added structure
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maltegrosse
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- [Hardware](architecture/hardware.md)
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- [Software](architecture/software.md)
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# (Basic Usage) Sandbox
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# Playground
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- [Overview](sandbox/overview.md)
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- [Services](sandbox/services.md)
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- [Getting Started](sandbox/services.md)
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- [Environments](sandbox/services.md)
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- [Resources](sandbox/services.md)
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- [Limitations](sandbox/services.md)
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- [Use Cases](sandbox/services.md)
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<!---
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environments
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-> feedback issues
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# (Advanced Usage) Sandbox
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--!>
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- [Development Environment](sandbox/dev_env.md)
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- [Getting Started](sandbox/dev_env#getting-started.md)
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- [Environments](sandbox/dev_env#environments.md)
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- [Resources](sandbox/dev_env#resources.md)
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- [Limitations](sandbox/dev_env#limitations.md)
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- [Use Cases](sandbox/use_cases.md)
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- [Data Pool](sandbox/data_pool.md)
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- [Training Environment](sandbox/training.md)
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- [Data Generator](sandbox/data_generator.md)
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# Data Generator
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Coming Soon ...
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# Data Pool
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# Storage
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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.
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## Inside the Sandbox
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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.
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## Object storage
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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:
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**Upload Example**
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```python
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import requests
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url = "https://share.storage.sandbox.iuk.hdm-stuttgart.de/upload"
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filename = "ds_salaries.csv"
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files = {'fileUpload': (filename, open(filename, 'rb'),'text/csv')}
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r = requests.post(url, files=files)
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```
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**Usage Example**
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```python
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import pandas as pd
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url = "https://storage.sandbox.iuk.hdm-stuttgart.de/upload/ec6c1c9c-ea9b-47ff-97cf-f92d870e8fb9/ds_salaries.csv"
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df = pd.read_csv(url)
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```
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## Git LFS
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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.
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For further information visit [Git LFS](https://git-lfs.com/).
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# Services
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# Development Environment
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## Introduction
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use cases, limitations + t&c
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# Playground
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## Getting Started
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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.
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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.
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@ -32,44 +34,21 @@ Currently, it is planned for the technical lectures that the students get first
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# Training
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Coming Soon ...
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## Technical Overview
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Which python packages are installed, How can I install a python package?
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### Environments
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#### Datascience environment
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* Available Data Science image is based on [Official Data Science Image](https://hub.docker.com/r/jupyter/datascience-notebook/tags/)
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* most common data analysis library's included for Julia, Python, R
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#### GPU environment
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* Available GPU image is based on [Official GPU Image](https://hub.docker.com/r/cschranz/gpu-jupyter)
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* support added for the NVIDIA GPU A100 calculations based on python most common GPU-able libraries Tensorflow, PyTorch and Keras.
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# Storage
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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.
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## Inside the Sandbox
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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.
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### Resources
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## Object storage
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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:
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**Upload Example**
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```python
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import requests
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url = "https://share.storage.sandbox.iuk.hdm-stuttgart.de/upload"
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filename = "ds_salaries.csv"
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files = {'fileUpload': (filename, open(filename, 'rb'),'text/csv')}
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r = requests.post(url, files=files)
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```
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**Usage Example**
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```python
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import pandas as pd
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url = "https://storage.sandbox.iuk.hdm-stuttgart.de/upload/ec6c1c9c-ea9b-47ff-97cf-f92d870e8fb9/ds_salaries.csv"
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df = pd.read_csv(url)
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```
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## Git LFS
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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.
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For further information visit [Git LFS](https://git-lfs.com/).
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## Tmp Binary
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headless binary curl
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### Limitations
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# Overview Sandbox
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resources
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## images on Sandbox
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## Development Environment
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Playground...Getting started... Use cases
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Which python packages are installed, How can I install a python package?
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## Use Cases
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### Datascience environment
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* Available Data Science image is based on [Official Data Science Image](https://hub.docker.com/r/jupyter/datascience-notebook/tags/)
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* most common data analysis library's included for Julia, Python, R
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### GPU environment
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* Available GPU image is based on [Official GPU Image](https://hub.docker.com/r/cschranz/gpu-jupyter)
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* support added for the NVIDIA GPU A100 calculations based on python most common GPU-able libraries Tensorflow, PyTorch and Keras.
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### CLEANUP --> at the end of each Semester
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## Data Pool
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## Training Environment
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(tbd)
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## Data Generator
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(tbd)
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# Training Environment
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# Training
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Coming Soon ...
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@ -0,0 +1,12 @@
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# Use Cases
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tbd
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ipynb example syntax + markdown + tex + voila slider (interactive dashboards)
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idee:
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- bild use case example
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- python code example
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- markdown example (bild,headline, bullet points)
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- tex
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- voila
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