.. _ray-debugger: 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 deprecated. Use the :doc:`Ray Distributed Debugger <../../ray-distributed-debugger>` instead. 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: .. testcode:: :skipif: True 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: .. code-block:: bash 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: .. code-block:: bash ray debug The ``ray debug`` command will print an output like this: .. code-block:: text 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: .. code-block:: text (Pdb) bt /home/ubuntu/ray/python/ray/workers/default_worker.py(170)() -> 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: .. testcode:: :skipif: True 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: .. code-block:: shell Enter breakpoint index or press enter to refresh: 0 > /home/ubuntu/tmp/stepping.py(16)() -> 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: .. code-block:: shell -> 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: .. code-block:: shell Enter breakpoint index or press enter to refresh: 0 > /home/ubuntu/tmp/stepping.py(14)() -> 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)()->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: .. code-block:: bash pip install "ray[serve]" scikit-learn Next, copy the following code into a file called ``serve_debugging.py``: .. testcode:: :skipif: True 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``): .. code-block:: bash 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 .. code-block:: bash 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: .. code-block:: text 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 :ref:`package-ref-debugging-apis`.