Client application to interface with the BranchKey system

# BranchKey Python Client Application  This application runs against the BranchKey backend aggregation service for Federated Learning. It provides a Python interface to login/logout a client, upload files to the system for aggregation, and download aggregated output files. # Install - `pip install branchkey` # Build 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: ```python import json from branchkey.client import Client credentials = {"leaf_name": "leaf-1", "leaf_id": "46780841-9787-41e6-ac14-e3ee160e158a", "leaf_session_token": "46780841-9787-41e6-ac14-e3ee160e158a", "performance_analyser_username": "hello", "performance_analyser_password": "world", "response_host":"response.branchkey.com"} host = "http://api.branchkey.com" # initialise the client # it implicitly authenticates the leaf_session # and fetches the run_details of the parent branch c = Client(credentials,host) # upload the file to the system c.file_upload("./file/path.npy") # 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 if not c.queue.empty(): aggregation_id = c.queue.get(block=False) c.file_download(aggregation_id) # To push performance analysis metrics for this aggregation: # mode can be test, train or non-federated data = json.dumps({"key1":"val1","key2":"val2"}) mode = "test" c.send_performance_metrics(aggregation_id, data, mode) ``` ## File format Weights file in a numpy `.npy` format: ```python with open("./test.npy", "wb") as f: np.save(f, parameter_array) [num_samples, [n_d parameter matrix]] ``` ``` num_samples - the number of samples that contributed to this update n_d parameter matrix - parameters ``` ### Required file format The required numpy arrays after exports ```python [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... ``` ```python (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 ```