView collector should store the historical information. It is
Falseby default, and must be specified as
Truein the local configuration file to enable data collection.
Once these settings are in place in the configuration file for the
HTCondorView server host, create the directory specified in
POOL_HISTORY_DIR and make it writable by the user the HTCondorView
collector is running as. This is the same user that owns the
CollectorLog file in the
log directory. The user is usually
If using the existing condor_collector as the HTCondorView collector, no further configuration is needed. To run a different condor_collector to act as the HTCondorView collector, configure HTCondor to automatically start it.
If using a separate host for the HTCondorView collector, to start it,
add the value
DAEMON_LIST, and restart HTCondor on
that host. To run the HTCondorView collector on the same host as another
condor_collector, ensure that the two condor_collector daemons use
different network ports. Here is an example configuration in which the
main condor_collector and the HTCondorView collector are started up
by the same condor_master daemon on the same machine. In this
example, the HTCondorView collector uses port 12345.
VIEW_SERVER = $(COLLECTOR) VIEW_SERVER_ARGS = -f -p 12345 VIEW_SERVER_ENVIRONMENT = "_CONDOR_COLLECTOR_LOG=$(LOG)/ViewServerLog" DAEMON_LIST = MASTER, NEGOTIATOR, COLLECTOR, VIEW_SERVER
For this change to take effect, restart the condor_master on this host. This may be accomplished with the condor_restart command, if the command is run with administrator access to the pool.
Running HTCondor Jobs within a Virtual Machine¶
HTCondor jobs are formed from executables that are compiled to execute on specific platforms. This in turn restricts the machines within an HTCondor pool where a job may be executed. An HTCondor job may now be executed on a virtual machine running VMware, Xen, or KVM. This allows Windows executables to run on a Linux machine, and Linux executables to run on a Windows machine.
In older versions of HTCondor, other parts of the system were also referred to as virtual machines, but in all cases, those are now known as slots. A virtual machine here describes the environment in which the outside operating system (called the host) emulates an inner operating system (called the inner virtual machine), such that an executable appears to run directly on the inner virtual machine. In other parts of HTCondor, a slot (formerly known as virtual machine) refers to the multiple cores of a multi-core machine. Also, be careful not to confuse the virtual machines discussed here with the Java Virtual Machine (JVM) referenced in other parts of this manual. Targeting an HTCondor job to run on an inner virtual machine is also different than using the vm universe. The vm universe lands and starts up a virtual machine instance, which is the HTCondor job, on an execute machine.
HTCondor has the flexibility to run a job on either the host or the inner virtual machine, hence two platforms appear to exist on a single machine. Since two platforms are an illusion, HTCondor understands the illusion, allowing an HTCondor job to be executed on only one at a time.
Installation and Configuration¶
HTCondor must be separately installed, separately configured, and separately running on both the host and the inner virtual machine.
The configuration for the host specifies
. This specifies host names or IP addresses of
all inner virtual machines running on this host. An example
configuration on the host machine:
VMP_VM_LIST = vmware1.domain.com, vmware2.domain.com
The configuration for each separate inner virtual machine specifies
VMP_HOST_MACHINE . This specifies the
host for the inner virtual machine. An example configuration on an inner
VMP_HOST_MACHINE = host.domain.com
Given this configuration, as well as communication between HTCondor
daemons running on the host and on the inner virtual machine, the policy
for when jobs may execute is set by HTCondor. While the host is
executing an HTCondor job, the
START policy on the inner virtual
machine is overridden with
False, so no HTCondor jobs will be
started on the inner virtual machine. Conversely, while the inner
virtual machine is executing an HTCondor job, the
START policy on
the host is overridden with
False, so no HTCondor jobs will be
started on the host.
The inner virtual machine is further provided with a new syntax for
referring to the machine ClassAd attributes of its host. Any machine
ClassAd attribute with a prefix of the string
refers to the host’s ClassAd attributes. The
START policy on the
inner virtual machine ought to use this syntax to avoid starting jobs
when its host is too busy processing other items. An example
START on an inner virtual machine:
START = ( (KeyboardIdle > 150 ) && ( HOST_KeyboardIdle > 150 ) \ && ( LoadAvg <= 0.3 ) && ( HOST_TotalLoadAvg <= 0.3 ) )
HTCondor’s Dedicated Scheduling¶
The dedicated scheduler is a part of the condor_schedd that handles the scheduling of parallel jobs that require more than one machine concurrently running per job. MPI applications are a common use for the dedicated scheduler, but parallel applications which do not require MPI can also be run with the dedicated scheduler. All jobs which use the parallel universe are routed to the dedicated scheduler within the condor_schedd they were submitted to. A default HTCondor installation does not configure a dedicated scheduler; the administrator must designate one or more condor_schedd daemons to perform as dedicated scheduler.
Selecting and Setting Up a Dedicated Scheduler¶
We recommend that you select a single machine within an HTCondor pool to act as the dedicated scheduler. This becomes the machine from upon which all users submit their parallel universe jobs. The perfect choice for the dedicated scheduler is the single, front-end machine for a dedicated cluster of compute nodes. For the pool without an obvious choice for a submit machine, choose a machine that all users can log into, as well as one that is likely to be up and running all the time. All of HTCondor’s other resource requirements for a submit machine apply to this machine, such as having enough disk space in the spool directory to hold jobs. See Directories for more information.
