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update dev_environment

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Cornelius Specht 2024-05-02 14:13:45 +02:00
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@ -11,14 +11,15 @@ Please carefully handle the resources and follow the fair use principle and as w
## Getting Started
Within the sandbox, the different disciplines of the IKID project can provide tasks to be worked on by the respective student groups. Both text-based tasks and programmatic tasks can be processed. For example, Markdown files can be created for editing textual tasks.
Currently, it is planned for the technical lectures that students get in touch with the programming language Python for the first time. Therefore, the Sandbox platform was created, in which experiments with Python can be carried out.
Currently, inside the technical lectures students (in the role User) get in touch with prorgramming languages for the first time. Therefore, the Sandbox platform was created, in which experiments and Usecases with Python of all User levels can be carried out.
1. **Sign in**, use your **HdM Credentials** at [Sandbox](https://sandbox.iuk.hdm-stuttgart.de/)
2. Select the image you want to start (two options)
2. Select the image you want to start (three options)
1. **Datascience environment**
2. **Datascience GPU environment** (choose only if you realy need the graphic card, otherwise you block resources from those who need them)
2. **GPU PyTorch environment** (PyTorch library with GPU support, choose only if you realy need the graphic card, otherwise you block resources from those who need them)
3. **GPU TF environment** (Tensorflow library with GPU support, choose only if you realy need the graphic card, otherwise you block resources from those who need them)
3. Create or upload a .ipynb file:
1. . **create a empty .ipynb file:**
![sandbox launcher](res/sandbox_launcher.png "Sandbox Launcher")
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## Technical Overview
### Environments
The Sandbox provides multiple scientific environments. If any additional packages or libraries are need, please open an issue on our [GIT](https://git.sandbox.iuk.hdm-stuttgart.de/).
The Sandbox provides multiple scientific environments. If any additional packages or libraries are needed, check if they are available on [PyPi](https://pypi.org/) and use pip to install it. If a problem occurs, please open an issue on our [GIT](https://git.sandbox.iuk.hdm-stuttgart.de/).
#### Datascience environment
* Available Data Science image is based on [Data Science Container](https://git.sandbox.iuk.hdm-stuttgart.de/grosse/-/packages/container/jupyterlab-datascience/latest)
* most common data analysis library's included for Julia, Python, R are available
* most common data analysis library's included for Python are available.
Please open an [issue](https://git.sandbox.iuk.hdm-stuttgart.de/grosse/jupyterlab-datascience/issues/new) if any additional packages are needed or issues occurred.
#### Datascience GPU environment
#### GPU PyTorch environment
* Available GPU image is based on [Datascience GPU Container](https://git.sandbox.iuk.hdm-stuttgart.de/grosse/-/packages/container/jupyterlab-datascience-gpu/latest)
* support added for the NVIDIA GPU A100 computations based on python most common GPU-able libraries like Tensorflow, PyTorch and Keras.
* support added for the NVIDIA GPU A100 computations based on python library PyTorch.
Please open an [issue](https://git.sandbox.iuk.hdm-stuttgart.de/grosse/jupyterlab-datascience-gpu/issues/new) if any additional packages are needed or issues occurred.
#### GPU TF environment
* Available GPU image is based on [Datascience GPU Container](https://git.sandbox.iuk.hdm-stuttgart.de/grosse/-/packages/container/jupyterlab-datascience-gpu/latest)
* support added for the NVIDIA GPU A100 computations based on python library Tensorflow.
Please open an [issue](https://git.sandbox.iuk.hdm-stuttgart.de/grosse/jupyterlab-datascience-gpu/issues/new) if any additional packages are needed or issues occurred.
### Resources
Each instance has following resource limits: