معرفی شرکت ها
databand-1.0.9.1
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
1.0.9.1
1.0.8.1
1.0.7.4
1.0.7.3
1.0.7.2
1.0.6.9
1.0.6.8
1.0.6.6
1.0.6.5
1.0.6.4
1.0.6.3
1.0.6.2
1.0.6.15
1.0.6.14
1.0.6.13
1.0.6.12
1.0.6.1
1.0.5.8
1.0.5.7
1.0.5.6
1.0.5.5
1.0.5.4
1.0.5.2
1.0.5.1
1.0.4.9
1.0.4.8
1.0.4.7
1.0.4.6
1.0.4.4
1.0.4.3
1.0.4.22
1.0.4.21
1.0.4.2
1.0.4.19
1.0.4.18
1.0.4.17
1.0.4.16
1.0.4.15
1.0.4.14
1.0.4.13
1.0.4.10
1.0.4.1
1.0.3.8
1.0.3.7
1.0.3.6
1.0.3.5
1.0.3.4
1.0.3.3
1.0.3.2
1.0.3.10
1.0.3.1
1.0.2.5
1.0.2.4
1.0.2.2
1.0.2.1
1.0.12.9
1.0.12.8
1.0.12.5
1.0.12.3
1.0.12.2
1.0.12.13
1.0.12.12
1.0.12.11
1.0.12.10
1.0.12.1
1.0.11.1
1.0.1.8
1.0.1.7
1.0.1.6
1.0.1.5
1.0.1.4
1.0.1.2
1.0.1.10
1.0.1.1
1.0.0.0
0.90.9
0.90.8
0.90.7
0.90.6
0.90.2
0.90.10
0.90.1
0.89.9
0.89.8
0.89.7
0.89.6
0.89.5
0.89.4
0.89.3
0.89.2
0.89.11
0.88.5
0.88.4
0.88.3
0.88.2
0.88.1
0.87.4
0.87.3
0.87.2
0.87.1
0.86.3
0.85.9
0.85.8
0.85.6
0.85.4
0.85.2
0.84.5
0.84.4
0.84.3
0.84.2
0.84.1
0.83.5
0.83.4
0.83.3
0.83.2
0.83.1
0.82.9
0.82.8
0.82.5
0.82.4
0.82.3
0.82.2
0.82.1
0.81.4
0.81.2
0.81.1
0.80.9
0.80.7
0.80.6
0.80.5
0.80.4
0.80.3
0.80.12
0.80.11
0.80.10
0.80.1
0.79.9
0.79.8
0.79.7
0.79.6
0.79.4
0.79.3
0.79.2
0.79.17
0.79.16
0.79.15
0.79.13
0.79.11
0.79.10
0.79.1
0.78.9
0.78.8
0.78.7
0.78.6
0.78.5
0.78.4
0.78.3
0.78.2
0.78.10
0.78.1
0.76.9
0.76.7
0.76.6
0.76.5
0.76.4
0.76.2
0.76.1
0.75.9
0.75.8
0.75.7
0.75.6
0.75.5
0.75.4
0.75.3
0.75.2
0.75.15
0.75.14
0.75.13
0.75.12
0.75.11
0.75.10
0.74.9
0.74.8
0.74.7
0.74.6
0.74.5
0.74.4
0.74.3
0.74.2
0.74.18
0.74.17
0.74.16
0.74.15
0.74.14
0.74.13
0.74.12
0.74.11
0.74.10
0.74.1
0.73.9
0.73.8
0.73.7
0.73.6
0.73.5
0.73.4
0.73.3
0.73.2
0.73.11
0.73.10
0.73.1
0.71.9
0.71.8
0.71.7
0.71.6
0.71.5
0.71.4
0.71.3
0.71.2
0.71.1
0.70.9
0.70.8
0.70.7
0.70.6
0.70.5
0.70.4
0.70.3
0.70.2
0.70.1
0.69.6
0.69.5
0.69.4
0.69.3
0.69.2
0.69.1
0.68.1
0.67.4
0.67.3
0.67.2
0.67.1
0.66.9
0.66.8
0.66.7
