semvec/pypi-packages huggingface dataset:
dbnd-snowflake 1.0.30.1
Machine Learning Orchestration
Keywords: orchestration, data, machinelearning

# DBND Snowflake

## The plugin

`dbnd-snowflake` Log preview, schema, size and system resource usage for Snowflake tables.

### What does it do?

It uses system Snowflake tables and views to fetch meta data.

### Installation

```bash
pip install dbnd-snowflake
```
dbnd-redshift 1.0.30.1
Machine Learning Orchestration
Keywords: orchestration, data, machinelearning

# DBND Redshift

## The plugin

`dbnd-redshift` logs redshift resource usage of queries using `log_redshift_resource_usage`.

### What does it do?

It uses internal/system redshift tables to get query data after they are completed.

### Installation

```bash
pip install dbnd-redshift
```
dbnd-postgres 1.0.30.1
Machine Learning Orchestration
Keywords: orchestration, data, machinelearning

# DBND Postgres

## The plugin

`dbnd-postgres` create histograms for postgres tables.
The plugin supports Redshift.

### What does it do?

It uses internal/system postgres tables to create the histograms. It doesn't query user tables at all.

### Installation

```bash
pip install dbnd-postgres
```
dbnd-monitor 1.0.30.1
Machine Learning Orchestration
Keywords: orchestration, data, machinelearning

# Databand Monitor

Databand Monitor is a module providing functionality shared across *monitors* within Databand system, like Databand Airflow Monitor.

## Installation with setup tools

```bash
cd modules/dbnd-monitor
pip install -e .
```

## Usage

```python
from dbnd_monitor.base_integration import BaseIntegration
from dbnd_monitor.base_monitor_config import BaseMonitorConfig
from dbnd_monitor.multiserver_monitor_starter import monitor_cmd


class ExampleMonitorConfig(BaseMonitorConfig):
    ...


class ExampleIntegartion(BaseIntegration):
    MONITOR_TYPE = "example_monitor"
    CONFIG_CLASS = ExampleMonitorConfig
    config: ExampleMonitorConfig


monitor_cmd(obj=ExampleIntegartion)
```
dbnd-mlflow 1.0.30.1
Machine Learning Orchestration
Keywords: orchestration, data, machinelearning

# Overview

The `dbnd-mlflow` plugin allows storing [mlflow](https://github.com/mlflow/mlflow) metrics to DBND tracker together with duplicating them to the mlflow store.

# Install

```bash
pip install dbnd-mlflow
# or
pip install databand[mlflow]
```

# Config

```ini
[core]
# Databand store url should be defined
databand_url=http://localhost:8080

[mlflow_tracking]
# Enable tracking to Databand store
databand_tracking=True

# Optionally, define a URI for mlflow store,
# mlflow.get_tracking_uri() is used by default
; duplicate_tracking_to=http://mlflow-store/
```

# Run example

You might need to install examples at first `pip install dbnd-examples`.

```bash
dbnd run dbnd_examples.tracking.tracking_mlflow.task_with_mflow

# or set configs manually
dbnd run dbnd_examples.tracking.tracking_mlflow.task_with_mflow --set-config mlflow_tracking.databand_tracking=True
```

# Explanation

<details><summary>mlflow_example code</summary>
<p>

```python
from dbnd import task
from mlflow import start_run, end_run
from mlflow import log_metric, log_param

@task
def mlflow_example():
    start_run()
    # params
    log_param("param1", randint(0, 100))
    log_param("param2", randint(0, 100))
    # metrics
    log_metric("foo1", random())
    log_metric("foo2", random())
    end_run()
```

