Handling Dependencies

This page might be useful for you if you’re trying to:

  • Run a distributed Ray library or application.

  • Run a distributed Ray script which imports some local files.

  • Quickly iterate on a project with changing dependencies and files while running on a Ray cluster.

What problem does this page solve?

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 use Ray’s runtime environments.


  • 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. The recommended way to connect to a remote Ray cluster is to use Ray Client, and we will call the machine running Ray Client your local machine.

  • Job. A period of execution between connecting to a cluster with ray.init() and disconnecting by calling ray.shutdown() or exiting the Ray script.

Runtime Environments


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 only supported on macOS and Linux.

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.

Runtime environments let you transition your Ray application from running on your local machine to running on a remote cluster, without any manual environment setup.

import ray
import requests

runtime_env = {"working_dir": "/data/my_files", "pip": ["requests", "pendulum==2.1.2"]}

# To transition from a local single-node cluster to a remote cluster,
# simply change to ray.init("ray://123.456.7.8:10001", runtime_env=...)

def f():
  return requests.get("https://www.ray.io")
runtime_env = {
    "conda": {
        ["toolz", "dill", "pip", {
            "pip": ["pendulum", "ray[serve]"]
    "env_vars": {"TF_WARNINGS": "none"}

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 or using Ray Client:

# Starting a single-node local Ray cluster
# Connecting to remote cluster using Ray Client
ray.init("ray://123.456.7.89:10001", runtime_env=runtime_env)

This will install the dependencies to the remote cluster. Any tasks and actors used in the job will use this runtime environment unless otherwise specified.


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 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.

# 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.
def g():

# 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.
class MyClass:

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.)


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.

# /path/to/files is a directory on the local machine.
# /path/to/files/hello.txt contains the string "Hello World!"

import ray

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

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

print(ray.get(f.remote())) # Hello World!


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.

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 an isolated 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"]})

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

print(ray.get(reqs.remote())) # <Response [200]>

You may also specify your pip dependencies either via a Python list or a requirements.txt file. Alternatively, you can specify a conda environment, either as a Python dictionary or via a environment.yml file. This conda environment can include pip packages. For details, head to the API Reference.


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


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.

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]})

def test_my_module():
    # No need to import my_module inside this function.


API Reference

The runtime_env is a Python dictionary including one or more of the following fields:

  • working_dir (str): Specifies the working directory for the Ray workers. This must either be an existing directory on the local machine with total size at most 100 MiB, or a URI to a remotely-stored zip file containing the working directory for your job. 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"

      • "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 your local directory contains a .gitignore file, the files and paths specified therein will not be 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 directory, (2) a URI to a remote zip file (see Remote URIs for details), or (3) a Python module object.

    • Examples of entries in the list:

      • "."

      • "/local_dependency/my_module"

      • "s3://bucket/my_module.zip"

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

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

    Note: Setting options (1) and (3) 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 your local directory contains a .gitignore file, the files and paths specified therein will not be uploaded to the cluster.

  • 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 also supports the pattern-matching syntax used by .gitignore files: see https://git-scm.com/docs/gitignore for details.

    • Example: ["my_file.txt", "path/to/dir", "*.log"]

  • pip (List[str] | str): Either a list of pip requirements specifiers, or a string containing the path to a pip “requirements.txt” file. This will be installed in the Ray workers at runtime. To use a library like Ray Serve or Ray Tune, you will need to include "ray[serve]" or "ray[tune]" here.

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

    • Example: "./requirements.txt"

  • conda (dict | str): Either (1) a dict representing the conda environment YAML, (2) a string containing the path to a 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"). 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. 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"

  • env_vars (Dict[str, str]): Environment variables to set.

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

  • 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}


The runtime environment is inheritable, so it will apply to all tasks/actors within a job and all child tasks/actors of a task or actor once set, unless it is overridden.

If an actor or task specifies a new runtime_env, it will override the parent’s runtime_env (i.e., the parent actor/task’s runtime_env, or the job’s runtime_env if there is no parent actor or task) 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.


# 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"}}

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. 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. You can use the following command in the Terminal to do so:

zip -r example.zip /some_path/example_dir

In general, to compress a directory called directory_to_zip into a zip file called zip_file_name.zip, the command is:

# General command
zip -r zip_file_name.zip directory_to_zip

There should be no other files or directories at the top level of the zip file, other than example_dir. 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). 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", ...}


Check for hidden files and metadata directories (e.g. __MACOSX/) 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. This will cause Ray to throw an error because the structure of the zip file is invalid since there is more than a single directory at the top level. You can avoid this by using the zip -r command directly on the directory you want to compress.

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"}

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.


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.