global19 / debugpy

An implementation of the Debug Adapter Protocol for Python

debugpy - a debugger for Python

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This debugger implements the Debug Adapter Protocol: debugProtocol.json

debugpy CLI Usage

Debugging a script file

To run a script file with debugging enabled, but without waiting for the client to attach (i.e. code starts executing immediately):

-m debugpy --listen localhost:5678 myfile.py

To wait until the client attaches before running your code, use the --wait-for-client switch.

-m debugpy --listen localhost:5678 --wait-for-client myfile.py

The hostname passed to --listen specifies the interface on which the debug adapter will be listening for connections from DAP clients. It can be omitted, with only the port number specified:

-m debugpy --listen 5678 ...

in which case the default interface is 127.0.0.1.

To be able to attach from another machine, make sure that the adapter is listening on a public interface - using 0.0.0.0 will make it listen on all available interfaces:

-m debugpy --listen 0.0.0.0:5678 myfile.py

This should only be done on secure networks, since anyone who can connect to the specified port can then execute arbitrary code within the debugged process.

To pass arguments to the script, just specify them after the filename. This works the same as with Python itself - everything up to the filename is processed by debugpy, but everything after that becomes sys.argv of the running process.

Debugging a module

To run a module, use the -m switch instead of filename:

-m debugpy --listen localhost:5678 -m mymodule

Same as with scripts, command line arguments can be passed to the module by specifying them after the module name. All other debugpy switches work identically in this mode; in particular, --wait-for-client can be used to block execution until the client attaches.

Attaching to a running process by ID

The following command injects the debugger into a process with a given PID that is running Python code. Once the command returns, a debugpy server is running within the process, as if that process was launched via -m debugpy itself.

-m debugpy --listen localhost:5678 --pid 12345

Ignoring subprocesses

The following command will ignore subprocesses started by the debugged process.

-m debugpy --listen localhost:5678 --pid 12345 --config-subProcess False

debugpy Import usage

Enabling debugging

At the beginning of your script, import debugpy, and call debugpy.listen() to start the debug adapter, passing a (host, port) tuple as the first argument.

import debugpy
debugpy.listen(("localhost", 5678))
...

As with the --listen command line switch, hostname can be omitted, and defaults to "127.0.0.1":

debugpy.listen(5678)
...

Waiting for the client to attach

Use the debugpy.wait_for_client() function to block program execution until the client is attached.

import debugpy
debugpy.listen(5678)
debugpy.wait_for_client()  # blocks execution until client is attached
...

breakpoint() function

In Python 3.7 and above, debugpy supports the standard breakpoint() function. Use debugpy.breakpoint() function for similar behavior and compatibility with older versions of Python. If the debugger is attached when either of these functions is invoked, it will pause execution on the calling line, as if it had a breakpoint set. If there's no client attached, the functions do nothing, and the code continues to execute normally.

import debugpy
debugpy.listen(...)

while True:
    ...
    breakpoint()  # or debugpy.breakpoint() on 3.6 and below
    ...

Debugger logging

To enable debugger internal logging via CLI, the --log-to switch can be used:

-m debugpy --log-to path/to/logs ...

When using the API, the same can be done with debugpy.log_to():

debugpy.log_to('path/to/logs')
debugpy.listen(...)

In both cases, the environment variable DEBUGPY_LOG_DIR can also be set to the same effect.

When logging is enabled, debugpy will create several log files with names matching debugpy*.log in the specified directory, corresponding to different components of the debugger. When subprocess debugging is enabled, separate logs are created for every subprocess.