Configuring Ray¶

Note

For running Java applications, please see Java Applications.

This page discusses the various way to configure Ray, both from the Python API and from the command line. Take a look at the ray.init documentation for a complete overview of the configurations.

Important

For the multi-node setting, you must first run ray start on the command line to start the Ray cluster services on the machine before ray.init in Python to connect to the cluster services. On a single machine, you can run ray.init() without ray start, which will both start the Ray cluster services and connect to them.

Cluster Resources¶

Ray by default detects available resources.

# This automatically detects available resources in the single machine.
ray.init()

If not running cluster mode, you can specify cluster resources overrides through ray.init as follows.

# If not connecting to an existing cluster, you can specify resources overrides:
ray.init(num_cpus=8, num_gpus=1)

# Specifying custom resources
ray.init(num_gpus=1, resources={'Resource1': 4, 'Resource2': 16})

When starting Ray from the command line, pass the --num-cpus and --num-gpus flags into ray start. You can also specify custom resources.

# To start a head node.
$ ray start --head --num-cpus=<NUM_CPUS> --num-gpus=<NUM_GPUS>

# To start a non-head node.
$ ray start --address=<address> --num-cpus=<NUM_CPUS> --num-gpus=<NUM_GPUS>

# Specifying custom resources
ray start [--head] --num-cpus=<NUM_CPUS> --resources='{"Resource1": 4, "Resource2": 16}'

If using the command line, connect to the Ray cluster as follow:

# Connect to ray. Notice if connected to existing cluster, you don't specify resources.
ray.init(address=<address>)

Note

Ray sets the environment variable OMP_NUM_THREADS=1 by default. This is done to avoid performance degradation with many workers (issue #6998). You can override this by explicitly setting OMP_NUM_THREADS. OMP_NUM_THREADS is commonly used in numpy, PyTorch, and Tensorflow to perform multi-threaded linear algebra. In multi-worker setting, we want one thread per worker instead of many threads per worker to avoid contention. Some other libraries may have their own way to configure parallelism. For example, if you’re using OpenCV, you should manually set the number of threads using cv2.setNumThreads(num_threads) (set to 0 to disable multi-threading).

Logging and Debugging¶

Each Ray session will have a unique name. By default, the name is session_{timestamp}_{pid}. The format of timestamp is %Y-%m-%d_%H-%M-%S_%f (See Python time format for details); the pid belongs to the startup process (the process calling ray.init() or the Ray process executed by a shell in ray start).

For each session, Ray will place all its temporary files under the session directory. A session directory is a subdirectory of the root temporary path (/tmp/ray by default), so the default session directory is /tmp/ray/{ray_session_name}. You can sort by their names to find the latest session.

Change the root temporary directory by passing --temp-dir={your temp path} to ray start.

(There is not currently a stable way to change the root temporary directory when calling ray.init(), but if you need to, you can provide the _temp_dir argument to ray.init().)

You can also use default_worker.py --temp-dir={your temp path} to start a new worker with the given root temporary directory.

Layout of logs:

/tmp
└── ray
    └── session_{datetime}_{pid}
        ├── logs  # for logging
        │   ├── log_monitor.err
        │   ├── log_monitor.out
        │   ├── monitor.err
        │   ├── monitor.out
        │   ├── raylet.err  # outputs of the raylet process
        │   ├── raylet.out
        │   ├── redis-shard_0.err   # outputs of redis shards
        │   ├── redis-shard_0.out
        │   ├── redis.err  # redis
        │   ├── redis.out
        │   ├── webui.err  # ipython notebook web ui
        │   ├── webui.out
        │   ├── worker-{worker_id}.err  # redirected output of workers
        │   ├── worker-{worker_id}.out
        │   └── {other workers}
        └── sockets  # for sockets
            ├── plasma_store
            └── raylet  # this could be deleted by Ray's shutdown cleanup.

Ports configurations¶

Ray requires bi-directional communication among its nodes in a cluster. Each of node is supposed to open specific ports to receive incoming network requests.

All Nodes¶

--node-manager-port: Raylet port for node manager. Default: Random value.
--object-manager-port: Raylet port for object manager. Default: Random value.

The node manager and object manager run as separate processes with their own ports for communication.

The following options specify the range of ports used by worker processes across machines. All ports in the range should be open.

--min-worker-port: Minimum port number worker can be bound to. Default: 10000.
--max-worker-port: Maximum port number worker can be bound to. Default: 10999.

Port numbers are how Ray disambiguates input and output to and from multiple workers on a single node. Each worker will take input and give output on a single port number. Thus, for example, by default, there is a maximum of 1,000 workers on each node, irrespective of number of CPUs.

In general, it is recommended to give Ray a wide range of possible worker ports, in case any of those ports happen to be in use by some other program on your machine. However, when debugging it is useful to explicitly specify a short list of worker ports such as --worker-port-list=10000,10001,10002,10003,10004 (note that this will limit the number of workers, just like specifying a narrow range).

Head Node¶

In addition to ports specified above, the head node needs to open several more ports.

--port: Port of GCS. Default: 6379.
--redis-shard-ports: Comma-separated list of ports for non-primary Redis shards. Default: Random values.
--gcs-server-port: GCS Server port. GCS server is a stateless service that is in charge of communicating with the GCS. Default: Random value.
If --include-dashboard is true (the default), then the head node must open --dashboard-port. Default: 8265.

