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Using Condor An Introduction Condor Week 2008

Using Condor An Introduction Condor Week 2008. The Condor Project (Established ‘85). Distributed High Throughput Computing research performed by a team of ~35 faculty, full time staff and students. The Condor Project (Established ‘85).

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Using Condor An Introduction Condor Week 2008

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  1. Using Condor An IntroductionCondor Week 2008

  2. The Condor Project (Established ‘85) Distributed High Throughput Computing research performed by a team of ~35 faculty, full time staff and students.

  3. The Condor Project (Established ‘85) Distributed High Throughput Computing research performed by a team of ~35 faculty, full time staff and students who: • face software engineering challenges in a distributed UNIX/Linux/NT environment • are involved in national and international grid collaborations, • actively interact with academic and commercial users, • maintain and support large distributed production environments, • and educate and train students.

  4. Some free software produced by the Condor Project • MW • NeST / LotMan • Stork • And others… all as open source • Condor System • VDT • Metronome • ClassAd Library • DAGMan • Hawkeye • GCB

  5. Full featured system • Flexible scheduling policy engine via ClassAds • Preemption, suspension, requirements, preferences, groups, quotas, settable fair-share, system hold… • Facilities to manage BOTH dedicated CPUs (clusters) and non-dedicated resources (desktops) • Transparent Checkpoint/Migration for many types of serial jobs • No shared file-system required • Federate clusters w/ a wide array of Grid Middleware • Workflow management (inter-dependencies) • Support for many job types – serial, parallel, etc. • Fault-tolerant: can survive crashes, network outages, no single point of failure. • Development APIs: via SOAP / web services, DRMAA (C), Perl package, GAHP, flexible command-line tools, MW • Platforms: Linux i386/IA64, Windows 2k/XP, MacOS, Solaris, IRIX, HP-UX, Compaq Tru64, … lots.

  6. She is a scientist with a big problem. Meet Frieda.

  7. Frieda’s Application … Run a Parameter Sweep of F(x,y,z) for 20 values of x, 10 values of y and 3 values of z • 20×10×3 = 600 combinations • F takes on the average 6 hours to compute on a “typical” workstation (total = 600 × 6 = 3600 hours) • F requires a “moderate” (256MB) amount of memory • F performs “moderate” I/O - (x,y,z) is 5 MB and F(x,y,z) is 50 MB

  8. I have 600simulations to run.Where can I get help?

  9. While sharing a beverage with some colleagues, she shares her problem. Somebody asks “Have you tried Condor? It’s free.”

  10. Getting Condor • Available as a free download from http://www.cs.wisc.edu/condor • Download Condor for your operating system • Available for most UNIX (including Linux and Apple’s OS/X) platforms • Also for Windows XP / 2003 / Vista

  11. Condor Releases • Stable / Developer Releases • Version numbering scheme similar to that of the (pre 2.6) Linux kernels … • Major.minor.release • Minor is even (a.b.c): Stable • Examples: 6.6.3, 6.8.4, 6.8.5 • Very stable, mostly bug fixes • Minor is odd (a.b.c): Developer • New features, may have some bugs • Examples: 6.7.11, 6.9.1, 6.9.2

  12. Frieda Installs a “Personal Condor” on her machine… • What do we mean by a “Personal” Condor? • Condor on your own workstation • No root / administrator access required • No system administrator intervention needed • After installation, Frieda submits her jobs to her Personal Condor…

  13. personal Condor your workstation 600 Condor jobs

  14. Personal Condor?!What’s the benefit of a Condor “Pool” with just one user and one machine?

  15. Your Personal Condor will ... • Keep an eye on your jobs and will keep you posted on their progress • Implement your policy on the execution order of the jobs • Keep a log of your job activities • Add fault tolerance to your jobs • Implement your policy on when the jobs can run on your workstation

  16. Definitions • Job • The Condor representation of your work • Machine • The Condor representation of computers and that can perform the work • Match Making • Matching a job with a machine “Resource”

  17. Job Jobs state their requirements and preferences: I need a Linux/x86 platform I want the machine with the most memory I prefer a machine in the chemistry department

  18. Machine Machines state their requirements and preferences: Run jobs only when there is no keyboard activity I prefer to run Frieda’s jobs I am a machine in the physics department Never run jobs belonging to Dr. Smith

  19. The Magic of Matchmaking • Jobs and machines state their requirements and preferences • Condor matches jobs with machines based on requirements and preferences

  20. Getting Started:Submitting Jobs to Condor • Overview: • Choose a “Universe” for your job • Make your job “batch-ready” • Create a submit description file • Run condor_submitto put your job in the queue

