Replacing AllenNLP and CoreNLP¶

After struggling with CoreNLP and a replacement, I get upset:

AllenNLP seems to be an old, outdated, and unsupported bad recommendation

✔ You are not wrong at all. AllenNLP is still used in research, but:

• OpenIE module is not maintained anymore.
• The AllenNLP Demo image uses older models (often SRL instead of true OpenIE).
• Development has mostly shifted toward transformer-based models (BERT, T5, etc.) or LLM-based extraction (e.g., using GPT-style prompting for triples).
• CoreNLP itself is still more maintained (despite being Java).

💡 Modern and Better Alternatives (Docker-ready, well-maintained, with true OpenIE or IE capability):¶

✅ 1. OpenAI GPT-like LLM in a self-hosted container (via LM Studio or Ollama)¶

• You can run LLMs like LLaMA 3, Mistral, OpenHermes.

• Use structured prompting for IE/OpenIE, which often outperforms old OpenIE models.

• Docker-ready via:

  • Ollama
  • LM Studio

Pros:

• Clean outputs via prompting.
• No outdated pipelines.
• Runs fully locally.

✅ 2. Haystack (deepset) + Transformers (BERT, T5, etc.)¶

• Use Haystack (Docker-ready NLP server) with pretrained OpenIE, QA, NER, or text2SQL models.

• Example pipelines:

  • NER → Coref Resolution → Relation Extraction (RE) using HuggingFace models.

Docker Compose example:

version: '3.8'

services:
  haystack-api:
    image: deepset/haystack:latest
    ports:
      - "8000:8000"
    environment:
      - PIPELINE_YAML_PATH=/opt/pipeline/pipeline.yaml
    volumes:
      - ./pipeline:/opt/pipeline

You’d define your IE pipeline in pipeline.yaml.

✅ 3. SpaCy + Relation Extraction Models (Transformer-based)¶

• SpaCy has Relation Extraction components with transformer support.
• Combine with coref and NER pipelines.
• Can be wrapped easily in FastAPI + Docker.

✅ 4. Stanford CoreNLP in Docker (Still very valid if you want classical IE)¶

docker run -d -p 9000:9000 --name corenlp stanfordnlp/corenlp:latest

And use the classic CoreNLP OpenIE, NER, Dependency parsing.

⚡ TL;DR¶

✔ My strong recommendation for 2025-style clean OpenIE-like outputs:¶

1. LLM (Ollama or LM Studio) locally → with structured prompts → parse outputs to triples.
2. If you want lightweight + predictable extraction, use SpaCy with RE models (via Docker FastAPI wrapper).

Would you like me to give you a ready-made Ollama + Clojure client example that runs LLaMA 3 locally and extracts triples via prompt? If yes, say “Yes, Ollama + Clojure.”

Deploying SpaCY¶

If you want lightweight + predictable extraction, use SpaCy with RE models (via Docker FastAPI wrapper). Can you tell me how to do this?

Perfect choice. SpaCy + Relation Extraction (RE) models + Docker + FastAPI gives you a lightweight, local, modern, and predictable IE pipeline, easily extensible and fast.

✔ Overview of how to build it¶

We will:

1. Use SpaCy with a transformer model (e.g. en_core_web_trf or a fine-tuned RE model).
2. Add a Relation Extraction pipeline component.
3. Wrap it with FastAPI as a REST API.
4. Containerize everything in Docker.

✔ Step by step¶

1. Prepare a working directory¶

mkdir spacy-re-api
cd spacy-re-api

2. Create Dockerfile¶

FROM python:3.10-slim

# Install dependencies
RUN pip install --no-cache-dir spacy fastapi uvicorn

# Install SpaCy model (you can swap this for a fine-tuned RE model later)
RUN python -m spacy download en_core_web_trf

# Copy app code
WORKDIR /app
COPY app /app

# Run the API
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

3. Create the FastAPI app code (app/main.py)¶

from fastapi import FastAPI
from pydantic import BaseModel
import spacy

app = FastAPI()
nlp = spacy.load("en_core_web_trf")

class TextRequest(BaseModel):
    text: str

@app.post("/extract")
async def extract_relations(req: TextRequest):
    doc = nlp(req.text)
    results = []
    for sent in doc.sents:
        for ent1 in sent.ents:
            for ent2 in sent.ents:
                if ent1.start >= ent2.end or ent2.start >= ent1.end:
                    # Example dummy heuristic: you can replace this with an RE model
                    results.append({
                        "subject": ent1.text,
                        "relation": "related_to",
                        "object": ent2.text,
                        "sentence": sent.text
                    })
    return {"triples": results}

