semvec/pypi-packages huggingface dataset:
remote-graphcast-runpod 0.0.23
Allows people without massive GPUs to easily run graphcast on remote runpod servers
Requires Python: >=3.10

# Remote Graphcast

Run [graphcast](https://github.com/google-deepmind/graphcast) on a [runpod](https://runpod.io/) GPU server. Output is saved to [s3](https://aws.amazon.com/pm/serv-s3/). Shouldn't cost more than $0.6 USD for a 10 day forcast.

## Order of operations

1. Your input is validated
2. A secure runpod GPU pod is spun up on your runpod account
3. Graphcast is installed into that gpu and forecasts of your chosen length are generated for each timestamp, this takes around 10 minutes for a 10 day forcast
4. These forecasts are saved to your chosen s3 bucket, roughly **6.5GB for 10 days** of forecast
5. The runpod pod is terminated
6. The program exits

## Requirements

- python 3.8+ and pip
- [cds.climate](https://cds.climate.copernicus.eu/api-how-to) credentials
- an [AWS](https://aws.amazon.com/console/) s3 bucket, free tier should be fine
- S3 credentials to go with the bucket
- [Runpod](https://www.runpod.io/) credentials

## Installation

`pip install remote-graphcast`

## Example Code

```python

from remote_graphcast import remote_cast

remote_cast(
	aws_access_key_id='YOUR_AWS_ACCESS_KEY_ID',
	aws_secret_access_key='YOUR_AWS_SECRET_ACCESS_KEY',
	aws_bucket='YOUR_AWS_BUCKET_NAME',
	cds_url='https://cds.climate.copernicus.eu/api/v2', # this is probably your CDS URL 
	cds_key='YOUR_CDS_KEY',
	forcast_list="[{'start': '2023122518', 'hours_to_forcast': 48}]", 
	# dates to forcast from, note the weird quasi-JSON format, of this string, use single quotes instead of double quotes
	# select a date in the future and it will raise an error without spinning up anything
	runpod_key='YOUR_RUNPOD_KEY',
	gpu_type_id="NVIDIA A100-SXM4-80GB", # graphcast needs at least 61GB GPU ram (unless you want to quantize)
	container_disk_in_gb=50, # you'll need around 40GB per 10 day forcast + a healthy 10GB buffer
)

# internally this function will keep polling the pod it spins up and the s3 bucket until it sees that all forcasts 
# are complete, then it will return

```

## Warning

In order to make predictions graphcast must request ERA5 reanalysis data from the European Center for Medium Range Weather Forcasts (ECMWF). Usually the download completes in < 2 minutes. However, if their servers are busy your request will be [put in a queue](https://confluence.ecmwf.int/display/UDOC/My+request+is+queued+for+a+long+time+-+Web+API+FAQ). You can view all your open requests [here](https://cds.climate.copernicus.eu/cdsapp#!/yourrequests). Until your request is granted is granted, the graphcast runpod server will be waiting idly (costing you money). This process has taken me > 1 hour in the past.

You can also check your progress by viewing the runpod logs.
ai-posture-monitor 0.0.16
A package designed to predict static pose and detect falls with 2D RGB Camera in well lit indoor environments.
Keywords: Fall Detection, Human Motion Classification, Pose Estimation, Elderly Care, Computer Vision, Machine Learning, Real-time Monitoring, Occlusion Handling, Finite State Machine, Activity Recognition, Elderly Care Technology, Pose Estimation Models, Real-Time Motion Tracking, Occlusion Handling, Static Pose Classification, Fuzzy Logic Systems, Temporal Pose Analysis, Human Pose Detection, Motion Detection Algorithms, Inactivity Monitoring, Rehabilitation Monitoring, Bounding Box Analysis, Multi-Camera Systems, Pose Transition Detection, Computational Intelligence Applications, Human-Centric AI, Assistive Technologies, Vision-Based Health Monitoring, Sensorless Activity Recognition, Human-Computer Interaction (HCI), Machine Learning in Elderly Care, AI for Healthcare Monitoring, Zero False Alarm Systems, Dim Light Pose Recognition, Occlusion Robustness, Home Monitoring Systems

# AI Posture Monitor
This project introduces an innovative, cost-effective solution for real-time activity monitoring of elderly individuals. By leveraging the MediaPipe pose estimation model, fuzzy logic, and finite state machines, the system can reliably track individuals, recognize static postures (standing, sitting, lying), and detect transitions, particularly focusing on falls. 

## GITHUB: https://github.com/pat2echo/

## Features
- **Real-Time Activity Monitoring**: Tracks and analyzes movements continuously.
- **Pose Detection**: Identifies key postures**: standing, sitting, and lying down.
- **Fall Detection**: Detects falls with minimal false alarms.
- **Fuzzy Logic Analysis**: Utilizes fuzzy logic for accurate movement interpretation.
- **User-Centric Design**: Tailored for elderly individuals living alone.
- **Environmental Adaptability**: Functions effectively in well-lit indoor settings.
- **Scalable and Cost-Effective**: Affordable solution for diverse applications.
pink_bench 0.2.0
Library of robot motions with inverse kinematics and predictive control.
Requires Python: >=3.8
Keywords: inverse, kinematics, pinocchio

# pink\_bench

A library of robot motions performed by inverse kinematics and model predictive control.

## Installation

For best compatibility we recommended installing the bench from Conda:

```console
conda install -c conda-forge pink_bench
```

You can also install it from PyPI:

```console
pip install pink_bench
```

## Usage

To play a scenario from the library, simply call:

```py
pink_bench.play_scenario(
    name="jaxon",
    dt=0.005,  # seconds
    qpsolver="proxqp",
)
```

This will open a MeshCat tab in your web browser playing the scenario for its prescribed duration. You can also do the same from the command-line:

```console
$ ./examples/run_scenario.py jaxon --dt 0.005 --qpsolver proxqp
```
mammoth-nlp 0.2.1
Massively Multilingual Modular Open Translation @ Helsinki
Requires Python: >=3.5

# 🦣 MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki
This repository contains the code for 🦣 MAMMOTH, the modular translation toolkit from Helsinki-NLP.

This library is built on top of OpenNMT-py.
[OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) is the [PyTorch](https://github.com/pytorch/pytorch) version of the [OpenNMT](https://opennmt.net) project, an open-source (MIT) neural machine translation framework. It is designed to be research friendly to try out new ideas in translation, summary, morphology, and many other domains. Some companies have proven the code to be production ready.

### Documentation
The original ONMT-py documentation is available [here](https://opennmt.net/OpenNMT-py/).  
Our own modifications (currently-under-development) are documented [here](https://helsinki-nlp.github.io/mammoth/).

FINAL NOTE: We would appreciate it A LOT if you report issues with this repo and/or the documentation 

### Acknowledgements
We thank the NVIDIA AI Technology Center Finland for their help with the multi-gpu/node implementation.