Environment Dependencies#

Your Ray application may have dependencies that exist outside of your Ray script. For example:

  • Your Ray script may import/depend on some Python packages.

  • Your Ray script may be looking for some specific environment variables to be available.

  • Your Ray script may import some files outside of the script.

One frequent problem when running on a cluster is that Ray expects these “dependencies” to exist on each Ray node. If these are not present, you may run into issues such as ModuleNotFoundError, FileNotFoundError and so on.

To address this problem, you can (1) prepare your dependencies on the cluster in advance (e.g. using a container image) using the Ray Cluster Launcher, or (2) use Ray’s runtime environments to install them on the fly.

For production usage or non-changing environments, we recommend installing your dependencies into a container image and specifying the image using the Cluster Launcher. For dynamic environments (e.g. for development and experimentation), we recommend using runtime environments.

Concepts#

  • Ray Application. A program including a Ray script that calls ray.init() and uses Ray tasks or actors.

  • Dependencies, or Environment. Anything outside of the Ray script that your application needs to run, including files, packages, and environment variables.

  • Files. Code files, data files or other files that your Ray application needs to run.

  • Packages. External libraries or executables required by your Ray application, often installed via pip or conda.

  • Local machine and Cluster. Usually, you may want to separate the Ray cluster compute machines/pods from the machine/pod that handles and submits the application. You can submit a Ray Job via the Ray Job Submission mechanism, or use ray attach to connect to a cluster interactively. We call the machine submitting the job your local machine.

  • Job. A Ray job is a single application: it is the collection of Ray tasks, objects, and actors that originate from the same script.

Preparing an environment using the Ray Cluster launcher#

The first way to set up dependencies is to prepare a single environment across the cluster before starting the Ray runtime.

  • You can build all your files and dependencies into a container image and specify this in your your Cluster YAML Configuration.

  • You can also install packages using setup_commands in the Ray Cluster configuration file (reference); these commands will be run as each node joins the cluster. Note that for production settings, it is recommended to build any necessary packages into a container image instead.

  • You can push local files to the cluster using ray rsync_up (reference).

Runtime environments#

Note

This feature requires a full installation of Ray using pip install "ray[default]". This feature is available starting with Ray 1.4.0 and is currently supported on macOS and Linux, with beta support on Windows.

The second way to set up dependencies is to install them dynamically while Ray is running.

A runtime environment describes the dependencies your Ray application needs to run, including files, packages, environment variables, and more. It is installed dynamically on the cluster at runtime and cached for future use (see Caching and Garbage Collection for details about the lifecycle).

Runtime environments can be used on top of the prepared environment from the Ray Cluster launcher if it was used. For example, you can use the Cluster launcher to install a base set of packages, and then use runtime environments to install additional packages. In contrast with the base cluster environment, a runtime environment will only be active for Ray processes. (For example, if using a runtime environment specifying a pip package my_pkg, the statement import my_pkg will fail if called outside of a Ray task, actor, or job.)

Runtime environments also allow you to set dependencies per-task, per-actor, and per-job on a long-running Ray cluster.

import ray

runtime_env = {"pip": ["emoji"]}

ray.init(runtime_env=runtime_env)

@ray.remote
def f():
  import emoji
  return emoji.emojize('Python is :thumbs_up:')

print(ray.get(f.remote()))

Python is 👍

A runtime environment can be described by a Python dict:

runtime_env = {
    "pip": ["emoji"],
    "env_vars": {"TF_WARNINGS": "none"}
}

Alternatively, you can use ray.runtime_env.RuntimeEnv:

from ray.runtime_env import RuntimeEnv
runtime_env = RuntimeEnv(
    pip=["emoji"],
    env_vars={"TF_WARNINGS": "none"}
)

For more examples, jump to the API Reference.

There are two primary scopes for which you can specify a runtime environment:

Specifying a Runtime Environment Per-Job#

You can specify a runtime environment for your whole job, whether running a script directly on the cluster, using the Ray Jobs API, or submitting a KubeRay RayJob:

# Option 1: Starting a single-node local Ray cluster or connecting to existing local cluster
ray.init(runtime_env=runtime_env)

# Option 2: Using Ray Jobs API (Python SDK)
from ray.job_submission import JobSubmissionClient

client = JobSubmissionClient("http://<head-node-ip>:8265")
job_id = client.submit_job(
    entrypoint="python my_ray_script.py",
    runtime_env=runtime_env,
)

# Option 3: Using Ray Jobs API (CLI). (Note: can use --runtime-env to pass a YAML file instead of an inline JSON string.)
$ ray job submit --address="http://<head-node-ip>:8265" --runtime-env-json='{"working_dir": "/data/my_files", "pip": ["emoji"]}' -- python my_ray_script.py

# Option 4: Using KubeRay RayJob. You can specify the runtime environment in the RayJob YAML manifest.
# [...]
spec:
  runtimeEnvYAML: |
    pip:
      - requests==2.26.0
      - pendulum==2.1.2
    env_vars:
      KEY: "VALUE"

Warning

Specifying the runtime_env argument in the submit_job or ray job submit call ensures the runtime environment is installed on the cluster before the entrypoint script is run.

