nbox provides first class CLI + python package support for all NimbleBox infrastructure and services. You can

# on macos find the correct wheel file based on python version: https://github.com/pietrodn/grpcio-mac-arm-build/releases/tag/1.51.1
pip install <wheel_url>

pip install nbox

Next you need to authenticate yourself with the CLI:

nbx login

Status: The library is currently undergoing heavy development.

• nbx projects:
  • nbx projects - artifacts --help stable ✅
  • nbx projects - run --help stable ✅
• nbx jobs --help: mostly stable 🟡
• nbx serve --help: mostly stable 🟡

• Write and execute code in Python
• Document your code that supports mathematical equations
• Create/Upload/Share notebooks
• Import notebooks from your local machine
• Import/Publish notebooks from/to GitHub
• Import external datasets (e.g. from Kaggle)
• Integrate PyTorch, TensorFlow, Keras, OpenCV
• Share your projects
• Collaborate with your team

Install the package from pypi:

When loading nbox for the first time, it will prompt you the username and password and create a secrets file at ~/.nbx/secrets.json. This file then contains all the information that you don’t have to fetch manually again.

Our APIs are deep, user functions are kept to minimum and most relavant. This documentation contains the full spec of everything, but here’s all the APIs you need to know:

nbox
├── Model          # Framework agnostic Model
│   ├── __call__
│   ├── deploy
│   ├── train_on_instance (WIP)
│   └── train_on_jobs (WIP)
├── Operators      # How jobs are combinations of operators
│   ├── __call__
│   └── deploy
├── Jobs           # For controlling all your jobs
│   ├── logs       # stream logs right on your terminal
│   └── trigger    # manually trigger a job
└── Instance
   ├── __call__    # Run any command on the instance
   └── mv (WIP)    # Move files to and from NBX-Build

Let's take this script as an example

from nbox import operator, Operator
from nbox.lib.shell import ShellCommand

# define your function and wrap it as an operator
@operator()
def foo(x: Dict):
  return "bar"

# or use OO to deploy an API
@operator()
class Baz():
  def __init__(self, power: int = 2):
    # load any model that you want
    self.model = load_tf_pt_model()
    self.power = power
  
  def forward(self, x: float = 1.0):
    return {"pred": x ** self.power}    

Through your CLI:

# to deploy a job
nbx jobs upload file:foo 'my_first_job'

# to deploy an API
nbx serve upload file:Baz 'my_first_api'

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The code in thist repo is licensed as Apache License 2.0. Please check for individual repositories for licenses.