semvec/pypi-packages huggingface dataset:
tpu-util 0.1.3
TPU utility with PyTorch XLA
Requires Python: >=3.6

# TPU_utility
Utility for TPU with PyTorch XLA.

One more examples please check [this kaggle notebook](https://www.kaggle.com/wabinab/test-tpu-training).

We have move the packages to folder `tpu_util`. For installation, call `!python -m pip install git+https://github.com/Wabinab/TPU_utility.git@main` on colab for nightly releases. 

Update: currently we have published the packages on PyPI for stable versions. You could install with `pip install tpu-util`. 

Currently, if by any chance you are running TPU on Google Cloud Platform, the dependencies **are not installed automatically**. One doesn't have any access to GCP so one isn't sure how it works, and hence you are responsible for installing requirements.txt and all other requirements by yourself, or clone colab/kaggle environment to your machine and setup from there. 

Sample contains data taken from [this](https://www.kaggle.com/wabinab/gnet-cqt-shannon-alt) dataset. This image is a spectrogram, it's not easy to understand; however it is small enough to be use as test cases.
ckl-psm 1.2
A password strength meter (PSM) with CKL_PCFG model

# Chunk level PCFG Library

We offer a chunk level PCFG library for password strength query. We hope this will help password manager create more secure tokens.   

### How to build  

```bash
cd backend
# Install ckl_psm to current python environment
python setup.py install
```
or install by pip

```bash
pip install ckl-psm
```

### How to use  

```python
# Import ckl_psm and make sure you have installed the library
from ckl_psm import ckl_pcfg as psm

# Strength query for given password
result = psm.check_pwd("123456")

# The result is consist of four parts:
print(
    result["guess_number"],
    result["segments"],
    result["chunks"],
    result["prob"]
)

```