NoMachine is supported on Windows, MAC and Linux computers. You can get the latest version of the enterprise client from NoMachine download page. Ubuntu/Mint users should download the Debian version (DEB) of the NoMachine client. MAC Clients must install XCode and XQuartz.
In order to access S3DF, host is set to s3dfnx.slac.stanford.edu, port to 22, and protocol to SSH. See the snapshots below for standard NoMachine settings for S3DF usage.
Open OnDemand is a web-based terminal. As long as you keep your web browser open, or are not using your browsers private browsing feature, you should only need to authenticate again about once a day.
?> TODO more about module avail etc.
We also provide common compilation tools...
?> TODO describe compilation tools etc.
You might want to know how much of your quota is used; reaching the
limit might be the cause of error messages whose reason is not
obvious. To do so for your home, simply type df -h ~/ in a terminal
window. More in general type df -h <folder> to determine the quota
for any folder.
The SLAC-wide legacy file systems AFS, GPFS, and SDF Lustre will be mounted read-only, and only on the interactive pools, to enable the migration of legacy data to S3DF storage:
-
AFS:
/fs/afsThe current plan is to use the afsnfs translator since AFS ACLs do not map to POSIX anyway. Some experimentation is underway to see what issues might exist in any potential transfer to S3DF. -
GPFS:
/fs/gpfsThe current plan is to use NFS as the access method. Affected systems: ACD, ATLAS, CryoEM, DES + DarkSky, Fermi, KIPAC, LSSTcam, MCC/EED, StaaS. -
Lustre:
/fs/ddnAlthough Lustre is currently operating in read-write mode, groups will likely benefit from moving data to S3DF storage as it becomes available to them.
Current backup/archive situation
| File system | Snapshot | Tape backup (TSM) | Tape archive (HPSS) | Done by | Paid by |
|---|---|---|---|---|---|
| sdfhome | To flash | Yes | No | S3DF | SLAC |
| sdfscratch | No | No | No | - | - |
| sdfk8s | To flash | No | No | S3DF | SLAC |
| sdfdata | To tiering layer | On demand | Active, from files | S3DF | SLAC |
| sdfdata:/u | To tiering layer | On demand | No | S3DF | Group |
| S3 direct | No | No | No | No | No |
Target backup/archive strategy
| File system | Snapshot | Tape backup (TSM-like) | Tape archive (HPSS-like) | Done by | Paid by |
|---|---|---|---|---|---|
| sdfhome | To S3 bucket | No | From S3 snapshots | S3DF | SLAC |
| sdfscratch | No | No | No | - | - |
| sdfk8s | To S3 bucket | No | From S3 snapshots | S3DF | SLAC |
| sdfdata | To S3 bucket | No | Active, from files | Group | Group |
| sdfdata:/u | To S3 bucket | No | From S3 snapshots | S3DF | Group |
| S3 direct | No | No | From S3 | Group | Group |
?> sdfdata:/u indicates folders that logically belong to sdfgroup but are too large to be paid for by SLAC.
?> The target backup strategy is aspirational, we need to find the right tools to copy from S3 to tape.
Please feel free to send us suggestions and modifications to this documentation! The content is hosted on github and as long as you have a github account, you can send us pull request where we can review your changes and merge them into this site.
?> TIP At the top of each page, there is also a 'Edit Page' button that will send you directly to the page on github to be edited
The documentation uses docsify.js to render markdown documents into a static website using only javascript. Whilst not necessary to submit changes, you may want to view the webpage locally for development purposes. Once you have cloned this document repository, you can start a local web server to preview all changes. Please see docsify for more details.
The official Slurm documentation can be found at the SchedMD site.
Whilst your desktop or laptop computer has a fast processor and quick access to data stored on its local hard disk/ssd; you may want to run large compute tasks that require more CPU/GPU/memory to run, or to process a large amount of data. Our compute servers are part of a Batch system that allows you to accomplish such tasks in a reasonable amount of time. Our servers also have fast access to centralised storage, have widely-used, common software packages and images pre-installed, and will enable you to run these larger compute tasks without impacting your own local desktop/laptop resources.