0.66.6
0.66.5
0.66.4
0.66.3
0.66.2
0.66.11
0.66.10
0.66.1
0.65.8
0.65.7
0.65.6
0.65.5
0.65.4
0.65.3
0.65.2
0.65.1
0.64.2
0.64.1
0.63.6
0.63.5
0.63.4
0.63.3
0.63.2
0.63.1
0.62.9
0.62.8
0.62.7
0.62.6
0.62.5
0.62.4
0.62.3
0.62.2
0.62.16
0.62.15
0.62.14
0.62.13
0.62.12
0.62.11
0.62.10
0.62.1
0.61.3
0.61.2
0.61.1
0.60.3
0.60.2
0.60.1
0.59.3
0.58.6
0.58.5
0.58.4
0.58.3
0.58.2
0.58.1
0.57.5
0.57.4
0.57.3
0.57.2
0.57.1
0.56.7
0.56.6
0.56.5
0.56.4
0.56.3
0.56.2
0.56.1
0.55.4
0.55.3
0.55.2
0.55.1
0.54.2
0.54.1
0.53.8
0.53.7
0.53.6
0.53.5
0.53.4
0.53.3
0.52.2
0.52.1
0.52.0
0.51.5
0.51.4
0.51.3
0.51.2
0.51.1
0.51.0
0.50.6
0.50.5
0.50.4
0.50.3
0.50.2
0.50.1
0.49.6
0.49.4
0.49.3
0.49.2
0.49.0
0.48.7
0.48.6
0.48.5
0.48.3
0.48.2
0.48.1
0.48.0
0.47.4
0.47.3
0.47.2
0.47.1
0.47.0
0.46.5
0.46.4
0.46.3
0.46.2
0.46.1
0.46.0
0.45.9
0.45.8
0.45.7
0.45.6
0.45.5
0.45.4
0.45.3
0.45.2
0.45.1
0.45.0
0.44.7
0.44.6
0.44.5
0.44.4
0.44.3
0.44.2
0.44.1
0.44.0
0.43.5
0.43.4
0.43.3
0.43.2
0.43.1
0.42.5
0.42.4
0.42.3
0.42.2
0.42.1
0.41.6
0.41.5
0.41.4
0.41.3
0.41.2
0.41.1
0.41.0
0.40.3
0.40.2
0.40.1
0.40.0
0.39.3
0.39.2
0.39.1
0.39.0
0.38.7
0.38.6
0.38.5
0.38.4
0.38.3
0.38.2
0.38.1
0.38.0
0.37.5
0.37.4
0.37.3
0.37.2
0.37.1
0.37.0
0.36.5
0.36.4
0.36.3
0.36.2
0.36.1
0.36.0
0.35.2
0.35.1
0.34.7
0.34.6
0.34.5
0.34.4
0.34.3
0.34.2
0.34.1
0.34.0
0.33.4
0.33.1
0.33.0
0.32.9
0.32.8
0.32.7
0.32.6
0.32.5
0.32.4
0.32.3
0.32.2
0.32.13
0.32.12
0.32.11
0.32.10
0.32.1
0.32.0
0.31.2
0.31.1
0.31.0
0.30.8
0.30.6
0.30.5
0.30.4
0.30.3
0.30.2
0.30.1
0.30.0
0.29.9
0.29.8
0.29.7
0.29.6
0.29.5
0.29.3
0.29.2
0.29.10
0.29.1
0.29.0
0.28.9
0.28.8
0.28.7
0.28.5
0.28.3
0.28.28
0.28.27
0.28.26
0.28.25
0.28.24
0.28.22
0.28.21
0.28.20
0.28.2
0.28.19
0.28.18
0.28.17
0.28.16
0.28.15
0.28.12
0.28.11
0.28.10
0.28.1
0.28.0
0.27.9
0.27.7
0.27.6
0.27.4
0.27.2
0.27.19
0.27.18
0.27.17
0.27.16
0.27.15
0.27.14
0.27.11
0.27.10
0.27.1
0.27.0
0.26.4
0.26.3
