Using the 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

Warning

The Ray Debugger is an experimental feature and is currently unstable. Interfaces are subject to change.

Getting Started

Note

On Python 3.6, the breakpoint() function is not supported and you need to use ray.util.pdb.set_trace() instead.

Take the following example:

import ray

@ray.remote
def f(x):
    breakpoint()
    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 2 executed tasks will drop into a breakpoint when the line breakpoint() 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:

2021-07-13 16:30:40,112     INFO scripts.py:216 -- Connecting to Ray instance at 192.168.2.61:6379.
2021-07-13 16:30:40,112     INFO worker.py:740 -- Connecting to existing Ray cluster at address: 192.168.2.61:6379
Active breakpoints:
index | timestamp           | Ray task | filename:lineno
0     | 2021-07-13 23:30:37 | ray::f() | debugging.py:6
1     | 2021-07-13 23:30:37 | 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
  /home/ubuntu/ray/python/ray/workers/default_worker.py(170)<module>()
-> ray.worker.global_worker.main_loop()
  /home/ubuntu/ray/python/ray/worker.py(385)main_loop()
-> self.core_worker.run_task_loop()
> /home/ubuntu/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!

Running on a Cluster

The Ray debugger supports setting breakpoints inside of tasks and actors that are running across your Ray cluster. In order to attach to these from the head node of the cluster using ray debug, you’ll need to make sure to pass in the --ray-debugger-external flag to ray start when starting the cluster (likely in your cluster.yaml file or k8s Ray cluster spec).

Note that this flag will cause the workers to listen for PDB commands on an external-facing IP address, so this should only be used if your cluster is behind a firewall.

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.remote
def fact(n):
    if n == 1:
        return n
    else:
        n_ref = fact.remote(n - 1)
        return n * ray.get(n_ref)

@ray.remote
def compute():
    breakpoint()
    result_ref = fact.remote(5)
    result = ray.get(result_ref)

ray.get(compute.remote())

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
> /home/ubuntu/tmp/stepping.py(16)<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--
> /home/ubuntu/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_ref = fact.remote(n - 1)
 11                 return n * ray.get(n_ref)
(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
> /home/ubuntu/tmp/stepping.py(14)<module>()
-> result_ref = fact.remote(5)
(Pdb) remote
*** Connection closed by remote host ***
Continuing pdb session in different process...
--Call--
> /home/ubuntu/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--
> /home/ubuntu/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--
> /home/ubuntu/tmp/stepping.py(5)fact()->120
-> @ray.remote
(Pdb) get
*** Connection closed by remote host ***
Continuing pdb session in different process...
--Return--
> /home/ubuntu/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. To get started, install the required dependencies:

pip install "ray[serve]" scikit-learn

Next, copy the following code into a file called serve_debugging.py:

import time

from sklearn.datasets import load_iris
from sklearn.ensemble import GradientBoostingClassifier

import ray
from ray import serve

serve.start()

# Train model
iris_dataset = load_iris()
model = GradientBoostingClassifier()
model.fit(iris_dataset["data"], iris_dataset["target"])

# Define Ray Serve model,
@serve.deployment
class BoostingModel:
    def __init__(self):
        self.model = model
        self.label_list = iris_dataset["target_names"].tolist()

    async def __call__(self, starlette_request):
        payload = (await starlette_request.json())["vector"]
        print(f"Worker: received request with data: {payload}")

        prediction = self.model.predict([payload])[0]
        human_name = self.label_list[prediction]
        return {"result": human_name}

# Deploy model
serve.run(BoostingModel.bind(), route_prefix="/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:
index | timestamp           | Ray task                                     | filename:lineno
0     | 2021-07-13 23:49:14 | ray::RayServeWrappedReplica.handle_request() | /home/ubuntu/ray/python/ray/serve/backend_worker.py:249
Traceback (most recent call last):

  File "/home/ubuntu/ray/python/ray/serve/backend_worker.py", line 242, in invoke_single
    result = await method_to_call(*args, **kwargs)

  File "serve_debugging.py", line 24, in __call__
    prediction = self.model.predict([payload])[0]

  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/_gb.py", line 1188, in predict
    raw_predictions = self.decision_function(X)

  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/_gb.py", line 1143, in decision_function
    X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr')

  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 63, in inner_f
    return f(*args, **kwargs)

  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 673, in check_array
    array = np.asarray(array, order=order, dtype=dtype)

  File "/home/ubuntu/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.