stdpio 1.0.0

A library to interact with the stdp.io REST API

stdp.io client library

A library to interact with the stdp.io REST API and a framework to keep your local Akida models synced with your cloud stored models. You can train your models in the cloud and have them synced to your local devices.

authenticating

Authenticate via username and password or via token. Specify the directory you want fetched models to be stored and synced.

from stdpio import stdpio

stdp = stdpio(username="username", password="password", model_dir="/tmp/")

or, if you already have a token

from stdpio import stdpio

stdp = stdpio(token="example-token", model_dir="/tmp/")

to get a token:

from stdpio import stdpio

stdp = stdpio(username="username", password="password")
token = stdp.get_token()

fetching your Akida models

This will query the stdp.io API to bring back a list of your models.

from stdpio import stdpio

stdp = stdpio(username="username", password="password")

# get a list of your models
models = stdp.my_models()

# fetch all the model files & knowns
for model in models:

    # download the model file from stdp.io
    stdp.fetch_model_file(model)

    # print the labels and trained neurons for the model
    print(stdp.fetch_known(model))

You can also add search paramaters:

from stdpio import stdpio

stdp = stdpio(username="username", password="password")

params = {"name__icontains": "mobilenet"}
stdp.my_models(**params)

syncing your Akida models

When stdpio is initialised, it will begin a timer that will check models updated_at date. If you want to keep models synced with stdp.io, simply at it to the list of models to sync. The interval defaults to 1 second checks, this can be changed by passing in the keyword argument 'initerval'

from stdpio import stdpio

stdp = stdpio(token="example-token", model_dir="/tmp/", interval=1)

# to start syncing a model

stdp.sync_model("dbe69029-6ad0-4609-a06b-b0958e892f15")

# to stop syncing a model

stdp.unsync_model("dbe69029-6ad0-4609-a06b-b0958e892f15")

running inference while syncing models

import time
from stdpio import stdpio
from akida import Model
import tensorflow as tf
from tf.keras.utils import load_img
import numpy as np

# sync an Akida model
model_key = "058cdf7b-b913-44a9-9fd9-52d41d4295d0"
stdp = stdpio(token="example-token", model_dir="")
stdp.sync_model(model_key)

# loading / fetching the models via API

model_path = False
while not model_path:
    model_path = stdp.get_model_path(model_key)

model_object = stdp.get_model_object(model_key)
akida_model = Model(filename=model_path)

while True:
    
    # check for model changes
    minted_model_path = stdp.get_model_path(model_key)
    if minted_model_path:
        akida_model = Model(filename=minted_model_path)

    if akida_model:
        image = load_img("thor.png")
        input_arr = tf.keras.preprocessing.image.img_to_array(image)
        input_arr = tf.image.resize_with_crop_or_pad(input_arr, 224, 224)
        input_arr = np.array([input_arr], dtype="uint8")
        predictions = akida_model.predict(input_arr, num_classes=model_object["model_output_features"])

    time.sleep(1)

to get more information about the model object

from stdpio import stdpio

stdp = stdpio(token="example-token", model_dir="")
model_key = "058cdf7b-b913-44a9-9fd9-52d41d4295d0"
model_object = stdp.get_model_object(model_key)
print(model_object)

an example model object will look like this:

{
    "unique_id": "058cdf7b-b913-44a9-9fd9-52d41d4295d0",
    "name": "testing",
    "short_description": "test",
    "labels": {
        "0": "test",
        "3": "mouse"
    },
    "learned": [
        0,
        3
    ],
    "base_model": "mobilenet_imagenet",
    "updated_at": "2021-10-11T11:27:22.811873Z",
    "model_input_shape": "[224, 224, 3]",
    "model_output_shape": "[1, 1, 10]",
    "model_output_features": 10
}