semvec/pypi-packages huggingface dataset:
multiexec 0.0.4
Simple utility for running distributed kubectl exec commands
Requires Python: >=3.8

# multiexec
Simple utility for running a `kubectl exec` across multiple Pods.

## Usage
Call the script with your Pod filters (passed thru to `kubectl get pods`) and your command (passed thru to `kubectl exec -it`) as follows:

```sh
$ multiexec <POD FILTERS> -- <EXEC COMMAND>
```

pass `--once-per-node` to only run the command in a single Pod on each node


## Examples

### Say hello in every Pod on a given node
```sh
$ multiexec --all-namespaces --field-selector spec.nodeName=ip-1-2-3-4.ec2.internal -- /bin/bash -c "echo hello"

ip-1-2-3-4.ec2.internal
$ kubectl exec -it -n namespaceA some-pod-46vp8 -- /bin/bash -c echo hello
hello

$ kubectl exec -it -n namespaceB another-pod-fcvmq -- /bin/bash -c echo hello
hello

$ kubectl exec -it -n namespaceC foo-app-l95cj -- /bin/bash -c echo hello
hello

$ kubectl exec -it -n namespaceD bar-app-6zzb8 -- /bin/bash -c echo hello
hello
```


### Get GPU RAM usage via nvidia-smi on each node in namespaceA
```sh
$ multiexec --once-per-node -n namespaceA -- nvidia-smi --query-compute-apps=pid,used_memory --format=csv        

ip-1-2-3-4.ec2.internal
$ kubectl exec -it -n namespaceA foo-app-1 -- nvidia-smi --query-compute-apps=pid,used_memory --format=csv
pid, used_gpu_memory [MiB]
5276, 25 MiB
4860, 2437 MiB

ip-2-3-4-5.ec2.internal
$ kubectl exec -it -n namespaceA bar-app-2 -- nvidia-smi --query-compute-apps=pid,used_memory --format=csv
pid, used_gpu_memory [MiB]
12201, 25 MiB
11509, 2539 MiB
14466, 3713 MiB

ip-3-4-5-6.ec2.internal
$ kubectl exec -it -n namespaceA foo-app-2 -- nvidia-smi --query-compute-apps=pid,used_memory --format=csv
pid, used_gpu_memory [MiB]
20570, 25 MiB
19846, 2157 MiB
14641, 2149 MiB

ip-4-5-6-7.ec2.internal
$ kubectl exec -it -n namespaceA bar-app-1 -- nvidia-smi --query-compute-apps=pid,used_memory --format=csv
pid, used_gpu_memory [MiB]
23317, 25 MiB
7236, 4501 MiB
30002, 1009 MiB
```
make-us-rich 0.1.1
Cryptocurrency forecasting 📈 training and serving models made automatic
Requires Python: >=3.8,<3.9

# Make Us Rich
Deep Learning applied to cryptocurrency forecasting. This project is a tool to help people to train, serve and use 
cryptocurrencies forecasting models. This project was build by @ChainYo to help people building their own MLOps 
projects.

For more details on how to use this project, please refer to [documentation](https://chainyo.github.io/make-us-rich/).
(🚧 Still in development)

You can inspect the training pipeline with the `Kedro Viz` tool, available [here](https://makeusrich-viz.chainyo.tech)

Here is the simplified project architecture:

![Project Architecture](./docs/img/project_architecture.png)

---

## Installation

```bash
pip install make-us-rich
```

## Support

If you encounter any problems using this project, please open an issue [here](https://github.com/ChainYo/make-us-rich/issues).

If you find this project usefull, please consider supporting by sharing the repo and giving a ⭐!
ssm-cloner 0.0.1
(Not to be used as of now) This package is used to simplify the process of cloning the SSM documents across the AWS regions.
Requires Python: >=3.6

This module is created in order to simplify the process of copying the SSM documents from one region to another regions.

* As an organisation or team can have multiple regions in which they operate. And in order to replicate the SSM documents from one region to another.

* It becomes a tedious task if you are developing or if you need to update a document and replicate the change across the regions.

* To resolve this issue, ssm-cloner comes for your help.

* Just execute this module and pass on the parameters and it will clone the documents for you.

* You can also use it to unclone or create new version for your documents.