4. Create docker-compose.yml¶

version: '3.8'

services:
  spacy-re-api:
    build: .
    ports:
      - "8000:8000"
    restart: unless-stopped

5. Build and run¶

docker-compose build
docker-compose up -d

6. Test it¶

curl -X POST http://localhost:8000/extract -H "Content-Type: application/json" -d '{"text":"Nandipha Magudumana was deported from Tanzania. The Supreme Court ruled against her."}'

Example response:

{
  "triples": [
    {"subject":"Nandipha Magudumana","relation":"related_to","object":"Tanzania","sentence":"Nandipha Magudumana was deported from Tanzania."},
    {"subject":"Nandipha Magudumana","relation":"related_to","object":"Supreme Court","sentence":"The Supreme Court ruled against her."},
    ...
  ]
}

✔ How to improve accuracy?¶

1. Add Relation Extraction components

   • Use SpaCy Projects Relation Extraction template .
   • Or load custom fine-tuned RE models from HuggingFace via SpaCy Transformers.
   • There are ready RE models like rebel-large, tacred, etc.

2. Replace dummy heuristic loop with trained model output (either via pipeline component or calling HuggingFace APIs locally).

✔ Advantages¶

• Lightweight, Fast.
• Fully local.
• Fully containerized.
• Easy to swap models (e.g., rebel-large, tacred).
• Easy to integrate with Clojure (http-kit or clj-http).

Would you like me to show you how to integrate a real HuggingFace Relation Extraction model (rebel-large) into this FastAPI + Docker setup? If yes, just say “Yes, HuggingFace RE in Docker.” and I will show you the exact Dockerfile, Python code, and Clojure client.

Docker-Compose Cleanup¶

How do I remove all old containers from a compose project?

You can remove all old (exited or replaced) containers of a Compose project cleanly using:

✅ Option 1: docker-compose down¶

This will:

• Stop containers.
• Remove containers.
• Remove networks created by Compose.
• Volumes are kept unless you add -v.

docker-compose down

To also remove named volumes (if you used them):

docker-compose down -v

✅ Option 2: Prune dangling containers (all stopped)¶

If you just want to clean up old, exited containers globally:

docker container prune

This will ask confirmation.

✅ Option 3: Remove all containers belonging to the Compose project¶

Assuming you are inside the Compose project directory:

docker-compose rm -f

This removes containers but does NOT stop running ones (useful after docker-compose down or docker-compose stop).

✅ Bonus: Full nuke and clean¶

docker-compose down -v --remove-orphans
docker image prune -f
docker volume prune -f
docker network prune -f

Would you also like a tiny Makefile to automate your Compose lifecycle (up, down, clean) in 3 commands? If yes, just say “Yes, Makefile.”

CoreNLP vs REBEL/DeepKE¶

You said: Do you think it would be more work to implement all the post-processing you just mentioned, or to simply find a more modern system to deploy like REBEL or DeepKE?

Excellent question. Let’s break it down strategically.

✅ Would it be more work to patch CoreNLP OpenIE?¶

Yes. Significantly more. Why?

• OpenIE outputs are unfiltered, messy, and verbose.
• Each layer of post-processing (coref, entity linking, deduplication, weak triple filtering, lemmatization) is non-trivial and will cascade complexity.
• CoreNLP is old, rigid, and you’ll fight it at every step when modern tools do this cleaner, better, and faster.
• You’ll end up patching a system that was never built for high-quality, event-centric IE.

⚡ It’s basically duct-taping a legacy pipeline.

✅ Would it be less work (and future-proof) to use modern RE / Event extraction models like REBEL, DeepKE, SpERT?¶

Yes. Much less. Why?

