Ray Debugger¶
Ray has a built in debugger that allows you to debug your distributed applications. It allows to set breakpoints in your Ray tasks and actors and when hitting the breakpoint you can drop into a PDB session that you can then use to:
Inspect variables in that context
Step within that task or actor
Move up or down the stack
Note
It is currently an experimental feature and under active development. Interfaces are subject to change.
Getting Started¶
Take the following example:
import ray
ray.init()
@ray.remote
def f(x):
ray.util.pdb.set_trace()
return x * x
futures = [f.remote(i) for i in range(2)]
print(ray.get(futures))
Put the program into a file named debugging.py and execute it using:
python debugging.py
Each of the 4 executed tasks will drop into a breakpoint when the line
ray.util.pdb.set_trace() is executed. You can attach to the debugger by running
the following command on the head node of the cluster:
ray debug
The ray debug command will print an output like this:
2020-11-04 15:35:50,011 INFO worker.py:672 -- Connecting to existing Ray cluster at address: 192.168.1.105:6379
Active breakpoints:
0: ray::f() | debugging.py:6
1: ray::f() | debugging.py:6
Enter breakpoint index or press enter to refresh:
You can now enter 0 and hit Enter to jump to the first breakpoint. You will be dropped into PDB
at the break point and can use the help to see the available actions. Run bt to see a backtrace
of the execution:
(Pdb) bt
/Users/pcmoritz/ray/python/ray/workers/default_worker.py(170)<module>()
-> ray.worker.global_worker.main_loop()
/Users/pcmoritz/ray/python/ray/worker.py(385)main_loop()
-> self.core_worker.run_task_loop()
> /Users/pcmoritz/tmp/debugging.py(7)f()
-> return x * x
You can inspect the value of x with print(x). You can see the current source code with ll
and change stack frames with up and down. For now let us continue the execution with c.
After the execution is continued, hit Control + D to get back to the list of break points. Select
the other break point and hit c again to continue the execution.
The Ray program debugging.py now finished and should have printed [0, 1]. Congratulations, you
have finished your first Ray debugging session!
Debugger Commands¶
The Ray debugger supports the same commands as PDB.
Stepping between Ray tasks¶
You can use the debugger to step between Ray tasks. Let’s take the following recursive function as an example:
import ray
ray.init()
@ray.remote
def fact(n):
if n == 1:
return n
else:
n_id = fact.remote(n - 1)
return n * ray.get(n_id)
ray.util.pdb.set_trace()
result_ref = fact.remote(5)
result = ray.get(result_ref)
After running the program by executing the Python file and calling
ray debug, you can select the breakpoint by pressing 0 and
enter. This will result in the following output:
Enter breakpoint index or press enter to refresh: 0
> /Users/pcmoritz/tmp/stepping.py(14)<module>()
-> result_ref = fact.remote(5)
(Pdb)
You can jump into the call with the remote command in Ray’s debugger.
Inside the function, print the value of n with p(n), resulting in
the following output:
-> result_ref = fact.remote(5)
(Pdb) remote
*** Connection closed by remote host ***
Continuing pdb session in different process...
--Call--
> /Users/pcmoritz/tmp/stepping.py(5)fact()
-> @ray.remote
(Pdb) ll
5 -> @ray.remote
6 def fact(n):
7 if n == 1:
8 return n
9 else:
10 n_id = fact.remote(n - 1)
11 return n * ray.get(n_id)
(Pdb) p(n)
5
(Pdb)
Now step into the next remote call again with
remote and print n. You an now either continue recursing into
the function by calling remote a few more times, or you can jump
to the location where ray.get is called on the result by using the
get debugger comand. Use get again to jump back to the original
call site and use p(result) to print the result:
Enter breakpoint index or press enter to refresh: 0
> /Users/pcmoritz/tmp/stepping.py(14)<module>()
-> result_ref = fact.remote(5)
(Pdb) remote
*** Connection closed by remote host ***
Continuing pdb session in different process...