  21. 1. Choose the “Universe” • Controls how Condor handles jobs • Choices include: • Vanilla • Standard • Grid • Java • Parallel • VM

  22. Using the Vanilla Universe • The Vanilla Universe: • Allows running almost any “serial” job • Provides automatic file transfer, etc. • Like vanilla ice cream • Can be used in just about any situation

  23. 2. Make your job batch-ready Must be able to run in the background • No interactive input • No GUI/window clicks • No music ;^)

  24. Make your job batch-ready (continued)… • Job can still use STDIN, STDOUT, and STDERR (the keyboard and the screen), but files are used for these instead of the actual devices • Similar to UNIX shell: • $ ./myprogram <input.txt >output.txt

  25. 3. Create a Submit Description File • A plain ASCII text file • Condor does not care about file extensions • Tells Condor about your job: • Which executable, universe, input, output and error files to use, command-line arguments, environment variables, any special requirements or preferences (more on this later) • Can describe many jobs at once (a “cluster”), each with different input, arguments, output, etc.

  26. Simple Submit Description File # Simple condor_submit input file # (Lines beginning with # are comments) # NOTE: the words on the left side are not # case sensitive, but filenames are! Universe = vanilla Executable = my_job Output = output.txt Queue

  27. 4. Run condor_submit • You give condor_submit the name of the submit file you have created: • condor_submit my_job.submit • condor_submit: • Parses the submit file, checks for errors • Creates a “ClassAd” that describes your job(s) • Puts job(s) in the Job Queue

  28. ClassAd ? • Condor’s internal data representation • Similar to classified ads (as the name implies) • Represent an object & its attributes • Usually many attributes • Can also describe what an object matches with

  29. ClassAd Details • ClassAds can contain a lot of details • The job’s executable is analysis.exe • The machine’s load average is 5.6 • ClassAds can specify requirements • I require a machine with Linux • ClassAds can specify preferences • This machine prefers to run jobs from the physics group

  30. ClassAd Details (continued) • ClassAds are: • semi-structured • user-extensible • schema-free • Attribute = Expression

  31. Example: MyType = "Job" TargetType = "Machine" ClusterId = 1377 Owner = "roy" Cmd = "sim.exe" Requirements = (Arch == "INTEL") && (OpSys == "LINUX") && (Disk >= DiskUsage) && ((Memory * 1024)>=ImageSize) … String Number Boolean ClassAd Example

  32. ClassAd Type = “Dog” Color = “Brown” Price = 12 ClassAd for the “Job” . . . Requirements = (type == “Dog”) && (color == “Brown”) && (price <= 15) . . . The Dog

  33. The Job Queue • condor_submit sends your job’s ClassAd(s) to the schedd • The schedd (more details later): • Manages the local job queue • Stores the job in the job queue • Atomic operation, two-phase commit • “Like money in the bank” • View the queue with condor_q

  34. Examplecondor_submit and condor_q % condor_submit my_job.submit Submitting job(s). 1 job(s) submitted to cluster 1. % condor_q -- Submitter: perdita.cs.wisc.edu : <128.105.165.34:1027> : ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 1.0 frieda 6/16 06:52 0+00:00:00 I 0 0.0 my_job 1 jobs; 1 idle, 0 running, 0 held %

  35. Input, output & error files • Controlled by submit file settings • You can define the job’s standard input, standard output and standard error: • Read job’s standard input from “input_file”: • Input = input_file • Shell equivalent: program <input_file • Write job’s standard ouput to “output_file”: • Output = output_file • Shell equivalent: program >output_file • Write job’s standard error to “error_file”: • Error = error_file • Shell equivalent: program 2>error_file

  36. Email about your job • Condor sends email about job events to the submitting user • Specify “notification” in your submit file to control which events: Notification = complete Notification = never Notification = error Notification = always Default

  37. Feedback on your job • Create a log of job events • Add to submit description file: log = sim.log • Becomes the Life Story of a Job • Shows all events in the life of a job • Always have a log file

  38. Sample Condor User Log 000 (0001.000.000) 05/25 19:10:03 Job submitted from host: <128.105.146.14:1816> ... 001 (0001.000.000) 05/25 19:12:17 Job executing on host: <128.105.146.14:1026> ... 005 (0001.000.000) 05/25 19:13:06 Job terminated. (1) Normal termination (return value 0) ...