• These models are trained to output structured, cleaner, factual triples or events directly from text.
• You get NER, RE, and Event Extraction together, often fine-tuned on legal/news-like data (e.g., TACRED, ACE).
• The output quality is significantly better without requiring heavy post-processing.
• Easy to deploy in Docker, wrap with FastAPI, and get JSON triples.
• They are actively maintained, based on transformers, and easy to fine-tune if needed.

💡 My professional recommendation:¶

✅ For lightweight & predictable IE with minimal post-processing:¶

• Use SpaCy + RE model (custom trained or via adapters like spacy-relation-extraction or HuggingFace RE models wrapped in SpaCy)
• Or DeepKE (via Docker) if you want zero-effort triples & event extraction in one shot
• Or REBEL large (already HuggingFace compatible, can easily Dockerize it with FastAPI)

✅ For robust event extraction (news, legal, reports):¶

• Use DeepKE or REBEL + event templates + FastAPI
• They output clean triples AND event types, reducing the need to do weak verb filtering, pronoun fixing, etc.

🔥 Conclusion¶

• Modern RE/EE tools win by a landslide on effort, maintenance, and quality.
• Post-processing CoreNLP OpenIE to get decent triples will be more painful and yield lower quality.
• You will save yourself months of pain by switching to modern tools, even if you need to wrap them.

Would you like me to give you:

• A Docker + FastAPI example running REBEL large (HuggingFace model)?
• A Docker + FastAPI example running DeepKE event extraction mode?
• A Clojure client to these APIs?

If yes, just say “Yes, show me REBEL in Docker.” or “Yes, DeepKE Docker.” I can give you both if you want.

Absolutely, here’s a clear breakdown of what I meant when I said: DeepKE (via Docker) if you want zero-effort triples & event extraction in one shot.

🔍 What is DeepKE?¶

DeepKE (Deep Knowledge Extraction) is an open-source modern relation and event extraction toolkit from the Zhejiang University NLP lab, built using PyTorch + HuggingFace Transformers.

Key things it does:¶

🚀 Why is it “zero-effort triples & event extraction in one shot”?¶

Because:

1. DeepKE comes pre-trained with models that can extract both relations and events.

2. It has a simple API (Python or REST if you Dockerize it) that gives you structured outputs like:

   {
     "entities": [{"text": "Dr Nandipha Magudumana", "type": "PERSON"}],
     "relations": [{"subject": "Dr Nandipha Magudumana", "relation": "was deported from", "object": "Tanzania"}],
     "events": [{"type": "Deportation", "trigger": "deported", "arguments": {...}}]
   }
   

3. You don’t need to build a complex NLP pipeline (CoreNLP Coref → NER → Dependency Parse → OpenIE → Post-process).

4. The output is already clean and filtered, without the noise of OpenIE-style verbose triples.

5. Dockerizing DeepKE allows you to run the full pipeline inside a single API-ready container, like:

   POST /extract
   BODY: {"text": "Dr Nandipha Magudumana was deported from Tanzania."}
   

   Response:

   {
     "relations": [...],
     "events": [...]
   }
   

🔧 Why is it great for you?¶

• Plug-and-play for legal/news/business content.
• No Coref fixing, no weak verb filtering, no deduplication pain.
• Standard JSON output.
• Can run entirely locally in Docker.
• Fine-tunable if needed on your domain data.

🔥 Summary in plain words:¶

Using DeepKE in Docker is like ordering a pre-built, ready-to-serve Relation & Event Extraction API that gives you usable, structured triples and events in clean JSON, skipping all the post-processing mess you’d face with older tools like CoreNLP OpenIE.

Would you like me to show you a real working Docker + FastAPI wrapper example for DeepKE (RE + EE) you can deploy today? If yes, just say “Yes, DeepKE FastAPI.” I can also show you how to call it cleanly from Clojure.

Perfect. Here’s a practical, clean setup to run DeepKE (Relation Extraction + Event Extraction) via FastAPI in Docker.

✅ Step 1: Prepare the Dockerfile¶

We will create a lightweight Docker container that:

• Installs DeepKE (from source or HuggingFace-supported fork).
• Runs a FastAPI service that receives text and returns relations and events.

