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


It is currently an experimental feature and under active development. Interfaces are subject to change.

Getting Started

Take the following example:

import ray

def f(x):
    return x * x

futures = [f.remote(i) for i in range(2)]

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:
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
-> ray.worker.global_worker.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.

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")


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 Debugger APIs.