If runtime_env is specified from ray.init(runtime_env=...), the runtime env is only applied to all children Tasks and Actors, not the entrypoint script (Driver) itself.

If runtime_env is specified by both ray job submit and ray.init, the runtime environments are merged. See Runtime Environment Specified by Both Job and Driver for more details.

Note

There are two options for when to install the runtime environment:

  1. As soon as the job starts (i.e., as soon as ray.init() is called), the dependencies are eagerly downloaded and installed.

  2. The dependencies are installed only when a task is invoked or an actor is created.

The default is option 1. To change the behavior to option 2, add "eager_install": False to the config of runtime_env.

Specifying a Runtime Environment Per-Task or Per-Actor#

You can specify different runtime environments per-actor or per-task using .options() or the @ray.remote decorator:

# Invoke a remote task that will run in a specified runtime environment.
f.options(runtime_env=runtime_env).remote()

# Instantiate an actor that will run in a specified runtime environment.
actor = SomeClass.options(runtime_env=runtime_env).remote()

# Specify a runtime environment in the task definition.  Future invocations via
# `g.remote()` will use this runtime environment unless overridden by using
# `.options()` as above.
@ray.remote(runtime_env=runtime_env)
def g():
    pass

# Specify a runtime environment in the actor definition.  Future instantiations
# via `MyClass.remote()` will use this runtime environment unless overridden by
# using `.options()` as above.
@ray.remote(runtime_env=runtime_env)
class MyClass:
    pass

This allows you to have actors and tasks running in their own environments, independent of the surrounding environment. (The surrounding environment could be the job’s runtime environment, or the system environment of the cluster.)

Warning

Ray does not guarantee compatibility between tasks and actors with conflicting runtime environments. For example, if an actor whose runtime environment contains a pip package tries to communicate with an actor with a different version of that package, it can lead to unexpected behavior such as unpickling errors.

Common Workflows#

This section describes some common use cases for runtime environments. These use cases are not mutually exclusive; all of the options described below can be combined in a single runtime environment.

Using Local Files#

Your Ray application might depend on source files or data files. For a development workflow, these might live on your local machine, but when it comes time to run things at scale, you will need to get them to your remote cluster.

The following simple example explains how to get your local files on the cluster.

import os
import ray

os.makedirs("/tmp/runtime_env_working_dir", exist_ok=True)
with open("/tmp/runtime_env_working_dir/hello.txt", "w") as hello_file:
  hello_file.write("Hello World!")

# Specify a runtime environment for the entire Ray job
ray.init(runtime_env={"working_dir": "/tmp/runtime_env_working_dir"})

# Create a Ray task, which inherits the above runtime env.
@ray.remote
def f():
    # The function will have its working directory changed to its node's
    # local copy of /tmp/runtime_env_working_dir.
    return open("hello.txt").read()

print(ray.get(f.remote()))

Hello World!

Note

The example above is written to run on a local machine, but as for all of these examples, it also works when specifying a Ray cluster to connect to (e.g., using ray.init("ray://123.456.7.89:10001", runtime_env=...) or ray.init(address="auto", runtime_env=...)).

The specified local directory will automatically be pushed to the cluster nodes when ray.init() is called.

You can also specify files via a remote cloud storage URI; see Remote URIs for details.

If you specify a working_dir, Ray always prepares it first, and it’s present in the creation of other runtime environments in the ${RAY_RUNTIME_ENV_CREATE_WORKING_DIR} environment variable. This sequencing allows pip and conda to reference local files in the working_dir like requirements.txt or environment.yml. See pip and conda sections in API Reference for more details.

Using conda or pip packages#

Your Ray application might depend on Python packages (for example, pendulum or requests) via import statements.

Ray ordinarily expects all imported packages to be preinstalled on every node of the cluster; in particular, these packages are not automatically shipped from your local machine to the cluster or downloaded from any repository.

However, using runtime environments you can dynamically specify packages to be automatically downloaded and installed in a virtual environment for your Ray job, or for specific Ray tasks or actors.

import ray
import requests

# This example runs on a local machine, but you can also do
# ray.init(address=..., runtime_env=...) to connect to a cluster.
ray.init(runtime_env={"pip": ["requests"]})

@ray.remote
def reqs():
    return requests.get("https://www.ray.io/").status_code

print(ray.get(reqs.remote()))

200

You may also specify your pip dependencies either via a Python list or a local requirements.txt file. Consider specifying a requirements.txt file when your pip install command requires options such as --extra-index-url or --find-links; see https://pip.pypa.io/en/stable/reference/requirements-file-format/# for details. Alternatively, you can specify a conda environment, either as a Python dictionary or via a local environment.yml file. This conda environment can include pip packages. For details, head to the API Reference.