Historically, SLAC has used IBM's LSF as our Batch scheduler software. However, with the addition of new hardware such as our NVIDIA GPUs, we have decided to switch to Slurm to schedule compute jobs as it is also commonly used across other academic and laboratory environments. We hope that this commonality and consistency with other facilities will enable easier usage for users, as well as simpler administration for the Science Computing team here at SLAC.
The purpose of a batch system is to enable efficient sharing of the CPUs, GPUs, memory, and ephemeral storage that exists in a compute Cluster. The cluster is comprised of many servers - often called batch nodes. As the number of these batch nodes and their resources (such as GPUs) in our environment is finite, we need to keep account of which users consume which resources so that we can provide access to all users in a fair manner.
A partition is a logical grouping of batch nodes. These are servers of a similar technical specification (eg Cascade Lake CPUs, Telsa GPUs etc). Examples of partition names are roma and milano.
To view the status of the nodes on SDF from the command line use sinfo. The following produces a reasonably informative summary of all the nodes on SDF:
sinfo --Node --format="%10N %.6D %10P %10T %20E %.4c %.8z %8O %.6m %10e %.6w %.60f"
To get only information on a specific partition use --partition=<partition>. To get more information on a specfic node, use the following scontrol command:
scontrol show node <node name>
The names of the nodes can be found in the left-most column of the above sinfo command (called NODELIST) for some reason.
There are two ways to interact with slurm
- Using command line tools on the interactive pools.
- Using the ondemand web interface.
Common actions that you may want to perform are:
| Submit a job | srun or sbatch |
request a quick job to be ran - eg an interactive terminal for srun and a longer job(s) with sbatch |
| Show information about a job | scontrol show job <jobid> |
shows detailed information about the state, resources requested etc. for a job |
| Cancel or terminate a job | scancel <jobid> |
cancel a job; you can also use --signal=INT to send a unix signal to the job to cleanly terminate |
| Show position in squeue | sprio |
shows the fairshare calculations that determine your place in line for the job to start |
| Show running statistics about a job | sstat |
show job usage details |
| Modify accout/add users to partitions etc. | sacctmgr |
manage Associations |
In order to submit a batch job, you have to:
- create a text file containing some slurm commands (lines starting with
#SBATCH) and a list of commands/programs that you wish to run. This is called a batch script. - submit this batch script to the cluster using the
sbatchcommand - monitor the job using
scontrol show job
Create a job submission script (text file) script.sh (or whatever filename you wish):
#!/bin/bash
#SBATCH --partition=milano
#
#SBATCH --job-name=test
#SBATCH --output=output-%j.txt
#SBATCH --error=output-%j.txt
#
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=12
#SBATCH --mem-per-cpu=1g
#
#SBATCH --time=0-00:10:00
#
#SBATCH --gpus 1
<commands here>
In the above example, we write a batch script for a job named 'test' (using the --job-name). You can choose what ever name you wish to give the job so that you may be able to quickly identify it yourself. Both stdout and stderr will be outputted the same file output-%j.txt in the current working working - %j will be replaced with the slurm job id (using the --output and --error options). We request a single Task (think of it as an MPI rank) and that single task will request 12 CPUs; each of which will be allocated 1GB of RAM - so a total of 12GB. By default, the --ntasks will be equivalent to the number of nodes (servers) asked for. In order to aid scheduling (and potentially prioritising the Job), we limit the duration of the Job to 10 minutes. The format of the time limit field is D-HH:MM:SS. We also request a single GPU with the Job. This will be exposed via CUDA_VISIBLE_DEVICES.
?> TIP: only lines starting with #SBATCH will be processed by the slurm interpretor. As the script itself is just a bash script, any line beginning with # will be ignored. As such you may also comment out slurm directives by using somethign like ##SBATCH
We can define where the job will run using the --partition option.
?> You can think of the batch script as a shell script but with some extra directives that only slurm understand. As such, you can also just run the same script in hte command line to ensure that your job will work; ie sh script.sh will run the same set of commands but on the local host. This therefore also means that if you already have a shell script that runs your code, you can 'slurmify' it by adding slurm directives with #SBATCH. Please note, however, that if you are using GPUs in your code etc. the login node may not have any GPUs and hence your local run will fail.
