Your Ray project may depend on environment variables, local files, and Python packages.
Ray makes managing these dependencies easy, even when working with a remote cluster.
You can specify dependencies dynamically at runtime using :ref:`Runtime Environments<runtime-environments>`. This is useful for quickly iterating on a project with changing dependencies and local code files, or for running jobs, tasks and actors with different environments all on the same Ray cluster.
Alternatively, you can prepare your Ray cluster's environment once, when your cluster nodes start up. This can be
accomplished using ``setup_commands`` in the Ray Cluster launcher; see the :ref:`documentation<cluster-configuration-setup-commands>` for details.
You can still use
runtime environments on top of this, but they will not inherit anything from the base
cluster environment.
.._runtime-environments:
Runtime Environments
--------------------
..note::
This API is in beta and may change before becoming stable.
..note::
This feature requires a full installation of Ray using ``pip install "ray[default]"``.
On Mac OS and Linux, Ray 1.4+ supports dynamically setting the runtime environment of tasks, actors, and jobs so that they can depend on different Python libraries (e.g., conda environments, pip dependencies) while all running on the same Ray cluster.
The ``runtime_env`` is a (JSON-serializable) dictionary that can be passed as an option to tasks and actors, and can also be passed to ``ray.init()``.
The runtime environment defines the dependencies required for your workload.
You can specify a runtime environment for your whole job, whether running a script directly on the cluster or using :ref:`Ray Client<ray-client>`:
-``working_dir`` (str): Specifies the working directory for your job. This can be the path of an existing local directory with a total size of at most 100 MiB.
Alternatively, it can be a URI to a remotely-stored zip file containing the working directory for your job. See the "Remote URIs" section below for more info.
The directory will be cached on the cluster, so the next time you connect with Ray Client you will be able to skip uploading the directory contents.
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``.
-``eager_install`` (bool): A boolean indicates whether to install runtime env eagerly before the workers are leased. This flag is set to True by default and only job level is supported now.
- 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.
If a child actor or task specifies a new ``runtime_env``, it will be merged with the parent’s ``runtime_env`` via a simple dict update.
For example, if ``runtime_env["pip"]`` is specified, it will override the ``runtime_env["pip"]`` field of the parent.
The one exception is the field ``runtime_env["env_vars"]``. This field will be `merged` with the ``runtime_env["env_vars"]`` dict 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.
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``).
-``S3``: ``S3`` refers to URIs starting with ``s3://`` that point to compressed packages stored in `AWS S3 <https://aws.amazon.com/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 <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html>`_ to learn how to configure these.
-``GS``: ``GS`` refers to URIs starting with ``gs://`` that point to compressed packages stored in `Google Cloud Storage <https://cloud.google.com/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 <https://cloud.google.com/docs/authentication/getting-started>`_ guide to set up your credentials, which allow Ray to access your remote package.
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.
Option 1: Download Zip (quicker to implement, but not recommended for production environments)
The second option is to manually create this URL by pattern-matching your specific use case with one of the following examples.
**This is recommended** because it provides finer-grained control over which repository branch and commit to use when generating your dependency zip file.
These options prevent consistency issues on Ray Clusters (see the warning above for more info).
To create the URL, pick a URL template below that fits your use case, and fill in all parameters in brackets (e.g. [username], [repository], etc.) with the specific values from your repository.
For instance, suppose your GitHub username is ``example_user``, the repository's name is ``example_repository``, and the desired commit hash is ``abcdefg``.
If ``example_repository`` is public and you want to retrieve the ``abcdefg`` commit (which matches the first example use case), the URL would be:
