Setting up PYME cluster hardware and software

This document details the steps to set up a linux cluster for data analysis. In this document we focus on high-thoughput analysis of full data rate (800MB/s) sCMOS data, although the same infrastructure can be used for conventional PALM/STORM experiments, see localization analysis docs for key differences.

Software installation

Setting up the software on the cluster is simple for someone with experience with Linux and Python. We recommend using a dedicated cluster with limited connection to the external network (nodes need internet access through a proxy /NAT for software updates, but should not be directly accessible from outside the cluster). This is both for performance reasons to limit any extraneous network traffic and for security reasons.

Warning

The PYME cluster architecture should be considered insecure and should only be used on a trusted network.

Note

For testing, all the processes that make up the cluster be run on the same computer, and on operating systems other than Linux. Much of the development work was done on OSX. Performance on windows hosts is likely to be poor due to file system limitations (although this can be partially mitigated by packing individual pzf ‘files’ inside an HDF5 container - see “cluster of one” docs).

In short the following steps should be followed:

On each node:

  1. Set up an identical install of Linux (64 bit, we recommend Ubuntu)

  2. Create a user for PYME

  3. Install the python 3.6 version of miniconda

  4. Add the david_baddeley conda channel

  5. conda install python-microscopy

  6. Create /etc/PYME/config.yaml (or ~/.PYME/config.yaml) with the following contents (modified appropriately):

    dataserver-root: "/path/to/directory/to/serve"
dataserver-filter: ""

    Note

    dataserver-root should point to a directory which will be dedicated to cluster data (not home or similar) and which must be writeable by the PYME user. Anything in this directory will be made visible through the cluster file system. This should ideally be on a hard mount (not an auto-mount under /media/) to ensure that permissions don’t get screwed up. Note: It should be sufficient for the directory to be writeable by the user, but if in doubt, a directory owned by the user is arguably safer.

    dataserver-filter lets you specify a filter that will allow multiple distinct clusters to run on the same network. The default value of "" will match all running servers. This is appropriate in the recommended case where the cluster is isolated from the general network behind a dedicated switch. If this is not possible, setting dataserver-filter is recommended (the typical use case here would be a “Cluster of One” on an acquisition computer for standard low throughput analysis).

  7. Optional, but strongly recommended for high-throughput - enable GPU fitting (PYME will allow CPU based fitting in the absence of these steps)

    1. Install CUDA

    2. Install pyme-warp-drive following instructions on [github](https://github.com/python-microscopy/pyme-warp-drive)

    3. Optional, Install pyNVML so GPU usage can be graphically displayed in the clusterUI web interface. A Python 2 package is hosted in the david_baddeley conda channel, and installable with conda install nvidia-ml-py.

On the master/interface node:

The master node runs 3 extra server processes that do not run on standard cluster nodes - a Web UI to the cluster, a task scheduler for distributed compute tasks, and, optionally, a WebDAV server to permit the cluster to be mapped as a drive on windows or OSX. It is also reasonable to use the master node as a gateway/proxy into the cluster, in which case it should have 2 network interfaces. In our installs to date the master node is one of the standard cluster nodes, just running the extra processes but it could also be a standalone machine.

  1. Follow the individual node steps (optionally without configuring the data server if this is not also a storage node)

  2. Checkout the PYME source from [github](github.com/python-microscopy/python-microscopy) to get the clusterUI sources. clusterUI is a Django web app for browsing the cluster.

  3. conda install django=2.1

Running the software

The following steps should be ideally added to init scripts so that the cluster automatically comes back up after a power outage. For testing purposes, they can be executed manually. All these processes should run as an unprivileged user - in no circumstances should they run as root.

On each node:

  1. Run PYMEDataServer to launch the distributed file system server

  2. [optional] run PYMEClusterDup to start the data duplication processes

    Warning

    PYMEClusterDup is not particularly well tested (we ran out of space on our development cluster and disabled duplication). It might not play well with files saved using the __aggregate_ endpoints.

On the master node:

  1. Run PYMERuleServer to launch the process which oversees the task distribution

  2. Change to the clusterUI directory in PYME source distribution and run python manage.py runserver 9000 to run clusterUI using the Django builtin development server.