Configuration Examples for Dedicated Resources¶
Each execute machine may have its own policy for the execution of jobs,
as set by configuration. Each machine with aspects of its configuration
that are dedicated identifies the dedicated scheduler. And, the ClassAd
representing a job to be executed on one or more of these dedicated
machines includes an identifying attribute. An example configuration
file with the following various policy settings is
Each execute machine defines the configuration variable
DedicatedScheduler , which
identifies the dedicated scheduler it is managed by. The local
configuration file contains a modified form of
DedicatedScheduler = "DedicatedScheduler@full.host.name" STARTD_ATTRS = $(STARTD_ATTRS), DedicatedScheduler
Substitute the host name of the dedicated scheduler machine for the string “full.host.name”.
If running personal HTCondor, the name of the scheduler includes the user name it was started as, so the configuration appears as:
DedicatedScheduler = "DedicatedScheduler@firstname.lastname@example.org" STARTD_ATTRS = $(STARTD_ATTRS), DedicatedScheduler
All dedicated execute machines must have policy expressions which allow for jobs to always run, but not be preempted. The resource must also be configured to prefer jobs from the dedicated scheduler over all other jobs. Therefore, configuration gives the dedicated scheduler of choice the highest rank. It is worth noting that HTCondor puts no other requirements on a resource for it to be considered dedicated.
Job ClassAds from the dedicated scheduler contain the attribute
Scheduler. The attribute is defined by a string of the form
Scheduler = "DedicatedScheduler@full.host.name"
The host name of the dedicated scheduler substitutes for the string full.host.name.
Different resources in the pool may have different dedicated policies by varying the local configuration.
- Policy Scenario: Machine Runs Only Jobs That Require Dedicated Resources
One possible scenario for the use of a dedicated resource is to only run jobs that require the dedicated resource. To enact this policy, configure the following expressions:
START = Scheduler =?= $(DedicatedScheduler) SUSPEND = False CONTINUE = True PREEMPT = False KILL = False WANT_SUSPEND = False WANT_VACATE = False RANK = Scheduler =?= $(DedicatedScheduler)
STARTexpression specifies that a job with the
Schedulerattribute must match the string corresponding
DedicatedSchedulerattribute in the machine ClassAd. The
RANKexpression specifies that this same job (with the
Schedulerattribute) has the highest rank. This prevents other jobs from preempting it based on user priorities. The rest of the expressions disable any other of the condor_startd daemon’s pool-wide policies, such as those for evicting jobs when keyboard and CPU activity is discovered on the machine.
- Policy Scenario: Run Both Jobs That Do and Do Not Require Dedicated Resources
While the first example works nicely for jobs requiring dedicated resources, it can lead to poor utilization of the dedicated machines. A more sophisticated strategy allows the machines to run other jobs, when no jobs that require dedicated resources exist. The machine is configured to prefer jobs that require dedicated resources, but not prevent others from running.
To implement this, configure the machine as a dedicated resource as above, modifying only the
START = True
- Policy Scenario: Adding Desktop Resources To The Mix
A third policy example allows all jobs. These desktop machines use a preexisting
STARTexpression that takes the machine owner’s usage into account for some jobs. The machine does not preempt jobs that must run on dedicated resources, while it may preempt other jobs as defined by policy. So, the default pool policy is used for starting and stopping jobs, while jobs that require a dedicated resource always start and are not preempted.
RANKpolicies are set in the global configuration. Locally, the configuration is modified to this hybrid policy by adding a second case.
SUSPEND = Scheduler =!= $(DedicatedScheduler) && ($(SUSPEND)) PREEMPT = Scheduler =!= $(DedicatedScheduler) && ($(PREEMPT)) RANK_FACTOR = 1000000 RANK = (Scheduler =?= $(DedicatedScheduler) * $(RANK_FACTOR)) \ + $(RANK) START = (Scheduler =?= $(DedicatedScheduler)) || ($(START))
RANK_FACTORto be a larger value than the maximum value possible for the existing rank expression.
RANKis a floating point value, so there is no harm in assigning a very large value.
Preemption with Dedicated Jobs¶
The dedicated scheduler can be configured to preempt running parallel universe jobs in favor of higher priority parallel universe jobs. Note that this is different from preemption in other universes, and parallel universe jobs cannot be preempted either by a machine’s user pressing a key or by other means.
By default, the dedicated scheduler will never preempt running parallel
universe jobs. Two configuration variables control preemption of these
SCHEDD_PREEMPTION_RANK . These
variables have no default value, so if either are not defined,
preemption will never occur.
True for a machine to be a candidate for this kind of
preemption. If more machines are candidates for preemption than needed
to satisfy a higher priority job, the machines are sorted by
SCHEDD_PREEMPTION_RANK, and only the highest ranked machines are
Note that preempting one node of a running parallel universe job requires killing the entire job on all of its nodes. So, when preemption occurs, it may end up freeing more machines than are needed for the new job. Also, as HTCondor does not produce checkpoints for parallel universe jobs, preempted jobs will be re-run, starting again from the beginning. Thus, the administrator should be careful when enabling preemption of these dedicated resources. Enable dedicated preemption with the configuration:
STARTD_JOB_ATTRS = JobPrio SCHEDD_PREEMPTION_REQUIREMENTS = (My.JobPrio < Target.JobPrio) SCHEDD_PREEMPTION_RANK = 0.0
In this example, preemption is enabled by user-defined job priority. If a set of machines is running a job at user priority 5, and the user submits a new job at user priority 10, the running job will be preempted for the new job. The old job is put back in the queue, and will begin again from the beginning when assigned to a newly acquired set of machines.