--Call--
> /Users/pcmoritz/tmp/stepping.py(5)fact()
-> @ray.remote
(Pdb) p(n)
5
(Pdb) remote
*** Connection closed by remote host ***
Continuing pdb session in different process...
--Call--
> /Users/pcmoritz/tmp/stepping.py(5)fact()
-> @ray.remote
(Pdb) p(n)
4
(Pdb) get
*** Connection closed by remote host ***
Continuing pdb session in different process...
--Return--
> /Users/pcmoritz/tmp/stepping.py(5)fact()->120
-> @ray.remote
(Pdb) get
*** Connection closed by remote host ***
Continuing pdb session in different process...
--Return--
> /Users/pcmoritz/tmp/stepping.py(14)<module>()->None
-> result_ref = fact.remote(5)
(Pdb) p(result)
120
(Pdb)
Post Mortem Debugging¶
Often we do not know in advance where an error happens, so we cannot set a breakpoint. In these cases, we can automatically drop into the debugger when an error occurs or an exception is thrown. This is called post-mortem debugging.
We will show how this works using a Ray serve application. Copy the following code into a file called
serve_debugging.py:
import time
import ray
from ray import serve
from sklearn.datasets import load_iris
from sklearn.ensemble import GradientBoostingClassifier
# Train model
iris_dataset = load_iris()
model = GradientBoostingClassifier()
model.fit(iris_dataset["data"], iris_dataset["target"])
# Define Ray Serve model,
class BoostingModel:
def __init__(self):
self.model = model
self.label_list = iris_dataset["target_names"].tolist()
def __call__(self, flask_request):
payload = flask_request.json["vector"]
print("Worker: received flask request with data", payload)
prediction = self.model.predict([payload])[0]
human_name = self.label_list[prediction]
return {"result": human_name}
# Deploy model
client = serve.start()
client.create_backend("iris:v1", BoostingModel)
client.create_endpoint("iris_classifier", backend="iris:v1", route="/iris")
time.sleep(3600.0)
Let’s start the program with the post-mortem debugging activated (RAY_PDB=1):
RAY_PDB=1 python serve_debugging.py
The flag RAY_PDB=1 will have the effect that if an exception happens, Ray will
drop into the debugger instead of propagating it further. Let’s see how this works!
First query the model with an invalid request using
python -c 'import requests; response = requests.get("http://localhost:8000/iris", json={"vector": [1.2, 1.0, 1.1, "a"]})'
When the serve_debugging.py driver hits the breakpoint, it will tell you to run
ray debug. After we do that, we see an output like the following:
Active breakpoints:
0: ray::RayServeWorker_BoostingModel.handle_request() | /Users/pcmoritz/ray/python/ray/serve/backend_worker.py:249
Traceback (most recent call last):
File "/Users/pcmoritz/ray/python/ray/serve/backend_worker.py", line 244, in invoke_single
result = await method_to_call(arg)
File "/Users/pcmoritz/ray/python/ray/async_compat.py", line 29, in wrapper
return func(*args, **kwargs)
File "serve_debugging.py", line 23, in __call__
prediction = self.model.predict([payload])[0]
File "/Users/pcmoritz/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/_gb.py", line 2165, in predict
raw_predictions = self.decision_function(X)
File "/Users/pcmoritz/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/_gb.py", line 2120, in decision_function
X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr')
File "/Users/pcmoritz/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 531, in check_array
array = np.asarray(array, order=order, dtype=dtype)
File "/Users/pcmoritz/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py", line 83, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: 'a'
Enter breakpoint index or press enter to refresh:
We now press 0 and then Enter to enter the debugger. With ll we can see the context and with
print(a) we an print the array that causes the problem. As we see, it contains a string ('a')
instead of a number as the last element.
In a similar manner as above, you can also debug Ray actors. Happy debugging!
Debugging APIs¶
See Debugging APIs.