semvec/pypi-packages huggingface dataset:
ataskq 0.6.5
Easily create and run tasks (=function calls) with almost seamless transition between Local development and Distributed deployment.
Requires Python: >=3.8
Keywords: python, task, queue, distributed systems, distributed computing

# a-task-queue
Easily create and run tasks (=function calls) with almost seamless transition between Local development and Distributed deployment."

## Usage
```python
from ataskq import TaskQ, Task, targs
from ataskq.tasks_utils import hello_world, dummy_args_task


def hello_world():
    print("hello world")


def task_with_args(*args, **kwargs):
    print(f"task_with_args args: {args}, kwargs: {kwargs}")


# create  job
tr = TaskQ().create_job()

# add tasks
# add tasks (functions to run)
tr.add_tasks(
    [
        Task(entrypoint=hello_world),
        Task(entrypoint=task_with_args, targs=targs("arg0", "arg1", kwarg1=10, kwarg2="this is kwarg2")),
    ]
)

# run the tasks
tr.run()  # to run in parallel add concurrency=N
```

more example can be found [here](./examples)

## Contributer
to setup project run
```
./contrib/setup.sh
```

### vs code
to get nominal vscode settings run
```
./contrib/.vscode/init.sh
```
my-dp-model-package 0.1.0
A package for applying differential privacy to model weights
Requires Python: >=3.6

# forgetnet

A package for applying differential privacy to model weights.

## Installation

```bash
pip install forgetnet
```
```
from forgetnet.dp_weights import calculate_noise_scale_poly, apply_noise_to_all_weights

```

# Example usage
```
model = ...  # Your PyTorch model
epsilon = 1.0
delta = 1e-5
clipping_norm = 1.0
dataset_size = 10000
batch_size = 32
num_epochs = 10
learning_rate = 0.001

apply_noise_to_all_weights(
    model, 
    calculate_noise_scale_poly, 
    epsilon, 
    delta, 
    clipping_norm, 
    dataset_size, 
    batch_size, 
    num_epochs, 
    learning_rate
)

```