</p>
</details>

## Execution flow:

1. Run `dbnd run mlflow_example --set-config mlflow_tracking.databand_tracking=True`
2. dbnd creates a new dbnd context
3. `dbnd_on_pre_init_context` hook from `dbnd_mlflow` is triggered
    - a new uri is computed to be used by mlflow, e.g.:
        - `dbnd://localhost:8080?duplicate_tracking_to=http%253A%252F%252Fmlflow-store%253A80%252F`
    - the new uri is set to be used with `mlflow.set_tracking_uri()`
4. `mlflow_example` task starts:
    1. `mlflow.start_run()`
        1. `mlflow` reads `entry_points` for each installed package and finds:
            - "dbnd = dbnd_mlflow.tracking_store:get_dbnd_store",
            - "dbnd+s = dbnd_mlflow.tracking_store:get_dbnd_store",
            - "databand = dbnd_mlflow.tracking_store:get_dbnd_store",
            - "databand+s = dbnd_mlflow.tracking_store:get_dbnd_store",
        2. `mlflow` creates `TrackingStoreClient` using the new uri
        3. uri schema instructs to use `dbnd_mlflow.tracking_store:get_dbnd_store`
            - `get_dbnd_store` creates dbnd `TrackingAPIClient`
            - `get_dbnd_store` creates mlflow tracking store to duplicate tracking to
            - `get_dbnd_store` returns `DatabandStore` instance
    2. `log_param()`/`log_metric()`
        - calls to `DatabandStore`
            - calls to `TrackingAPIClient`
            - calls to mlflow tracking store to duplicate tracking to
    3. `mlflow.end_run()`
5. `mlflow_example` ends
6. `dbnd_on_exit_context` hook from `dbnd_mlflow` is triggered
    - restore original mlflow tracking uri
dbnd-airflow-auto-tracking 1.0.30.1
Machine Learning Orchestration
Keywords: orchestration, data, machinelearning

# DBND Airflow Auto Tracking

## The plugin

`dbnd-airflow-auto-tracking` is a plugin for Airflow, enabling dbnd tracking to all DAGs and tasks without any code changes.

### What does it do?

The plugin will go over tasks and modify them if needed.
It uses Airflow's policy mechanism by adding dbnd tracking to policy function.
If the user had already defined its own policy function it will be executed in addition to dbnd tracking.

### Installation

```bash
pip install dbnd-airflow-auto-tracking
```
dbnd 1.0.30.1
Machine Learning Orchestration
Requires Python: <3.14,>=3.6
Keywords: orchestration, data, machinelearning

[![pipeline status](https://gitlab.com/databand-ai/dbnd/badges/master/pipeline.svg)](https://gitlab.com/databand-ai/dbnd/pipelines)

![PyPI - Downloads](https://img.shields.io/pypi/dm/dbnd) ![PyPI](https://img.shields.io/pypi/v/dbnd)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dbnd) ![PyPI - License](https://img.shields.io/pypi/l/dbnd)
![Code style: ](https://img.shields.io/badge/code%20style-black-000000.svg)

# DBND

DBND an open source framework for building and tracking data pipelines. DBND is used for processes ranging from data ingestion, preparation, machine learning model training and production.

DBND includes a Python library, set of APIs, and CLI that enables you to collect metadata from your workflows, create a system of record for runs, and easily orchestrate complex processes.

DBND simplifies the process of building and running data pipelines
from dbnd import task

```python
from dbnd import task

@task
def say_hello(name: str = "databand.ai") -> str:
    value = "Hello %s!" % name
    return value
```

And makes it easy to track your critical pipeline metadata

```python
from dbnd import log_metric, log_dataframe

log_dataframe("my_dataset", my_dataset)
log_metric("r2", r2)
```

## Getting Started

See our [documentation](https://www.ibm.com/docs/en/dobd) with examples and quickstart guides to get up and running with DBND.

## The Latest and Greatest

For using DBND, we recommend that you work with a virtual environment like [Virtualenv](https://virtualenv.pypa.io/en/latest/) or [Conda](https://docs.conda.io/en/latest/). Update to the latest and greatest:

```shell script
pip install dbnd
```

If you would like access to our latest features, or have any questions, feedback, or contributions we would love to here from you! Get in touch through contact@databand.ai
databand 1.0.30.1
Machine Learning Orchestration
Keywords: orchestration, data, machinelearning

[![pipeline status](https://gitlab.com/databand-ai/dbnd/badges/master/pipeline.svg)](https://gitlab.com/databand-ai/dbnd/pipelines)

![PyPI - Downloads](https://img.shields.io/pypi/dm/dbnd) ![PyPI](https://img.shields.io/pypi/v/dbnd)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dbnd) ![PyPI - License](https://img.shields.io/pypi/l/dbnd)
![Code style: ](https://img.shields.io/badge/code%20style-black-000000.svg)

# DBND

DBND an open source framework for building and tracking data pipelines. DBND is used for processes ranging from data ingestion, preparation, machine learning model training and production.