0.26.2
0.26.1
0.26.0
0.25.8
0.25.7
0.25.6
0.25.5
0.25.4
0.25.3
0.25.2
0.25.15
0.25.14
0.25.13
0.25.12
0.25.11
0.25.10
0.25.1
0.25.0
0.24.36
0.24.35
0.24.34
0.24.33
0.24.30
0.24.29
0.24.28
0.24.27
0.24.26
0.24.25
0.24.24
0.24.23
0.24.21
0.24.19
0.24.17
0.24.16
0.24.14
0.24.11
1.0.9.1
1.0.8.1
1.0.7.4
1.0.7.3
1.0.7.2
1.0.6.9
1.0.6.8
1.0.6.6
1.0.6.5
1.0.6.4
1.0.6.3
1.0.6.2
1.0.6.15
1.0.6.14
1.0.6.13
1.0.6.12
1.0.6.1
1.0.5.8
1.0.5.7
1.0.5.6
1.0.5.5
1.0.5.4
1.0.5.2
1.0.5.1
1.0.4.9
1.0.4.8
1.0.4.7
1.0.4.6
1.0.4.4
1.0.4.3
1.0.4.22
1.0.4.21
1.0.4.2
1.0.4.19
1.0.4.18
1.0.4.17
1.0.4.16
1.0.4.15
1.0.4.14
1.0.4.13
1.0.4.10
1.0.4.1
1.0.3.8
1.0.3.7
1.0.3.6
1.0.3.5
1.0.3.4
1.0.3.3
1.0.3.2
1.0.3.10
1.0.3.1
1.0.2.5
1.0.2.4
1.0.2.2
1.0.2.1
1.0.12.9
1.0.12.8
1.0.12.5
1.0.12.3
1.0.12.2
1.0.12.13
1.0.12.12
1.0.12.11
1.0.12.10
1.0.12.1
1.0.11.1
1.0.1.8
1.0.1.7
1.0.1.6
1.0.1.5
1.0.1.4
1.0.1.2
1.0.1.10
1.0.1.1
1.0.0.0
0.90.9
0.90.8
0.90.7
0.90.6
0.90.2
0.90.10
0.90.1
0.89.9
0.89.8
0.89.7
0.89.6
0.89.5
0.89.4
0.89.3
0.89.2
0.89.11
0.88.5
0.88.4
0.88.3
0.88.2
0.88.1
0.87.4
0.87.3
0.87.2
0.87.1
0.86.3
0.85.9
0.85.8
0.85.6
0.85.4
0.85.2
0.84.5
0.84.4
0.84.3
0.84.2
0.84.1
0.83.5
0.83.4
0.83.3
0.83.2
0.83.1
0.82.8
0.82.5
0.82.4
0.82.3
0.82.2
0.82.1
0.81.4
0.81.2
0.81.1
0.80.9
0.80.7
0.80.6
0.80.5
0.80.4
0.80.3
0.80.12
0.80.11
0.80.10
0.80.1
0.79.9
0.79.8
0.79.7
0.79.6
0.79.4
0.79.3
0.79.2
0.79.17
0.79.16
0.79.15
0.79.13
0.79.11
0.79.10
0.79.1
0.78.9
0.78.8
0.78.7
0.78.6
0.78.5
0.78.4
0.78.3
0.78.2
0.78.10
0.78.1
0.76.9
0.76.7
0.76.6
0.76.5
0.76.4
0.76.2
0.76.1
0.75.9
0.75.8
0.75.7
0.75.6
0.75.5
0.75.4
0.75.3
0.75.2
0.75.15
0.75.14
0.75.13
0.75.12
0.75.11
0.75.10
0.74.9
0.74.8
0.74.7
0.74.6
0.74.5
0.74.4
0.74.3
0.74.2
0.74.18
0.74.17
0.74.16
0.74.15
0.74.14
0.74.13
0.74.12
0.74.11
0.74.10
0.74.1
0.73.9
0.73.8
0.73.7
0.73.6
0.73.5
0.73.4
0.73.3
0.73.2
0.73.11
0.73.10
0.73.1
0.71.9
0.71.8
0.71.7
0.71.6
0.71.5
