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38 changes: 38 additions & 0 deletions docs/index.md
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Expand Up @@ -30,6 +30,7 @@ Notable features include:

The AWS Deadline Cloud client can be installed by the standard python packaging mechanisms:
```sh

$ pip install deadline
```

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An example usage is to create a shortcut called "Deadline Settings" on your desktop that runs `C:\path\to\deadlinew.exe config gui`.
Opening the shortcut will show the Deadline Settings dialog without a terminal window behind it.

## Manual Installation Deadline[GUI] Instructions
**Use Case:** This allows users to install Deadline GUI components on machines without internet access by downloading packages on a connected machine and transferring them.

Prerequisites: Ensure the Deadline Client is installed on a machine with internet access.

### Step 1: Download the packages

Use the Python version bundled with the installer to download the Deadline GUI components:
```bash
path/to/python -m pip download -d path/to/download/directory deadline[gui]=={DEADLINE_CLIENT_VERSION}
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One nit - I think we need to mention the same python package is needed on both machines?

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I believe the steps helps with the same, with step to step instructions to follow by the customer.

```

### Step 2: Verify the download

Check that all packages were downloaded successfully:
```bash
ls path/to/download/directory
# Should show multiple .whl and .tar.gz files
```

### Step 3: Transfer the packages

Copy the entire download directory to your offline machine using USB drive, network share, or other transfer method.

### Step 4: Install the packages

Install the packages using the downloaded files:
```bash
path/to/python -m pip install --no-index --find-links path/to/download/directory --target path/to/target/python/modules deadline[gui]
```

**Arguments explained:**
- `--no-index`: Don't use PyPI, only use local files
- `--find-links`: Directory containing the downloaded packages
- `--target`: Where to install the packages


## Job-related Files
For job-related files and data, AWS Deadline Cloud supports either transferring files to AWS using job attachments or reading files from network storage that is shared between both your local workstation and your farm.

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