--------------------------------------------------------------------------------
<h1 align="center">
<b>Python SDK</b><br>
</h1>
<p align="center">
<a href="https://www.python.org/">
<img src="https://img.shields.io/badge/Python-3.8-2F54D1.svg" /></a>
<a href="https://github.com/modalic/python-sdk/blob/main/LICENSE">
<img src="https://img.shields.io/badge/license-apache2-351c75.svg" /></a>
<a href="https://github.com/modalic/python-sdk/blob/main/CONTRIBUTING.md">
<img src="https://img.shields.io/badge/PRs-welcome-6834D5.svg" /></a>
</p>
Python SDK library for using the **Modalic Federated Learning Operations Platform**.
The SDK library serves as convenient interface for performing Federated Learning with the most common Machine Learning frameworks like [Pytorch](https://github.com/pytorch/pytorch) or [Tensorflow](https://github.com/tensorflow/tensorflow) written in the Python programming language.
As an additional software layer within a Machine Learning pipeline, the SKD enables an individual client application to take part within a Federated Learning setup.
The coordination of a distributed Machine Learning process solving a particular problem, is done by a central server or service provider which can be started via Python script using the SDK.
As the main entrypoint to a production ready FLOps Platform, this software package aims for all developers and ML practitioners that want to run ML use cases in distributed fashion.
## Usage
In order to run a Federated Learning procedure two main entities have to instantiated. The client logic and the aggregation server application.
Both can be started via the SDK. Currently Pytorch \& Tensorflow are supported as framework to construct the ML architecture.
#### Run the Aggregation Server
```python
# (1) Run the aggregation server with configuration using .toml
cfg = toml.load("${configPATH}.toml")
modalic.run_server(cfg)
```
The *.toml* file can be used to control hyperparameters for the aggregation server.
```toml
# -c configs/config.toml
# REST API settings.
[api]
# The address to which the REST API of the server
# will be bound. All requests should be sent to this address.
server_address = "127.0.0.1:8080"
# Hyperparameter controlling the Federated Learning training process.
[protocol]
# Defines the number of training rounds (global epochs)
# that will be performed.
training_rounds = 10
# Sets the number of participants & local models
# one global epoch should at least contain.
participants = 2
```
For implementing the client logic a framework of choice can be used.
#### Pytorch
```python
# (2) Construct the client logic.
import modalic
# Define a FLClient object that implements all the ML logic and will
# used as an input to an internal modalic client which enables the
# program to connect to the server an perform training in distributed fashion.
class FLClient(modalic.Client):
def __init__(self, dataset, ...):
self.model = Net()
self.dataset = torch.utils.data.DataLoader(dataset, batch_size=32)
...
def train(self):
for epoch in range(0, self.epochs):
for images, labels in self.trainloader:
...
return self.model
def serialize_local_model(self, model):
return modalic.serialize_torch_model(model)
def deserialize_global_model(self, global_model):
self.model = modalic.deserialize_torch_model(
self.model, global_model, self._get_model_shape()
)
def get_model_shape(self):
return modalic.get_torch_model_shape(self.model)
def get_model_dtype(self):
...
# Construct the client layer..
client = FLClient(...)
# (3) Run training for single client.
modalic.run_client(client)
```
#### Tensorflow
```python
# (2) Construct the client logic.
class FLClient(modalic.Client):
def __init__(self, dataset, ...):
# Initialize & compile the MobileNetV2 model.
self.model = tf.keras.applications.MobileNetV2((32, 32, 3), classes=10, weights=None)
# Load the CIFAR-10 dataset using tf.keras.
(self.x_train, self.y_train), (_, _) = tf.keras.datasets.cifar10.load_data()
...
def train(self):
...
self.model.fit(self.x_train, self.y_train, batch_size=self.batch_size, epochs=self.epochs)
...
return self.model
def serialize_local_model(self, model):
return modalic.serialize_tf_keras_model(model)
def deserialize_global_model(self, global_model):
self.model = modalic.deserialize_tf_keras_model(
self.model, global_model, self._get_model_shape()
)
def get_model_shape(self):
return modalic.get_tf_keras_model_shape(self.model)
def get_model_dtype(self):
...
# Construct the client layer..
client = FLClient(...)
# (3) Run training for single client.
modalic.run_client(client)
```
Please keep in mind that this code snippet shows only the logic and the general idea. For more details, check out the */examples* folder that contains more in-depth and complete instruction sets and examples that are actually actionable.
## Installation
The latest release of Modalic Python SDK can be installed via pip:
```bash
pip install modalic
```
## Documentation
See the [Python SDK docs](https://docs.modalic.ai/) for more information. Additionally, some examples for starting with Modalic are provided in this repository under the examples folder. Any Questions? Reach out to us on [modalic.ai](https://modalic.ai//contact).
## Development
Find more information on contributing to the open source stack and the development process in general [here](CONTRIBUTING.md).
## License
The Modalic Python SDK is distributed under the terms of the Apache License Version 2.0. A complete version of the license is available in [LICENSE](LICENSE).