0.71.4
0.71.3
0.71.2
0.71.1
0.70.9
0.70.8
0.70.7
0.70.6
0.70.5
0.70.4
0.70.3
0.70.2
0.70.1
0.69.6
0.69.5
0.69.4
0.69.3
0.69.2
0.69.1
0.68.1
0.67.4
0.67.3
0.67.2
0.67.1
0.66.9
0.66.8
0.66.7
0.66.6
0.66.5
0.66.4
0.66.3
0.66.2
0.66.11
0.66.10
0.66.1
0.65.8
0.65.7
0.65.6
0.65.5
0.65.4
0.65.3
0.65.2
0.65.1
0.64.2
0.64.1
0.63.6
0.63.5
0.63.4
0.63.3
0.63.2
0.63.1
0.62.9
0.62.8
0.62.7
0.62.6
0.62.5
0.62.4
0.62.3
0.62.2
0.62.16
0.62.15
0.62.14
0.62.13
0.62.12
0.62.11
0.62.10
0.62.1
0.61.3
0.61.2
0.61.1
0.60.3
0.60.2
0.60.1
0.59.3
0.58.6
0.58.5
0.58.4
0.58.3
0.58.2
0.58.1
0.57.5
0.57.4
0.57.3
0.57.2
0.57.1
0.56.7
0.56.6
0.56.5
0.56.4
0.56.3
0.56.2
0.56.1
0.55.4
0.55.3
0.55.2
0.55.1
0.54.2
0.54.1
0.53.8
0.53.7
0.53.6
0.53.5
0.53.4
0.53.3
0.52.2
0.52.1
0.52.0
0.51.5
0.51.4
0.51.3
0.51.2
0.51.1
0.51.0
0.50.6
0.50.5
0.50.4
0.50.3
0.50.2
0.50.1
0.49.6
0.49.4
0.49.3
0.49.2
0.49.0
0.48.7
0.48.6
0.48.5
0.48.3
0.48.2
0.48.1
0.48.0
0.47.4
0.47.3
0.47.2
0.47.1
0.47.0
0.46.5
0.46.4
0.46.3
0.46.2
0.46.1
0.46.0
0.45.9
0.45.8
0.45.7
0.45.6
0.45.5
0.45.4
0.45.3
0.45.2
0.45.1
0.45.0
0.44.7
0.44.6
0.44.5
0.44.4
0.44.3
0.44.2
0.44.1
0.44.0
0.43.5
0.43.4
0.43.3
0.43.2
0.43.1
0.42.5
0.42.4
0.42.3
0.42.2
0.42.1
0.41.6
0.41.5
0.41.4
0.41.3
0.41.2
0.41.1
0.41.0
0.40.3
0.40.2
0.40.1
0.40.0
0.39.3
0.39.2
0.39.1
0.39.0
0.38.7
0.38.6
0.38.5
0.38.4
0.38.3
0.38.2
0.38.1
0.38.0
0.37.5
0.37.4
0.37.3
0.37.2
0.37.1
0.37.0
0.36.5
0.36.4
0.36.3
0.36.2
0.36.1
0.36.0
0.35.2
0.35.1
0.34.7
0.34.6
0.34.5
0.34.4
0.34.3
0.34.2
0.34.1
0.34.0
0.33.4
0.33.1
0.33.0
0.32.9
0.32.8
0.32.7
0.32.6
0.32.5
0.32.4
0.32.3
0.32.2
0.32.13
0.32.12
0.32.11
0.32.10
0.32.1
0.32.0
0.31.2
0.31.1
0.31.0
0.30.8
0.30.6
0.30.5
0.30.4
0.30.3
0.30.2
0.30.1
0.30.0
0.29.9
0.29.8
0.29.7
0.29.6
0.29.5
0.29.3
0.29.2
0.29.10
0.29.1
0.29.0
0.28.9
0.28.8
0.28.7
0.28.5
0.28.3
0.28.28
0.28.27
0.28.26
0.28.25
0.28.24
0.28.22
0.28.21
0.28.20
0.28.2
0.28.19
0.28.18
0.28.17
