Adding LLM private fine-tuning example

examples/llm_private_fine_tuning/.gitattributes

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+components/model-reference-relocation-server/models/gpt2-large/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+components/model-relocation-server/models/gpt2/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

examples/llm_private_fine_tuning/.gitignore

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examples/llm_private_fine_tuning/README.md

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+# Private Fine-Tuning
+
+Privately fine-tune a LLM using Parameter Efficient Fine-Tuning with private data.
+
+```mermaid
+graph TD;
+    dataset-relocation-server-->dataset-pvc[(dataset-pvc)];
+    model-relocation-server-->base-model-pvc[(base-model-pvc)];
+    tokenizer-relocation-server-->base-tokenizer-pvc[(base-tokenizer-pvc)];
+    dataset-pvc-->model-peft-server;
+    base-model-pvc-->model-peft-server;
+    base-tokenizer-pvc-->model-peft-server;
+    
+    model-peft-server-->fine-tuned-model-pvc[(fine-tuned-model-pvc)];
+
+    model-reference-relocation-server-->reference-model-pvc[(reference-model-pvc)];
+    reference-model-pvc-->model-evaluation-server;
+
+    tensorboard(((tensorboard)))-->fine-tuned-model-pvc;
+    fine-tuned-model-pvc-->model-evaluation-server;
+    fine-tuned-model-pvc-->inference-server;
+    base-tokenizer-pvc-->inference-server;
+```
+
+## [Pushing components to registries](docs/registries.md)
+
+## Updating the models
+
+Updating the base and reference models from GPT2 is a matter of updarting the files and the occurrences of `gpt2` in the code.
+
+> Because we are storing the full model, we need to track the large files using `git lfs` before pushing to a different GitHub repository:  
+
+```bash
+git lfs track components/model-reference-relocation-server/models/gpt2-large/pytorch_model.bin
+git lfs track components/model-relocation-server/models/gpt2/pytorch_model.bin
+```
+
+
+## Local development with KinD
+
+Single node required:
+
+```bash
+kind create cluster
+```
+
+### Installing
+
+With skaffold everything can be built and run with one command for local iteration, the first time will take a while
+because all the images are being created.
+
+```bash
+skaffold run --port-forward=true
+```
+
+> Note that the evaluation server takes over 80 minutes.
+But since the inference server is created in parallel, we can use it while evaluation happens.
+
+
+After running the `skaffold` command, it should display something like:
+```bash
+Waiting for deployments to stabilize...
+ - deployment/model-inference-server is ready.
+Deployments stabilized in 4.135 seconds
+Port forwarding service/model-inference-service in namespace default, remote port 5000 -> http://127.0.0.1:5002
+```
+
+And the inference server is ready to receive requests:
+```bash
+ curl -XPOST 127.0.0.1:5002/prompt -d '{"input_prompt":"What is the best hotel in Seville?"}' -H 'Content-Type: application/json'
+```
+
+## Using KubeFlow
+
+KubeFlow automates all the creation of Kubernetes objects, their synchronization and adds utilities to help us with experiments and visualization.
+
+### Building the pipeline
+
+[Using the notebook](kubeflow/GeneratePipeline.ipynb) or running [pipeline.py](kubeflow/pipeline.py)
+
+### Visualising data in TensorBoard
+
+Select the PVC from `fine-tuned-model-pvc`.
+With the following mount path: `model/gpt2/logs/`.
+
+> Note that since some environments can't easily create Persistent Volumes with `ReadWriteMany`, we have to wait for completion or to delete the board once we analyzed it.

examples/llm_private_fine_tuning/components/dataset-relocation-server/.dockerignore

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+k8s/

examples/llm_private_fine_tuning/components/dataset-relocation-server/Dockerfile

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+FROM python:3.9
+
+WORKDIR /code 
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --upgrade pip
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+WORKDIR $HOME/app
+COPY . $HOME/app
+
+CMD ["python3", "app.py"]

examples/llm_private_fine_tuning/components/dataset-relocation-server/app.py

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+import argparse
+import shutil
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset_destination_path', type=str, help='dataset destination path')
+
+    opt = parser.parse_args()
+    dataset_destination_path = opt.dataset_destination_path
+    print("Relocating to: " + dataset_destination_path)
+    shutil.copytree("./datasets", dataset_destination_path, dirs_exist_ok=True)