branchkey 2.0.4.1

Client application to interface with the branchkey system

BranchKey Python Client Application

This application runs against the BranchKey backend aggregation service. It allows to perform federated averaging across a sample of the given files

It provides python interface to login/logout a client, upload files to the system for aggregation, and download aggregated output files. It also spawns a rabbitmq consumer thread to receive updates whenever an aggregated output is available.

Setup Instructions

• To build the dependencies:
  • make setup, or
  • pip install -r requirements.txt
• To run the tests: make test
  • make test, or
  • python3 -m unittest -v

Usage instructions:

• To use a client:

  from branchkey.client import Client
  
  credentials = {"leaf_name": "guest",
                   "leaf_password": "abc123",
                   "tree_id": "tree-1",
                   "branch_id": "group-1",
                   "queue_password": "guest"}
  
  
  # initialise the client
  c = Client(credentials)
  
  # login and authenticate your credentials
  c.login()
  
  # upload the file to the system
  c.file_upload("./file/path")
  
  # Download a file with the file_id value
  # same as the one received from the consumer
  # It downloads the files in the ./aggregated_files directory
  c.file_download("file-id")
  

File format

In src/examples there is a sample weights.npy file.

Weights file numpy format:

[num_samples, [n_d parameter matrix]]

num_samples - the number of samples that contributed to this update
n_d parameter matrix - parameters

From model export; parameter.data.numpy() values for all in parameters to get required file format

NOTE:

These parameters are not the same as those used in the numpy example below

(2486, [['conv1.weight', Parameter containing:
tensor([[[[-4.8906e-02, -1.1447e-03, -2.7956e-02, -1.7628e-01,  1.2711e-01],
          [-1.3940e-02, -1.7490e-01,  1.9408e-01, -1.4146e-01, -1.9384e-01],
          [ 1.6216e-01, -5.7605e-02, -2.6069e-02, -9.5061e-02, -8.6440e-02],
          [ 4.1506e-02, -9.2765e-02,  2.3566e-02, -6.4725e-02,  1.1439e-01],
          [-1.1091e-01,  6.8872e-02,  1.6387e-01,  5.6428e-02,  1.4058e-01]]]]]],
       device='cuda:0', requires_grad=True)], ['conv1.bias', Parameter containing:
tensor([ 0.1031, -0.1715, -0.1133, -0.0628, -0.0625,  0.0822, -0.0405, -0.1773,
         0.1003,  0.0762, -0.0489, -0.1638, -0.1598, -0.0859,  0.0661,  0.1164,
        -0.0803,  0.1263,  0.1396, -0.1557, -0.1488, -0.0836,  0.0559, -0.1944,
        -0.1192, -0.0261, -0.1164,  0.1215, -0.1154, -0.0822,  0.1301, -0.1932],
       device='cuda:0', requires_grad=True)], ['conv2.weight', Parameter containing:
tensor([[[[-1.7591e-02, -1... etc

Required file format

The required numpy arrays after exports

[1329, list([array([[[[ 1.71775490e-01,    [[[ 8.74867663e-02,  5.19692302e-02, -1.64664671e-01,,          -2.23452481e-03,  1.11475676e-01],,    [-1.75505821e-02, -1...

(1329, [array([[[[ 1.71775490e-01,  3.02851666e-02,  2.90171858e-02,
          -4.27578250e-03,  1.14474617e-01],
         [-8.07138346e-03,  1.44909814e-01, -5.36724664e-02,
          -3.51673253e-02, -1.82426855e-01],
         [ 6.75795972e-02, -1.72839850e-01, -7.25025982e-02,
          -1.59504730e-02,  1.60634145e-01],
         [ 6.62277341e-02, -2.26575769e-02, -1.65369093e-01,
          -8.67117420e-02,  1.80021569e-01],
         [-6.11407161e-02, -1.59245610e-01,  1.45820528e-01,
          -5.40512279e-02, -5.19061387e-02]]],
        ....
         [-1.44068539e-01,  6.15987852e-02,  1.83321223e-01,
          -1.79076958e-02, -1.53445438e-01],
         [-7.76787996e-02,  7.64556080e-02,  9.43044946e-02,
           1.63337544e-01, -1.69042274e-01],
         [-8.55994076e-02, -1.23661250e-01,  1.48442864e-01,
          -1.35983482e-01,  2.05254350e-02]]]], dtype=float32), array([ 0.13065006,  0.12797254, -0.12818147, -0.09621437,  0.04100017,
       -0.07248228,  0.02753541,  0.00476395, -0.11270998,  0.11353076,
       -0.0167569 ,  0.12654744, -0.05019006, -0.07281244,  0.03892357,
       -0.09698197, -0.06845284, -0.04604543, -0.01372138, -0.052395  ,
        0.04833373,  0.16228785,  0.09982517,  0.19556762,  0.10631064,
        0.02496212, -0.14297573, -0.10442089,  0.01970248, -0.1684099 ,
       -0.05076171,  0.19325127], dtype=float32), array([[[[-3.42470817e-02,  8.76816106e-04, -2.13724039e-02,
          -2.62880027e-02, -1.86583996e-02],
         [ 2.56936941e-02, -1.97169576e-02, -3.45735364e-02,
          -4.32738848e-03, -1.22306980e-02],
         [ 8.36322457e-03,  3.26042138e-02, -1.50063485e-02,
          -1.85401291e-02,  2.39207298e-02],
         [-1.15280924e-02, -3.47947963e-02,  2.17274204e-02,
           1.80862695e-02,  2.19682772e-02],
...
etc