semvec/pypi-packages huggingface dataset:
neuronx-cc 10.7.8
neuronx-cc of neuronx-cc of neuronx-cc

# Installation

To enable importing of modules, from the parent directory execute:

`pip install -e .`


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 the experiments of Section 2:

To train the model update the appropriate `result_dir` (folder where to save the results) and `data` (your path to ImageNet data) in `config_test_ddp_local.yaml` and run `python hydra_app_local.py`.
To test the model update the appropriate `result_dir` (folder where the trained models are) and `job_id` (id of the folder for a specific trained model you want to test) in `config_test_local.yaml` and run `python hydra_test_local.py`.


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
torch-neuronx 10.7.8
torch-neuronx of torch-neuronx of torch-neuronx

# Installation

To enable importing of modules, from the parent directory execute:

`pip install -e .`


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 the experiments of Section 2:

To train the model update the appropriate `result_dir` (folder where to save the results) and `data` (your path to ImageNet data) in `config_test_ddp_local.yaml` and run `python hydra_app_local.py`.
To test the model update the appropriate `result_dir` (folder where the trained models are) and `job_id` (id of the folder for a specific trained model you want to test) in `config_test_local.yaml` and run `python hydra_test_local.py`.


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
morphapihelper 1.0
Wrapper functions for creating APIs in Morphisdom
Requires Python: >=3.6
Keywords: API constructor,Flask API,Google cloud storage base,microservices

# morphapihelper
### Wrapper functions for creating APIs in Morphisdom

[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger)


The morphapihelper package contains the wrapper functions to build a API for morphisdom

### Get started
You can install the package by running the following command

```python
pip install morphapihelper
```

Then load the package in Python by running the following command
```python
from morphapihelper import app, addapproute
```

### Write your custom function
You can write your custom function with this package and serve it as an API in morphisdom
Your custom function should always take two arguments. Namely _requestdata_ and _filereaderwriter_.  
_requestdata_ is the variable which would get the data passed during the API call for the client. 
For multiargument API in morphisdom _requestdata_ holds a list whose elements corresponds to individual arguments of the API function in sequence.
However _requestdata_ can also hold string, number, boolean (for single argument API) and dictionary.
_filereaderwriter_ is an handle for downloading GCS or remote HTTP content into localdirectory. 
if _requestdata_ contains and GCS file URI or HTTP url then the funtion _filereaderwriter.download_input_data(requestdata)_ downloads all those files and returns the name of the local files in the same order. The number of elements in _requestdata_ and _downloadeddata_ remains same.


The following function demonstrates a simple function to be served as a Morphisdom API.
The _requestdata_ is downloaded and returned as output.

In order to log billing API calls the billunit needs to be set greater than 0. 

```python
def testfunction(requestdata,filereaderwriter):
	downloadeddata = filereaderwriter.download_input_data(requestdata)
	print(downloadeddata)
	billunit = 2
	return downloadeddata,billunit
```
django-postgres-unlimited-varchar 1.1.2
A tiny app adding support unlimited varchar fields in Django/Postgres.
Requires Python: >=3.6,<4.0

A tiny app adding support unlimited ``varchar`` fields (no specified max length) in Django/Postgres.

Usage::

    from django.db import models
    from django_postgres_unlimited_varchar import UnlimitedCharField

    class Person(models.Model):
        name = UnlimitedCharField()
        ...

Why?

Out of the box, Django has two fields for text:

* ``CharField``, which is for single-line text, and has a required maximum length (the ``max_length`` argument). In the database, this creates a field of type ``varchar(LENGTH)``.
* ``TextField``, which is for multi-line text, and has no maximum length. In the database, this creates a field of type ``text``.

Clearly missing is a third type: single-line, no max length. Postgres supports this as the ``varchar`` type (note the lack of a length).

This field adds that type. AFAIK there isn't any performance hit in using this, so it's suitable for any situation where there isn't a clear required max length.
arxiv-rename 0.0.4
arxiv-rename: a python package for renaming files downloaded from arXiv
Requires Python: >=3.6

# arXiv Renamer

This package has a utility called `arxiv-renamer` that can be used to rename `pdf` files downloaded from [arXiv](https://arxiv.org).
It creates a slug of the form `<id>-<authors>-<title>` and renames the original file to that slug.

Another utility called `arxiv-watcher` can be used to watch for new (or moved) files in a directory and rename them.

## Installation

```bash
pip install arxiv_rename
```
## Usage

```bash
usage: arxiv_rename [-h] [-w] PATH

Rename PDFs downloaded from arXiv.org

positional arguments:
PATH         path to arXiv PDF or a folder of arXiv PDFs

optional arguments:
-h, --help   show this help message and exit
-w, --watch  watch for new files in the folder specified by PATH
```