Warning

Since the packages in the runtime_env are installed at runtime, be cautious when specifying conda or pip packages whose installations involve building from source, as this can be slow.

Note

When using the "pip" field, the specified packages will be installed “on top of” the base environment using virtualenv, so existing packages on your cluster will still be importable. By contrast, when using the conda field, your Ray tasks and actors will run in an isolated environment. The conda and pip fields cannot both be used in a single runtime_env.

Note

The ray[default] package itself will automatically be installed in the environment. For the conda field only, if you are using any other Ray libraries (for example, Ray Serve), then you will need to specify the library in the runtime environment (e.g. runtime_env = {"conda": {"dependencies": ["pytorch", "pip", {"pip": ["requests", "ray[serve]"]}]}}.)

Note

conda environments must have the same Python version as the Ray cluster. Do not list ray in the conda dependencies, as it will be automatically installed.

Library Development#

Suppose you are developing a library my_module on Ray.

A typical iteration cycle will involve

  1. Making some changes to the source code of my_module

  2. Running a Ray script to test the changes, perhaps on a distributed cluster.

To ensure your local changes show up across all Ray workers and can be imported properly, use the py_modules field.

import ray
import my_module

ray.init("ray://123.456.7.89:10001", runtime_env={"py_modules": [my_module]})

@ray.remote
def test_my_module():
    # No need to import my_module inside this function.
    my_module.test()

ray.get(test_my_module.remote())

API Reference#

The runtime_env is a Python dictionary or a Python class ray.runtime_env.RuntimeEnv including one or more of the following fields:

  • working_dir (str): Specifies the working directory for the Ray workers. This must either be (1) an local existing directory with total size at most 100 MiB, (2) a local existing zipped file with total unzipped size at most 100 MiB (Note: excludes has no effect), or (3) a URI to a remotely-stored zip file containing the working directory for your job (no file size limit is enforced by Ray). See Remote URIs for details. The specified directory will be downloaded to each node on the cluster, and Ray workers will be started in their node’s copy of this directory.

    • Examples

      • "."  # cwd

      • "/src/my_project"

      • "/src/my_project.zip"

      • "s3://path/to/my_dir.zip"

    Note: Setting a local directory per-task or per-actor is currently unsupported; it can only be set per-job (i.e., in ray.init()).

    Note: If the local directory contains a .gitignore file, the files and paths specified there are not uploaded to the cluster. You can disable this by setting the environment variable RAY_RUNTIME_ENV_IGNORE_GITIGNORE=1 on the machine doing the uploading.

    Note: If the local directory contains symbolic links, Ray follows the links and the files they point to are uploaded to the cluster.

  • py_modules (List[str|module]): Specifies Python modules to be available for import in the Ray workers. (For more ways to specify packages, see also the pip and conda fields below.) Each entry must be either (1) a path to a local file or directory, (2) a URI to a remote zip or wheel file (see Remote URIs for details), (3) a Python module object, or (4) a path to a local .whl file.

    • Examples of entries in the list:

      • "."

      • "/local_dependency/my_dir_module"

      • "/local_dependency/my_file_module.py"

      • "s3://bucket/my_module.zip"

      • my_module # Assumes my_module has already been imported, e.g. via 'import my_module'

      • my_module.whl

      • "s3://bucket/my_module.whl"

    The modules will be downloaded to each node on the cluster.

    Note: Setting options (1), (3) and (4) per-task or per-actor is currently unsupported, it can only be set per-job (i.e., in ray.init()).

    Note: For option (1), if the local directory contains a .gitignore file, the files and paths specified there are not uploaded to the cluster. You can disable this by setting the environment variable RAY_RUNTIME_ENV_IGNORE_GITIGNORE=1 on the machine doing the uploading.

  • excludes (List[str]): When used with working_dir or py_modules, specifies a list of files or paths to exclude from being uploaded to the cluster. This field uses the pattern-matching syntax used by .gitignore files: see https://git-scm.com/docs/gitignore for details. Note: In accordance with .gitignore syntax, if there is a separator (/) at the beginning or middle (or both) of the pattern, then the pattern is interpreted relative to the level of the working_dir. In particular, you shouldn’t use absolute paths (e.g. /Users/my_working_dir/subdir/) with excludes; rather, you should use the relative path /subdir/ (written here with a leading / to match only the top-level subdir directory, rather than all directories named subdir at all levels.)

    • Example: {"working_dir": "/Users/my_working_dir/", "excludes": ["my_file.txt", "/subdir/", "path/to/dir", "*.log"]}

  • pip (dict | List[str] | str): Either (1) a list of pip requirements specifiers, (2) a string containing the path to a local pip “requirements.txt” file, or (3) a python dictionary that has three fields: (a) packages (required, List[str]): a list of pip packages, (b) pip_check (optional, bool): whether to enable pip check at the end of pip install, defaults to False. (c) pip_version (optional, str): the version of pip; Ray will spell the package name “pip” in front of the pip_version to form the final requirement string. The syntax of a requirement specifier is defined in full in PEP 508. This will be installed in the Ray workers at runtime. Packages in the preinstalled cluster environment will still be available. To use a library like Ray Serve or Ray Tune, you will need to include "ray[serve]" or "ray[tune]" here. The Ray version must match that of the cluster.