    Note

    This will launch a webserver on port 9000 (the django default of 8080 is the default port for the dataserver, and so should be avoided). Ideally the clusterUI app should be deployed behind a webserver - e.g. apache - following the Django instructions, although this currently results in unresolved performance problems.

    Tip

    The clusterUI app can be run from any computer with an interface on the cluster subnet, PYME installed (from source), and the same dataserver-filter entry in the config.yaml file (see above).

  3. [optional] Run PYMEWebDav for the WebDAV server to enable the cluster to be mapped as a network drive on windows and mac. The webdav server will bind to port 9090, and has a default username:password combo of test:test.

    Warning

    PYMEWebDav is really buggy, and just barely functional. In order to use it on modern versions of windows you will need to set a registry key enabling support for the (insecure) authentication model it uses (googling windows and WebDAV turns up the relevant instructions pretty quickly). Look at PYME/ParallelTasks/webdav.py for info on setting custom passwords.

  4. [optional] Install the svgwrite package to display recipes graphically in the cluster user interface. We do not currently maintain a conda package for svgwrite, but it can be found in, e.g., the conda-forge channel.

On each node:

  1. Run PYMERuleNodeServer to launch the distributed analysis clients.

    Note

    PYMERuleServer should be running on the master before the node server is launched. TODO - make the nodeserver wait for a ruleserver to become available so that startup scripts are more robust.

Spooling data

On the instrument computer

  1. Make a development install of PYME following the instructions at http://python-microscopy.org/doc/Installation/InstallationFromSource.html#installationfromsource .

  2. Either use the PYMEAcquire acquisition program, or adapt the code in PYME/experimental/dcimgFileChucker.py to interface with your acquisition program.

Troubleshooting

mDNS server advertisements point to loopback, rather than external interface

Example symptom: running PYMEDataServer logs INFO:root:Serving HTTP on 127.0.1.1 port 15348 … rather than an IP address on the cluster network.

PYME binds to the IP address associated with the host computer name. On linux this is association is set in the /etc/hosts file, which often defaults to

This configuration is incomplete, and there are two ways to resolve it:

The right way:

  • Make sure DNS (e.g. dnsmasq) and, optionally DHCP, are configured correctly within the cluster

  • Comment out / delete the 127.0.1.1 <hostname> line in /etc/hosts

The quick and dirty way:

NOTE: this only works if you have assigned static IPs to your nodes

  • Change the 127.0.1.1 <hostname> line to map to your correct static IP

ClusterUI doesn’t show files

  • Assuming that PYMEDataServer is running this is likely to be a permissions error on the data directory. It’s easiest if the PYME user owns the directory in question.

  • Check that the computer running the clusterUI app has an interface on the cluster subnet and an appropriate dataserver-filter entry in its config.yaml file.

getdents: Bad file descriptor

  • We default to using a low-level directory counting function for a speed improvement. We have run

into issues with it on later kernels (Ubuntu 16, 18), which can present as PYMEDataServer failing (and e.g. clusterUI timing out when navigating to <ip:port>/files). The offending function call can be avoided by adding the following to .PYME/config.yaml

Poor clusterIO performance

If you are seeing timeout or retry errors on clusterIO.get_file calls, consider disabling the PYME hybrid nameserver (SQL and zeroconf) and using the PYME zeroconf nameserver only by adding the following to .PYME/config.yaml

If you are performing sliding-window background estimation during localization analysis, you may also want to play with the chunksize used in HTTPSpooler on the instrument computer (or wherever you are spooling data from). It defaults to 50 frames; depending on the window sizes you use in analysis you may consider increasing this to increase data locality (and decrease network I/O). This can be done in .PYME/config.yaml. For 100 frame chunks, you would have:

Footnotes

1

In practice this means an ‘enterprise class’ switch, not the cheapest 10 port switch you can get

2

1GbE is sufficient if there are enough nodes. On new hardware, it might be possible to get enough compute power using fewer nodes and 10 GbE connections should be considered if the number of nodes is < 6. It might also be worth considering 10GbE for the ‘master’ node.