blobhub 0.1.5

BlobHub Python SDK

BlobHub Python SDK

Overview

BlobHub is a service offering versioned storage of structured data.

BlobHub SDK provides developers with convenient access to the functionality of BlobHub platform.

BlobHub Portal

Registration

Free BlobHub account needs to be created in order to make use of the SDK.

Follow these steps to get started with BlobHub:

1. Create free BlobHub account at:
   https://blobhub.io/
2. Create new organization to store your blobs:
   https://blobhub.io/p/me/org/create
3. Create blob of desired type (see the section on Presets library below for examples).
4. Under blob's Settings create API key with write access to the blob.

Access Control

Some of the principles in the foundation of BlobHub access model:

• Blob (and organization holding it) can remain private until it is ready to be exposed for public consumption.
• Private Blobs can be shared with BlobHub members directly.
• API keys provide ability to enable mutable access to blob data (via API and SDK).

Blob Types

Blob type defines the structure of the data stored within the blob. It also determines the set of available operations.

The following blob types are supported at the moment:

• ONNX Model
  Provides storage of deep neural network computational graphs in ONNX format.

Installation

To install, use:

pip install blobhub

Usage

Authentication

Use the following snippet to get access to the blob:

from blobhub.blob import Blob, Revision

blob = Blob(
    org_id="<< organization alias or id >>", 
    blob_id="<< blob alias or id >>", 
    api_key="<< API key from blob settings >>")
revision = blob.revisions.latest()
assert blob[Blob.FIELD_STATUS] == Blob.STATUS_READY
assert revision[Revision.FIELD_STATUS] == Revision.STATUS_READY
assert revision[Revision.FIELD_PHASE] == Revision.PHASE_DRAFT

Presets Library

Presets library is created to simplify operating with blobs of various types (ONNX, OrientDB Graph, etc.).

ONNX Module

ONNX Model blobs are designed to store deep neural network models exported in ONNX format.

Follow these steps to make use of the module:

• Initialize preset:

from blobhub.presets.onnx import Onnx, Model

onnx = Onnx(revision=revision)

• Upload local model:

local_model = Model.from_local_file(onnx=onnx, path="/path/to/local/model.onnx")
success = onnx.upload(model=local_model)
assert True == success

• Download model:

downloaded_model = onnx.download()
assert None != downloaded_model

• (Optional) Confirm that downloaded model is identical to the original one:

assert downloaded_model.is_identical(model=local_model)