Development Tips


To speed up compilation, be sure to install Ray with

cd ray/python
pip install -e . --verbose

The -e means “editable”, so changes you make to files in the Ray directory will take effect without reinstalling the package. In contrast, if you do python install, files will be copied from the Ray directory to a directory of Python packages (often something like /home/ubuntu/anaconda3/lib/python3.6/site-packages/ray). This means that changes you make to files in the Ray directory will not have any effect.

If you run into Permission Denied errors when running pip install, you can try adding --user. You may also need to run something like sudo chown -R $USER /home/ubuntu/anaconda3 (substituting in the appropriate path).

If you make changes to the C++ files, you will need to recompile them. However, you do not need to rerun pip install -e .. Instead, you can recompile much more quickly by doing

cd ray/build
make -j8


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:


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 ray.experimental.state.GlobalState Python 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:

# Returns current information about the nodes in the cluster, such as:
# [{'ClientID': '2a9d2b34ad24a37ed54e4fcd32bf19f915742f5b',
#   'IsInsertion': True,
#   'NodeManagerAddress': '',
#   '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

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

import redis
r = redis.StrictRedis(host='', 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:

ray start

This will print any RAY_LOG(DEBUG) lines in the source code to the raylet.err file, which you can find in the Temporary Files.

Testing locally

Suppose that one of the tests (e.g., is failing. You can run that test locally by running python -m pytest -v python/ray/tests/ However, doing so will run all of the tests which can take a while. To run a specific test that is failing, you can do

cd ray
python -m pytest -v python/ray/tests/

When running tests, usually only the first test failure matters. A single test failure often triggers the failure of subsequent tests in the same script.


Running linter locally: To run the Python linter on a specific file, run
something like flake8 ray/python/ray/ You may need to first run pip install flake8.
Autoformatting code. We use yapf for
linting, and the config file is located at .style.yapf. We recommend running scripts/ prior to pushing to format changed files. Note that some projects such as dataframes and rllib are currently excluded.