# 
[](https://badge.fury.io/py/starpack)
[](https://codecov.io/gh/a360-starpack/starpack)
[](https://github.com/psf/black)
Starpack is tool to package and deploy production-ready packages of machine learning models.
This repository contains the code for the CLI and Python library for interacting with Starpack. To run Starpack locally,
you will need to install a Python version listed in the badge above and
[Docker Desktop](https://docs.docker.com/get-docker/) installed locally.
Currently, Starpack only supports packaging and deployment in local Docker Desktop or Docker Engine environments,
but the generated Docker images can be pushed to remote repositories and deployed manually.
It is currently on our roadmap to support deployment through Andromeda 360 down the line.
## Table of Contents
<!-- TOC -->
* [Starpack](#)
* [Table of Contents](#table-of-contents)
* [Quickstart](#quickstart)
* [Full Command List](#full-command-list)
* [Init](#init)
* [Upload](#upload)
* [Package](#package)
* [Metadata](#metadata)
* [Artifacts](#artifacts)
* [Steps](#steps)
* [Deploy](#deploy)
* [Engine](#engine)
* [Start](#start)
* [Terminate](#terminate)
* [Plugins](#plugins)
* [Examples](#examples)
* [Basic Example](#basic-example)
* [Troubleshooting](#troubleshooting)
<!-- TOC -->
## Quickstart
To begin using Starpack, open a terminal and run
```bash
pip install starpack
```
Then, you can initialize an existing or new directory with starter code and files by running the following in a terminal
```bash
starpack init ./path/you/want
```
Reconfigure your `starpack.yaml`, `predict.py`, and `requirements.txt` files to point to your model artifacts, properly ingest your data, and list your project dependencies, respectively. An example of these files can be found in the `examples` folder.
Finally, run the following terminal command to package and deploy your model locally
```bash
starpack deploy ./path/you/want
```
or alternatively in Python (such as in a notebook), the process can be run as follows:
```python
import starpack
from pathlib import Path
desired_directory = Path("./path/you/want")
starpack.initialize_directory(desired_directory)
# Reconfigure your files that have been initialized for your specific project
starpack.deploy_directory(desired_directory)
```
## Full Command List
A full command list can be found by running
```bash
starpack --help
```
Currently, the following top-level flags are available:
| Name | Flag | Description |
|--------------------|------------------------|-----------------------------------------------------------------------------------------------------------------|
| Version | `--version`, `-v` | Returns the version of the Starpack CLI installed in your environment |
| Install Completion | `--install-completion` | Installs the auto-complete definitions for Starpack in your current terminal environment |
| Show Completion | `--show-completion` | Shows completion for your current shell so that you can copy or customize the configuration |
| Help | `--help` | Shows a nicely formatted table displaying the currently available commands, their flags, and their descriptions |
Additionally, we have a variety of commands to manage the Starpack Engine and generate packages.
### Init
The command, `starpack init`, when provided with a local path as an argument, will generate files to help you start developing your package definition. By default, Starpack will prompt for permission to overwrite existing files, unless the `--ovewrite/-o` flag is given.
| Filename | Usage |
|--------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `requirements.txt` | Define your Python requirements that are necessary to run your deployment script. Dependencies are installed using `pip`. |
| `predict.py` | Python script that defines how to handle an ingested Pandas DataFrame of data and transform it into your prediction output. This script should handle any data transformations and model loading required for inference. |
| `starpack.yaml` | YAML definition of your Starpack packaging and deployment information. This file should define the locations of all necessary artifacts and build steps. |
### Upload
The command, `starpack upload`, takes in a directory path and uploads its contents to the Starpack Engine.
The name of this directory will be the name of the artifacts location within the Engine for when you package and deploy your model.
### Package
The command, `starpack package`, takes in a path, either a directory or `starpack.yaml` in order to package your model.
If given a directory, Starpack will upload the contents to the Starpack Engine, then find any `starpack.yaml` file in
the given directory to send as a payload to the Starpack Engine.
The `starpack.yaml` file contains two main sections, `package` and `deployment`.
Furthermore, the YAML packaging section contains three main subsections:
1. Metadata
2. Artifacts
3. Steps
#### Metadata
Within the metadata section, you can define things such as the name, version, and author of the package.
These fields should be generated when creating a directory using [`starpack init`](#init)
#### Artifacts
This section defines the name of your model artifacts location in the Engine system, as well as where you can find files such as your validation data, prediction script, and dependencies.
#### Steps
This section is an ordered list of your build steps. Each of your build steps uses either built-in or
externally written plugins that can be called by name and optionally constrained by version of the plugin.
Some plugins may need additional data, which will be included within the step's definition.
### Deploy
The command, `starpack deploy`, takes in a path, either a directory or `starpack.yaml` in order to deploy your packaged model.
If given a path, the assumption is made that the contents should be uploaded to the Engine, the YAML should be parsed for any packaging information, and finally, the deployment should be processed.
If given just a starpack.yaml, only the `deployment` step will be run, with the assumption that the other steps have already been run previously.
