stdpio 0.0.5

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

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

to get a token:

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.

#get 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:

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'

stdp = stdpio(token="example-token", model_dir="/tmp/", interval=1)
stdp.sync_model("dbe69029-6ad0-4609-a06b-b0958e892f15")

```

and to stop syncing a model

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