highlighter-sdk 0.5.2.0

Package to interact with the Highlighter Perception System

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Highlighter SDK

The Highlighter SDK Python library provides convenient access to the Highlighter API from applications written in Python. There is also a CLI to access the Highlighter API in your shell.

The library also provides other features. For example:

• Easy configuration for fast setup and use across multiple Highlighter accounts.
• Functions to help get datasets in and out of Highlighter
• Helpers for pagination.

Installation

pip install highlighter-sdk

API Tokens, Environment Variables and Profiles

If you have just a single set of Highlighter credentials you can simply set the appropriate environment variables.

export HL_WEB_GRAPHQL_ENDPOINT="https://<client-account>.highlighter.ai/graphql"
export HL_WEB_GRAPHQL_API_TOKEN="###"

# Only required if you have datasets stored outside Highlighter's managed
# aws s3 storage
export AWS_ACCESS_KEY_ID=###
export AWS_SECRET_ACCESS_KEY=###
export AWS_DEFAULT_REGION=###

If you have several Highlighter credentials we suggest you use the profiles option. You can create a ~/.highlighter-profiles.yaml via the cli.

hl config create --name my-profile --api-token ### --endpoint https://...

Other commands for managing your profiles can be seen with the --help flag,

If you're a Maverick Renegade you can manage the ~/.highlighter-profiles.yaml manually. Below is an example,

# Example ~/.highlighter-profiles.yaml

my_profile:
  endpoint_url: https://<client-account>.highlighter.ai/graphql
  api_token: ###

  # Only required if you have datasets stored outside Highlighter's managed
  # aws s3 storage
  cloud:
    - type: aws-s3
      aws_access_key_id: ###
      aws_secret_access_key: ###
      aws_default_region: ###

To use as a profile in the cli simply use,

hl --profile <profile-name> <command>

In a script you can use,

# in a script
from highlighter import HLClient

client = HLClient.from_profile("profile-name")

Additionally HLClient can be initialized using environment variables or by passing credentials direcly

from highlighter import HLClient

client = HLClient.from_env()

# or

api_token = "###"
endpoint_url = "https://...highligher.ai/graphql
client = HLCient.from_credential(api_token, endpoint_url)

Finally, if you are in the position where you want to write a specified profile's credentials to an evnironment file such as .env or .envrc you can use the write command. This will create or append to the specified file.

hl --profile my-profile write .envrc

Python API

Once you have a HLClient object you can use it perform queries or mutations. Here is a simple example.

from highlighter import HLClient
from pydantic import BaseModel

client = HLClient.from_env()

# You don't always need to define your own BaseModels
# many common BaseModels are defined in highlighter.base_models
# this is simply for completeness
class ObjectClassType(BaseModel):
  id: int
  name: str

id = 1234  # add an object class id from you account

result = client.ObjectClass(
    return_type=ObjectClassType,
    id=id,
    )

print(result)

Some queries may return arbitrarily many results. These queries are paginated and are called Connections and the queries are named accordingly. We have provided a paginate function to help with these Connections

from highlighter import HLClient, paginate
from pydantic import BaseModel

client = HLClient.from_env()
uuids = [
   "abc123-abc123-abc123-abc123",
   "xyz456-xyz456-xyz456-xyz456",
]

# The following BaseModels are all defined in
# highlighter.base_models. They are simply here
# for completeness
class PageInfo(BaseModel):
    hasNextPage: bool
    endCursor: Optional[str]

class ObjectClass(BaseModel):
    id: str
    uuid: str
    name: str

class ObjectClassTypeConnection(BaseModel):
    pageInfo: PageInfo
    nodes: List[ObjectClass]

generator = paginate(
     client..objectClassConnection,
     ObjectClassTypeConnection,
     uuid=uuids,
     )

for object_class in generator:
  print(object_class)

Datasets

Highlighter SDK provides a dataset representation that can populated from several sources {HighlighterWeb.Assessments | Local files {.hdf, records.json, coco.json} | S3Bucket}. Once populated the Highlighter.Datasets object contains 2 Pandas.DataFrames (data_files_df and annotations_df) that you can manipulate as required. When you're ready you can write to disk or upload to Highligher using one of the Writer classes to dump your data to disk in a format that can be consumed by your downstream code. If you need you can also roll-your-own Writer by implementing the highlighter.datasets.interfaces.IWriter interface.

Documentation

See https://highlighter-docs.netlify.app/