Grouping Dedicated Nodes into Parallel Scheduling Groups¶
In some parallel environments, machines are divided into groups, and jobs should not cross groups of machines. That is, all the nodes of a parallel job should be allocated to machines within the same group. The most common example is a pool of machine using InfiniBand switches. For example, each switch might connect 16 machines, and a pool might have 160 machines on 10 switches. If the InfiniBand switches are not routed to each other, each job must run on machines connected to the same switch. The dedicated scheduler’s Parallel Scheduling Groups feature supports this operation.
Each condor_startd must define which group it belongs to by setting
variable in the configuration file, and advertising it into the machine
ClassAd. The value of this variable is a string, which should be the
same for all condor_startd daemons within a given group. The property
must be advertised in the condor_startd ClassAd by appending
ParallelSchedulingGroup to the
The submit description file for a parallel universe job which must not cross group boundaries contains
+WantParallelSchedulingGroups = True
The dedicated scheduler enforces the allocation to within a group.
Configuring HTCondor for Running Backfill Jobs¶
HTCondor can be configured to run backfill jobs whenever the condor_startd has no other work to perform. These jobs are considered the lowest possible priority, but when machines would otherwise be idle, the resources can be put to good use.
Currently, HTCondor only supports using the Berkeley Open Infrastructure for Network Computing (BOINC) to provide the backfill jobs. More information about BOINC is available at http://boinc.berkeley.edu.
The rest of this section provides an overview of how backfill jobs work in HTCondor, details for configuring the policy for when backfill jobs are started or killed, and details on how to configure HTCondor to spawn the BOINC client to perform the work.
Overview of Backfill jobs in HTCondor¶
Whenever a resource controlled by HTCondor is in the Unclaimed/Idle state, it is totally idle; neither the interactive user nor an HTCondor job is performing any work. Machines in this state can be configured to enter the Backfill state, which allows the resource to attempt a background computation to keep itself busy until other work arrives (either a user returning to use the machine interactively, or a normal HTCondor job). Once a resource enters the Backfill state, the condor_startd will attempt to spawn another program, called a backfill client, to launch and manage the backfill computation. When other work arrives, the condor_startd will kill the backfill client and clean up any processes it has spawned, freeing the machine resources for the new, higher priority task. More details about the different states an HTCondor resource can enter and all of the possible transitions between them are described in Policy Configuration for Execute Hosts and for Submit Hosts, especially the condor_startd Policy Configuration and condor_schedd Policy Configuration sections.
At this point, the only backfill system supported by HTCondor is BOINC. The condor_startd has the ability to start and stop the BOINC client program at the appropriate times, but otherwise provides no additional services to configure the BOINC computations themselves. Future versions of HTCondor might provide additional functionality to make it easier to manage BOINC computations from within HTCondor. For now, the BOINC client must be manually installed and configured outside of HTCondor on each backfill-enabled machine.
Defining the Backfill Policy¶
There are a small set of policy expressions that determine if a condor_startd will attempt to spawn a backfill client at all, and if so, to control the transitions in to and out of the Backfill state. This section briefly lists these expressions. More detail can be found in condor_startd Configuration File Macros.
A boolean value to determine if any backfill functionality should be used. The default value is
A string that defines what backfill system to use for spawning and managing backfill computations. Currently, the only supported string is
A boolean expression to control if an HTCondor resource should start a backfill client. This expression is only evaluated when the machine is in the Unclaimed/Idle state and the
A boolean expression that is evaluated whenever an HTCondor resource is in the Backfill state. A value of
Trueindicates the machine should immediately kill the currently running backfill client and any other spawned processes, and return to the Owner state.
The following example shows a possible configuration to enable backfill:
# Turn on backfill functionality, and use BOINC ENABLE_BACKFILL = TRUE BACKFILL_SYSTEM = BOINC # Spawn a backfill job if we've been Unclaimed for more than 5 # minutes START_BACKFILL = $(StateTimer) > (5 * $(MINUTE)) # Evict a backfill job if the machine is busy (based on keyboard # activity or cpu load) EVICT_BACKFILL = $(MachineBusy)
Overview of the BOINC system¶
The BOINC system is a distributed computing environment for solving large scale scientific problems. A detailed explanation of this system is beyond the scope of this manual. Thorough documentation about BOINC is available at their website: http://boinc.berkeley.edu. However, a brief overview is provided here for sites interested in using BOINC with HTCondor to manage backfill jobs.
BOINC grew out of the relatively famous SETI@home computation, where volunteers installed special client software, in the form of a screen saver, that contacted a centralized server to download work units. Each work unit contained a set of radio telescope data and the computation tried to find patterns in the data, a sign of intelligent life elsewhere in the universe, hence the name: “Search for Extra Terrestrial Intelligence at home”. BOINC is developed by the Space Sciences Lab at the University of California, Berkeley, by the same people who created SETI@home. However, instead of being tied to the specific radio telescope application, BOINC is a generic infrastructure by which many different kinds of scientific computations can be solved. The current generation of SETI@home now runs on top of BOINC, along with various physics, biology, climatology, and other applications.
