Data Explorer Client SDK

Introduction

akride is a Python SDK that facilitates interaction with Akridata's Data Explorer, providing seamless integration and tools for streamlined data analysis.

Akridata's Data Explorer allows you to save time building the best training and test sets for your application.

akride will help you:

• Visualize your data
• Detect and remove outliers
• Check class imbalance
• Sample the data and remove duplications
• Apply Image-based-Search
• Analyze model's accuracy

and much more!

Installation

You can install the latest stable version of akride via pip -

for a CPU-only version:

pip install akride[cpu]

or on a GPU-enabled machine:

pip install akride[gpu]

You can upgrade your existing installation to the latest version by running

pip install -U akride[cpu]

or

pip install -U akride[gpu]

QuickStart

Start using Akride by downloading the SDK configuration through the steps outlined here and paste the following code snippet into your Python terminal:

from akride import AkriDEClient
SAAS_ENDPOINT="https://app.akridata.ai"
API_KEY=<your-api-key-from-sdk-config>
# API Key Configurations
sdk_config_dict = {
  "saas_endpoint": SAAS_ENDPOINT,
  "api_key": API_KEY,
  "mode": "saas"
}


client = AkriDEClient(sdk_config_dict=sdk_config_dict)
print(client.get_server_version())

Documentation

For detailed documentation on how to use the akride client and its capabilities, please refer to the official SDK documentation.

For more information about Akridata's Data Explorer, please refer to the official Akridata documentation.

Examples

Check out the akride-examples repository for examples of using akride client to interact with DataExplorer

For more information about AkriData, please visit akridata.ai.

Docker command to ingest data

CPU docker image

docker run -v ${PWD}/data:/data akridata/akride:latest akride ingest [-n <dataset_name> | -d <dataset_id>] -i /data -e <de_endpoint> -a <api_key>

GPU docker image

docker run --gpus=all -v ${PWD}/data:/data akridata/akride-gpu:latest akride ingest [-n <dataset_name> | -d <dataset_id>] -i /data -e <de_endpoint> -a <api_key>