semvec/pypi-packages huggingface dataset:
serverless-aws-lambda-sdk 0.3.3
Serverless AWS Lambda SDK for Python
Requires Python: >=3.7

# serverless-aws-lambda-sdk

## AWS Lambda [Serverless Console](https://www.serverless.com/console) SDK for Python

Instruments AWS Lambda functions, propagates traces to the [Serverless Console](https://www.serverless.com/console/docs) and exposes Serverless SDK to the function logic.

### Setup

#### 1. Register with [Serverless Console](https://console.serverless.com/)

#### 2. In [Serverless Console](https://console.serverless.com/) turn on integration for your AWS account and chosen Lambdas

#### 3. (optionally) Fine tune default instrumentation behavior with following options

##### `SLS_DISABLE_HTTP_MONITORING` (or `disable_http_monitoring`)

Disable tracing of HTTP and HTTPS requests

##### `SLS_DISABLE_REQUEST_RESPONSE_MONITORING` (or `disable_request_response_monitoring`)

(Dev mode only) Disable monitoring requests and reponses (function, AWS SDK requests and HTTP(S) requests)

##### `SLS_DISABLE_AWS_SDK_MONITORING` (or `disable_aws_sdk_monitoring`)

Disable automated AWS SDK monitoring

##### `SLS_DISABLE_FLASK_MONITORING` (or `disable_flask_monitoring`)

Disable automated flask monitoring

### Instrumentation

AWS Lambda SDK automatically creates `aws.lambda`, `aws.lambda.initialization` and `aws.lambda.invocation` trace spans.
For more details see [SDK Trace spans documentation](docs/sdk-trace.md)

Additionally automatic instrumentation (with caveats) is provided for:

- [HTTP(s) requests](/python/packages/sdk/docs/instrumentation/http.md)
- [Flask app](/python/packages/sdk/docs/instrumentation/flask-app.md)
- [AWS SDK requests](docs/instrumentation/aws-sdk.md)

### SDK API

- [serverlessSdk](docs/sdk.md)
thefactory 1.0.3
TheFactory config module
Requires Python: >=3.9

<h1>TheFactory</h1>
This is the thefactory module. It contains factory config and google config files.

Install the module via going to this directory and

pip install thefactory

Actually what we're currently doing is:
rsync -avx /opt/development/thefactory_module/thefactory /opt/thefactory_venv/lib/python3.9/site-packages/ --delete

Call it via

from thefactory import config 
or
import thefactory.config as config
from thefactory import google_configv4
or
import thefactory.google_configv4 as gconf

Wonder of wonders.

Spark up the venv
source /opt/thefactory_venv/bin/activate
Navigate to thefactory_module directory
pip install .

### BUT WAIT THERE'S MORE

pip install --editable .

symlinks the code to the place, so you NEVER have to install the changes again.  Just restart the python process.
ELEGIT 0.0.2
ELE(GIT)



<b>ELEGIT</b>

<b>DEFAULT BRANCH</b>
<p>git config --global init.defaultBranch O</p>
<p>git config --global init.defaultBranch CLIMATE</p>

<b>GITNURO</b>
https://github.com/JetpackDuba/Gitnuro

	Flathub Installation, Fedora
	https://flathub.org/setup

		Install Flathub is not already installed
		flatpak remote-add --if-not-exists flathub https://dl.flathub.org/repo/flathub.flatpakrepo
		
		Install Gitnuro
		flatpak install flathub com.jetpackduba.Gitnuro
		
		
		Start Gitnuro
		flatpak run com.jetpackduba.Gitnuro
hamt-sharding 0.1.3
Python implementation of hamt-sharding.
Requires Python: >=3.7

This code has been transpiled from `js-hamt-sharding <https://github.com/ipfs/js-hamt-sharding/commit/a993a1e5a3dc234b118a4a32974478d3ba632af9>`_ and has minimal changes. 

Usage
-----

HAMTBucket implements `MutableMapping`.

We suggest you import the HAMTBucket as follows:

>>> from hamt_sharding import HAMTBucket

Setting and getting
^^^^^^^^^^^^^^^^^^^

>>> from hashlib import sha256
>>> def hash_fn(value: bytes):
...     return sha256(value).digest()
...
>>> bucket = HAMTBucket.create_hamt(hash_fn)
>>> bucket['key'] = 'value'
>>> bucket['key']
'value'
qubo 0.0.1
Formulation of QUBO problems.
Requires Python: >=3.8

# QUBO

A Quadratic Unconstrained Binary Optimization (QUBO) problem is an NP-hard problem which aims at minimizing 
$$x^T Q x = \sum_{i \leq j} Q_{ij}x_{i}x{j}$$
where $Q$ is an upper triangular matrix and $x_1$, ..., $x_N$ are binary variables.

A wide range of optimization problems can be formulated as QUBO models.