?> add something about submitting sbatch commands directy without using a batch script - ie --wrap
It is important that you specify a meaningful duration for which your expect your job to run for. This allows the slurm scheduler to appropriately priortize your job against other jobs that are competing for the limited resources in the cluster. The duration of a job may depend upon many different factors such as the type of hardware that you may be constraining your job to run against, how well your code/application scales with multiple nodes, the speed of memory and disk access etc. etc.
You can specify the expected duration with the --time option. Valid time formats are:
M (M minutes)
M:S (M minutes, S seconds)
H:M:S (H hours, M minutes, S seconds)
D-H (D days, H hours)
D-H:M (D days, H hours, M minutes)
D-H:M:S (D days, H hours, M minutes, S seconds)
Once the job exceeds the specified job time, it will terminate. Unless you checkpoint your application as it progresses this may result in wasted cycles and the need to submit the job again with a longer duration.
!> TODO
!> TODO
!> TODO
!> TODO
!> diff between cd'ing on the script and --workingdir
?> note stuff about workign directories etc.
After you have created a batch script, you then need to tell slurm to queue it so that it may run. The command to you is sbatch and is synonymous with the bsub command in LSF. Therfore to submit the script script.sh we simply run
sbatch script.sh
If successful, it should provide you with the job id that the script will run as. You can use this job id to monitor your job progress.
?> TIP: you can also submit the slurm directives directly on the command line rather than within the batch script. When submitted as arguments to srun or sbatch, they will take precedence over any same directives that may already be specified in the batch script. e.g. if you run sbatch --partition ml script.sh and your script.sh contains a definiting to use the shared partition, your job will be submitted into the ml partition.
You can then use the command to monitor your job progress
squeue
And you can cancel the job with
scancel <jobid>
You can use the --gpus to specify gpus for your jobs: Using a number will request the number of any gpu that is available on the parition that yo choose. The type of gpu you get will depend upon the partition (cluster) that you request.
# request single gpu
srun -A <account name> -p ampere -n 1 --gpus 1 --pty /bin/bash
# sinfo -o "%12P %5D %14F %7z %7m %10d %11l %42G %38N %f"
PARTITION NODES NODES(A/I/O/T) S:C:T MEMORY TMP_DISK TIMELIMIT GRES NODELIST AVAIL_FEATURES
roma* 61 56/0/5/61 2:64:1 512000 0 10-00:00:00 (null) sdfrome[003-063] CPU_GEN:RME,CPU_SKU:7702,CPU_FRQ:2.00GHz
milano 135 52/78/5/135 2:64:1 512000 0 10-00:00:00 (null) sdfmilan[001-072,101-131,201-232] CPU_GEN:RME,CPU_SKU:7713,CPU_FRQ:2.00GHz
ampere 23 23/0/0/23 2:64:2 1029344 0 10-00:00:00 gpu:a100:4 sdfampere[001-023] CPU_GEN:RME,CPU_SKU:7542,CPU_FRQ:2.10GHz,GPU_GEN:AMP,GPU_SKU:A100,GPU_MEM:40GB,GPU_CC:8.0
This is often due to limited resources. The simplest way is to request less CPU (--cpus) or less memory (--memfor your Job. However, this will also likely increase the amount of time that you need for the Job to complete. Note that perfect scaling is often very difficult (ie using 16 CPUs will not run twice as fast as 8 CPUs, as will using 4 nodes via MPI will not run twice as fast as 2 nodes), so it may be beneficial to submit many smaller Jobs if your code allows it. You can also set the --time option to specify that your job will only run upto that amount of time so that the scheduler can better fit your job in.
You can also make use of the Scavenger QoS such that your job may run on any available resources available at SLAC. This, however, has the disadvantage that should higher priority jobs run on the same resources, your jobs may be terminated (preempted) - possibly before it has completed.
For a detailed explanation of how to use Apptainer on SDF, you can go through Yee-Ting Li's presentation here where you can find both the slides and the zoom recording.
