Getting Started

Part 1: Install EVA

EVA supports Python (versions >= 3.7). 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, run the following command on the terminal:

eva_server &

Part 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

To connect to the EVA server in the notebook, use the following Python 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 = 5432)

# 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 is used to load a video onto a table in EVA server.

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

Part 3: Register an user-defined function (UDF)

User-defined functions allow us to combine SQL with deep learning models. These functions wrap around deep learning models.

Download the user-defined function for classifying MNIST images.

!wget -nc https://raw.githubusercontent.com/georgia-tech-db/eva/master/tutorials/apps/mnist/eva_mnist_udf.py

cursor.execute("""CREATE UDF IF NOT EXISTS MnistCNN
                  INPUT  (data NDARRAY (3, 28, 28))
                  OUTPUT (label TEXT(2))
                  TYPE  Classification
                  IMPL  'eva_mnist_udf.py';
                """)
response = cursor.fetch_all()
print(response)

Run a query using the newly registered UDF!

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

Visualize the output

The output of the query is visualized in the notebook.

Part 5: Start a Command Line Client

Besides the notebook interface, EVA also exports a command line interface for querying the server. This interface allows for quick querying from the terminal:

>>> eva_client
eva=# LOAD VIDEO "mnist.mp4" INTO MNISTVid;
@status: ResponseStatus.SUCCESS
@batch:

0 Video successfully added at location: mnist.p4
@query_time: 0.045

eva=# SELECT id, data FROM MNISTVid WHERE id < 1000;
@status: ResponseStatus.SUCCESS
@batch:
            mnistvid.id     mnistvid.data
    0          0             [[[ 0 2 0]\n [0 0 0]\n...
    1          1             [[[ 2 2 0]\n [1 1 0]\n...
    2          2             [[[ 2 2 0]\n [1 2 2]\n...
    ..       ...
    997        997           [[[ 0 2 0]\n [0 0 0]\n...
    998        998           [[[ 0 2 0]\n [0 0 0]\n...
    999        999           [[[ 2 2 0]\n [1 1 0]\n...

[1000 rows x 2 columns]
@query_time: 0.216

eva=# exit