    • Example: ["requests==1.0.0", "aiohttp", "ray[serve]"]

    • Example: "./requirements.txt"

    • Example: {"packages":["tensorflow", "requests"], "pip_check": False, "pip_version": "==22.0.2;python_version=='3.8.11'"}

    When specifying a path to a requirements.txt file, the file must be present on your local machine and it must be a valid absolute path or relative filepath relative to your local current working directory, not relative to the working_dir specified in the runtime_env. Furthermore, referencing local files within a requirements.txt file isn’t directly supported (e.g., -r ./my-laptop/more-requirements.txt, ./my-pkg.whl). Instead, use the ${RAY_RUNTIME_ENV_CREATE_WORKING_DIR} environment variable in the creation process. For example, use -r ${RAY_RUNTIME_ENV_CREATE_WORKING_DIR}/my-laptop/more-requirements.txt or ${RAY_RUNTIME_ENV_CREATE_WORKING_DIR}/my-pkg.whl to reference local files, while ensuring they’re in the working_dir.

  • uv (dict | List[str] | str): Alpha version feature. Either (1) a list of uv requirements specifiers, (2) a string containing the path to a local uv “requirements.txt” file, or (3) a python dictionary that has three fields: (a) packages (required, List[str]): a list of uv packages, (b) uv_version (optional, str): the version of uv; Ray will spell the package name “uv” in front of the uv_version to form the final requirement string. (c) uv_check (optional, bool): whether to enable pip check at the end of uv install, default to False. (d) uv_pip_install_options (optional, List[str]): user-provided options for uv pip install command, default to ["--no-cache"]. To override the default options and install without any options, use an empty list [] as install option value. The syntax of a requirement specifier is the same as pip requirements. This will be installed in the Ray workers at runtime. Packages in the preinstalled cluster environment will still be available. To use a library like Ray Serve or Ray Tune, you will need to include "ray[serve]" or "ray[tune]" here. The Ray version must match that of the cluster.

    • Example: ["requests==1.0.0", "aiohttp", "ray[serve]"]

    • Example: "./requirements.txt"

    • Example: {"packages":["tensorflow", "requests"], "uv_version": "==0.4.0;python_version=='3.8.11'"}

    When specifying a path to a requirements.txt file, the file must be present on your local machine and it must be a valid absolute path or relative filepath relative to your local current working directory, not relative to the working_dir specified in the runtime_env. Furthermore, referencing local files within a requirements.txt file isn’t directly supported (e.g., -r ./my-laptop/more-requirements.txt, ./my-pkg.whl). Instead, use the ${RAY_RUNTIME_ENV_CREATE_WORKING_DIR} environment variable in the creation process. For example, use -r ${RAY_RUNTIME_ENV_CREATE_WORKING_DIR}/my-laptop/more-requirements.txt or ${RAY_RUNTIME_ENV_CREATE_WORKING_DIR}/my-pkg.whl to reference local files, while ensuring they’re in the working_dir.

  • conda (dict | str): Either (1) a dict representing the conda environment YAML, (2) a string containing the path to a local conda “environment.yml” file, or (3) the name of a local conda environment already installed on each node in your cluster (e.g., "pytorch_p36") or its absolute path (e.g. "/home/youruser/anaconda3/envs/pytorch_p36") . In the first two cases, the Ray and Python dependencies will be automatically injected into the environment to ensure compatibility, so there is no need to manually include them. The Python and Ray version must match that of the cluster, so you likely should not specify them manually. Note that the conda and pip keys of runtime_env cannot both be specified at the same time—to use them together, please use conda and add your pip dependencies in the "pip" field in your conda environment.yaml.

    • Example: {"dependencies": ["pytorch", "torchvision", "pip", {"pip": ["pendulum"]}]}

    • Example: "./environment.yml"

    • Example: "pytorch_p36"

    • Example: "/home/youruser/anaconda3/envs/pytorch_p36"

    When specifying a path to a environment.yml file, the file must be present on your local machine and it must be a valid absolute path or a relative filepath relative to your local current working directory, not relative to the working_dir specified in the runtime_env. Furthermore, referencing local files within a environment.yml file isn’t directly supported (e.g., -r ./my-laptop/more-requirements.txt, ./my-pkg.whl). Instead, use the ${RAY_RUNTIME_ENV_CREATE_WORKING_DIR} environment variable in the creation process. For example, use -r ${RAY_RUNTIME_ENV_CREATE_WORKING_DIR}/my-laptop/more-requirements.txt or ${RAY_RUNTIME_ENV_CREATE_WORKING_DIR}/my-pkg.whl to reference local files, while ensuring they’re in the working_dir.

