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- [Hardware](architecture/hardware.md)
- [Software](architecture/software.md)
# (Basic Usage) Sandbox
# Playground
- [Overview](sandbox/overview.md)
- [Services](sandbox/services.md)
- [Getting Started](sandbox/services.md)
- [Environments](sandbox/services.md)
- [Resources](sandbox/services.md)
- [Limitations](sandbox/services.md)
- [Use Cases](sandbox/services.md)
<!---
environments
-> feedback issues
# (Advanced Usage) Sandbox
--!>
- [Development Environment](sandbox/dev_env.md)
- [Getting Started](sandbox/dev_env#getting-started.md)
- [Environments](sandbox/dev_env#environments.md)
- [Resources](sandbox/dev_env#resources.md)
- [Limitations](sandbox/dev_env#limitations.md)
- [Use Cases](sandbox/use_cases.md)
- [Data Pool](sandbox/data_pool.md)
- [Training Environment](sandbox/training.md)
- [Data Generator](sandbox/data_generator.md)

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# Data Generator
Coming Soon ...

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# Data Pool
# Storage
In the following section we describe how to store data on the Sandbox. There are three diffrent ways to do so: Inside the Sandbox, Object Storage, Git LFS.
## Inside the Sandbox
To store the data inside the Sandbox, you just have to drag & drop or click on the upload button to save the file to your running instance. You can also create folders and new Notebooks. The only limitation is, that each user has 1GB of storage.
![file upload](res/sandbox_upload_file_selector.png "File Upload")
## Object storage
To use the Object storage, you can upload a file via REST-Interface and access it by the key you get provided in the response. If you want to upload your file:
**Upload Example**
```python
import requests
url = "https://share.storage.sandbox.iuk.hdm-stuttgart.de/upload"
filename = "ds_salaries.csv"
files = {'fileUpload': (filename, open(filename, 'rb'),'text/csv')}
r = requests.post(url, files=files)
```
**Usage Example**
```python
import pandas as pd
url = "https://storage.sandbox.iuk.hdm-stuttgart.de/upload/ec6c1c9c-ea9b-47ff-97cf-f92d870e8fb9/ds_salaries.csv"
df = pd.read_csv(url)
```
## Git LFS
The following solution we highly recomment only for users which are familiar with git command line tools! Git Large File Storage (LFS). An open source Git extension for versioning large files. Git LFS replaces large files (audio, sample, datasets, videos) by a text pointer inside git. The files get stored on our gitea Server.
For further information visit [Git LFS](https://git-lfs.com/).

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# Services
# Development Environment
![sandbox](https://uptime.monitoring.iuk.hdm-stuttgart.de/api/badge/1/status)
## Introduction
use cases, limitations + t&c
# Playground
## Getting Started
Within the sandbox, the different disciplines inside the IKID project can provide tasks to be worked on by the respective student groups. Both text-based tasks and programmatic tasks can be provided and processed. For example, Markdown files can be created for editing textual tasks. These files can be converted from the source form (unformatted) to the target form (formatted) using a simple syntax.
Currently, it is planned for the technical lectures that the students get first in touch with the programming language Python. Therefore, the Sandbox platform was created, in which experiments with Python can be carried out. But if needed, it is possible to add more supported languages in the future.
@ -32,44 +34,21 @@ Currently, it is planned for the technical lectures that the students get first
# Training
Coming Soon ...
## Technical Overview
Which python packages are installed, How can I install a python package?
### Environments
#### Datascience environment
* Available Data Science image is based on [Official Data Science Image](https://hub.docker.com/r/jupyter/datascience-notebook/tags/)
* most common data analysis library's included for Julia, Python, R
#### GPU environment
* Available GPU image is based on [Official GPU Image](https://hub.docker.com/r/cschranz/gpu-jupyter)
* support added for the NVIDIA GPU A100 calculations based on python most common GPU-able libraries Tensorflow, PyTorch and Keras.
# Storage
In the following section we describe how to store data on the Sandbox. There are three diffrent ways to do so: Inside the Sandbox, Object Storage, Git LFS.
## Inside the Sandbox
To store the data inside the Sandbox, you just have to drag & drop or click on the upload button to save the file to your running instance. You can also create folders and new Notebooks. The only limitation is, that each user has 1GB of storage.
![file upload](res/sandbox_upload_file_selector.png "File Upload")
### Resources
## Object storage
To use the Object storage, you can upload a file via REST-Interface and access it by the key you get provided in the response. If you want to upload your file:
**Upload Example**
```python
import requests
url = "https://share.storage.sandbox.iuk.hdm-stuttgart.de/upload"
filename = "ds_salaries.csv"
files = {'fileUpload': (filename, open(filename, 'rb'),'text/csv')}
r = requests.post(url, files=files)
```
**Usage Example**
```python
import pandas as pd
url = "https://storage.sandbox.iuk.hdm-stuttgart.de/upload/ec6c1c9c-ea9b-47ff-97cf-f92d870e8fb9/ds_salaries.csv"
df = pd.read_csv(url)
```
## Git LFS
The following solution we highly recomment only for users which are familiar with git command line tools! Git Large File Storage (LFS). An open source Git extension for versioning large files. Git LFS replaces large files (audio, sample, datasets, videos) by a text pointer inside git. The files get stored on our gitea Server.
For further information visit [Git LFS](https://git-lfs.com/).
## Tmp Binary
headless binary curl
### Limitations

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# Overview Sandbox
resources
![Sandbox Architecture](res/sandbox-architecture.png)
## images on Sandbox
## Development Environment
Playground...Getting started... Use cases
Which python packages are installed, How can I install a python package?
## Use Cases
### Datascience environment
* Available Data Science image is based on [Official Data Science Image](https://hub.docker.com/r/jupyter/datascience-notebook/tags/)
* most common data analysis library's included for Julia, Python, R
### GPU environment
* Available GPU image is based on [Official GPU Image](https://hub.docker.com/r/cschranz/gpu-jupyter)
* support added for the NVIDIA GPU A100 calculations based on python most common GPU-able libraries Tensorflow, PyTorch and Keras.
### CLEANUP --> at the end of each Semester
## Data Pool
## Training Environment
(tbd)
## Data Generator
(tbd)

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# Training Environment
# Training
Coming Soon ...

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# Use Cases
tbd
ipynb example syntax + markdown + tex + voila slider (interactive dashboards)
idee:
- bild use case example
- python code example
- markdown example (bild,headline, bullet points)
- tex
- voila