Contents

Debugging (internal)

Starting processes in a debugger

When processes are crashing, it is often useful to start them in a debugger. Ray currently allows processes to be started in the following:

  • valgrind

  • the valgrind profiler

  • the perftools profiler

  • gdb

  • tmux

To use any of these tools, please make sure that you have them installed on your machine first (gdb and valgrind on MacOS are known to have issues). Then, you can launch a subset of ray processes by adding the environment variable RAY_{PROCESS_NAME}_{DEBUGGER}=1. For instance, if you wanted to start the raylet in valgrind, then you simply need to set the environment variable RAY_RAYLET_VALGRIND=1.

To start a process inside of gdb, the process must also be started inside of tmux. So if you want to start the raylet in gdb, you would start your Python script with the following:

RAY_RAYLET_GDB=1 RAY_RAYLET_TMUX=1 python

You can then list the tmux sessions with tmux ls and attach to the appropriate one.

You can also get a core dump of the raylet process, which is especially useful when filing issues. The process to obtain a core dump is OS-specific, but usually involves running ulimit -c unlimited before starting Ray to allow core dump files to be written.

Inspecting Redis shards

To inspect Redis, you can use the global state API. The easiest way to do this is to start or connect to a Ray cluster with ray.init(), then query the API like so:

ray.init()
ray.nodes()
# Returns current information about the nodes in the cluster, such as:
# [{'ClientID': '2a9d2b34ad24a37ed54e4fcd32bf19f915742f5b',
#   'IsInsertion': True,
#   'NodeManagerAddress': '1.2.3.4',
#   'NodeManagerPort': 43280,
#   'ObjectManagerPort': 38062,
#   'ObjectStoreSocketName': '/tmp/ray/session_2019-01-21_16-28-05_4216/sockets/plasma_store',
#   'RayletSocketName': '/tmp/ray/session_2019-01-21_16-28-05_4216/sockets/raylet',
#   'Resources': {'CPU': 8.0, 'GPU': 1.0}}]

To inspect the primary Redis shard manually, you can also query with commands like the following.

r_primary = ray.worker.global_worker.redis_client
r_primary.keys("*")

To inspect other Redis shards, you will need to create a new Redis client. For example (assuming the relevant IP address is 127.0.0.1 and the relevant port is 1234), you can do this as follows.

import redis
r = redis.StrictRedis(host='127.0.0.1', port=1234)

You can find a list of the relevant IP addresses and ports by running

r_primary.lrange('RedisShards', 0, -1)

Backend logging

The raylet process logs detailed information about events like task execution and object transfers between nodes. To set the logging level at runtime, you can set the RAY_BACKEND_LOG_LEVEL environment variable before starting Ray. For example, you can do:

export RAY_BACKEND_LOG_LEVEL=debug
ray start

This will print any RAY_LOG(DEBUG) lines in the source code to the raylet.err file, which you can find in Logging and Debugging. If it worked, you should see as the first line in raylet.err:

logging.cc:270: Set ray log level from environment variable RAY_BACKEND_LOG_LEVEL to -1

(-1 is defined as RayLogLevel::DEBUG in logging.h.)

#include <atomic>
#include <functional>

Backend event stats

The raylet process also periodically dumps event stats to the debug_state.txt log file if the RAY_event_stats=1 environment variable is set. To also enable regular printing of the stats to log files, you can additional set RAY_event_stats_print_interval_ms=1000.

Event stats include ASIO event handlers, periodic timers, and RPC handlers. Here is a sample of what the event stats look like:

Event stats:
Global stats: 739128 total (27 active)
Queueing time: mean = 47.402 ms, max = 1372.219 s, min = -0.000 s, total = 35035.892 s
Execution time:  mean = 36.943 us, total = 27.306 s
Handler stats:
  ClientConnection.async_read.ReadBufferAsync - 241173 total (19 active), CPU time: mean = 9.999 us, total = 2.411 s
  ObjectManager.ObjectAdded - 61215 total (0 active), CPU time: mean = 43.953 us, total = 2.691 s
  CoreWorkerService.grpc_client.AddObjectLocationOwner - 61204 total (0 active), CPU time: mean = 3.860 us, total = 236.231 ms
  CoreWorkerService.grpc_client.GetObjectLocationsOwner - 51333 total (0 active), CPU time: mean = 25.166 us, total = 1.292 s
  ObjectManager.ObjectDeleted - 43188 total (0 active), CPU time: mean = 26.017 us, total = 1.124 s
  CoreWorkerService.grpc_client.RemoveObjectLocationOwner - 43177 total (0 active), CPU time: mean = 2.368 us, total = 102.252 ms
  NodeManagerService.grpc_server.PinObjectIDs - 40000 total (0 active), CPU time: mean = 194.860 us, total = 7.794 s
Ray Whitepapers Profiling (internal)