semvec/pypi-packages huggingface dataset:
kfpdist 0.1.8
Use Kubeflow Pipeline to run distributed training jobs
Requires Python: >=3.6

# Kubeflow Pipeline distributed training support

kfp-dist-train contains utilities to use together with
[Kubeflow Pipeline](https://www.kubeflow.org/docs/components/pipelines/)
to enable writing distributed training code directly using Kubeflow Pipeline SDK.


## Get Started

1. Setup an Kubeflow environment (maybe use https://github.com/alauda/kubeflow-chart).
2. Upload the example [kfp-dist-train.ipynb](./kfkp-dist-train.ipynb) into a Notebook
   instance, or setup local pipeline submit.
3. Execute the example to submit a workflow, you can configure the number of workers
   in the Kubeflow web UI. The job should look like below:

![](./doc/kfpdist.png)


# Some Roadmap

- support `kfpdist.component(dist=True)` decorator as an wrap of `dsl.component`
- support parameter server strategy
- support pytorch
fulltickerscsv 0.0.1
Generate a CSV of all current tickers
Keywords: python,stocks,tickers,algo-trading,finance


# FullTickersCSV

NASDAQ provides a list of all tickers, along with some basic information (i.e. Share price, count, industry, etc.) as downloadable for free.
This library allows for automated downloads of this CSV file, using a headless Selenium browser.
NASDAQ hosts this information in its entirety for free and this module only makes one page request to download the CSV file.


## Example


```python
from fulltickerscsv import GetCSV

def GetDataFolder():
	return filedialog.askdirectory(title = 'Select Folder')

def main():
	folder = GetDataFolder()
	GetCSV.GetCSVFile(folder, 10)
```

GetCSVFile(arg1, arg2):

arg1 = CSV output folder
arg2 = Seconds of time to wait to ensure file has properly downloaded (10 is recommended, but add more if slow connectivity is an issue)