As pulling and building a new image can use quite a lot of disk space, we recommend that you set the appropriate cache paths for apptainer to not use your $HOME directory. Therefore, before pulling or building an image, define the following environment variables:
export DIR=${SCRATCH}/.apptainer
mkdir -p $DIR
export APPTAINER_LOCALCACHEDIR=$DIR
export APPTAINER_CACHEDIR=$DIR
export APPTAINER_TMPDIR=$DIRTo pull an image from DockerHub, do:
apptainer pull docker://<user or organization>/<repository>:<tag>We offer various of GCC through (Redhat Software Collections](https://developers.redhat.com/products/softwarecollections/overview). These are wrapped up in Environment Modules and are available with a module load devtoolset/<version>.
We also provide symlinks to the appropriate GCC version to the above devtoolset packages with module load gcc/<version>.
We provide tunnel-less access to jupyter instances running on SDF via an easy to use web portal where you can 'bring-your-own' jupyter environments. See Interactive Jupyter for more information.
This section describes how to run Matab in the S3DF Parallel Computing environment. You can bring up matlab interactively on your desktop/laptop with nomachine or in an interactive slurm session with srun, submit jobs from within matlab, or submit jobs using sbatch from a job submission node. If you are going to use slurm, you will need to configure matlab for your account following instructions included below.
Install and configure nomachine on your system:
https://s3df.slac.stanford.edu/public/doc/#/reference?id=nomachine
https://confluence.slac.stanford.edu/display/SCSPub/NoMachine
Bring up nomachine, ssh to a submission host and issue:
module load matlab
matlab
ssh -X <batch submission host>
srun --x11 -A <account> -p <partition> -n 1 --pty /bin/bash
module load matlab
matlab
You will need to have access to a slurm account and a partition in order to submit jobs. Bring up matlab,
module load matlab
matlab
Once in the matlab environment run the following commands:
configCluster
c = parcluster
c.AdditionalProperties.AccountName = '<name of account>'
c.AdditionalProperties.QueueName = '<name of partition>'
c.saveProfile
These steps will create a .matlab/3p_cluster_jobs directory under your account with our site¿s configuration and you can submit jobs which will run on the partition and account specified.
You will need access to an account and a partition to submit matlab jobs with slurm, and you will have to configure matlab as described above. Below are two examples of batch submission scripts to run matlab.
#!/bin/sh
#SBATCH --partition=roma
#SBATCH --account=rubin
#SBATCH -n 1 # 1 instance of MATLAB
#SBATCH --cpus-per-task=8 # 8 cores per instance
#SBATCH --mem-per-cpu=4gb # 4 GB RAM per core
#SBATCH --time=00:30:00 # 10 minutes
# Add MATLAB to system path
module load matlab
# Run code
matlab -batch calc_pi
This is the sample calc_pi.m matlab job used in the above submission:
function calc_pi
c = parcluster('local');
% Query for available cores (assume either Slurm or PBS)
sz = str2num([getenv('SLURM_CPUS_PER_TASK') getenv('PBS_NP')]); %#ok<ST2NM>
if isempty(sz), sz = maxNumCompThreads; end
if isempty(gcp('nocreate')), c.parpool(sz); end
spmd
a = (labindex - 1)/numlabs;
b = labindex/numlabs;
fprintf('Subinterval: [%-4g, %-4g]\n', a, b)
myIntegral = integral(@quadpi, a, b);
fprintf('Subinterval: [%-4g, %-4g] Integral: %4g\n', a, b, myIntegral)
piApprox = gplus(myIntegral);
end
approx1 = piApprox{1}; % 1st element holds value on worker 1
fprintf('pi : %.18f\n', pi)
fprintf('Approximation: %.18f\n', approx1)
fprintf('Error : %g\n', abs(pi - approx1))
function y = quadpi(x)
%QUADPI Return data to approximate pi.
% Derivative of 4*atan(x)
y = 4./(1 + x.^2);
The job would look like this in the slurm queue:
[renata@sdfrome001 examples]$ squeue -u renata
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
1155746 roma matlab-s renata R 0:10 1 sdfrome027
and the output should look like:
Starting parallel pool (parpool) using the 'Processes' profile ...
Connected to the parallel pool (number of workers: 8).
