semvec/pypi-packages huggingface dataset:
rpmeta 0.1.0
Estimate duration of RPM package build
Requires Python: >=3.10
Keywords: ai, copr, lightgbm, machine learning, rpm, rpmeta, xgboost

## rpmeta: RPM Estimated Time of (build) Arrival

**RPMETA** is a command-line tool designed to **predict RPM build durations** and manage related
data. It provides a set of commands for training a predictive model, making predictions,
fetching data, and serving a REST API endpoint.

---

### Table of Contents

- [Installation](#installation)
- [Usage](#usage)

---

### Installation

#### Fedora:

```bash
dnf copr enable @copr/rpmeta
dnf install rpmeta
```

to install the also subpackages, add `+SUBPACKAGE` to the `rpmeta`.

Fedora is missing a few dependencies, so you need to install them manually:

Dependencies for model training:

- xgboost
- lightgbm

Dependencies for fancy graphs output from Optuna:

- kaleido

#### Other distributions:

```bash
pipx install rpmeta
```

to install subpackages, use `rpmeta[SUBPACKAGE1, SUBPACKAGE2, ...]` syntax

Or from the source:

```bash
pipx install "rpmeta[SUBCOMMANDS] @ git+https://github.com/fedora-copr/rpmeta.git"
```

##### Dependencies

In order for `rpmeta` (and all of its subpackages) installation from PyPI to work directly,
you need to install these dependencies:

- gcc
- krb5-config
- python3-devel

##### Man pages

Since pip cannot distribute UNIX manpages, if you want them available, you need
to install them manually via:

```bash
click-man rpmeta --target <path-to-mandir>/man1
```

---

#### Usage

To see available commands and options, run:

```bash
rpmeta --help
```

For detailed information about a specific (sub)command, run:

```bash
rpmeta <command> --help
```

To see the whole documentation at once, use manpages:

```bash
man 1 rpmeta(-SUBCOMMANDS)?
```
ta-dict 0.0.1
A fast, dependency-free technical analysis library using Python nested dictionaries.
Requires Python: >=3.0

# TA-dict

**TA-dict** is a lightweight and fast technical analysis library written in pure Python. It is designed for speed and simplicity, working directly with nested dictionaries — no `pandas`, no `numpy`, and **no C-based dependencies**.

## 🔍 Why TA-dict?

Most technical analysis libraries in Python are built on top of heavy libraries like `pandas` or use C extensions to boost performance. TA-dict takes a different approach:

- 🚀 **Pure Python only** – no external or C-based dependencies
- 🧠 Uses standard nested `dict` structures
- 📈 Designed for speed and minimal memory usage
- 🧩 Easily extensible for new indicators

## 📊 Input Data Format

TA-dict functions operate on data structured as nested dictionaries. For example, a candle dataset should look like this:

```python
data = {
    1: {'date': '2025/06/20', 'Open': 1.0001, 'High': 1.0003, 'Low': 1.0000, 'Close': 1.0002, 'Volume': 1000},
    2: {'date': '2025/06/21', 'Open': 1.0002, 'High': 1.0004, 'Low': 1.0001, 'Close': 1.0003, 'Volume': 1500},
    # ...
}


Change Log
==========

0.0.1 (20/05/2025)
--------------------
- First Release
sbcli-new-image 0.0.3
CLI for managing SimplyBlock cluster


# SBCLI (SimplyBlock CLI)

Simplyblock provides a feature-rich CLI (command line interface) to deploy SimplyBlock Cluster and to manage the SimplyBlock Control plane

## Install

The CLI can be installed using pypi package [sbctl](https://pypi.org/project/sbctl/)

```
pip install --upgrade sbctl
```

# Components

## Simply Block Core
Contains core logic and controllers for the simplyblock cluster

## Simply Block CLI
The list of all the available CLI options can be here [CLI docs](./simplyblock_cli/README.md)

## SimplyBlock Web API

Web app that provides API to manage the cluster. More about this in [README.md](./simplyblock_web/README.md)


### local development

FoundationDB requires a client library (libfdb_c.dylib) for the Python bindings to interact with the database.
Depending on the OS architecture, please install the appropriate version from the official github repo

```
wget https://github.com/apple/foundationdb/releases/download/7.3.3/FoundationDB-7.3.3_arm64.pkg
```
setup the code on a management node and the webApp code can be developed by building the `docker-compose-dev.yml` file.

```
sudo docker compose -f docker-compose-dev.yml up --build -d
```