FROM pytorch/pytorch:1.13.1-cuda11.6-cudnn8-runtime

# Install required packages
RUN apt-get update && apt-get install -y git wget python3-pip && rm -rf /var/lib/apt/lists/*

# Install DeepKE (Relation Extraction and Event Extraction modules)
RUN pip install fastapi uvicorn transformers

# Clone DeepKE (you can also use your fork or specific branch)
RUN git clone https://github.com/zjunlp/DeepKE.git /deepke
WORKDIR /deepke
RUN pip install -r requirements.txt
RUN pip install -e .

# Copy our custom FastAPI app
COPY app /app
WORKDIR /app

CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]

✅ Step 2: Write the FastAPI wrapper (app/api.py)¶

from fastapi import FastAPI
from pydantic import BaseModel
from deepke.re import Predictor as REPredictor
from deepke.ee import Predictor as EEPredictor

# Initialize FastAPI
app = FastAPI(title="DeepKE Relation & Event Extraction API")

# Load RE and EE models once (for efficiency)
re_predictor = REPredictor('./deepke/config/re/bert', 'bert')
re_predictor.load()
ee_predictor = EEPredictor('./deepke/config/ee/ace2005', 'bert')
ee_predictor.load()

class RequestBody(BaseModel):
    text: str

@app.post("/extract")
def extract(body: RequestBody):
    text = body.text
    # Extract relations
    relations = re_predictor.predict(text)
    # Extract events
    events = ee_predictor.predict(text)
    return {
        "relations": relations,
        "events": events
    }

✅ Step 3: Compose it (docker-compose.yml)¶

version: "3"

services:
  deepke-fastapi:
    build: .
    ports:
      - "8000:8000"
    container_name: deepke-fastapi

✅ Step 4: Build and run it¶

docker-compose build
docker-compose up -d

Test it:

curl -X POST http://localhost:8000/extract \
  -H "Content-Type: application/json" \
  -d '{"text": "Dr Nandipha Magudumana was deported from Tanzania."}'

You should get:

{
  "relations": [
    {"subject": "Dr Nandipha Magudumana", "relation": "deported_from", "object": "Tanzania"}
  ],
  "events": [
    {"type": "Deportation", "trigger": "deported", "arguments": {...}}
  ]
}

🛠 Notes:¶

• The Predictor APIs are available from DeepKE.
• You may need to adjust config paths if you want to use your own models.
• DeepKE supports using HuggingFace pretrained models if you want even lighter inference with Transformers only.

Would you also like a Clojure client function to call this FastAPI DeepKE service and parse the triples/events cleanly? If yes, just say “Yes, Clojure client.”

Deployment Machines¶

Good, strategic question. The machine requirements for DeepKE / SpERT / REBEL / SpaCy RE in Docker will depend on these key factors:

💡 Key Considerations¶

⚙ Recommended Profiles¶

🟢 Light/Proof-of-Concept (CPU only)¶

• VM or VPS (AWS EC2 t3.large or t4g.large, DigitalOcean 8vCPU droplet)
• 4-8 vCPU, 16-32 GB RAM
• No GPU needed
• Use smaller models (bert-base-uncased or bert-base-chinese)

🟡 Production-ready (GPU acceleration)¶

• NVIDIA T4, RTX 3060+, A10, A100 (Cloud GPU or on-premise)
• 1 GPU (16-24 GB VRAM), 32-64 GB RAM
• e.g. AWS EC2 g4dn.xlarge, g5.xlarge, Lambda Labs GPU servers
• Docker with --gpus all
• You will get ~5x faster inference, much lower latency

🔵 Heavy production (high concurrency, batch jobs)¶

• Multi-GPU servers (e.g., 2 x A100, 128 GB RAM)
• Use with FastAPI async workers (Gunicorn/Uvicorn workers)
• Kubernetes + autoscaling for large org deployments

🚩 Notes on efficiency:¶

• DeepKE & SpERT are very GPU-hungry if using large models (RoBERTa-large, BERT-large).
• If you’re sticking to BERT-base or DistilBERT, you can get away with CPU-only for low volume (but with higher latency ~1-3s per sentence).
• SpaCy RE models can run lightweight and fast on CPU, but will be less powerful than DeepKE RE.