Worker 1:
Subinterval: [0 , 0.125]
Subinterval: [0 , 0.125] Integral: 0.49742
Worker 2:
Subinterval: [0.125, 0.25]
Subinterval: [0.125, 0.25] Integral: 0.482495
Worker 3:
Subinterval: [0.25, 0.375]
Subinterval: [0.25, 0.375] Integral: 0.455168
Worker 4:
Subinterval: [0.375, 0.5 ]
Subinterval: [0.375, 0.5 ] Integral: 0.419508
Worker 5:
Subinterval: [0.5 , 0.625]
Subinterval: [0.5 , 0.625] Integral: 0.379807
Worker 6:
Subinterval: [0.625, 0.75]
Subinterval: [0.625, 0.75] Integral: 0.339607
Worker 7:
Subinterval: [0.75, 0.875]
Subinterval: [0.75, 0.875] Integral: 0.301316
Worker 8:
Subinterval: [0.875, 1 ]
Subinterval: [0.875, 1 ] Integral: 0.266273
pi : 3.141592653589793116
Approximation: 3.141592653589792672
Error : 4.44089e-16
In this case you would see 2 slurm jobs, one just to bring up matlab and the other to run the job:
[renata@sdfrome001 examples]$ squeue -u renata
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
1156422 roma Job15 renata R 0:12 1 sdfrome028
1156401 roma matlab-m renata R 0:49 1 sdfrome028
This is the slurm submission job:
#!/bin/sh
#SBATCH --partition=roma
#SBATCH --account=rubin
#SBATCH -n 1 # 1 instance of MATLAB
#SBATCH --cpus-per-task=1 # 1 core per instance
#SBATCH --mem-per-cpu=4gb # 4 GB RAM per core
#SBATCH --time=00:20:00 # 20 minutes
# Add MATLAB to system path
module load matlab
# Run code
matlab -batch calc_pi_multi_node
And this is the matlab job, calc_pi_multi_node.m used in the above script:
function calc_pi_multi_node
c = parcluster;
% Required fields
c.AdditionalProperties.WallTime = '00:20:00';
if isempty(gcp('nocreate')), c.parpool(20); end
spmd
a = (labindex - 1)/numlabs;
b = labindex/numlabs;
fprintf('Subinterval: [%-4g, %-4g]\n', a, b)
myIntegral = integral(@quadpi, a, b);
fprintf('Subinterval: [%-4g, %-4g] Integral: %4g\n', a, b, myIntegral)
piApprox = gplus(myIntegral);
end
approx1 = piApprox{1}; % 1st element holds value on worker 1
fprintf('pi : %.18f\n', pi)
fprintf('Approximation: %.18f\n', approx1)
fprintf('Error : %g\n', abs(pi - approx1))
function y = quadpi(x)
%QUADPI Return data to approximate pi.
% Derivative of 4*atan(x)
y = 4./(1 + x.^2);
The output should look like:
Starting parallel pool (parpool) using the 's3df R2022b' profile ...
additionalSubmitArgs =
'--ntasks=20 --cpus-per-task=1 --ntasks-per-core=1 -A rubin --mem-per-cpu=4gb -p roma -t 00:20:00'
Connected to the parallel pool (number of workers: 20).