DBND includes a Python library, set of APIs, and CLI that enables you to collect metadata from your workflows, create a system of record for runs, and easily orchestrate complex processes.

DBND simplifies the process of building and running data pipelines
from dbnd import task

```python
from dbnd import task

@task
def say_hello(name: str = "databand.ai") -> str:
    value = "Hello %s!" % name
    return value
```

And makes it easy to track your critical pipeline metadata

```python
from dbnd import log_metric, log_dataframe

log_dataframe("my_dataset", my_dataset)
log_metric("r2", r2)
```

## Getting Started

See our [documentation](https://www.ibm.com/docs/en/dobd) with examples and quickstart guides to get up and running with DBND.

## The Latest and Greatest

For using DBND, we recommend that you work with a virtual environment like [Virtualenv](https://virtualenv.pypa.io/en/latest/) or [Conda](https://docs.conda.io/en/latest/). Update to the latest and greatest:

```shell script
pip install dbnd
```

If you would like access to our latest features, or have any questions, feedback, or contributions we would love to here from you! Get in touch through contact@databand.ai
accelbyte-py-sdk-feat-token-validation 0.5.0
AccelByte Python SDK - Feature - Token Validation
Requires Python: >=3.9

# AccelByte Modular Python SDK - Feature Module

This is a feature module for the [AccelByte Modular Python SDK](https://github.com/AccelByte/accelbyte-python-modular-sdk) package.

## Setup

This SDK requires Python 3.9 to be installed.

### Install with Pip

Install the core package from PyPI

```sh
pip install accelbyte-py-sdk-core
```

install *this* service

```sh
pip install accelbyte-py-sdk-feat-token-validation
```

and then install any service or feature you want

```sh
pip install accelbyte-py-sdk-service-{ SERVICE }
```

```sh
pip install accelbyte-py-sdk-feat-{ FEATURE }
```

or install everything

```sh
pip install accelbyte-py-sdk-all
```

## Usage

See [README.md](https://github.com/AccelByte/accelbyte-python-modular-sdk/blob/main/README.md)
graph-thanhchinh 0.1.11
Một thư viện đơn giản để tạo và trực quan hóa đồ thị.
Requires Python: >=3.8

# 📊 graph-thanhchinh 🐍
### Một Bộ Công Cụ Python Hỗ Trợ Môn Học Toán Rời Rạc

`graph-thanhchinh` là một thư viện Python được phát triển nhằm cung cấp các công cụ tiện ích để tạo, thao tác và trực quan hóa đồ thị. Thư viện này đặc biệt hữu ích cho sinh viên và những người học Toán rời rạc, giúp đơn giản hóa việc thực hiện các bài tập lớn và dự án liên quan đến đồ thị.

---

## ✨ Tính năng nổi bật

* ➕ Tạo đồ thị vô hướng và có hướng một cách dễ dàng.
* 📈 Trực quan hóa đồ thị một cách rõ ràng bằng Matplotlib.
* 🧠 Hỗ trợ các thuật toán đồ thị cơ bản và nâng cao.
* 🛠️ Thêm nhiều chức năng hỗ trợ làm dự án, bài tập lớn.

---

## 🚀 Cài đặt

Bạn có thể dễ dàng cài đặt thư viện này thông qua `pip`:

```bash
pip install graph-thanhchinh
```

---

## 🧑‍💻Tác giả

Nguyễn Như Thành - @addictionnt

---

## 📜 Lịch sử Cập nhật (Changelog)

Tất cả các thay đổi và tính năng mới của từng phiên bản đều được ghi chép chi tiết trong mục [**Releases**](https://github.com/addictionnt/Graph-tool/releases).