The basic computational model for BOINC and the original SETI@home is the same: volunteers install BOINC client software, called the boinc_client, which runs whenever the machine would otherwise be idle. However, the BOINC installation on any given machine must be configured so that it knows what computations to work for instead of always working on a hard coded computation. The BOINC terminology for a computation is a project. A given BOINC client can be configured to donate all of its cycles to a single project, or to split the cycles between projects so that, on average, the desired percentage of the computational power is allocated to each project. Once the boinc_client starts running, it attempts to contact a centralized server for each project it has been configured to work for. The BOINC software downloads the appropriate platform-specific application binary and some work units from the central server for each project. Whenever the client software completes a given work unit, it once again attempts to connect to that project’s central server to upload the results and download more work.
BOINC participants must register at the centralized server for each project they wish to donate cycles to. The process produces a unique identifier so that the work performed by a given client can be credited to a specific user. BOINC keeps track of the work units completed by each user, so that users providing the most cycles get the highest rankings, and therefore, bragging rights.
Because BOINC already handles the problems of distributing the application binaries for each scientific computation, the work units, and compiling the results, it is a perfect system for managing backfill computations in HTCondor. Many of the applications that run on top of BOINC produce their own application-specific checkpoints, so even if the boinc_client is killed, for example, when an HTCondor job arrives at a machine, or if the interactive user returns, an entire work unit will not necessarily be lost.
Installing the BOINC client software¶
In HTCondor Version 9.9.1, the boinc_client must be manually downloaded, installed and configured outside of HTCondor. Download the boinc_client executables at http://boinc.berkeley.edu/download.php.
Once the BOINC client software has been downloaded, the boinc_client
binary should be placed in a location where the HTCondor daemons can use
it. The path will be specified with the HTCondor configuration variable
Additionally, a local directory on each machine should be created where
the BOINC system can write files it needs. This directory must not be
shared by multiple instances of the BOINC software. This is the same
restriction as placed on the
execute directories used
by HTCondor. The location of this directory is defined by
BOINC_InitialDir . The directory must
be writable by whatever user the boinc_client will run as. This user
is either the same as the user the HTCondor daemons are running as, if
HTCondor is not running as root, or a user defined via the
BOINC_Owner configuration variable.
Finally, HTCondor administrators wishing to use BOINC for backfill jobs must create accounts at the various BOINC projects they want to donate cycles to. The details of this process vary from project to project. Beware that this step must be done manually, as the boinc_client can not automatically register a user at a given project, unlike the more fancy GUI version of the BOINC client software which many users run as a screen saver. For example, to configure machines to perform work for the Einstein@home project (a physics experiment run by the University of Wisconsin at Milwaukee), HTCondor administrators should go to http://einstein.phys.uwm.edu/create_account_form.php, fill in the web form, and generate a new Einstein@home identity. This identity takes the form of a project URL (such as http://einstein.phys.uwm.edu) followed by an account key, which is a long string of letters and numbers that is used as a unique identifier. This URL and account key will be needed when configuring HTCondor to use BOINC for backfill computations.
Configuring the BOINC client under HTCondor¶
After the boinc_client has been installed on a given machine, the BOINC projects to join have been selected, and a unique project account key has been created for each project, the HTCondor configuration needs to be modified.
Whenever the condor_startd decides to spawn the boinc_client to perform backfill computations, it will spawn a condor_starter to directly launch and monitor the boinc_client program. This condor_starter is just like the one used to invoke any other HTCondor jobs. In fact, the argv of the boinc_client will be renamed to condor_exec, as described in the Renaming of argv section.
This condor_starter reads values out of the HTCondor configuration
files to define the job it should run, as opposed to getting these
values from a job ClassAd in the case of a normal HTCondor job. All of
the configuration variables names for variables to control things such
as the path to the boinc_client binary to use, the command-line
arguments, and the initial working directory, are prefixed with the
"BOINC_". Each of these variables is described as either a
required or an optional configuration variable.
Required configuration variables:
The full path and executable name of the boinc_client binary to use.
The full path to the local directory where BOINC should run.
The HTCondor universe used for running the boinc_client program. This must be set to
vanillafor BOINC to work under HTCondor.
What user the boinc_client program should be run as. This variable is only used if the HTCondor daemons are running as root. In this case, the condor_starter must be told what user identity to switch to before invoking the boinc_client. This can be any valid user on the local system, but it must have write permission in whatever directory is specified by
Optional configuration variables:
Command-line arguments that should be passed to the boinc_client program. For example, one way to specify the BOINC project to join is to use the -attach_project argument to specify a project URL and account key. For example:
BOINC_Arguments = --attach_project http://einstein.phys.uwm.edu [account_key]
Environment variables that should be set for the boinc_client.
Full path to the file where
stdoutfrom the boinc_client should be written. If this variable is not defined,
stdoutwill be discarded.
Full path to the file where
stderrfrom the boinc_client should be written. If this macro is not defined,
stderrwill be discarded.