### Engine
There are several
#### Start
The command, `starpack engine start`, is used to ensure the Starpack Engine is running or force the creation of a new container.
Finally, if you pass the `--force` or `-F` flags, you will force the deletion of any existing Starpack Engines and create a new Docker container for the Engine.
#### Terminate
The command, `starpack engine terminate`, is used to spin down any existing Starpack Engines running on your local machine.
Additionally, the `--all` or `-A` flag can be passed to additionally delete any existing Docker Volumes and associated data from your machine.
### Plugins
The following plugins are available to use in either packaging or deployment:
| Name | Description | Arguments |
|----------------------|------------------------------------------------------------------------------------------|----------------------------------------------------------|
| docker_desktop_push | Tags an image and stores it in the local Docker repository | `image_name`, `image_tags` (list), `wrapper` |
| local_docker_deploy | Deploys a packaged Starpack model with a given wrapper to the local Docker environment | `port`, `wrapper` (list) with `name` and `port` for each |
| local_docker_find | Finds already packaged model artifacts in the local Docker environment | `wrapper`, `image` with `name` and `tag` sub-arguments |
| fastapi | Packages a set of model artifacts with a FastAPI wrapper. | |
| streamlit | Packages a set of model artifacts with a Streamlit wrapper. | |
## Examples
### Basic Example
Under `examples/starpack_basic_example`, you can find a Jupyter notebook and associated files for deploying a Starpack package using both Streamlit and FastAPI. By following along with the notebook, then running `starpack deploy examples/starpack_basic_example` from the root of this repository, you will be able to see how one exports, defines, packages, and deploys a Starpack model.
## Troubleshooting
Before troubleshooting any issues, please run the two following commands in your terminal to ensure that you're running the latest version of both the Starpack CLI and Starpack Engine:
```
pip install starpack --upgrade
starpack engine start --force
```
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Raw data
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"keywords": "Swagger,OpenAPI,ModelOps,CLI,DataScience,DevOps,MLOps",
"author": "\"Andromeda 360, Inc\"",
"author_email": "irvin.shen@andromeda360.ai",
"download_url": "https://files.pythonhosted.org/packages/93/b8/ef5b496ea95423d03d44898f8798f220d252ef4efe598d68f960b3f482b5/starpack-0.2.0.tar.gz",
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"description": "# \n\n[](https://badge.fury.io/py/starpack)\n\n[](https://codecov.io/gh/a360-starpack/starpack)\n[](https://github.com/psf/black)\n\n\nStarpack is tool to package and deploy production-ready packages of machine learning models.\n\nThis repository contains the code for the CLI and Python library for interacting with Starpack. To run Starpack locally, \nyou will need to install a Python version listed in the badge above and \n[Docker Desktop](https://docs.docker.com/get-docker/) installed locally.\n\nCurrently, Starpack only supports packaging and deployment in local Docker Desktop or Docker Engine environments, \nbut the generated Docker images can be pushed to remote repositories and deployed manually. \nIt is currently on our roadmap to support deployment through Andromeda 360 down the line.\n\n\n## Table of Contents\n\n<!-- TOC -->\n* [Starpack](#)\n * [Table of Contents](#table-of-contents)\n * [Quickstart](#quickstart)\n * [Full Command List](#full-command-list)\n * [Init](#init)\n * [Upload](#upload)\n * [Package](#package)\n * [Metadata](#metadata)\n * [Artifacts](#artifacts)\n * [Steps](#steps)\n * [Deploy](#deploy)\n * [Engine](#engine)\n * [Start](#start)\n * [Terminate](#terminate)\n * [Plugins](#plugins)\n * [Examples](#examples)\n * [Basic Example](#basic-example)\n * [Troubleshooting](#troubleshooting)\n<!-- TOC -->\n\n## Quickstart\n\nTo begin using Starpack, open a terminal and run \n```bash\npip install starpack\n```\n\nThen, you can initialize an existing or new directory with starter code and files by running the following in a terminal\n\n```bash\nstarpack init ./path/you/want\n```\n\nReconfigure your `starpack.yaml`, `predict.py`, and `requirements.txt` files to point to your model artifacts, properly ingest your data, and list your project dependencies, respectively. An example of these files can be found in the `examples` folder.\n\nFinally, run the following terminal command to package and deploy your model locally\n\n```bash\nstarpack deploy ./path/you/want\n```\n\n\n\nor alternatively in Python (such as in a notebook), the process can be run as follows:\n\n```python\nimport starpack\nfrom pathlib import Path\n\ndesired_directory = Path(\"./path/you/want\")\n\nstarpack.initialize_directory(desired_directory)\n\n# Reconfigure your files that have been initialized for your specific project\n\nstarpack.deploy_directory(desired_directory)\n```\n\n\n## Full Command List\n\nA full command list can be found by running\n\n```bash\nstarpack --help\n```\n\nCurrently, the following top-level flags are available:\n\n| Name | Flag | Description |\n|--------------------|------------------------|-----------------------------------------------------------------------------------------------------------------|\n| Version | `--version`, `-v` | Returns the version of the Starpack CLI installed in your environment |\n| Install Completion | `--install-completion` | Installs the auto-complete definitions for Starpack in your current terminal environment |\n| Show Completion | `--show-completion` | Shows completion for your current shell so that you can copy or customize the configuration |\n| Help | `--help` | Shows a nicely formatted table displaying the currently available commands, their flags, and their descriptions |\n\nAdditionally, we have a variety of commands to manage the Starpack Engine and generate packages.