Scripts, utilities, and community for launching Spark on the Janelia Research Center cluster. We maintain a chatroom on gitter if you have any questions.
Check out the teamwork page, where we help coordinate usage among users.
These scripts automate many of the tasks required to start and manage a Spark job.
SSH in to one of Janelia's cluster login nodes (see Scientific Computing for more information).
ssh login2.int.janelia.org
If you have never done so, set up keyless SSH
ssh-keygen -t dsa
Accept all of the default settings by hitting "return" three times. Then continue with
cd .ssh
cat id_dsa.pub >> authorized_keys
Navigate to a location in your home directory where you will store these scripts, then call
git clone https://github.com/freeman-lab/spark-janelia
Add a line with this location to your bash profile (usually located in ~/.bash_profile)
export PATH=/path/to/spark-janelia:$PATH
While doing this, also add this line to point your PATH to a recent version of Python:
export PATH=/usr/local/python-2.7.6/bin/:$PATH
If you want to continue in the same session, source your profile with source ~/.bash_profile.
While logged in to one of Janelia cluster's login nodes, submit a request to run a group of nodes as a Spark cluster using:
spark-janelia launch -n <number_of_nodes>
Each node has 16 cores and 100GB of RAM for Spark to use. You should target the number of nodes you request based on the size of the data you are working with or the amount of neccessary computation (or both). A good rule of thumb is to use enough nodes so the total amount of RAM is about twice the total size of your data set. Check the status of your request using the qstat command. When the status is listed as r (for "ready"), proceed.
Every Spark cluster has a unique node designated as the "driver" from which all computations are initiated. To log in to the driver for your Spark cluster, use:
spark-janelia login
You can now run Spark applications as described below.
To start the Spark interactive shell in Python call
spark-janelia start
This will start a shell with Spark running and its modules available.
To start the Spark interactive shell in Scala call
spark-janelia start-scala
And to submit a Spark application to run call
spark-janelia submit -s <submit_arguments>
Where submit_arguments is a string of arguments you would normally pass to spark-submit, as described in the Spark documentation.
If you want to run an application with unknown runtime it will be helpful to have the Spark cluster shut down automatically after completion of the application.
spark-janelia <args> lsd -s <submit_arguments>will submit your application and delete the cluter job, once it has finished. lsd is short for launch-submit-and-destroy and the meaning of submit_arguments is along the lines of spark-janelia submit. For an example of how to set up a Python script for this workflow, see the example in examples/lsd-example.py.
Many of us at Janelia are using the Thunder library for working with neural data in Spark. The scripts in this repo also make installing and using Thunder easy.
First, to install, from your home directory type
thunder-janelia install
This will clone a copy of Thunder into your home directory. To install to a different location, use
thunder-janelia install -p <path_to_thunder>
To start Spark with Thunder, type
thunder-janelia start
This will open an interactive shell with Thunder functionality preloaded. See its project page for how to use this library in your data workflows and analyses.
If you want to grab the latest version, use
thunder-janelia update
If you have your own version of Thunder (e.g. because you have cloned its repo and are modifying its source code), you can specify a version to run by specifying the location directly
thunder-janelia start -p <path_to_thunder>
When running Spark in Python, the IPython notebook is a fantastic way to run analyses and inspect and visualize results. These scripts make it easy to set up the notebook for use on Janelia's cluster.
First, as a one time operation, call this script
setup-notebook-janelia
This will configure your IPython Notebook settings to be compatible with Janelia's cluster. The only thing you need to do is provide a password when prompted. This password will allow you to make a secure connection to the IPython Notebook server.
Now, when starting either Spark or Thunder, add the -i flag, as in:
spark-janelia start -i
thunder-janelia start -i
Instead of opening a shell, this will launch an IPython Notebook server on the driver. You will see a website addressed printed next to View your notebooks at.... Go to this address in a web browser. Your browser will complain that the connection is not trusted; just ignore and proceed.
When prompted, enter the password you chose above. You should now have a graphical interface to the directory from which you launched the IPython Notebook server. From here you can create a new notebook or edit a previously-existing one.
To shut down the IPython Notebook server, return to the terminal where the server is running and type Ctrl+C+C.
NOTE: You can't run Spark jobs in multiple notebooks at once. If you start one notebook, then start another, nothing will execute in the second one until you shutdown the first one (by clicking the shutdown button in the notebook browser).
When you are finished with all of your jobs, log out of the driver with the simple command exit in the command line. Finally, release your nodes with
spark-janelia destroy
You can check that these nodes have successfully been released with the qstat command.
Many Spark users at Janelia can be found in the Gitter chat rooom for this repository. If you have questions or comments, or just want to join the conversation, please drop in!