  • env_vars (Dict[str, str]): Environment variables to set. Environment variables already set on the cluster will still be visible to the Ray workers; so there is no need to include os.environ or similar in the env_vars field. By default, these environment variables override the same name environment variables on the cluster. You can also reference existing environment variables using ${ENV_VAR} to achieve the appending behavior. If the environment variable doesn’t exist, it becomes an empty string "".

    • Example: {"OMP_NUM_THREADS": "32", "TF_WARNINGS": "none"}

    • Example: {"LD_LIBRARY_PATH": "${LD_LIBRARY_PATH}:/home/admin/my_lib"}

    • Non-existant variable example: {"ENV_VAR_NOT_EXIST": "${ENV_VAR_NOT_EXIST}:/home/admin/my_lib"} -> ENV_VAR_NOT_EXIST=":/home/admin/my_lib".

  • nsight (Union[str, Dict[str, str]]): specifies the config for the Nsight System Profiler. The value is either (1) “default”, which refers to the default config, or (2) a dict of Nsight System Profiler options and their values. See here for more details on setup and usage.

    • Example: "default"

    • Example: {"stop-on-exit": "true", "t": "cuda,cublas,cudnn", "ftrace": ""}

  • container (dict): Require a given (Docker) image, and the worker process will run in a container with this image. The worker_path is the default_worker.py path. It is required only if ray installation directory in the container is different from raylet host. The run_options list spec is here.

    • Example: {"image": "anyscale/ray:2.31.0-py39-cpu", "worker_path": "/root/python/ray/workers/default_worker.py", "run_options": ["--cap-drop SYS_ADMIN","--log-level=debug"]}

    Note: container is experimental now. If you have some requirements or run into any problems, raise issues in github.

  • config (dict | ray.runtime_env.RuntimeEnvConfig): config for runtime environment. Either a dict or a RuntimeEnvConfig. Fields: (1) setup_timeout_seconds, the timeout of runtime environment creation, timeout is in seconds.

    • Example: {"setup_timeout_seconds": 10}

    • Example: RuntimeEnvConfig(setup_timeout_seconds=10)

    (2) eager_install (bool): Indicates whether to install the runtime environment on the cluster at ray.init() time, before the workers are leased. This flag is set to True by default. If set to False, the runtime environment will be only installed when the first task is invoked or when the first actor is created. Currently, specifying this option per-actor or per-task is not supported.

    • Example: {"eager_install": False}

    • Example: RuntimeEnvConfig(eager_install=False)

Caching and Garbage Collection#

Runtime environment resources on each node (such as conda environments, pip packages, or downloaded working_dir or py_modules directories) will be cached on the cluster to enable quick reuse across different runtime environments within a job. Each field (working_dir, py_modules, etc.) has its own cache whose size defaults to 10 GB. To change this default, you may set the environment variable RAY_RUNTIME_ENV_<field>_CACHE_SIZE_GB on each node in your cluster before starting Ray e.g. export RAY_RUNTIME_ENV_WORKING_DIR_CACHE_SIZE_GB=1.5.

When the cache size limit is exceeded, resources not currently used by any Actor, Task or Job are deleted.

Runtime Environment Specified by Both Job and Driver#

When running an entrypoint script (Driver), the runtime environment can be specified via ray.init(runtime_env=...) or ray job submit --runtime-env (See Specifying a Runtime Environment Per-Job for more details).

  • If the runtime environment is specified by ray job submit --runtime-env=..., the runtime environments are applied to the entrypoint script (Driver) and all the tasks and actors created from it.

  • If the runtime environment is specified by ray.init(runtime_env=...), the runtime environments are applied to all the tasks and actors, but not the entrypoint script (Driver) itself.

Since ray job submit submits a Driver (that calls ray.init), sometimes runtime environments are specified by both of them. When both the Ray Job and Driver specify runtime environments, their runtime environments are merged if there’s no conflict. It means the driver script uses the runtime environment specified by ray job submit, and all the tasks and actors are going to use the merged runtime environment. Ray raises an exception if the runtime environments conflict.

  • The runtime_env["env_vars"] of ray job submit --runtime-env=... is merged with the runtime_env["env_vars"] of ray.init(runtime_env=...). Note that each individual env_var keys are merged. If the environment variables conflict, Ray raises an exception.

  • Every other field in the runtime_env will be merged. If any key conflicts, it raises an exception.