The following example shows one possible usage of these settings:
# Define a shared macro that can be used to define other settings. # This directory must be manually created before attempting to run # any backfill jobs. BOINC_HOME = $(LOCAL_DIR)/boinc # Path to the boinc_client to use, and required universe setting BOINC_Executable = /usr/local/bin/boinc_client BOINC_Universe = vanilla # What initial working directory should BOINC use? BOINC_InitialDir = $(BOINC_HOME) # Where to place stdout and stderr BOINC_Output = $(BOINC_HOME)/boinc.out BOINC_Error = $(BOINC_HOME)/boinc.err
If the HTCondor daemons reading this configuration are running as root, an additional variable must be defined:
# Specify the user that the boinc_client should run as: BOINC_Owner = nobody
In this case, HTCondor would spawn the boinc_client as nobody, so the
directory specified in
$(BOINC_HOME) would have to be writable by
the nobody user.
A better choice would probably be to create a separate user account just
for running BOINC jobs, so that the local BOINC installation is not
writable by other processes running as nobody. Alternatively, the
BOINC_Owner could be set to daemon.
Attaching to a specific BOINC project
There are a few ways to attach an HTCondor/BOINC installation to a given BOINC project:
Use the -attach_project argument to the boinc_client program, defined via the
BOINC_Argumentsvariable. The boinc_client will only accept a single -attach_project argument, so this method can only be used to attach to one project.
The boinc_cmd command-line tool can perform various BOINC administrative tasks, including attaching to a BOINC project. Using boinc_cmd, the appropriate argument to use is called -project_attach. Unfortunately, the boinc_client must be running for boinc_cmd to work, so this method can only be used once the HTCondor resource has entered the Backfill state and has spawned the boinc_client.
Manually create account files in the local BOINC directory. Upon start up, the boinc_client will scan its local directory (the directory specified with
BOINC_InitialDir) for files of the form
account_[URL].xml, for example,
account_einstein.phys.uwm.edu.xml. Any files with a name that matches this convention will be read and processed. The contents of the file define the project URL and the authentication key. The format is:
<account> <master_url>[URL]</master_url> <authenticator>[key]</authenticator> </account>
<account> <master_url>http://einstein.phys.uwm.edu</master_url> <authenticator>aaaa1111bbbb2222cccc3333</authenticator> </account>
Of course, the <authenticator> tag would use the real authentication key returned when the account was created at a given project.
These account files can be copied to the local BOINC directory on all machines in an HTCondor pool, so administrators can either distribute them manually, or use symbolic links to point to a shared file system.
In the two cases of using command-line arguments for boinc_client or running the boinc_cmd tool, BOINC will write out the resulting account file to the local BOINC directory on the machine, and then future invocations of the boinc_client will already be attached to the appropriate project(s).
BOINC on Windows¶
The Windows version of BOINC has multiple installation methods. The preferred method of installation for use with HTCondor is the Shared Installation method. Using this method gives all users access to the executables. During the installation process
Deselect the option which makes BOINC the default screen saver
Deselect the option which runs BOINC on start up.
Do not launch BOINC at the conclusion of the installation.
There are three major differences from the Unix version to keep in mind when dealing with the Windows installation:
The Windows executables have different names from the Unix versions. The Windows client is called boinc.exe. Therefore, the configuration variable
BOINC_Executable = C:\PROGRA~1\BOINC\boinc.exe
The Unix administrative tool boinc_cmd is called boinccmd.exe on Windows.
When using BOINC on Windows, the configuration variable
BOINC_InitialDirwill not be respected fully. To work around this difficulty, pass the BOINC home directory directly to the BOINC application via the
BOINC_Argumentsconfiguration variable. For Windows, rewrite the argument line as:
BOINC_Arguments = --dir $(BOINC_HOME) \ --attach_project http://einstein.phys.uwm.edu [account_key]
As a consequence of setting the BOINC home directory, some projects may fail with the authentication error:
Scheduler request failed: Peer certificate cannot be authenticated with known CA certificates.
To resolve this issue, copy the
ca-bundle.crtfile from the BOINC installation directory to
$(BOINC_HOME). This file appears to be project and machine independent, and it can therefore be distributed as part of an automated HTCondor installation.
BOINC_Ownerconfiguration variable behaves differently on Windows than it does on Unix. Its value may take one of two forms:
user This form assumes that the user exists in the local domain (that is, on the computer itself).
Setting this option causes the addition of the job attribute
RunAsUser = True
to the backfill client. This further implies that the configuration variable
STARTER_ALLOW_RUNAS_OWNERbe set to
Trueto insure that the local condor_starter be able to run jobs in this manner. For more information on the
RunAsUserattribute, see Executing Jobs as the Submitting User. For more information on the the
STARTER_ALLOW_RUNAS_OWNERconfiguration variable, see Shared File System Configuration File Macros.
Per Job PID Namespaces¶
Per job PID namespaces provide enhanced isolation of one process tree from another through kernel level process ID namespaces. HTCondor may enable the use of per job PID namespaces for Linux RHEL 6, Debian 6, and more recent kernels.
Read about per job PID namespaces http://lwn.net/Articles/531419/.
The needed isolation of jobs from the same user that execute on the same machine as each other is already provided by the implementation of slot users as described in User Accounts in HTCondor on Unix Platforms. This is the recommended way to implement the prevention of interference between more than one job submitted by a single user. However, the use of a shared file system by slot users presents issues in the ownership of files written by the jobs.
The per job PID namespace provides a way to handle the ownership of files produced by jobs within a shared file system. It also isolates the processes of a job within its PID namespace. As a side effect and benefit, the clean up of processes for a job within a PID namespace is enhanced. When the process with PID = 1 is killed, the operating system takes care of killing all child processes.
