Debugging yt

There are several different convenience functions that allow you to control yt in perhaps unexpected and unorthodox manners. These will allow you to conduct in-depth debugging of processes that may be running in parallel on multiple processors, as well as providing a mechanism of signalling to yt that you need more information about a running process. Additionally, yt has a built-in mechanism for optional reporting of errors to a central server. All of these allow for more rapid development and debugging of any problems you might encounter.

Additionally, yt is able to leverage existing developments in the IPython community for parallel, interactive analysis. This allows you to initialize multiple yt processes through mpirun and interact with all of them from a single, unified interactive prompt. This enables and facilitates parallel analysis without sacrificing interactivity and flexibility.


A pastebin is a website where you can easily copy source code and error messages to share with yt developers or your collaborators. At a pastebin is available for placing scripts. With yt the script is distributed and wrapped with the pastebin and pastebin_grab commands, which allow for commandline uploading and downloading of pasted snippets. To upload a script you would supply it to the command:

$ yt pastebin

The URL will be returned. If you’d like it to be marked ‘private’ and not show up in the list of pasted snippets, supply the argument --private. All snippets are given either numbers or hashes. To download a pasted snippet, you would use the pastebin_grab option:

$ yt pastebin_grab 1768

The snippet will be output to the window, so output redirection can be used to store it in a file.

Use the Python Debugger

yt is almost entirely composed of python code, so it makes sense to use the python debugger as your first stop in trying to debug it.

Signaling yt to Do Something

During startup, yt inserts handlers for two operating system-level signals. These provide two diagnostic methods for interacting with a running process. Signalling the python process that is running your script with these signals will induce the requested behavior.

This will cause the python code to print a stack trace, showing exactly where in the function stack it is currently executing.
This will cause the python code to insert an IPython session wherever it currently is, with all local variables in the local namespace. It should allow you to change the state variables.

If your yt-running process has PID 5829, you can signal it to print a traceback with:

$ kill -SIGUSR1 5829

Note, however, that if the code is currently inside a C function, the signal will not be handled, and the stacktrace will not be printed, until it returns from that function.

Remote and Disconnected Debugging

If you are running a parallel job that fails, often it can be difficult to do a post-mortem analysis to determine what went wrong. To facilitate this, yt has implemented an XML-RPC interface to the Python debugger (pdb) event loop.

Running with the --rpdb command will cause any uncaught exception during execution to spawn this interface, which will sit and wait for commands, exposing the full Python debugger. Additionally, a frontend to this is provided through the yt command. So if you run the command:

$ mpirun -np 4 python2.7 --parallel --rpdb

and it reaches an error or an exception, it will launch the debugger. Additionally, instructions will be printed for connecting to the debugger. Each of the four processes will be accessible via:

$ yt rpdb 0

where 0 here indicates the process 0.

For security reasons, this will only work on local processes; to connect on a cluster, you will have to execute the command yt rpdb on the node on which that process was launched.