Example:

# `ray job submit --runtime_env=...`
{"pip": ["requests", "chess"],
"env_vars": {"A": "a", "B": "b"}}

# ray.init(runtime_env=...)
{"env_vars": {"C": "c"}}

# Driver's actual `runtime_env` (merged with Job's)
{"pip": ["requests", "chess"],
"env_vars": {"A": "a", "B": "b", "C": "c"}}

Conflict Example:

# Example 1, env_vars conflicts
# `ray job submit --runtime_env=...`
{"pip": ["requests", "chess"],
"env_vars": {"C": "a", "B": "b"}}

# ray.init(runtime_env=...)
{"env_vars": {"C": "c"}}

# Ray raises an exception because the "C" env var conflicts.

# Example 2, other field (e.g., pip) conflicts
# `ray job submit --runtime_env=...`
{"pip": ["requests", "chess"]}

# ray.init(runtime_env=...)
{"pip": ["torch"]}

# Ray raises an exception because "pip" conflicts.

You can set an environment variable RAY_OVERRIDE_JOB_RUNTIME_ENV=1 to avoid raising an exception upon a conflict. In this case, the runtime environments are inherited in the same way as Driver and Task and Actor both specify runtime environments, where ray job submit is a parent and ray.init is a child.

Inheritance#

The runtime environment is inheritable, so it applies to all Tasks and Actors within a Job and all child Tasks and Actors of a Task or Actor once set, unless it is overridden.

If an Actor or Task specifies a new runtime_env, it overrides the parent’s runtime_env (i.e., the parent Actor’s or Task’s runtime_env, or the Job’s runtime_env if Actor or Task doesn’t have a parent) as follows:

  • The runtime_env["env_vars"] field will be merged with the runtime_env["env_vars"] field of the parent. This allows for environment variables set in the parent’s runtime environment to be automatically propagated to the child, even if new environment variables are set in the child’s runtime environment.

  • Every other field in the runtime_env will be overridden by the child, not merged. For example, if runtime_env["py_modules"] is specified, it will replace the runtime_env["py_modules"] field of the parent.

Example:

# Parent's `runtime_env`
{"pip": ["requests", "chess"],
"env_vars": {"A": "a", "B": "b"}}

# Child's specified `runtime_env`
{"pip": ["torch", "ray[serve]"],
"env_vars": {"B": "new", "C": "c"}}

# Child's actual `runtime_env` (merged with parent's)
{"pip": ["torch", "ray[serve]"],
"env_vars": {"A": "a", "B": "new", "C": "c"}}

Frequently Asked Questions#

Are environments installed on every node?#

If a runtime environment is specified in ray.init(runtime_env=...), then the environment will be installed on every node. See Per-Job for more details. (Note, by default the runtime environment will be installed eagerly on every node in the cluster. If you want to lazily install the runtime environment on demand, set the eager_install option to false: ray.init(runtime_env={..., "config": {"eager_install": False}}.)

When is the environment installed?#

When specified per-job, the environment is installed when you call ray.init() (unless "eager_install": False is set). When specified per-task or per-actor, the environment is installed when the task is invoked or the actor is instantiated (i.e. when you call my_task.remote() or my_actor.remote().) See Per-Job Per-Task/Actor, within a job for more details.

Where are the environments cached?#

Any local files downloaded by the environments are cached at /tmp/ray/session_latest/runtime_resources.

How long does it take to install or to load from cache?#

The install time usually mostly consists of the time it takes to run pip install or conda create / conda activate, or to upload/download a working_dir, depending on which runtime_env options you’re using. This could take seconds or minutes.

On the other hand, loading a runtime environment from the cache should be nearly as fast as the ordinary Ray worker startup time, which is on the order of a few seconds. A new Ray worker is started for every Ray actor or task that requires a new runtime environment. (Note that loading a cached conda environment could still be slow, since the conda activate command sometimes takes a few seconds.)

You can set setup_timeout_seconds config to avoid the installation hanging for a long time. If the installation is not finished within this time, your tasks or actors will fail to start.

What is the relationship between runtime environments and Docker?#

They can be used independently or together. A container image can be specified in the Cluster Launcher for large or static dependencies, and runtime environments can be specified per-job or per-task/actor for more dynamic use cases. The runtime environment will inherit packages, files, and environment variables from the container image.

My runtime_env was installed, but when I log into the node I can’t import the packages.#

The runtime environment is only active for the Ray worker processes; it does not install any packages “globally” on the node.

Remote URIs#

The working_dir and py_modules arguments in the runtime_env dictionary can specify either local path(s) or remote URI(s).

A local path must be a directory path. The directory’s contents will be directly accessed as the working_dir or a py_module. A remote URI must be a link directly to a zip file or a wheel file (only for py_module). The zip file must contain only a single top-level directory. The contents of this directory will be directly accessed as the working_dir or a py_module.

For example, suppose you want to use the contents in your local /some_path/example_dir directory as your working_dir. If you want to specify this directory as a local path, your runtime_env dictionary should contain:

runtime_env = {..., "working_dir": "/some_path/example_dir", ...}

Suppose instead you want to host your files in your /some_path/example_dir directory remotely and provide a remote URI. You would need to first compress the example_dir directory into a zip file.

