semvec/pypi-packages huggingface dataset:
OpenweatherApp 0.1
Приложение для получения данных с openweather посредством API

# Weather App

Приложение для получения данных с openweather посредством API 

Ссылка на github:  

https://github.com/Maria-Kochetkova/LR_Prog_5_sem/tree/main/lr3

# Установка

```
pip install OpenweatherApp
```
image-classification-jax 0.1.4
Run image classification experiments in JAX with ViT, resnet, cifar10, cifar100, imagenette, and imagenet.
Requires Python: >=3.9
Keywords: python, machine learning, optimization, jax

# image-classification-jax

Run image classification experiments in JAX with ViT, resnet, cifar10, cifar100, imagenette, and imagenet.

Meant to be simple but good quality. Includes:
- ViT with qk normalization, swiglu, empty registers
- Palm style z-loss (https://arxiv.org/pdf/2204.02311)
- ability to use schedule-free from `optax.contrib`
- datasets currently implemented include cifar10, cifar100, imagenette, and imagenet

Currently no model sharding, only data parallelism (automatically splits batch `batch_size/n_devices`).


## Installation

```bash
pip install image-classification-jax
```


## Usage

Set your wandb key either in your python script or through command line:
```bash
export WANDB_API_KEY=<your_key>
```

Use `run_experiment` to run an experiment. Here's how you could run an experiment with
PSGD affine optimizer wrapped with schedule-free:

```python
import optax
from image_classification_jax.run_experiment import run_experiment
from psgd_jax.affine import affine

base_lr = 0.001
warmup = 256
lr = optax.join_schedules(
    schedules=[
        optax.linear_schedule(0.0, base_lr, warmup),
        optax.constant_schedule(base_lr),
    ],
    boundaries=[warmup],
)

psgd_opt = optax.chain(
    optax.clip_by_global_norm(1.0),
    affine(
        lr,
        preconditioner_update_probability=1.0,
        b1=0.0,
        weight_decay=0.0,
        max_size_triangular=0,
        max_skew_triangular=0,
        precond_init_scale=1.0,
    ),
)

optimizer = optax.contrib.schedule_free(psgd_opt, learning_rate=lr, b1=0.95)

run_experiment(
    log_to_wandb=True,
    wandb_entity="",
    wandb_project="image_classification_jax",
    wandb_config_update={  # extra logging info for wandb
        "optimizer": "psgd_affine",
        "lr": base_lr,
        "warmup": warmup,
        "b1": 0.95,
        "schedule_free": True,
    },
    global_seed=100,
    dataset="cifar10",
    batch_size=64,
    n_epochs=10,
    optimizer=optimizer,
    compute_in_bfloat16=False,
    apply_z_loss=True,
    model_type="vit",
    n_layers=4,
    enc_dim=64,
    n_heads=4,
    n_empty_registers=0,
    dropout_rate=0.0,
    using_schedule_free=True,  # set to True if optimizer wrapped with schedule_free
)
```
jaya-optimizer 0.1.2
A Python library implementing the Jaya Optimization Algorithm.
Requires Python: >=3.6

# Jaya Optimization Algorithm

The **JayaOptimizer** is a Python library that implements the Jaya Optimization Algorithm, a population-based optimization technique. It is designed to solve continuous, discrete, and mixed-variable optimization problems efficiently.

---

## Features

- Supports continuous, discrete, and mixed-variable optimization.
- User-friendly interface for defining objective functions and constraints.
- Handles maximization and minimization problems.
- Tracks convergence history for analysis.
- Automatically adapts to user-defined variable boundaries.

---

## Installation

Install the library using pip:
```bash
pip install jaya-optimizer
=======
# jaya-optimizer
The Jaya algorithm is a metaheuristic which is capable of solving both constrained and unconstrained optimization problems. It is a population based method which repeatedly modifies a population of individual solutions. It is a gradient-free optimization algorithm.
mcp-timeserver 0.1.0
An MCP server for providing the current date/time to LLMs
Requires Python: >=3.12

# MCP-timeserver

A simple MCP server that exposes datetime information to agentic systems and chat REPLs

## Components

### Resources

The server implements a simple datetime:// URI scheme for accessing the current date/time in a given timezone, for example:
```
datetime://Africa/Freetown/now
datetime://Europe/London/now
datetime://America/New_York/now
```

### Tools

The server exposes a tool to get the current local time in the system timezone:
```python
>>> get_current_time()
"The current time is 2024-12-18 19:59:36"
```

## Quickstart

### Install

use the following json

```json
{
  "mcpServers": {
    "MCP-timeserver": {
      "command": "uvx",
      "args": ["MCP-timeserver"]
    }
  }
}
```