If --include-dashboard is true but the --dashboard-port is not open on the head node, you will repeatedly get

WARNING worker.py:1114 -- The agent on node <hostname of node that tried to run a task> failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/grpc/aio/_call.py", line 285, in __await__
    raise _create_rpc_error(self._cython_call._initial_metadata,
grpc.aio._call.AioRpcError: <AioRpcError of RPC that terminated with:
  status = StatusCode.UNAVAILABLE
  details = "failed to connect to all addresses"
  debug_error_string = "{"description":"Failed to pick subchannel","file":"src/core/ext/filters/client_channel/client_channel.cc","file_line":4165,"referenced_errors":[{"description":"failed to connect to all addresses","file":"src/core/ext/filters/client_channel/lb_policy/pick_first/pick_first.cc","file_line":397,"grpc_status":14}]}"

(Also, you will not be able to access the dashboard.)

If you see that error, check whether the --dashboard-port is accessible with nc or nmap (or your browser).

$ nmap -sV --reason -p 8265 $HEAD_ADDRESS
Nmap scan report for compute04.berkeley.edu (123.456.78.910)
Host is up, received reset ttl 60 (0.00065s latency).
rDNS record for 123.456.78.910: compute04.berkeley.edu
PORT     STATE SERVICE REASON         VERSION
8265/tcp open  http    syn-ack ttl 60 aiohttp 3.7.2 (Python 3.8)
Service detection performed. Please report any incorrect results at https://nmap.org/submit/ .

Note that the dashboard runs as a separate subprocess which can crash invisibly in the background, so even if you checked port 8265 earlier, the port might be closed now (for the prosaic reason that there is no longer a service running on it). This also means that if that port is unreachable, if you ray stop and ray start, it may become reachable again due to the dashboard restarting.

If you don’t want the dashboard, set --include-dashboard=false.

Redis Port Authentication¶

Ray instances should run on a secure network without public facing ports. The most common threat for Ray instances is unauthorized access to Redis, which can be exploited to gain shell access and run arbitrary code. The best fix is to run Ray instances on a secure, trusted network.

Running Ray on a secured network is not always feasible. To prevent exploits via unauthorized Redis access, Ray provides the option to password-protect Redis ports. While this is not a replacement for running Ray behind a firewall, this feature is useful for instances exposed to the internet where configuring a firewall is not possible. Because Redis is very fast at serving queries, the chosen password should be long.

Note

The Redis passwords provided below may not contain spaces.

Redis authentication is only supported on the raylet code path.

To add authentication via the Python API, start Ray using:

ray.init(_redis_password="password")

To add authentication via the CLI or to connect to an existing Ray instance with password-protected Redis ports:

ray start [--head] --redis-password="password"

While Redis port authentication may protect against external attackers, Ray does not encrypt traffic between nodes so man-in-the-middle attacks are possible for clusters on untrusted networks.

One of most common attack with Redis is port-scanning attack. Attacker scans open port with unprotected redis instance and execute arbitrary code. Ray enables a default password for redis. Even though this does not prevent brute force password cracking, the default password should alleviate most of the port-scanning attack. Furthermore, redis and other ray services are bind to localhost when the ray is started using ray.init.

See the Redis security documentation for more information.

Java Applications¶

Important

For the multi-node setting, you must first run ray start on the command line to start the Ray cluster services on the machine before Ray.init() in Java to connect to the cluster services. On a single machine, you can run Ray.init() without ray start, which will both start the Ray cluster services and connect to them.

Code Search Path¶

If you want to run a Java application in a multi-node cluster, you must specify the code search path in your driver. The code search path is to tell Ray where to load jars when starting Java workers. Your jar files must be distributed to the same path(s) on all nodes of the Ray cluster before running your code.

$ java -classpath <classpath> \
    -Dray.address=<address> \
    -Dray.job.code-search-path=/path/to/jars/ \
    <classname> <args>

The /path/to/jars/ here points to a directory which contains jars. All jars in the directory will be loaded by workers. You can also provide multiple directories for this parameter.

$ java -classpath <classpath> \
    -Dray.address=<address> \
    -Dray.job.code-search-path=/path/to/jars1:/path/to/jars2:/path/to/pys1:/path/to/pys2 \
    <classname> <args>

You don’t need to configure code search path if you run a Java application in a single-node cluster.

See ray.job.code-search-path under Driver Options for more information.

Note

Currently we don’t provide a way to configure Ray when running a Java application in single machine mode. If you need to configure Ray, run ray start to start the Ray cluster first.

Driver Options¶

There is a limited set of options for Java drivers. They are not for configuring the Ray cluster, but only for configuring the driver.

Ray uses Typesafe Config to read options. There are several ways to set options:

System properties. You can configure system properties either by adding options in the format of -Dkey=value in the driver command line, or by invoking System.setProperty("key", "value"); before Ray.init().
A HOCON format configuration file. By default, Ray will try to read the file named ray.conf in the root of the classpath. You can customize the location of the file by setting system property ray.config-file to the path of the file.

Note

Options configured by system properties have higher priority than options configured in the configuration file.

The list of available driver options:

ray.address
- The cluster address if the driver connects to an existing Ray cluster. If it is empty, a new Ray cluster will be created.
- Type: String
- Default: empty string.
ray.local-mode
- If it’s set to true, the driver will run in Local mode.
- Type: Boolean
- Default: false
ray.job.code-search-path
- The paths for Java workers to load code from. Currently only directories are supported. You can specify one or more directories split by a :. You don’t need to configure code search path if you run a Java application in single machine mode or local mode. Code search path is also used for loading Python code if it’s specified. This is required for Cross-language programming. If code search path is specified, you can only run Python remote functions which can be found in the code search path.
- Type: String
- Default: empty string.
- Example: /path/to/jars1:/path/to/jars2:/path/to/pys1:/path/to/pys2

Best Practices: Ray with PyTorch Ray Dashboard

Ray v2.0.0.dev0

Configuring Ray¶

Cluster Resources¶

Logging and Debugging¶

Ports configurations¶

All Nodes¶

Head Node¶

Redis Port Authentication¶

Java Applications¶

Code Search Path¶

Driver Options¶