To enable the use of per job PID namespaces, set the configuration to include
USE_PID_NAMESPACES = True
This configuration variable defaults to
False, thus the use of per
job PID namespaces is disabled by default.
Group ID-Based Process Tracking¶
One function that HTCondor often must perform is keeping track of all processes created by a job. This is done so that HTCondor can provide resource usage statistics about jobs, and also so that HTCondor can properly clean up any processes that jobs leave behind when they exit.
In general, tracking process families is difficult to do reliably. By default HTCondor uses a combination of process parent-child relationships, process groups, and information that HTCondor places in a job’s environment to track process families on a best-effort basis. This usually works well, but it can falter for certain applications or for jobs that try to evade detection.
Jobs that run with a user account dedicated for HTCondor’s use can be
reliably tracked, since all HTCondor needs to do is look for all
processes running using the given account. Administrators must specify
in HTCondor’s configuration what accounts can be considered dedicated
User Accounts in HTCondor on Unix Platforms for
Ideally, jobs can be reliably tracked regardless of the user account they execute under. This can be accomplished with group ID-based tracking. This method of tracking requires that a range of dedicated group IDs (GID) be set aside for HTCondor’s use. The number of GIDs that must be set aside for an execute machine is equal to its number of execution slots. GID-based tracking is only available on Linux, and it requires that HTCondor daemons run as root.
GID-based tracking works by placing a dedicated GID in the supplementary group list of a job’s initial process. Since modifying the supplementary group ID list requires root privilege, the job will not be able to create processes that go unnoticed by HTCondor.
Once a suitable GID range has been set aside for process tracking,
GID-based tracking can be enabled via the
parameter. The minimum and
maximum GIDs included in the range are specified with the
MAX_TRACKING_GID settings. For
example, the following would enable GID-based tracking for an execute
machine with 8 slots.
USE_GID_PROCESS_TRACKING = True MIN_TRACKING_GID = 750 MAX_TRACKING_GID = 757
If the defined range is too small, such that there is not a GID available when starting a job, then the condor_starter will fail as it tries to start the job. An error message will be logged stating that there are no more tracking GIDs.
GID-based process tracking requires use of the condor_procd. If
USE_GID_PROCESS_TRACKING is true, the condor_procd will be used
regardless of the
MAX_TRACKING_GID require a full
restart of HTCondor.
Cgroup-Based Process Tracking¶
A new feature in Linux version 2.6.24 allows HTCondor to more accurately and safely manage jobs composed of sets of processes. This Linux feature is called Control Groups, or cgroups for short, and it is available starting with RHEL 6, Debian 6, and related distributions. Documentation about Linux kernel support for cgroups can be found in the Documentation directory in the kernel source code distribution. Another good reference is http://docs.redhat.com/docs/en-US/Red_Hat_Enterprise_Linux/6/html/Resource_Management_Guide/index.html Even if cgroup support is built into the kernel, many distributions do not install the cgroup tools by default.
The interface between the kernel cgroup functionality is via a (virtual) file system. When the condor_master starts on a Linux system with cgroup support in the kernel, it checks to see if cgroups are mounted, and if not, it will try to mount the cgroup virtual filesystem onto the directory /cgroup.
If your Linux distribution uses systemd, it will mount the cgroup file
system, and the only remaining item is to set configuration variable
BASE_CGROUP , as described below.
On Debian based systems, the memory cgroup controller is often not on by default, and needs to be enabled with a boot time option.
This setting needs to be inherited down to the per-job cgroup with the
following commands in
/usr/sbin/cgconfigparser -l /etc/cgconfig.conf /bin/echo 1 > /sys/fs/cgroup/htcondor/cgroup.clone_children
When cgroups are correctly configured and running, the virtual file
system mounted on
/cgroup should have several subdirectories under
it, and there should an
htcondor subdirectory under the directory
The condor_starter daemon uses cgroups by default on Linux systems to accurately track all the processes started by a job, even when quickly-exiting parent processes spawn many child processes. As with the GID-based tracking, this is only implemented when a condor_procd daemon is running.
Kernel cgroups are named in a virtual file system hierarchy. HTCondor
will put each running job on the execute node in a distinct cgroup. The
name of this cgroup is the name of the execute directory for that
condor_starter, with slashes replaced by underscores, followed by the
name and number of the slot. So, for the memory controller, a job
running on slot1 would have its cgroup located at
tasks file in this directory will contain a list of all the
processes in this cgroup, and many other files in this directory have
useful information about resource usage of this cgroup. See the kernel
documentation for full details.
Once cgroup-based tracking is configured, usage should be invisible to
the user and administrator. The condor_procd log, as defined by
PROCD_LOG, will mention that it is using this
method, but no user visible changes should occur, other than the
impossibility of a quickly-forking process escaping from the control of
the condor_starter, and the more accurate reporting of memory usage.
Limiting Resource Usage with a User Job Wrapper¶
An administrator can strictly limit the usage of system resources by
jobs for any job that may be wrapped using the script defined by the
. These are jobs within universes that
are controlled by the condor_starter daemon, and they include the
vanilla, java, local, and parallel
The job’s ClassAd is written by the condor_starter daemon. It will
need to contain attributes that the script defined by
USER_JOB_WRAPPER can use to implement platform specific resource
limiting actions. Examples of resources that may be referred to for
limiting purposes are RAM, swap space, file descriptors, stack size, and
core file size.