  39. Example Submit Description File With Logging # Example condor_submit input file # (Lines beginning with # are comments) # NOTE: the words on the left side are not # case sensitive, but filenames are! Universe = vanilla Executable = /home/frieda/condor/my_job.condor Log = my_job.log·Job log (from Condor) Input = my_job.in·Program’s standard input Output = my_job.out ·Program’s standard output Error = my_job.err·Program’s standard error Arguments = -a1 -a2·Command line arguments InitialDir = /home/frieda/condor/run Queue

  40. “Clusters” and “Processes” • If your submit file describes multiple jobs, we call this a “cluster” • Each cluster has a unique “cluster number” • Each job in a cluster is called a “process” • Process numbers always start at zero • A Condor “Job ID” is the cluster number, a period, and the process number (i.e. 2.1) • A cluster can have a single process • Job ID = 20.0 ·Cluster 20, process 0 • Or, a cluster can have more than one process • Job ID: 21.0, 21.1, 21.2 ·Cluster 21, process 0, 1, 2

  41. Submit File for a Cluster # Example submit file for a cluster of 2 jobs # with separate input, output, error and log files Universe = vanilla Executable = my_job Arguments = -x 0 log = my_job_0.log Input = my_job_0.in Output = my_job_0.out Error = my_job_0.err Queue·Job 2.0 (cluster 2, process 0) Arguments = -x 1 log = my_job_1.log Input = my_job_1.in Output = my_job_1.out Error = my_job_1.err Queue·Job 2.1 (cluster 2, process 1)

  42. Submitting The Job % condor_submit my_job.submit-file Submitting job(s). 2 job(s) submitted to cluster 2. % condor_q -- Submitter: perdita.cs.wisc.edu : <128.105.165.34:1027> : ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 1.0 frieda 4/15 06:52 0+00:02:11 R 0 0.0 my_job –a1 –a2 2.0 frieda 4/15 06:56 0+00:00:00 I 0 0.0 my_job –x 0 2.1 frieda 4/15 06:56 0+00:00:00 I 0 0.0 my_job –x 1 3 jobs; 2 idle, 1 running, 0 held %

  43. Back to our 600 jobs… • We could put all input, output, error & log files in the one directory • One of each type for each job • That’d be 2400 files (4 files × 600 jobs) • Difficult to sort through • Better: Create a subdirectory for each run

  44. Organize your files and directories for big runs • Create subdirectories for each “run” • run_0, run_1, … run_599 • Create input files in each of these • run_0/simulation.in • run_1/simulation.in • … • run_599/simulation.in • The output, error & log files for each job will be created by Condor from your job’s output

  45. simulation.in simulation.out simulation.err simulation.log simulation.in simulation.out simulation.err simulation.log Frieda’s simulation directory sim.exe sim.sub run_0 run_599

  46. Submit Description File for 600 Jobs # Cluster of 600 jobs with different directories Universe = vanilla Executable = sim Log = simulation.log ... Arguments = -x 0 InitialDir = run_0·Log, input, output & error files -> run_0 Queue ·Job 3.0 (Cluster 3, Process 0) Arguments = -x 1 InitialDir = run_1·Log, input, output & error files -> run_1 Queue ·Job 3.1 (Cluster 3, Process 1) ·Do this 598 more times…………

  47. Submit File for a BigCluster of Jobs • We just submitted 1 cluster with 600 processes • All the input/output files will be in different directories • The submit file is pretty unwieldy (over 1200 lines) • Isn’t there a better way?

  48. Submit File for a BigCluster of Jobs (the better way) #1 • We can queue all 600 in 1 “Queue” command • Queue 600 • Condor provides $(Process) and $(Cluster) • $(Process) will be expanded to the process number for each job in the cluster • 0, 1, … 599 • $(Cluster) will be expanded to the cluster number • Will be 4 for all jobs in this cluster

  49. Submit File for a BigCluster of Jobs (the better way) #2 • The initial directory for each job can be specified using $(Process) • InitialDir = run_$(Process) • Condor will expand these to “run_0”, “run_1”, … “run_599” directories • Similarly, arguments can be variable • Arguments = -x $(Process) • Condor will expand these to “-x 0”, “-x 1”, … “-x 599”

  50. Better Submit File for 600 Jobs # Example condor_submit input file that defines # a cluster of 600 jobs with different directories Universe = vanilla Executable = my_job Log = my_job.log Input = my_job.in Output = my_job.out Error = my_job.err Arguments = –x $(Process) ·–x 0, -x 1, … -x 599 InitialDir = run_$(Process) ·run_0 … run_599 Queue 600·Jobs 4.0 … 4.599

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