add GPU environment

Reviewed-on: #2

.woodpecker.yml

@@ -1,18 +1,21 @@
-pipeline:
+steps:
   create-book:
     image: peaceiris/mdbook:v0.4.30
     commands:
+      - mdbook init --theme light
       - mdbook build
-
-  publish-container:
-    image: woodpeckerci/plugin-docker-buildx:2.1.0
-    secrets: [docker_username, docker_password]
-    group: docker
+  build_and_release:
+    image: maltegrosse/woodpecker-buildah:0.0.12
     settings:
-      registry: https://git.sandbox.iuk.hdm-stuttgart.de
-      repo: git.sandbox.iuk.hdm-stuttgart.de/grosse/sandbox-docs-public
-      dockerfile: Dockerfile
-      tags: latest
+      registry: git.sandbox.iuk.hdm-stuttgart.de
+      repository: grosse/sandbox-docs-public
+      tag: latest
+      architectures: amd64
+      context: Dockerfile
+      imagename: sandbox-docs-public
+      username:
+        from_secret: docker_username
+      password:
+        from_secret: docker_password
+
 
-branches:
-  exclude: cspecht

Dockerfile

@@ -1,7 +1,5 @@
 FROM nginx:alpine3.17-slim
 WORKDIR /app
 
-COPY . .
-
 COPY ./nginx.conf /etc/nginx/nginx.conf
 COPY ./book /app/static

book.toml

@@ -1,3 +1,4 @@
 [book]
 title = "Sandbox Documentation"
 language = "en"
+[output.html]

src/SUMMARY.md

@@ -9,9 +9,9 @@
 - [Software](architecture/software.md)
 
 # Playground
-- [Overview](sandbox/overview.md)
-- [Development Environment](sandbox/dev_env.md)
+
 - [Use Cases](sandbox/use_cases.md) 
+- [Development Environment](sandbox/dev_env.md)
 - [Data Pool](sandbox/data_pool.md)
 - [Training Environment](sandbox/training.md)
-- [Data Generator](sandbox/data_generator.md)
+

src/architecture/overview.md

@@ -1,7 +1,7 @@
 # Architecture Overview 
 
 ## Conceptual Design
-![Sandbox Architecture](res/sandbox-architecture.png)
+![Sandbox Architecture](res/sb-overview.png)
 
 ## Use Cases
 

src/sandbox/data_pool.md

@@ -11,7 +11,7 @@ To store the data inside the Sandbox, you just have to drag & drop or click on t
 
 
 ## Share CLI (Headless)
-To use the headless object storage, you can upload a file via REST-Interface or curl. The json response message provides you the destination url.
+To use the headless object storage, you can upload a file via REST-Interface or curl. The json response message provides you the destination url. The upload is only available from the Sandbox.
 
 **Upload Example**
 ```python 
@@ -22,10 +22,17 @@ To use the headless object storage, you can upload a file via REST-Interface or
     files = {'fileUpload': (filename, open(filename, 'rb'),'text/csv')}
     r = requests.post(url, files=files)   
 ```
+
+or using curl as command line tool:
+
+```
+curl -F fileUpload=@file.zip https://share.storage.sandbox.iuk.hdm-stuttgart.de/upload
+```
+
 **Example JSON response**
 ```json
 {
-	"PublicUrl": "https://storage.sandbox.iuk.hdm-stuttgart.de/upload/a1236b2b-49bf-4047-a536-20dab15b7777/untitled.txt",
+	"PublicUrl": "https://share.storage.sandbox.iuk.hdm-stuttgart.de/upload/f2a69e9a-f60b-418d-a678-efce181fb8a5/untitled.txt",
 	"Size": 11,
 	"Expiration": "2023-10-04T00:00:00Z"
 }

src/sandbox/dev_env.md

@@ -60,6 +60,13 @@ Please open an [issue](https://git.sandbox.iuk.hdm-stuttgart.de/grosse/jupyterla
 
 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 Ollama environment
+* Available Ollama 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 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. 
+
+
 ### Resources
 Each instance has following resource limits:
 - maximum 2 physical CPUs, guranteed 0.5 CPUs
@@ -97,9 +104,29 @@ Please carefully follow the terms and conditions at [Sandbox](https://sandbox.iu
 - all my data is gone after the semester break: all persistent storages get recycled of every(!) user each semester break. Please backup your data locally if needed
 - package conflicts: common issue is to install a unspecific library version, please specify or upgrade all dependencies manually.
 
+## Large-Language-Models
+An advanced Pytorch development environment is preinstalled with [Ollama](https://ollama.com/), which makes it easy to download and run different LLMs. 
+
+If you want to run Ollama including the [OpenWebUI](https://openwebui.com/), following commands need to be executed in multiple Terminal windows: 
+![terminal](res/sandbox-terminal.png "Terminal")
+
+1. execute `ollama serve` to start the ollama backend
+2. execute `ollama run mistral:7b` to download and run a specific model in a second terminal. 
+
+Following steps are only needed if you want to access the WebUI. It also showcases how other http services can temporally be tunneled and exposed to public.
+![tunnel](res/sandbox-tunnel.png "Tunnel")
+
+3. execute `open-webui serve` to serve the WebUI locally on port 8080 in another terminal.
+4. visit [tun.iuk.hdm-stuttgart.de](https://tun.iuk.hdm-stuttgart.de) to obtain a token with your browser
+5. execute `pgrok init --remote-addr tun.iuk.hdm-stuttgart.de:80 --forward-addr https://{user}.tun.iuk.hdm-stuttgart.de --token {token}` in the terminal. Replace `{user}` and `{token}` with your username and the previous obtained token.
+6.  execute `pgrok http 8080` to run the tunnel and expose the webui. Now you are able to access the webui at ´https://{user}.tun.iuk.hdm-stuttgart. 
+
+### Notes
+Please use the tunnel only temporally and carefully. The tunnel only support http(s) tunnels. Tunneled services are public available and accessible by anyone! If you want to train/finetune any LLMs, please use the [Training Environment](training.md) instead.
 
 ## Useful Links
 
 - [Jupyter Documentation](https://docs.jupyter.org/en/latest/)
 - [pip](https://pip.pypa.io/en/stable/user_guide/)
 - [python](https://docs.python.org/3.11/)
+- [Ollama](https://ollama.com/)

src/sandbox/overview.md

@@ -1,18 +0,0 @@
-# Overview Sandbox
-
-![Sandbox Architecture](res/sandbox-architecture.png)
-
-## Use Cases
-A brief overview how to create [Use Cases](use_cases.md) on the Sandbox
-
-
-## Development Environment
-A [Development Environment](dev_env.md) or Playground for beginners and advanced users. 
-
-
-
-## Data Pool
-Details about how to store and manage data on the Sandbox: Additional information:  [Data Pool](data_pool.md)
-
-## Training Environment
-Detailed Information about how to perform a GPU supported model training on the Sandbox Environment. 

src/sandbox/training.md

@@ -58,7 +58,8 @@ steps:
 ```
 See the official [documentation](https://woodpecker-ci.org/docs/usage/workflow-syntax) for the syntax.
 
