https://github.com/a3darekar/fedn-chem-pytorch
https://github.com/a3darekar/fedn-chem-pytorch
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/a3darekar/fedn-chem-pytorch
- Owner: a3darekar
- Created: 2021-11-27T12:27:25.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-12-12T12:36:06.000Z (over 3 years ago)
- Last Synced: 2025-01-22T16:46:15.322Z (4 months ago)
- Language: Python
- Size: 81.3 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# fedn-chem-pytorch
## Configuring the Reducer
Navigate to 'https://localhost:8090' (or the url of your Reducer) and follow instructions to upload the compute package in 'package/package.tar.gz' and the initial model in 'initial_model/initial_model.npz'.
## Attaching a client to the federation
1. First, download 'client.yaml' from the Reducer 'Network' page, and replace the content in your local 'client.yaml'.
2. Start a client. Here there are different options (see below):
- Docker
- docker-compose
- [Native client (OSX/Linux)](https://github.com/scaleoutsystems/examples/tree/main/how-tos/start-native-fedn-client)
#### Docker
1. Build the image
``` bash
docker build . -t fedn-pytorch-client:latest
```
2. Start a client (edit the path of the volume mounts to provide the absolute path to your local folder.)
```
docker run -v /absolute-path-to-this-folder/data/:/app/data:ro -v /absolute-path-to-this-folder/client.yaml:/app/client.yaml --network fedn_default fedn-pytorch-client fedn run client -in client.yaml
```
(Repeat above steps as needed to deploy additional clients).
#### docker-compose
To start 2 clients:
```bash
docker-compose -f docker-compose.yaml -f private-network.yaml up --scale client=2
```
> If you are connecting to a Reducer part of a distributed setup or in Studio, you should omit 'private-network.yaml'.
### Start training
When clients are running, navigate to the 'Control' page of the Reducer to start the training.
### Configuring the client
We have made it possible to configure a couple of settings to vary the conditions for the training. These configurations are expsosed in the file 'settings.yaml':
```yaml
# Number of training samples used by each client
training_samples: 600
# Number of test samples used by each client (validation)
test_samples: 100
# How much to bias the client data samples towards certain classes (non-IID data partitions)
bias: 0.7
# Parameters for local training
batch_size: 32
epochs: 1
```
## Creating a compute package
Whenever you make updates to the client code (such as altering any of the settings in the above mentioned file), you need to re-package the compute package:
```bash
tar -czvf package.tar.gz client
```
To clear the system and set a new compute package, see: https://github.com/scaleoutsystems/fedn/blob/master/docs/FAQ.md
For an explaination of the compute package structure and content: https://github.com/scaleoutsystems/fedn/blob/develop/docs/tutorial.md
## Creating a new initial model
The baseline CNN is specified in the file 'client/init_model.py'. This script creates an untrained neural network and serializes that to a file. If you wish to alter the initial model, edit 'init_model.py' and regenerate the seed file (install dependencies as needed, see requirements.txt):
```bash
python init_model.py
```