Worker 1:
Subinterval: [0 , 0.05]
Subinterval: [0 , 0.05] Integral: 0.199834
Worker 2:
Subinterval: [0.05, 0.1 ]
Subinterval: [0.05, 0.1 ] Integral: 0.198841
Worker 3:
Subinterval: [0.1 , 0.15]
Subinterval: [0.1 , 0.15] Integral: 0.196885
Worker 4:
Subinterval: [0.15, 0.2 ]
Subinterval: [0.15, 0.2 ] Integral: 0.194022
Worker 5:
Subinterval: [0.2 , 0.25]
Subinterval: [0.2 , 0.25] Integral: 0.190332
Worker 6:
Subinterval: [0.25, 0.3 ]
Subinterval: [0.25, 0.3 ] Integral: 0.185913
Worker 7:
Subinterval: [0.3 , 0.35]
Subinterval: [0.3 , 0.35] Integral: 0.180872
Worker 8:
Subinterval: [0.35, 0.4 ]
Subinterval: [0.35, 0.4 ] Integral: 0.175326
Worker 9:
Subinterval: [0.4 , 0.45]
Subinterval: [0.4 , 0.45] Integral: 0.16939
Worker 10:
Subinterval: [0.45, 0.5 ]
Subinterval: [0.45, 0.5 ] Integral: 0.163175
Worker 11:
Subinterval: [0.5 , 0.55]
Subinterval: [0.5 , 0.55] Integral: 0.156782
Worker 12:
Subinterval: [0.55, 0.6 ]
Subinterval: [0.55, 0.6 ] Integral: 0.150305
Worker 13:
Subinterval: [0.6 , 0.65]
Subinterval: [0.6 , 0.65] Integral: 0.143823
Worker 14:
Subinterval: [0.65, 0.7 ]
Subinterval: [0.65, 0.7 ] Integral: 0.137403
Worker 15:
Subinterval: [0.7 , 0.75]
Subinterval: [0.7 , 0.75] Integral: 0.131101
Worker 16:
Subinterval: [0.75, 0.8 ]
Subinterval: [0.75, 0.8 ] Integral: 0.124959
Worker 17:
Subinterval: [0.8 , 0.85]
Subinterval: [0.8 , 0.85] Integral: 0.119012
Worker 18:
Subinterval: [0.85, 0.9 ]
Subinterval: [0.85, 0.9 ] Integral: 0.113284
Worker 19:
Subinterval: [0.9 , 0.95]
Subinterval: [0.9 , 0.95] Integral: 0.107791
Worker 20:
Subinterval: [0.95, 1 ]
Subinterval: [0.95, 1 ] Integral: 0.102542
pi : 3.141592653589793116
Approximation: 3.141592653589793116
Error : 0
You will need to have done the configuration steps shown above and have access to an account and partition in slurm in order to be able to submit jobs from within a matlab session. Following is a log of a session that runs the example batch script "j=batch(c,@pwd,1,{})" from within a matlab interactive session:
[renata@sdfrome001 ~]$ module load matlab
[renata@sdfrome001 ~]$ matlab
MATLAB is selecting SOFTWARE OPENGL rendering.
< M A T L A B (R) >
Copyright 1984-2022 The MathWorks, Inc.
R2022b Update 1 (9.13.0.2080170) 64-bit (glnxa64)
September 28, 2022
To get started, type doc.
For product information, visit www.mathworks.com.
>> c = parcluster
c =
Generic Cluster
Properties:
Profile: s3df R2022b
Modified: false
Host: sdfrome001.sdf.slac.stanford.edu
NumWorkers: 100000
NumThreads: 1
JobStorageLocation: /sdf/home/r/renata/.matlab/3p_cluster_jobs/s3df/R2022b/shared
ClusterMatlabRoot: /sdf/sw/matlab/R2022b
OperatingSystem: unix
RequiresOnlineLicensing: false
PluginScriptsLocation: /sdf/sw/matlab/R2022b/toolbox/local/IntegrationScripts/s3df
AdditionalProperties: List properties
Associated Jobs:
Number Pending: 0
Number Queued: 0
Number Running: 9
Number Finished: 3
>> c.AdditionalProperties
ans =
AdditionalProperties with properties:
AccountName: 'rubin'
AdditionalSubmitArgs: ''
Constraint: ''
EmailAddress: ''
EnableDebug: 0
MemUsage: '4gb'
ProcsPerNode: 0
QueueName: 'roma'
RequireExclusiveNode: 0
Reservation: ''
UseSmpd: 0
WallTime: ''
>>
>>
>> j=batch(c,@pwd,1,{})
additionalSubmitArgs =
'--ntasks=1 --cpus-per-task=1 --ntasks-per-core=1 -A rubin --mem-per-cpu=4gb -p roma'
j =
Job
Properties:
ID: 16
Type: independent
Username: renata
State: queued
SubmitDateTime: 23-Nov-2022 07:25:43
StartDateTime:
RunningDuration: 0 days 0h 0m 0s
NumThreads: 1
AutoAttachFiles: true
Auto Attached Files: {}
AttachedFiles: {}
AutoAddClientPath: true
AdditionalPaths: /sdf/home/r/renata/matlab
FileStore: [1x1 parallel.FileStore]
ValueStore: [1x1 parallel.ValueStore]
EnvironmentVariables: {}
Associated Tasks:
Number Pending: 1
Number Running: 0
Number Finished: 0
Task ID of Errors: []
Task ID of Warnings: []
Task Scheduler IDs: 1184718
>>
>>
>> j.fetchOutputs
ans =
1x1 cell array
{'/sdf/home/r/renata'}
Parallel computing documentation: https://www.mathworks.com/help/parallel-computing/index.html
Parallel computing coding examples: https://www.mathworks.com/products/parallel-computing.html
