semvec/pypi-packages huggingface dataset:
python-ecs-deploy 0.0.4
This script use ecs client of boto3 to instigate an automatic blue/green deployment.
License: MIT
Keywords: python aws-ecs ecs

# python-ecs-deploy

[![GitHub](https://img.shields.io/github/license/qiqiming/python-ecs-deploy.svg)](https://github.com/qiqiming/python-ecs-deploy/blob/master/LICENSE)
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inspired by <https://github.com/silinternational/ecs-deploy>, rewrite use python3

This script use ecs client of boto3 to instigate an automatic blue/green deployment.
It only updates the tag of the images in the task definition with the same image name you provided
This allows you to easily implement CI/CD

## usage
```
ecs-deploy

optional arguments:
  -h, --help                                                        show this help message and exit
  -k AWS_ACCESS_KEY, --aws-access-key AWS_ACCESS_KEY                AWS Access Key ID. May also be set as environment variable AWS_ACCESS_KEY_ID
  -s AWS_SECRET_ACCESS_KEY, --aws-secret-key AWS_SECRET_ACCESS_KEY  AWS Secret Access Key. May also be set as environment variable AWS_SECRET_ACCESS_KEY
  -r region_name, --region region_name                              AWS Region Name. May also be set as environment variable AWS_DEFAULT_REGION
  -c cluster_name, --cluster cluster_name                           Name of ECS cluster
  -n service_name, --service-name service_name                      Name of service to deploy
  -i images [images ...], --images images [images ...]              Name of Docker image to run(support multiple images)
                                                                    ex: --images repo/image:1.0 repo2/image2:8.0
  --scale nums                                                      Modifies the number(positive) of container instances of the current Task Definition
```

## example

You have a task definition that contains two docker images(aaa/foo:1.0, bbb/bar:2.0)

Update the service with the new image:

```
ecs-deploy -c test_cluster -n test_service -i aaa/foo:1.1 bbb/bar:3.0
```

Scale current service number of tasks

```
ecs-deploy -c test_cluster -n test_service --scale 5
```

## Installation

```
pip install python-ecs-depl
```
pdp 0.3.0
Build fast data processing pipelines easily
License: MIT
Keywords: pipeline parallel thread data processing augmentation

=======================
Pipline Data Processing
=======================

Why?
----
Many tasks in machine learning, deep learning and other fields require complex data processing that takes a lot of time. Ideally, this processing should run in parallel to the main process, preparing data for usage (by neural net, for instance). PDP provide simple interface to organize pipeline of data processing with simple blocks that satisfy most typical needs.

Use cases
--------------
* Neural Net training, where you need a way to train net, load data from the disk and augment it. PDP allows user to do all these things at the same time without need to use *threading* module directly.

Examples
--------
Are in repository in *examples* folder

Is it fast? 
-----------
Speed and parallel execution is a top priority. Right now threads are used to exchange information between pipline stages, because it's memory and CPU efficient to exchange data between threads and not processes. Python's threads are flawed by GIL, but it doesn't affect performance for IO-bound tasks and for numpy operations. Since all operations for data augmentations are likely to be done in numpy operations, performance will not be significantly affected by GIL.

Installation
------------
:code:`pip install pdp`