-Generally, the pipeline is based on different steps, and in each step, another container environment can be chosen. In the example above, first an official tensorflow container with python 3 is used to run the training python script. In the second step, the model gets compressed and pushed on the temp. sandbox storage.
+Generally, the pipeline is based on different steps, and in each step, another container environment can be chosen. In the example above, first an official tensorflow container with python 3 is used to run the training python script. In most cases you can find predefined containers at [Dockerhub](https://hub.docker.com/) or GPU supported containers at [NVIDIA](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch). If needed, custom images can be created and stored internally (on the Sandbox Git package repository) or any other public available container repository. In the second step, the model gets compressed and pushed on the temp. sandbox storage.
+
 3. Commit and push
 4. See current state of the pipelines at the [overview site](https://ci.sandbox.iuk.hdm-stuttgart.de/repos)
 
@@ -95,6 +96,12 @@ which returns a json with the download url of your uploaded file.
 - Choose a proper way to output some reasonable logs during your training, so it wont spam the logs too heavily
 - training exists after 60 minutes: increase maximum duration in the ci repository settings
 
+
+## Advanced Parameters (Matrix Workflos)
+The woodpecker cli yaml defintion files support [matrix workflows](https://woodpecker-ci.org/docs/usage/matrix-workflows), such that multiple pipeline runs are executed with all combinations of the predefined variables.
+See the [test-ci](https://git.sandbox.iuk.hdm-stuttgart.de/grosse/test-ci/src/branch/matrix) matrix branch as an example to define multiple pipeline runs with different epochs and optimizers. In the CI it is shown with different labels for each parameter:
+![repos](./res/matrix-ci.png)
+
 ## Useful Links
 - [Sandbox GIT](https://git.sandbox.iuk.hdm-stuttgart.de/)
 - [Sandbox CI](https://ci.sandbox.iuk.hdm-stuttgart.de)
@@ -104,3 +111,4 @@ which returns a json with the download url of your uploaded file.
 - [TensorFlow](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf)
 - [NVIDIA PyTorch Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch)
 - [NVIDIA Tensorflow Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow)
+- [Dockerhub](https://hub.docker.com/)

src/sandbox/use_cases.md

@@ -1,14 +1,11 @@
 # Use Cases
 
-tbd 
-ipynb example syntax + markdown + tex + voila slider (interactive dashboards)
-
-# Example Python
+## Example Python
 Inside the Notebook file you can write normal python syntax and can use plotting libaries to visualize your data and show the insights.
 
 ![Sandbox Example Python](res/sandbox_example_python.png)
 
-# Example Markdown 
+## Example Markdown 
 Inside Notebooks its possible to write Markdown text. This allows the user and the lectures to write formatted text inside the code editor, to create and answer Assignments. 
 
 | Markdown Syntax                                  | Description                                               |
@@ -33,8 +30,13 @@ if you need more or advanced syntax to format your text with markdown have a loo
 
 
 
-# Example interactive dashboard
+## Example interactive dashboard
 The following example shows the use of a interactive dashboard. The User Interface, makes it possible to the enduser to experiment/interact more easily with the Notebook.
 
 ![Sandbox Architecture](res/sandbox_example_ui.png)
 
+## Useful Links 
+
+- [Example Notebooks](https://git.sandbox.iuk.hdm-stuttgart.de/grosse/notebook-examples)           
+                                     
+- [Cheat Sheet](res/cheatsheet.pdf)