An initial sample of a
USER_JOB_WRAPPER script is provided in the
$(LIBEXEC)/condor_limits_wrapper.sh. Here is the
contents of that file:
#!/bin/bash # Copyright 2008 Red Hat, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. if [[ $_CONDOR_MACHINE_AD != "" ]]; then mem_limit=$((`egrep '^Memory' $_CONDOR_MACHINE_AD | cut -d ' ' -f 3` * 1024)) disk_limit=`egrep '^Disk' $_CONDOR_MACHINE_AD | cut -d ' ' -f 3` ulimit -d $mem_limit if [[ $? != 0 ]] || [[ $mem_limit = "" ]]; then echo "Failed to set Memory Resource Limit" > $_CONDOR_WRAPPER_ERROR_FILE exit 1 fi ulimit -f $disk_limit if [[ $? != 0 ]] || [[ $disk_limit = "" ]]; then echo "Failed to set Disk Resource Limit" > $_CONDOR_WRAPPER_ERROR_FILE exit 1 fi fi exec "$@" error=$? echo "Failed to exec($error): $@" > $_CONDOR_WRAPPER_ERROR_FILE exit 1
If used in an unmodified form, this script sets the job’s limits on a
per slot basis for memory and disk usage, with the limits defined by the
values in the machine ClassAd. This example file will need to be
modified and merged for use with a preexisting
If additional functionality is added to the script, an administrator is
likely to use the
USER_JOB_WRAPPER script in conjunction with
SUBMIT_ATTRS to force the job ClassAd to contain
attributes that the
USER_JOB_WRAPPER script expects to have defined.
The following variables are set in the environment of the the
USER_JOB_WRAPPER script by the condor_starter daemon, when the
USER_JOB_WRAPPER is defined.
The full path and file name of the file containing the machine ClassAd.
The full path and file name of the file containing the job ClassAd.
The full path and file name of the file that the
USER_JOB_WRAPPERscript should create, if there is an error. The text in this file will be included in any HTCondor failure messages.
Limiting Resource Usage Using Cgroups¶
While the method described to limit a job’s resource usage is portable,
and it should run on any Linux or BSD or Unix system, it suffers from
one large flaw. The flaw is that resource limits imposed are per
process, not per job. An HTCondor job is often composed of many Unix
processes. If the method of limiting resource usage with a user job
wrapper is used to impose a 2 Gigabyte memory limit, that limit applies
to each process in the job individually. If a job created 100 processes,
each using just under 2 Gigabytes, the job would continue without the
resource limits kicking in. Clearly, this is not what the machine owner
intends. Moreover, the memory limit only applies to the virtual memory
size, not the physical memory size, or the resident set size. This can
be a problem for jobs that use the
mmap system call to map in a
large chunk of virtual memory, but only need a small amount of memory at
one time. Typically, the resource the administrator would like to
control is physical memory, because when that is in short supply, the
machine starts paging, and can become unresponsive very quickly.
The condor_starter can, using the Linux cgroup capability, apply resource limits collectively to sets of jobs, and apply limits to the physical memory used by a set of processes. The main downside of this technique is that it is only available on relatively new Unix distributions such as RHEL 6 and Debian 6. This technique also may require editing of system configuration files.
To enable cgroup-based limits, first ensure that cgroup-based tracking
is enabled, as it is by default on supported systems, as described in
section 3.14.13. Once set, the
condor_starter will create a cgroup for each job, and set
attributes in that cgroup to control memory and cpu usage. These
attributes are the cpu.shares attribute in the cpu controller, and
two attributes in the memory controller, both
memory.limit_in_bytes, and memory.soft_limit_in_bytes. The
CGROUP_MEMORY_LIMIT_POLICY is set to the string
hard, the hard
limit will be set to the slot size, and the soft limit to 90% of the
slot size.. If set to
soft, the soft limit will be set to the slot
size and the hard limit will be set to the memory size of the whole startd.
By default, this whole size is the detected memory the size, minus
RESERVED_MEMORY. Or, if MEMORY is defined, that value is used..
No limits will be set if the value is
none. The default is
none. If the hard limit is in force, then the total amount of
physical memory used by the sum of all processes in this job will not be
allowed to exceed the limit. If the process goes above the hard
limit, the job will be put on hold.
The memory size used in both cases is the machine ClassAd
Memory. Note that
Memory is a static amount when using
static slots, but it is dynamic when partitionable slots are used. That
is, the limit is whatever the “Mem” column of condor_status reports for
CGROUP_MEMORY_LIMIT_POLICY is set, HTCondor will also also use
cgroups to limit the amount of swap space used by each job. By default,
the maximum amount of swap space used by each slot is the total amount
of Virtual Memory in the slot, minus the amount of physical memory. Note
that HTCondor measures virtual memory in kbytes, and physical memory in
megabytes. To prevent jobs with high memory usage from thrashing and
excessive paging, and force HTCondor to put them on hold instead, you
can tell condor that a job should never use swap, by setting
DISABLE_SWAP_FOR_JOB to true (the default is false).