There should be no other files or directories at the top level of the zip file, other than example_dir. You can use the following command in the Terminal to do this:

cd /some_path
zip -r zip_file_name.zip example_dir

Note that this command must be run from the parent directory of the desired working_dir to ensure that the resulting zip file contains a single top-level directory. In general, the zip file’s name and the top-level directory’s name can be anything. The top-level directory’s contents will be used as the working_dir (or py_module).

You can check that the zip file contains a single top-level directory by running the following command in the Terminal:

zipinfo -1 zip_file_name.zip
# example_dir/
# example_dir/my_file_1.txt
# example_dir/subdir/my_file_2.txt

Suppose you upload the compressed example_dir directory to AWS S3 at the S3 URI s3://example_bucket/example.zip. Your runtime_env dictionary should contain:

runtime_env = {..., "working_dir": "s3://example_bucket/example.zip", ...}

Warning

Check for hidden files and metadata directories in zipped dependencies. You can inspect a zip file’s contents by running the zipinfo -1 zip_file_name.zip command in the Terminal. Some zipping methods can cause hidden files or metadata directories to appear in the zip file at the top level. To avoid this, use the zip -r command directly on the directory you want to compress from its parent’s directory. For example, if you have a directory structure such as: a/b and you what to compress b, issue the zip -r b command from the directory a. If Ray detects more than a single directory at the top level, it will use the entire zip file instead of the top-level directory, which may lead to unexpected behavior.

Currently, three types of remote URIs are supported for hosting working_dir and py_modules packages:

  • HTTPS: HTTPS refers to URLs that start with https. These are particularly useful because remote Git providers (e.g. GitHub, Bitbucket, GitLab, etc.) use https URLs as download links for repository archives. This allows you to host your dependencies on remote Git providers, push updates to them, and specify which dependency versions (i.e. commits) your jobs should use. To use packages via HTTPS URIs, you must have the smart_open library (you can install it using pip install smart_open).

    • Example:

      • runtime_env = {"working_dir": "https://github.com/example_username/example_respository/archive/HEAD.zip"}

  • S3: S3 refers to URIs starting with s3:// that point to compressed packages stored in AWS S3. To use packages via S3 URIs, you must have the smart_open and boto3 libraries (you can install them using pip install smart_open and pip install boto3). Ray does not explicitly pass in any credentials to boto3 for authentication. boto3 will use your environment variables, shared credentials file, and/or AWS config file to authenticate access. See the AWS boto3 documentation to learn how to configure these.

    • Example:

      • runtime_env = {"working_dir": "s3://example_bucket/example_file.zip"}

  • GS: GS refers to URIs starting with gs:// that point to compressed packages stored in Google Cloud Storage. To use packages via GS URIs, you must have the smart_open and google-cloud-storage libraries (you can install them using pip install smart_open and pip install google-cloud-storage). Ray does not explicitly pass in any credentials to the google-cloud-storage’s Client object. google-cloud-storage will use your local service account key(s) and environment variables by default. Follow the steps on Google Cloud Storage’s Getting started with authentication guide to set up your credentials, which allow Ray to access your remote package.

    • Example:

      • runtime_env = {"working_dir": "gs://example_bucket/example_file.zip"}

Note that the smart_open, boto3, and google-cloud-storage packages are not installed by default, and it is not sufficient to specify them in the pip section of your runtime_env. The relevant packages must already be installed on all nodes of the cluster when Ray starts.

Hosting a Dependency on a Remote Git Provider: Step-by-Step Guide#

You can store your dependencies in repositories on a remote Git provider (e.g. GitHub, Bitbucket, GitLab, etc.), and you can periodically push changes to keep them updated. In this section, you will learn how to store a dependency on GitHub and use it in your runtime environment.

Note

These steps will also be useful if you use another large, remote Git provider (e.g. BitBucket, GitLab, etc.). For simplicity, this section refers to GitHub alone, but you can follow along on your provider.

First, create a repository on GitHub to store your working_dir contents or your py_module dependency. By default, when you download a zip file of your repository, the zip file will already contain a single top-level directory that holds the repository contents, so you can directly upload your working_dir contents or your py_module dependency to the GitHub repository.

Once you have uploaded your working_dir contents or your py_module dependency, you need the HTTPS URL of the repository zip file, so you can specify it in your runtime_env dictionary.

You have two options to get the HTTPS URL.

Debugging#

If runtime_env cannot be set up (e.g., network issues, download failures, etc.), Ray will fail to schedule tasks/actors that require the runtime_env. If you call ray.get, it will raise RuntimeEnvSetupError with the error message in detail.

import ray
import time

@ray.remote
def f():
    pass

@ray.remote
class A:
    def f(self):
        pass

start = time.time()
bad_env = {"conda": {"dependencies": ["this_doesnt_exist"]}}

# [Tasks] will raise `RuntimeEnvSetupError`.
try:
  ray.get(f.options(runtime_env=bad_env).remote())
except ray.exceptions.RuntimeEnvSetupError:
  print("Task fails with RuntimeEnvSetupError")

# [Actors] will raise `RuntimeEnvSetupError`.
a = A.options(runtime_env=bad_env).remote()
try:
  ray.get(a.f.remote())
except ray.exceptions.RuntimeEnvSetupError:
  print("Actor fails with RuntimeEnvSetupError")

Task fails with RuntimeEnvSetupError
Actor fails with RuntimeEnvSetupError

Full logs can always be found in the file runtime_env_setup-[job_id].log for per-actor, per-task and per-job environments, or in runtime_env_setup-ray_client_server_[port].log for per-job environments when using Ray Client.

