# reinautils
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
## The Utilities included are:
------------------------------------------------------------------------
<a
href="https://github.com/yuval6957/reinautils/blob/main/reinautils/parameters.py#LNone"
target="_blank" style="float:right; font-size:smaller">source</a>
### Parameters
> Parameters (**kargs)
A splecial class whos atributes can be referenced as attributs or as
dictionaty keys
------------------------------------------------------------------------
<a
href="https://github.com/yuval6957/reinautils/blob/main/reinautils/torchutils.py#LNone"
target="_blank" style="float:right; font-size:smaller">source</a>
### device_by_name
> device_by_name (name:str)
Return reference to cuda device by using Part of it’s name
Args: name: part of the cuda device name (shuuld be distinct)
Return: Reference to cuda device
Updated: Yuval 12/10/19
------------------------------------------------------------------------
<a
href="https://github.com/yuval6957/reinautils/blob/main/reinautils/torchutils.py#LNone"
target="_blank" style="float:right; font-size:smaller">source</a>
### DatasetCat
> DatasetCat (*datasets)
Concatenate datasets for Pytorch dataloader
The normal pytorch implementation does it only for raws. this is a
“column” implementation
Arges: datasets: list of datasets, of the same length
Updated: Yuval 12/10/2019
## Install
``` sh
pip install reinautils
```
## How to use
### Parameters
You can create a Parameters class from dict
``` python
params=Parameters(first=1,second='A')
print(params.first)
```
1
You can also creat a Parameters class and populate it from a json file
``` python
params2=Parameters().from_json('config_demo.json')
print(params2)
```
Parameters:
path : Parameters:
data : /workspace/hd/
tmp : /workspace/hd/tmp/
features : /workspace/nvme/features/
train : /workspace/nvme/train/
models : /workspace/hd/models/
output : /workspace/hd/outputs/
test : /workspace/nvme/test/
platform : myserver
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"description": "# reinautils\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n## The Utilities included are:\n\n------------------------------------------------------------------------\n\n<a\nhref=\"https://github.com/yuval6957/reinautils/blob/main/reinautils/parameters.py#LNone\"\ntarget=\"_blank\" style=\"float:right; font-size:smaller\">source</a>\n\n### Parameters\n\n> Parameters (**kargs)\n\nA splecial class whos atributes can be referenced as attributs or as\ndictionaty keys\n\n------------------------------------------------------------------------\n\n<a\nhref=\"https://github.com/yuval6957/reinautils/blob/main/reinautils/torchutils.py#LNone\"\ntarget=\"_blank\" style=\"float:right; font-size:smaller\">source</a>\n\n### device_by_name\n\n> device_by_name (name:str)\n\nReturn reference to cuda device by using Part of it\u2019s name\n\nArgs: name: part of the cuda device name (shuuld be distinct)\n\nReturn: Reference to cuda device\n\nUpdated: Yuval 12/10/19\n\n------------------------------------------------------------------------\n\n<a\nhref=\"https://github.com/yuval6957/reinautils/blob/main/reinautils/torchutils.py#LNone\"\ntarget=\"_blank\" style=\"float:right; font-size:smaller\">source</a>\n\n### DatasetCat\n\n> DatasetCat (*datasets)\n\nConcatenate datasets for Pytorch dataloader\n\nThe normal pytorch implementation does it only for raws. this is a\n\u201ccolumn\u201d implementation\n\nArges: datasets: list of datasets, of the same length\n\nUpdated: Yuval 12/10/2019\n\n## Install\n\n``` sh\npip install reinautils\n```\n\n## How to use\n\n### Parameters\n\nYou can create a Parameters class from dict\n\n``` python\nparams=Parameters(first=1,second='A')\nprint(params.first)\n```\n\n 1\n\nYou can also creat a Parameters class and populate it from a json file\n\n``` python\nparams2=Parameters().from_json('config_demo.json')\nprint(params2)\n```\n\n Parameters:\n path : Parameters:\n data : /workspace/hd/\n tmp : /workspace/hd/tmp/\n features : /workspace/nvme/features/\n train : /workspace/nvme/train/\n models : /workspace/hd/models/\n output : /workspace/hd/outputs/\n test : /workspace/nvme/test/\n platform : myserver\n\n\n",
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