0.28.16
0.28.15
0.28.12
0.28.11
0.28.10
0.28.1
0.28.0
0.27.9
0.27.7
0.27.6
0.27.4
0.27.2
0.27.19
0.27.18
0.27.17
0.27.16
0.27.15
0.27.14
0.27.11
0.27.10
0.27.1
0.27.0
0.26.4
0.26.3
0.26.2
0.26.1
0.26.0
0.25.8
0.25.7
0.25.6
0.25.5
0.25.4
0.25.3
0.25.2
0.25.15
0.25.14
0.25.13
0.25.12
0.25.11
0.25.10
0.25.1
0.25.0
0.24.36
0.24.35
0.24.34
0.24.33
0.24.30
0.24.29
0.24.28
0.24.27
0.24.26
0.24.25
0.24.24
0.24.23
0.24.21
0.24.19
0.24.17
0.24.16
0.24.14
0.24.11
توضیحات
Machine Learning Orchestration
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | databand-1.0.9.1 |
| نام | databand |
| نسخه کتابخانه | 1.0.9.1 |
| نگهدارنده | ['Evgeny Shulman'] |
| ایمیل نگهدارنده | ['evgeny.shulman@databand.ai'] |
| نویسنده | Evgeny Shulman |
| ایمیل نویسنده | evgeny.shulman@databand.ai |
| آدرس صفحه اصلی | https://github.com/databand-ai/dbnd |
| آدرس اینترنتی | https://pypi.org/project/databand/ |
| مجوز | - |
[](https://gitlab.com/databand-ai/dbnd/pipelines)
 
 
# 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://docs.databand.ai/docs) 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
نیازمندی
| مقدار | نام |
|---|---|
| ==1.0.12.13 | dbnd |
| ==1.0.12.13 | dbnd-airflow |
| ==1.0.12.13 | dbnd-airflow-auto-tracking |
| ==1.0.12.13 | dbnd-airflow-export |
| ==1.0.12.13 | dbnd-airflow-versioned-dag |
| ==1.0.12.13 | dbnd-aws |
| ==1.0.12.13 | dbnd-azure |
| ==1.0.12.13 | dbnd-databricks |
| ==1.0.12.13 | dbnd-docker |
| ==1.0.12.13 | dbnd-gcp |
| ==1.0.12.13 | dbnd-hdfs |
| ==1.0.12.13 | dbnd-docker |
| ==1.0.12.13 | dbnd-luigi |
| ==1.0.12.13 | dbnd-mlflow |
| ==1.0.12.13 | dbnd-postgres |
| ==1.0.12.13 | dbnd-qubole |
| ==1.0.12.13 | dbnd-redshift |
| ==1.0.12.13 | dbnd-snowflake |
| ==1.0.12.13 | dbnd-spark |
| ==1.0.12.13 | dbnd-tensorflow |
نحوه نصب
نصب پکیج whl databand-1.0.9.1:
pip install databand-1.0.9.1.whl
نصب پکیج tar.gz databand-1.0.9.1:
pip install databand-1.0.9.1.tar.gz