Getting Started

Step 1: Install EVA

EVA supports Python (versions >= 3.8). To install EVA, we recommend using the pip package manager:

pip install evadb

Launch EVA server

EVA is based on a client-server architecture. To launch the EVA server using the pip package, run the following command on the terminal:

eva_server &

Step 2: Start a Jupyter Notebook Client

Here is an illustrative Jupyter notebook focusing on MNIST image classification using EVA. The notebook works on Google Colab.

Connect to the EVA server

Connect to the EVA server in the notebook using the following code:

# allow nested asyncio calls for client to connect with server
import nest_asyncio
nest_asyncio.apply()
from eva.server.db_api import connect

# hostname and port of the server where EVA is running
connection = connect(host = '0.0.0.0', port = 8803)

# cursor allows the notebook client to send queries to the server
cursor = connection.cursor()

Load video for analysis

Download the MNIST video for analysis.

!wget -nc https://www.dropbox.com/s/yxljxz6zxoqu54v/mnist.mp4

Use the LOAD statement to load a video onto a table in EVA server.

cursor.execute('LOAD VIDEO "mnist.mp4" INTO MNISTVideoTable;')
response = cursor.fetch_all()
print(response)

Step 3: Run an AI Query on the loaded video

User-defined functions (UDFs) allow us to combine SQL with AI models. These functions wrap around AI models. In this query, we use the MnistImageClassifier UDF that wraps around a model trained for classifying MNIST images.

cursor.execute("""SELECT data, MnistImageClassifier(data).label
                  FROM MNISTVideoTable
                  WHERE id = 30;""")
response = cursor.fetch_all()

Visualize the output

The output of the query is visualized in the notebook.