# 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
```