Parallel computing tutorials: https://www.mathworks.com/videos/series/parallel-and-gpu-computing-tutorials-97719.html
Parallel computing videos: https://www.mathworks.com/videos/search.html?q=&fq[]=product:DM&page=1
Parallel computing webinars: https://www.mathworks.com/videos/search.html?q=&fq[]=product:DM&fq[]=video-external-category:recwebinar&page=1
Non-general purpose software should be installed locally by you under $HOME or project directories. The latter is preferred as your home space is limited and the project space offers better visibility to a larger audience.
It is not recommended to store your conda environments in your $HOME due to 1) quota limits, and 2) an inability to share conda environments across groups. We generally recommend that you install software into your $GROUP space (eg /sdf/group/<group_name>/sw - please see $GROUP storage).
Download the latest version of Miniconda from the conda website and follow the Instructions. Change the installion prefix to point to an appropriate $GROUP directory:
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/Miniconda3-latest-Linux-x86_64.sh
bash /tmp/Miniconda3-latest-Linux-x86_64.sh -p /sdf/group/<group_name>/sw/conda/Replacing <group_name> appropriately.
We can also modify our ~/.condarc file as follows;
channels:
- defaults
- anaconda
- conda-forge
- pytorch
envs_dirs:
- /sdf/group/<group_name/sw/conda/envs
pkgs_dirs:
- /sdf/group/<group_name/sw/conda/pkgs
auto_activate_base: falseConda environments are a nice way of switching between different software versions/packages without multiple conda installs.
There is no unique way to create a conda environment, we illustrate here how to do so from a .yaml file (see the conda documentation for more details).
In order to create an environment called mytest with python=3.6 and numpy and pandas in it, create mytest-environment.yaml:
name: mytest
dependencies:
- python=3.6
- numpy
- pandasThen run the following command: conda env create -f mytest-environment.yaml.
If successful, you should see mytest when listing your environments: conda env list.
You can now activate your environment and use it: conda activate test. To double-check that you have the right packages, you can type conda list once in the environment and check that you see numpy and pandas.
Please acknowledge the role that the use of S3DF resources at SLAC had in your publications. We would also appreciate letting us know when your work is published or presented - this will allow us to highlight your work as well.
If your work uses resources that were made available as a result of explicit contributions from your project to S3DF, you may use the following suggested acknowledgement:
"This work used the resources of the SLAC Shared Science Data Facility (S3DF) at SLAC National Accelerator Laboratory, funded by [Your Funding Source] under contract [your contract number]. S3DF is a shared High-Performance Computing facility that supports the scientific and data-intensive computing needs of all experimental facilities and programs of the SLAC National Accelerator Laboratory. SLAC is operated by Stanford University for the U.S. Department of Energy’s Office of Science."
If your work used generally available resources on S3DF without explicit contributions to S3DF, you may use the following suggested acknowledgement:
"This work used the resources of the SLAC Shared Science Data Facility (S3DF) at SLAC National Accelerator Laboratory. S3DF is a shared High-Performance Computing facility, operated by SLAC, that supports the scientific and data-intensive computing needs of all experimental facilities and programs of the SLAC National Accelerator Laboratory. SLAC is operated by Stanford University for the U.S. Department of Energy’s Office of Science."
We appreciate your cooperation in the acknowledgment and publication notifications. This will help the S3DF in communicating the importance of its role in science to SLAC and our funding agencies and help assure the continued availability of this valuable resource.