SDK User Guide

SDK Contents:

Find the latest support doc here!

Rendered.ai’s SDK: anatools

Anatools is a python package with modules for developing with the Ana Platform from Rendered.AI. With AnaTools you can generate and access synthetic datasets, and much more!

>>> import anatools
>>> ana = anatools.AnaClient()
'Enter your credentials for Ana.'
'email:' example@rendered.ai
'password:' ***************
>>> ana.get_channels()
['mychannel1', 'mychannel2']
>>> graphs = ana.get_graphs()
>>> datasets = ana.get_datasets()


Install the AnaTools Package

(Optional) Create a new Conda Environment

  1. Install conda for your operating system: https://www.anaconda.com/products/individual.

  2. Create a new conda environment and activate it.

  3. Install anatools from the Python Package Index. .. code-block:: sh

    $ conda create -n renderedai python=3.7 $ conda activate renderedai

Install AnaTools to the Python Environment

  1. Install AnaTools from the Python Package Index. .. code-block:: sh

    $ pip install anatools

Dependencies

The anatools package requires python 3.6 or higher and has dependencies on the following packages:

Package

Description

pyrebase

A python wrapper for Google Firebase API.

jwt

A python library for encoding and decoding JSON web tokens.

keyring

A python library for storing and accessing passwords securely.

docker

A python library for the Docker Engine API.

sphinx

A python documentation generator.

pytest

A python testing framework.

pyyaml

A python YAML parser and emitter.

If you have any questions or comments, contact Rendered.AI at info@rendered.ai.


Quickstart Guide

What is Ana?

Ana is a synthetic dataset generation tool where graphs describe what and how synthetic datasets are generated.

Terms

Definitions

workspace

A workspace is a collection of data used for a particular use-case, for example workspaces can be used to organize data for different projects.

dataset

A dataset is a collection of data, for many use-cases these are images with text-based annotation files.

graph

A graph is defined by nodes and links, it describes the what and the how a dataset is generated.

node

A node can be described as an executable block of code, it has inputs and runs some algorithm to generate outputs.

link

A link is used to transfer data from the output of one node, to the input of other nodes.

channel

A channel is a collection of nodes, it is used to limit the scope of what is possible to generate in a dataset (like content from a tv channel).

How do you use Ana?

Ana creates synthetic datasets by processing a graph, so we will need to create our Ana client, create a graph, then create a dataset.

  1. Execute the python command line, create a client and login to Ana. In this example we are instantiating a client with no workspace or environment variables, so it is setting our default workspace. To access the tool, you will need to use your email and password for https://deckard.rendered.ai. .. code-block:: python

    >>> import anatools
>>> ana = anatools.AnaClient()
'Enter your credentials for Ana.'
'email:' example@rendered.ai
'password:' ***************

  2. Create a graph file called ‘graph.yml’ with the code below. We are defining a simplistic graph for this example with multiple cubes dropped into a container. Graphs are usually stored in json or yaml format, but read into a python dictionary to be passed to the API.

    nodes:

Cube:
 class: CubeGenerator

ObjectPlacement:
 class: RandomPlacement
 inputs:
   object_generator: {$link: [Cube, object_generator]}
   object_number: '5'

Container:
 class: ContainerGenerator

DropObjects:
 class: DropObjectsNode
 inputs:
   objects: {$link: [ObjectPlacement, objects]}
   container_generator: {$link: [Container, object_generator]}

Render:
 class: RenderNode
 inputs:
   frame_number: 150
   target_object: {$link: [DropObjects, target_object]}
   annotated_objects: {$link: [DropObjects, annotated_objects]}

  3. Create a graph using the client. To create a new graph, we load the graph defined above into a python dictionary using the yaml python package. Then we create a graph using the client, this graph is being named ‘testgraph’ and is using the ‘example’ channel. The client will return a graphid so we can reference this graph later. .. code-block:: python

    >>> import yaml
>>> with open('graph.yml') as graphfile:
>>>     graph = yaml.safe_load(graphfile)
>>> graphid = ana.create_graph(name='testgraph',channel='example',graph=graph)
>>> graphid
'e175a44e-23a5-11eb-adc1-0242ac120002'

  4. Create a dataset using the client. Using the graphid, we can create a new job to generate a dataset.

The client will return a datasetid that can be used for reference later.

>>> datasetid = ana.create_dataset(name='testdataset',graphid=graphid,interpretations='10',priority='1',seed='1',description='A simple dataset with cubes in a container.')
>>> datasetid
'ce66e81c-23a6-11eb-adc1-0242ac120002 '

Indices and tables