In addition to memory, the condor_starter can also control the total
amount of CPU used by all processes within a job. To do this, it writes
a value to the cpu.shares attribute of the cgroup cpu controller. The
value it writes is copied from the
Cpus attribute of the machine
slot ClassAd multiplied by 100. Again, like the
this value is fixed for static slots, but dynamic under partitionable
slots. This tells the operating system to assign cpu usage
proportionally to the number of cpus in the slot. Unlike memory, there
is no concept of
hard, so this limit only applies when
there is contention for the cpu. That is, on an eight core machine, with
only a single, one-core slot running, and otherwise idle, the job
running in the one slot could consume all eight cpus concurrently with
this limit in play, if it is the only thing running. If, however, all
eight slots where running jobs, with each configured for one cpu, the
cpu usage would be assigned equally to each job, regardless of the
number of processes or threads in each job.
Concurrency limits allow an administrator to limit the number of concurrently running jobs that declare that they use some pool-wide resource. This limit is applied globally to all jobs submitted from all schedulers across one HTCondor pool; the limits are not applied to scheduler, local, or grid universe jobs. This is useful in the case of a shared resource, such as an NFS or database server that some jobs use, where the administrator needs to limit the number of jobs accessing the server.
The administrator must predefine the names and capacities of the resources to be limited in the negotiator’s configuration file. The job submitter must declare in the submit description file which resources the job consumes.
The administrator chooses a name for the limit. Concurrency limit names are case-insensitive. The names are formed from the alphabet letters ‘A’ to ‘Z’ and ‘a’ to ‘z’, the numerical digits 0 to 9, the underscore character ‘_’ , and at most one period character. The names cannot start with a numerical digit.
For example, assume that there are 3 licenses for the X software, so HTCondor should constrain the number of running jobs which need the X software to 3. The administrator picks XSW as the name of the resource and sets the configuration
XSW_LIMIT = 3
XSW is the invented name of this resource, and this name is
appended with the string
_LIMIT. With this limit, a maximum of 3
jobs declaring that they need this resource may be executed
In addition to named limits, such as in the example named limit
configuration may specify a concurrency limit for all resources that are
not covered by specifically-named limits. The configuration variable
sets this value. For example,
CONCURRENCY_LIMIT_DEFAULT = 1
will enforce a limit of at most 1 running job that declares a usage of
an unnamed resource. If
CONCURRENCY_LIMIT_DEFAULT is omitted from
the configuration, then no limits are placed on the number of
concurrently executing jobs for which there is no specifically-named
The job must declare its need for a resource by placing a command in its submit description file or adding an attribute to the job ClassAd. In the submit description file, an example job that requires the X software adds:
concurrency_limits = XSW
This results in the job ClassAd attribute
ConcurrencyLimits = "XSW"
Jobs may declare that they need more than one type of resource. In this case, specify a comma-separated list of resources:
concurrency_limits = XSW, DATABASE, FILESERVER
The units of these limits are arbitrary. This job consumes one unit of each resource. Jobs can declare that they use more than one unit with syntax that follows the resource name by a colon character and the integer number of resources. For example, if the above job uses three units of the file server resource, it is declared with
concurrency_limits = XSW, DATABASE, FILESERVER:3
If there are sets of resources which have the same capacity for each
member of the set, the configuration may become tedious, as it defines
each member of the set individually. A shortcut defines a name for a
set. For example, define the sets called
CONCURRENCY_LIMIT_DEFAULT = 5 CONCURRENCY_LIMIT_DEFAULT_LARGE = 100 CONCURRENCY_LIMIT_DEFAULT_SMALL = 25
To use the set name in a concurrency limit, the syntax follows the set
name with a period and then the set member’s name. Continuing this
example, there may be a concurrency limit named
which gets the capacity of the default defined for the
which is 100. A concurrency limit named
LARGE.DBSESSION will also
have a limit of 100. A concurrency limit named
receive the default limit of 5, as there is no set named
A concurrency limit may be evaluated against the attributes of a matched
machine. This allows a job to vary what concurrency limits it requires
based on the machine to which it is matched. To implement this, the job
uses submit command
Consider an example in which execute machines are located on one of two
local networks. The administrator sets a concurrency limit to limit the
number of network intensive jobs on each network to 10. Configuration of
each execute machine advertises which local network it is on. A machine
NETWORK = "NETWORK_A" STARTD_ATTRS = $(STARTD_ATTRS) NETWORK
and a machine on
NETWORK = "NETWORK_B" STARTD_ATTRS = $(STARTD_ATTRS) NETWORK
The configuration for the negotiator sets the concurrency limits:
NETWORK_A_LIMIT = 10 NETWORK_B_LIMIT = 10
Each network intensive job identifies itself by specifying the limit within the submit description file:
concurrency_limits_expr = TARGET.NETWORK
The concurrency limit is applied based on the network of the matched machine.
An extension of this example applies two concurrency limits. One limit
is the same as in the example, such that it is based on an attribute of
the matched machine. The other limit is of a specialized application
"SWX" in this example. The negotiator configuration is
extended to also include
SWX_LIMIT = 15
The network intensive job that also uses two units of the
application identifies the needed resources in the single submit
concurrency_limits_expr = strcat("SWX:2 ", TARGET.NETWORK)
Submit command concurrency_limits_expr may not be used together with submit command concurrency_limits.
Note that it is possible, under unusual circumstances, for more jobs to be started than should be allowed by the concurrency limits feature. In the presence of preemption and dropped updates from the condor_startd daemon to the condor_collector daemon, it is possible for the limit to be exceeded. If the limits are exceeded, HTCondor will not kill any job to reduce the number of running jobs to meet the limit.