You can also enable runtime_env debugging log streaming by setting an environment variable RAY_RUNTIME_ENV_LOG_TO_DRIVER_ENABLED=1 on each node before starting Ray, for example using setup_commands in the Ray Cluster configuration file (reference). This will print the full runtime_env setup log messages to the driver (the script that calls ray.init()).

Example log output:

ray.init(runtime_env={"pip": ["requests"]})

(pid=runtime_env) 2022-02-28 14:12:33,653       INFO pip.py:188 -- Creating virtualenv at /tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/virtualenv, current python dir /Users/user/anaconda3/envs/ray-py38
(pid=runtime_env) 2022-02-28 14:12:33,653       INFO utils.py:76 -- Run cmd[1] ['/Users/user/anaconda3/envs/ray-py38/bin/python', '-m', 'virtualenv', '--app-data', '/tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/virtualenv_app_data', '--reset-app-data', '--no-periodic-update', '--system-site-packages', '--no-download', '/tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/virtualenv']
(pid=runtime_env) 2022-02-28 14:12:34,267       INFO utils.py:97 -- Output of cmd[1]: created virtual environment CPython3.8.11.final.0-64 in 473ms
(pid=runtime_env)   creator CPython3Posix(dest=/private/tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/virtualenv, clear=False, no_vcs_ignore=False, global=True)
(pid=runtime_env)   seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/private/tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/virtualenv_app_data)
(pid=runtime_env)     added seed packages: pip==22.0.3, setuptools==60.6.0, wheel==0.37.1
(pid=runtime_env)   activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator
(pid=runtime_env)
(pid=runtime_env) 2022-02-28 14:12:34,268       INFO utils.py:76 -- Run cmd[2] ['/tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/virtualenv/bin/python', '-c', 'import ray; print(ray.__version__, ray.__path__[0])']
(pid=runtime_env) 2022-02-28 14:12:35,118       INFO utils.py:97 -- Output of cmd[2]: 3.0.0.dev0 /Users/user/ray/python/ray
(pid=runtime_env)
(pid=runtime_env) 2022-02-28 14:12:35,120       INFO pip.py:236 -- Installing python requirements to /tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/virtualenv
(pid=runtime_env) 2022-02-28 14:12:35,122       INFO utils.py:76 -- Run cmd[3] ['/tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/virtualenv/bin/python', '-m', 'pip', 'install', '--disable-pip-version-check', '--no-cache-dir', '-r', '/tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/requirements.txt']
(pid=runtime_env) 2022-02-28 14:12:38,000       INFO utils.py:97 -- Output of cmd[3]: Requirement already satisfied: requests in /Users/user/anaconda3/envs/ray-py38/lib/python3.8/site-packages (from -r /tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/requirements.txt (line 1)) (2.26.0)
(pid=runtime_env) Requirement already satisfied: idna<4,>=2.5 in /Users/user/anaconda3/envs/ray-py38/lib/python3.8/site-packages (from requests->-r /tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/requirements.txt (line 1)) (3.2)
(pid=runtime_env) Requirement already satisfied: certifi>=2017.4.17 in /Users/user/anaconda3/envs/ray-py38/lib/python3.8/site-packages (from requests->-r /tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/requirements.txt (line 1)) (2021.10.8)
(pid=runtime_env) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/user/anaconda3/envs/ray-py38/lib/python3.8/site-packages (from requests->-r /tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/requirements.txt (line 1)) (1.26.7)
(pid=runtime_env) Requirement already satisfied: charset-normalizer~=2.0.0 in /Users/user/anaconda3/envs/ray-py38/lib/python3.8/site-packages (from requests->-r /tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/requirements.txt (line 1)) (2.0.6)
(pid=runtime_env)
(pid=runtime_env) 2022-02-28 14:12:38,001       INFO utils.py:76 -- Run cmd[4] ['/tmp/ray/session_2022-02-28_14-12-29_909064_87908/runtime_resources/pip/0cc818a054853c3841171109300436cad4dcf594/virtualenv/bin/python', '-c', 'import ray; print(ray.__version__, ray.__path__[0])']
(pid=runtime_env) 2022-02-28 14:12:38,804       INFO utils.py:97 -- Output of cmd[4]: 3.0.0.dev0 /Users/user/ray/python/ray

See Logging Directory Structure for more details.