Add docs about private PyPI indexes

docs/deployments/python-packages.md

@@ -14,33 +14,28 @@ You can install your required PyPI packages and import them in your Python files
 └── requirements.txt
 ```
 
-If you want to use `conda` to install your python packages, see the [Conda section](#conda) below.
+If you want to use `conda` to install your python packages, see the [Conda section](#conda-packages) below.
 
 Note that some packages are pre-installed by default (see "pre-installed packages" for your Predictor type in the [Predictor documentation](predictors.md)).
 
-## Installing with Setup
+## Private PyPI packages
 
-Python packages can also be installed by providing a `setup.py` that describes your project's modules. Here's an example directory structure:
+To install packages from a private PyPI index, create a `pip.conf` inside the same directory as `requirements.txt`, and add the following contents:
 
 ```text
-./iris-classifier/
-├── cortex.yaml
-├── predictor.py
-├── ...
-├── mypkg
-│   └── __init__.py
-├── requirements.txt
-└── setup.py
+[global]
+extra-index-url = https://<username>:<password>@<my-private-index>.com/pip
 ```
 
-In this case, `requirements.txt` will have this form:
-```text
-# requirements.txt
+In same directory, create a [`dependencies.sh` script](system-packages.md#bash-script) and add the following contents:
 
-.
+```bash
+cp pip.conf /etc/pip.conf
 ```
 
-## Installing from GitHub
+You may now add packages to `requirements.txt` which are found in the private index.
+
+## GitHub packages
 
 You can also install public/private packages from git registries (such as GitHub) by adding them to `requirements.txt`. Here's an example for GitHub:
 
@@ -56,7 +51,29 @@ git+https://<personal access token>@github.com/<username>/<repo name>.git@<tag o
 
 On GitHub, you can generate a personal access token by following [these steps](https://help.github.com/en/github/authenticating-to-github/creating-a-personal-access-token-for-the-command-line).
 
-## Conda
+## Installing with Setup
+
+Python packages can also be installed by providing a `setup.py` that describes your project's modules. Here's an example directory structure:
+
+```text
+./iris-classifier/
+├── cortex.yaml
+├── predictor.py
+├── ...
+├── mypkg
+│   └── __init__.py
+├── requirements.txt
+└── setup.py
+```
+
+In this case, `requirements.txt` will have this form:
+```text
+# requirements.txt
+
+.
+```
+
+## Conda packages
 
 Cortex supports installing Conda packages. We recommend only using Conda when your required packages are not available in PyPI. Cortex looks for a `conda-packages.txt` file in the top level Cortex project directory (i.e. the directory which contains `cortex.yaml`):
 

docs/deployments/system-packages.md

@@ -14,12 +14,11 @@ Cortex looks for a file named `dependencies.sh` in the top level Cortex project
 └── dependencies.sh
 ```
 
-`dependencies.sh` is executed during the initialization of each replica (before installing Python packages in `requirements.txt` or `conda-packages.txt`). Typical use cases include installing required system packages to be used in your Predictor, building Python packages from source, etc.
+`dependencies.sh` is executed with `bash` shell during the initialization of each replica (before installing Python packages in `requirements.txt` or `conda-packages.txt`). Typical use cases include installing required system packages to be used in your Predictor, building Python packages from source, etc.
 
 Here is an example `dependencies.sh`, which installs the `tree` utility:
 
 ```bash
-#!/bin/bash
 apt-get update && apt-get install -y tree
 ```