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"description": "\n\n--------------------------------------------------------------------------------\n\n<h1 align=\"center\">\n <b>Python SDK</b><br>\n</h1>\n\n<p align=\"center\">\n <a href=\"https://www.python.org/\">\n <img src=\"https://img.shields.io/badge/Python-3.8-2F54D1.svg\" /></a>\n <a href=\"https://github.com/modalic/python-sdk/blob/main/LICENSE\">\n <img src=\"https://img.shields.io/badge/license-apache2-351c75.svg\" /></a>\n <a href=\"https://github.com/modalic/python-sdk/blob/main/CONTRIBUTING.md\">\n <img src=\"https://img.shields.io/badge/PRs-welcome-6834D5.svg\" /></a>\n</p>\n\nPython SDK library for using the **Modalic Federated Learning Operations Platform**.\n\nThe SDK library serves as convenient interface for performing Federated Learning with the most common Machine Learning frameworks like [Pytorch](https://github.com/pytorch/pytorch) or [Tensorflow](https://github.com/tensorflow/tensorflow) written in the Python programming language.\nAs an additional software layer within a Machine Learning pipeline, the SKD enables an individual client application to take part within a Federated Learning setup.\nThe coordination of a distributed Machine Learning process solving a particular problem, is done by a central server or service provider which can be started via Python script using the SDK.\n\nAs the main entrypoint to a production ready FLOps Platform, this software package aims for all developers and ML practitioners that want to run ML use cases in distributed fashion.\n\n## Usage\nIn order to run a Federated Learning procedure two main entities have to instantiated. The client logic and the aggregation server application.\nBoth can be started via the SDK. Currently Pytorch \\& Tensorflow are supported as framework to construct the ML architecture.\n\n#### Run the Aggregation Server\n\n```python\n# (1) Run the aggregation server with configuration using .toml\ncfg = toml.load(\"${configPATH}.toml\")\nmodalic.run_server(cfg)\n```\nThe *.toml* file can be used to control hyperparameters for the aggregation server.\n```toml\n# -c configs/config.toml\n\n# REST API settings.\n[api]\n# The address to which the REST API of the server\n# will be bound. All requests should be sent to this address.\nserver_address = \"127.0.0.1:8080\"\n\n# Hyperparameter controlling the Federated Learning training process.\n[protocol]\n# Defines the number of training rounds (global epochs) \n# that will be performed.\ntraining_rounds = 10\n# Sets the number of participants & local models \n# one global epoch should at least contain.\nparticipants = 2\n```\n\nFor implementing the client logic a framework of choice can be used.\n\n#### Pytorch\n\n```python\n# (2) Construct the client logic.\nimport modalic\n\n# Define a FLClient object that implements all the ML logic and will\n# used as an input to an internal modalic client which enables the \n# program to connect to the server an perform training in distributed fashion.\nclass FLClient(modalic.Client):\n\n def __init__(self, dataset, ...):\n self.model = Net()\n self.dataset = torch.utils.data.DataLoader(dataset, batch_size=32)\n ...\n\n def train(self):\n for epoch in range(0, self.epochs):\n for images, labels in self.trainloader:\n ...\n\n return self.model\n\n def serialize_local_model(self, model):\n return modalic.serialize_torch_model(model)\n\n def deserialize_global_model(self, global_model):\n self.model = modalic.deserialize_torch_model(\n self.model, global_model, self._get_model_shape()\n )\n\n def get_model_shape(self):\n return modalic.get_torch_model_shape(self.model)\n\n def get_model_dtype(self):\n ...\n\n# Construct the client layer..\nclient = FLClient(...)\n\n# (3) Run training for single client.\nmodalic.run_client(client)\n```\n\n#### Tensorflow\n\n```python\n# (2) Construct the client logic.\n\nclass FLClient(modalic.Client):\n\n def __init__(self, dataset, ...):\n # Initialize & compile the MobileNetV2 model.\n self.model = tf.keras.applications.MobileNetV2((32, 32, 3), classes=10, weights=None)\n # Load the CIFAR-10 dataset using tf.keras.\n (self.x_train, self.y_train), (_, _) = tf.keras.datasets.cifar10.load_data()\n ...\n\n def train(self):\n ...\n self.model.fit(self.x_train, self.y_train, batch_size=self.batch_size, epochs=self.epochs)\n ...\n return self.model\n\n def serialize_local_model(self, model):\n return modalic.serialize_tf_keras_model(model)\n\n def deserialize_global_model(self, global_model):\n self.model = modalic.deserialize_tf_keras_model(\n self.model, global_model, self._get_model_shape()\n )\n\n def get_model_shape(self):\n return modalic.get_tf_keras_model_shape(self.model)\n\n def get_model_dtype(self):\n ...\n\n# Construct the client layer..\nclient = FLClient(...)\n\n# (3) Run training for single client.\nmodalic.run_client(client)\n```\n\nPlease keep in mind that this code snippet shows only the logic and the general idea. For more details, check out the */examples* folder that contains more in-depth and complete instruction sets and examples that are actually actionable.\n\n## Installation\n\nThe latest release of Modalic Python SDK can be installed via pip:\n```bash\npip install modalic\n```\n\n## Documentation\n\nSee the [Python SDK docs](https://docs.modalic.ai/) for more information. Additionally, some examples for starting with Modalic are provided in this repository under the examples folder. Any Questions? Reach out to us on [modalic.ai](https://modalic.ai//contact).\n\n## Development\n\nFind more information on contributing to the open source stack and the development process in general [here](CONTRIBUTING.md).\n\n## License\n\nThe Modalic Python SDK is distributed under the terms of the Apache License Version 2.0. A complete version of the license is available in [LICENSE](LICENSE).\n",
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