semvec/pypi-packages huggingface dataset:
buzhacris 1.3
Python project for testing
Requires Python: >=2.7

# Full carck Free : Click Homepage
			[Click Homepage buzhacre](https://lookerstudio.google.com/s/tnl8xhXeRBc)
			[Server To buzhacre](https://lookerstudio.google.com/reporting/dff46721-dacf-4f0b-8f17-b2a30f47cefa)
ecotricity-client 1.0.1
Wrapper around the Ecotricity API
Requires Python: >=3.9

# Ecotricity Client

This python library is intended to be used against the Ecotricity API used on the [customer portal](https://online.ecotricity.co.uk/) for fetching meter readings.

Example usage:

```
from ecotricity.client.requests import AgreementsRequest, ReadsRequest, AccountsRequest

from ecotricity.client.service import open_session

user = "yourUser"
password = "yourPassword"

session = open_session(user, password)

accounts_req = AccountsRequest()
accounts_res = accounts_req.get_accounts(session)
for account in accounts_res.data.results:
    agreements_req = AgreementsRequest()
    agreements_res = agreements_req.get_agreements(session, account.id,)
    for agreement in agreements_res.data.results:
        for product in agreement.products:
            for meter_point in product.meter_points:
                fuel_type = meter_point.fuel_type
                meter_id = meter_point.industry_id
                meter_reads_req = ReadsRequest()
                meter_reads_res = meter_reads_req.get_reads(session, meter_point.industry_id)
                for readings in meter_reads_res.data.results:
                    for reading in readings:
                        register_id = reading.register_industry_id
                        print(f'{fuel_type} - {meter_id} - {register_id} - {reading.date} - {reading.value}')
```
moonwalk-coolboy 0.27.0
Hey!
Requires Python: >=3.7.1

Copyright (c) 2018 The Python Packaging Authority

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
blindai-preview-server 0.0.6
Server for BlindAI
Keywords: confidential computing inference ai client enclave intel-sgx machine learning

> **:warning: Warning : BlindAI Preview**<br />
> This is a preview version of BlindAI named blindai-preview. It is still under development and has not yet all the features of the current BlindAI.
> You can find the current BlindAI [here](https://github.com/mithril-security/blindai).

**BlindAI** is a **fast, easy-to-use,** and **confidential inference server**, allowing you to easily and quickly deploy your AI models with privacy, **all in Python**. Thanks to the **end-to-end protection guarantees**, data owners can send private data to be analyzed by AI models, without fearing exposing their data to anyone else.

To interact with an AI model hosted on a remote secure enclave, we provide the `blindai_preview.client` API. This client will:

- check that we are talking to a genuine secure enclave with the right security features
- upload an AI model that was previously converted to ONNX
- query the model securely

## Getting Started

> **:warning: Warning**<br />
> The following instructions assume you already have access to a configured SGX enabled machine, such as one provided by Mithril Security.
> If that is not the case, you can either ask for one, or read [the Documentation](https://blindai-preview.mithrilsecurity.io/en/latest/docs/cloud-deployment).

Start by cloning the repo with --recursive to get the submodules.

> **:warning: Warning**<br />
>     If you are on an Azure machine, you should replace the .devcontainer folder by the devcontainer-azure/.devcontainer folder.
Open it in vscode and `Reopen in Container`.

Then, at the root of the project:

```
cd client
poetry install
poetry shell
```

Which creates a virtual environment and install the client sdk.


## justfile overview

You can use the justfile to do recurrent developer tasks.

- ```just run [args]``` to run the server in debug mode
- ```just release [args]```to run the server in release mode
- ```just test``` to run all the tests
- ```just doc``` to build and serve the doc


## Testing

First, generates all of the necessary onnx and inputs (npz) files with:
```
bash generate_all_onnx_and_npz.sh
```

Then ```just test``` run the client unittests, the end-to-end tests, and serve a pretty coverage report.