![digikam face recognition tutorial digikam face recognition tutorial](https://www.lffl.org/wp-content/uploads/2020/07/digikam-ronaldo.png)
To run that same test over Infiniband I just modified the export in the batch script to force it to use IB (and thus fail if it couldn’t talk between the two nodes): #!/bin/bash Giving these latency results: MPI]$ cat slurm-1734137.out Srun shifter python /home/tutorial/mpi4py_benchmarks/osu_latency.py #SBATCH -image=chrissamuel/docker.openmpi:latest Running the test over TCP/IP is simply a matter of submitting this batch script which forces it onto 2 separate nodes: #!/bin/bash Fortunately the author of the dispel4py/docker.openmpi has their implementation published on Github and so I forked their repo, signed up for Docker and pushed a version which simply adds the libmlx4-1 package I needed.
#Digikam face recognition tutorial install#
Running this over TCP/IP is trivial with the dispel4py/docker.openmpi container, but of course it’s lacking the Mellanox library I need and as the whole point of Shifter is security I can’t get root access inside the container to install the package. This also begs the question then – what does this do for latency? The image contains a Python version of the OSU latency testing program which uses different message sizes between 2 MPI ranks to provide a histogram of performance. Open-MPI allows you to specify what transports to use, so adding one line to my batch script: export OMPI_MCA_btl=tcp,self,smĬleans up the output a lot: Ubuntu 14.04.4 LTS \n \l The problem is that this container doesn’t include the Mellanox user-space library needed to make use of the IB cards and so you get warnings that they aren’t working and that it will fall back to a different mechanism (in this case TCP/IP over gigabit Ethernet). This is because Shifter is (as designed) exposing the systems /sys directory to the container. It successfully demonstrates that it is using an Ubuntu container on 3 nodes, but the warnings are triggered because Open-MPI in Ubuntu is built with Infiniband support and it is detecting the presence of the IB cards on the host nodes.
![digikam face recognition tutorial digikam face recognition tutorial](https://www.huawei.com/minisite/ascend/images/en/a_10.png)
Hello, World! I am process 2 of 3 on bruce003. ,2]: A high-performance Open MPI point-to-point messaging module
![digikam face recognition tutorial digikam face recognition tutorial](http://analyticsinsight.b-cdn.net/wp-content/uploads/2020/11/Facial-Recognition-1.jpg)
Hello, World! I am process 1 of 3 on bruce002. ,1]: A high-performance Open MPI point-to-point messaging module Hello, World! I am process 0 of 3 on bruce001. Was unable to find any relevant network interfaces:Īnother transport will be used instead, although this may result in ,0]: A high-performance Open MPI point-to-point messaging module
#Digikam face recognition tutorial driver#
Libibverbs: Warning: no userspace device-specific driver found for /sys/class/infiniband_verbs/uverbs0 Libibverbs: Warning: couldn't open config directory '/etc/libibverbs.d'. But the output bemused me a little: Python]$ cat slurm-1734135.out This prints the MPI rank to demonstrate that the MPI wire up was successful and I forced it to run the tasks on separate nodes and print the hostnames to show it’s communicating over a network, not via shared memory on the same node. Srun shifter python /home/tutorial/mpi4py_benchmarks/helloworld.py I grabbed the dispel4py/docker.openmpi Docker container with shifterimg pull dispel4py/docker.openmpi and tried its Python version of the MPI hello world program: #!/bin/bash You can even run MPI applications this way successfully. That’s great for single CPU jobs, but what about parallel applications? Well turns out that’s easy too – you just request the configuration you need and slap srun in front of the shifter command.
![digikam face recognition tutorial digikam face recognition tutorial](https://www.linuxhowto.net/wp-content/uploads/2020/09/ShowFoto.png)
If you need to run something in a different image you just pass the -image option to shifter and then it will need to set up & tear down that container, but the one you specified for your batch job is still there. The advantage of using the plugin and this way of specifying the images is that the plugin will prep the container for us at the start of the batch job and keep it around until it ends so you can keep running commands in your script inside the container without the overhead of having to create/destroy it each time. Results in the following on our RHEL compute nodes: Shifter]$ cat slurm-1734069.out One that is done you can add the shifter programs arguments to your Slurm batch script and then just call shifter inside it to run a process, for instance: #!/bin/bash My test config was just: required /usr/lib64/shifter/shifter_slurm.so shifter_config=/etc/shifter/nfĪs I was installing by building RPMs (out preferred method is to install the plugin into our shared filesystem for the cluster so we don’t need to have it in the RAM disk of our diskless nodes). Getting Shifter to work in Slurm is pretty easy, it includes a plugin that you must install and tell Slurm about. This is continuing on from my previous blog about NERSC’s Shifter which lets you safely use Docker containers in an HPC environment.