# Managing dependencies with runtime\_env
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Ray `runtime_env` installs pip packages and ships a working directory to the cluster at run time. You add a dependency without rebuilding a container image, which keeps interactive development fast.

## Inject dependencies interactively
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Pass `runtime_env` to `ray.init()`. Ray installs the packages and uploads the working directory to the cluster before your code runs.

```
import ray

ray.init(runtime_env={
    "pip": ["pandas==2.2.2", "scikit-learn"],
    "working_dir": "{{./src}}",
})
```

## Inject dependencies for a submitted job
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For a job you submit from the command line, pass the same environment with `--working-dir` and `--runtime-env-json`.

```
ray job submit \
    --address sagemaker_ray://{{my-cluster}}/{{my-namespace}} \
    --working-dir {{./src}} \
    --runtime-env-json '{"pip": ["pandas==2.2.2", "scikit-learn"]}' \
    -- python {{my-script.py}}
```

For the full set of `runtime_env` fields, including conda environments and environment variables, see [Ray runtime environments](https://docs.ray.io/en/latest/ray-core/handling-dependencies.html) in the Ray documentation.