\n\n### Init\n\nThe command, `starpack init`, when provided with a local path as an argument, will generate files to help you start developing your package definition. By default, Starpack will prompt for permission to overwrite existing files, unless the `--ovewrite/-o` flag is given.\n\n\n| Filename | Usage |\n|--------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `requirements.txt` | Define your Python requirements that are necessary to run your deployment script. Dependencies are installed using `pip`. |\n| `predict.py` | Python script that defines how to handle an ingested Pandas DataFrame of data and transform it into your prediction output. This script should handle any data transformations and model loading required for inference. |\n| `starpack.yaml` | YAML definition of your Starpack packaging and deployment information. This file should define the locations of all necessary artifacts and build steps. |\n\n\n### Upload\n\nThe command, `starpack upload`, takes in a directory path and uploads its contents to the Starpack Engine. \nThe name of this directory will be the name of the artifacts location within the Engine for when you package and deploy your model.\n\n### Package\n\nThe command, `starpack package`, takes in a path, either a directory or `starpack.yaml` in order to package your model. \nIf given a directory, Starpack will upload the contents to the Starpack Engine, then find any `starpack.yaml` file in \nthe given directory to send as a payload to the Starpack Engine.\n\n\nThe `starpack.yaml` file contains two main sections, `package` and `deployment`. \nFurthermore, the YAML packaging section contains three main subsections:\n\n1. Metadata\n2. Artifacts\n3. Steps\n\n\n#### Metadata\n\nWithin the metadata section, you can define things such as the name, version, and author of the package. \nThese fields should be generated when creating a directory using [`starpack init`](#init)\n\n#### Artifacts\n\nThis section defines the name of your model artifacts location in the Engine system, as well as where you can find files such as your validation data, prediction script, and dependencies.\n\n#### Steps\n\nThis section is an ordered list of your build steps. Each of your build steps uses either built-in or \nexternally written plugins that can be called by name and optionally constrained by version of the plugin. \nSome plugins may need additional data, which will be included within the step's definition.\n\n### Deploy\n\nThe command, `starpack deploy`, takes in a path, either a directory or `starpack.yaml` in order to deploy your packaged model.\nIf given a path, the assumption is made that the contents should be uploaded to the Engine, the YAML should be parsed for any packaging information, and finally, the deployment should be processed.\nIf given just a starpack.yaml, only the `deployment` step will be run, with the assumption that the other steps have already been run previously.\n\n\n### Engine\n\nThere are several \n\n#### Start\n\nThe command, `starpack engine start`, is used to ensure the Starpack Engine is running or force the creation of a new container.\n\nFinally, if you pass the `--force` or `-F` flags, you will force the deletion of any existing Starpack Engines and create a new Docker container for the Engine.\n\n#### Terminate\n\nThe command, `starpack engine terminate`, is used to spin down any existing Starpack Engines running on your local machine. \nAdditionally, the `--all` or `-A` flag can be passed to additionally delete any existing Docker Volumes and associated data from your machine.\n\n### Plugins\n\nThe following plugins are available to use in either packaging or deployment:\n\n| Name | Description | Arguments |\n|----------------------|------------------------------------------------------------------------------------------|----------------------------------------------------------|\n| docker_desktop_push | Tags an image and stores it in the local Docker repository | `image_name`, `image_tags` (list), `wrapper` |\n| local_docker_deploy | Deploys a packaged Starpack model with a given wrapper to the local Docker environment | `port`, `wrapper` (list) with `name` and `port` for each |\n| local_docker_find | Finds already packaged model artifacts in the local Docker environment | `wrapper`, `image` with `name` and `tag` sub-arguments |\n| fastapi | Packages a set of model artifacts with a FastAPI wrapper. | |\n| streamlit | Packages a set of model artifacts with a Streamlit wrapper. | |\n\n## Examples\n\n### Basic Example\n\nUnder `examples/starpack_basic_example`, you can find a Jupyter notebook and associated files for deploying a Starpack package using both Streamlit and FastAPI. By following along with the notebook, then running `starpack deploy examples/starpack_basic_example` from the root of this repository, you will be able to see how one exports, defines, packages, and deploys a Starpack model.\n\n\n## Troubleshooting\n\nBefore troubleshooting any issues, please run the two following commands in your terminal to ensure that you're running the latest version of both the Starpack CLI and Starpack Engine:\n\n```\npip install starpack --upgrade\nstarpack engine start --force\n```\n\n Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n\n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n\n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n\n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n\n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n\n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n\n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n\n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n\n 2. Grant of Copyright License. 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