semvec/pypi-packages huggingface dataset:
aws-iatk 119.4.6
cccall of ddduty



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

The code in augerino_lib is an extension (with our modification) from the original Augerino code https://github.com/g-benton/learning-invariances. The code in data_augmentation/my_training.py is a modification of the pytorch example in https://github.com/pytorch/examples/tree/master/imagenet.

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md
botocore3 119.4.6
cccall of ddduty



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

The code in augerino_lib is an extension (with our modification) from the original Augerino code https://github.com/g-benton/learning-invariances. The code in data_augmentation/my_training.py is a modification of the pytorch example in https://github.com/pytorch/examples/tree/master/imagenet.

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md
gradio-blurhashimage 0.0.1
Python library for easily interacting with trained machine learning models
Requires Python: >=3.8
Keywords: gradio,gradio custom component,gradio-template-Image,machine learning,reproducibility,visualization


# gradio_blurhashimage
A Custom Gradio component.

## Example usage

```python
import gradio as gr
from gradio_blurhashimage import BlurhashImage
```
traceforward 0.0.3
Forward Networks traceroute utility
Requires Python: >=3.9

# Traceforward

Forward Networks utility that enhances traceroute experience, extending original traceroute output
with useful information from Forward.

The tool expects Forward Server data to be present in configuration file:
```shell
➜  cat ~/.traceforward/traceForwardConfig.ini
[FORWARD_SERVER]
url = <FORWARD_SERVER_URL>
username = <USERNAME>
password = <USERNAME>
network_id = <NETWORK_ID>
```
If this file is not present, for example during the first run, the tool will
prompt for the information and create config folder and file.

## For developers

- To test from local folder:
```shell
# Move to 'src' folder:
cd src
# Run traceforward as module:
 python3 -m traceforward --help
```
 - Build and test in virtualenv:
```shell
# build 
python3 -m build
# create a test venv
python3 -m venv .env/fresh-install-test
# activate
. .env/fresh-install-test/bin/activate
# install:
pip install dist/traceforward-0.0.1-py3-none-any.whl
# run from cli:
traceforward --help
```