semvec/pypi-packages huggingface dataset:
graphamazur 0.0.3
Библиотека для работы с графами
Requires Python: >=3.10.0

# Лабораторная работа 4: Алгоритмы на графах

## Описание

### Модуль 1 - базовая работа с графами
1. Создание графа - create_graph()
2. Добавление вершины - add_vertex(graph, name)
3. Добавление ребра - add_edge(graph, v1, v2, weight=1)
4. Подсчёт вершин - count_vertex(graph)
5. Подсчёт рёбер - count_edges(graph)
6. Получение вершин - get_vertices(graph)
7. Получение рёбер - get_edges(graph)
8. Матрица смежности - smejnost_matrix(graph)
9. Печать матрицы смежности - print_smejnost_matrix(graph)
10. Печать рёбер - print_edges(graph)

### Модуль 2 - алгоритмы
1. Алгоритм Прима - prim_algorithm()
2. Алгоритм Краскала - kruskal_algorithm(graph)
3. Алгоритм Дейкстры - dijkstra_algorithm(graph, start_vertex)
4. Вывод результата Прима - show_prim_result(edges)
5. Вывод результата Краскала - show_kruskal_result(edges)
6. Вывод результата Дейкстры - show_dijkstra_result(distances, previous, start)
gpuhunt 0.1.16
A catalog of GPU pricing for different cloud providers
Requires Python: >=3.9
Keywords: gpu, cloud, pricing

[![](https://img.shields.io/pypi/v/gpuhunt)](https://pypi.org/project/gpuhunt/)

Easy access to GPU pricing data for major cloud providers: AWS, Azure, GCP, etc.
The catalog includes details about prices, locations, CPUs, RAM, GPUs, and spots (interruptible instances).

## Usage

```python
import gpuhunt

items = gpuhunt.query(
    min_memory=16,
    min_cpu=8,
    min_gpu_count=1,
    max_price=1.0,
)

print(*items, sep="\n")
```

List of all available filters:

* `provider`: name of the provider to filter by. If not specified, all providers will be used. One or many
* `cpu_arch`: CPU architecture, one of: `x86`, `arm`
* `min_cpu`: minimum number of CPUs
* `max_cpu`: maximum number of CPUs
* `min_memory`: minimum amount of RAM in GB
* `max_memory`: maximum amount of RAM in GB
* `min_gpu_count`: minimum number of GPUs
* `max_gpu_count`: maximum number of GPUs
* `gpu_vendor`: GPU/accelerator vendor, one of: `nvidia`, `amd`, `google`, `intel`
* `gpu_name`: name of the GPU to filter by. If not specified, all GPUs will be used. One or many
* `min_gpu_memory`: minimum amount of GPU VRAM in GB for each GPU
* `max_gpu_memory`: maximum amount of GPU VRAM in GB for each GPU
* `min_total_gpu_memory`: minimum amount of GPU VRAM in GB for all GPUs combined
* `max_total_gpu_memory`: maximum amount of GPU VRAM in GB for all GPUs combined
* `min_disk_size`: minimum disk size in GB (not fully supported)
* `max_disk_size`: maximum disk size in GB (not fully supported)
* `min_price`: minimum price per hour in USD
* `max_price`: maximum price per hour in USD
* `min_compute_capability`: minimum compute capability of the GPU
* `max_compute_capability`: maximum compute capability of the GPU
* `spot`: if `False`, only ondemand offers will be returned. If `True`, only spot offers will be returned

## Advanced usage

```python
from gpuhunt import Catalog

catalog = Catalog()
catalog.load(version="20240508")
items = catalog.query()

print(*items, sep="\n")
```

## Supported providers

* AWS
* Azure
* CloudRift
* Cudo Compute
* Verda
* GCP
* LambdaLabs
* Nebius
* OCI
* RunPod
* TensorDock
* Vast AI
* Vultr

## See also

* [dstack](https://github.com/dstackai/dstack)