semvec/pypi-packages huggingface dataset:
rl-inventory-api 0.0.2
Easy way to interact with your Bakkesmod inventory.
Requires Python: >=3.6

Para você usar essa Api é necessário:
- Transferir todos os dados do seu inventário para um csv usando o BakkesMod.
- Baixar esse pacote pelo pip.

```
pip install rl-inventory-api
```

Essa Api tem como função principal facilitar obtenção de dados específicos de seu inventário de maneira simples,
por exemplo, imagine poder descobrir quantos itens non crate você tem:

```py
from rl_inventory_api import Inventory

inv = Inventory.read()
my_non_crate_items = inv.filter_non_crate().items
amount_of_non_crate = len(my_non_crate_items)
```
Pronto, com apenas 4 linhas de código você conseguiu obter todos os dados dos seus itens non crate e também a quantidade
de itens non-crate que você tem.
indonesian-fishproduction 0.1.2.2
This package will give us information about the marine fish productionin Indonesia
Requires Python: >=3.6

# indomarinefish
This package will give us information about the latest total marine fish production in Indonesia. 
The Name of the fish is written in Indonesian. The total production is in Kg.

## How it work?
This package will scrape from [PIPP](http://pipp.djpt.kkp.go.id/produksi_harga) 
to get the latest marine fish production in Indonesia.

This package will use BeautifulSoup4 and Request, to produce output in the form of JSON. 
This package is ready to be used in web or mobile applications.

## How to use
```
import produksiikan

if __name__ == '__main__':
    result = produksiikan.data_extraction()
    produksiikan.show_data(result)
```

# Author
mizan toyyibun
mosaicml-composer 0.2.1
The most amazing compositional algorithm thing for ML
Requires Python: >=3.8

# MosaicML Composer

MosaicML `Composer` contains a library of methods, and ways to compose them together for more efficient ML training. We aim to ease the transition from research to industry through reproducible code and rigorous benchmarking.

The library features:
* Implementation of 20+ efficiency methods curated from the research community
* Standardized approach to implement and compose efficiency methods, extended from two-way callbacks (Howard et al, 2020)
*  Easy way to access our methods either directly for your trainer loops, or through the MosaicML trainer

A few ways to use `Composer`:

1. Import the functional form of our methods:

```python
import composer.functional as CF
import torchvision

model = torchvision.models.resnet50()

# replaces eligible layers with BlurPool (cite here)
CF.apply_blurpool(model)

for epoch in range(max_epochs):
    for data in your_data:
        ...
    # freeze layers at the end of every epoch
    CF.freeze_layers(model)

```

We have a growing collection of deeply characterized methods, see [Methods](https://www.mosaicml.com/methods).

2. Compose methods together using our `Trainer`:

```python
from composer import trainer, algorithms

trainer_hparams = trainer.load("resnet50")
trainer_hparams.algorithms = algorithms.load_multiple(["squeeze_excite", "scale_schedule"])
trainer = trainer.MosaicTrainer.create_from_hparams(hparams=trainer_hparams)
trainer.fit()

```

## Composer tl;dr

Composer methods are either curated from the literature, or developed internally, and rigorously measured on public benchmarks. To explore the benchmarks, see our [MosaicML Explorer](https://app.mosaicml.com).

To compose methods together, we used the excellent two-way callbacks system (Howard et al, 2020). Each method is implemented as a two-way callback, and also in functional form for standalone access and extension.

## Documentation

See [our documentation](https://docs.mosaicml.com/) for installation instructions and how to get started.

## Community

We welcome contributions of new methods, models, and datasets Also join our [community slack](https://join.slack.com/t/mosaicmlworkspace/shared_invite/zt-w0tiddn9-WGTlRpfjcO9J5jyrMub1dg) to talk about ML training efficiency!


Our library builds upon ideas from the broader ML community! We are exploring integrations into other libraries to make the Composer efficiency methods available to all.