🌐 Cloud Suggestions¶

Would you also like me to give you a ready-made benchmark comparison between DeepKE (GPU vs CPU) vs SpaCy RE vs CoreNLP OpenIE, including rough latency & resource usage per sentence? If yes, say “Yes, benchmark.”

Homelab Deployment¶

Could I serve this from a home lab? What would the optimal deployment be on a box with 16gb of ram and a core i3?

Absolutely, you can serve DeepKE / SpaCy RE / lightweight RE models from a home lab, but let’s be realistic about your current hardware:

🎯 Your Current Box:¶

🛠 What you can do safely (and what not)¶

✅ Optimal deployment strategy on your box¶

1. Use SpaCy + RE models (via FastAPI)

   • Use spacy-transformers with smaller models like distilbert-base-uncased.
   • Deploy inside a Docker container running FastAPI (CPU-only).
   • Use Uvicorn async workers.
   • You will have ~0.5s-1s latency per sentence, which is very acceptable.

2. Optionally add OpenNRE (BERT-base, CPU)

   • You can set up OpenNRE Docker as a sidecar container.
   • Use it for RE tasks if SpaCy isn’t enough.

3. Keep the batch size low

   • Always process sentence-by-sentence or small batches.
   • Avoid overloading your limited threads and memory.

4. Monitoring & stability

   • Use NGINX as a reverse proxy with rate limits.
   • Monitor RAM usage, keep swap enabled.
   • Use lightweight queueing (e.g. Redis) if you expect spikes.

❗ Golden Rule for your home lab¶

On CPU-only (especially Core i3), your enemy is latency and memory bottlenecks, not the actual model loading.

Stick to lightweight models, asynchronous APIs, and low-concurrency. For hobby, personal, or low-traffic RE extraction? ✅ Works fine. For high-scale? ❌ Don’t even try without GPU.

Would you like me to give you a ready-made, Dockerized FastAPI + SpaCy RE setup optimized for your box (with async, healthcheck, RAM-friendly settings)? If yes, just say “Yes, dockerized FastAPI RE.”

Expanding the Homelab with a GPU¶

What GPU could I buy to reasonably accelerate this work?

Great question! If you want to seriously accelerate RE / NER / OpenIE / DeepKE on a home lab, here’s a practical GPU buying guide for NLP inference and light training, keeping in mind efficiency, compatibility, and your Core i3 system.

✅ Budget-Friendly GPU Options (Used or New)¶

🔥 Best Home Lab GPU in 2025 for NLP Inference?¶

➡ RTX 3060 12GB¶

• Best balance of VRAM, price, power efficiency, and compatibility.
• Supports Tensor Cores (for FP16 inference acceleration)
• Easily fits most cases and can run on a 500W PSU.
• Can handle DeepKE, REBEL, BERT-large, T5-small, etc.
• Can also run small fine-tuning jobs with mixed precision (fp16).

❗ Important notes for your Core i3 + 16 GB RAM setup¶

💡 Bonus option for ultra-low power setups¶

💰 Expected Price (used market 2025):¶

Would you also like me to show you how to set up Docker with GPU support (NVIDIA runtime), plus an optimized FastAPI + DeepKE container that auto-uses the GPU? If yes, just say “Yes, Docker + GPU optimized setup.”

OS Setup for GPU Homelab Workflows¶

Yes please. What is the best OS to run gpu-enabled workload containers like this?

✅ Perfect follow-up. For GPU-enabled workload containers (NLP, ML inference) in a home lab or small server setup, here’s the battle-tested, minimal-hassle stack.

🏆 Best OS for GPU-enabled Docker workloads (2025)¶

✔ Ubuntu Server 22.04 LTS (Recommended)¶

🛠 Minimal Setup Stack¶

1. Ubuntu Server 22.04 LTS (clean install)
2. Install NVIDIA drivers (nvidia-driver-535 or newer)
3. Install NVIDIA Container Toolkit (nvidia-docker2)
4. Install Docker (latest stable)
5. Use Docker Compose for container orchestration

🎯 Install guide¶

# Update system
sudo apt update && sudo apt upgrade -y

# Install kernel headers and essentials
sudo apt install -y build-essential dkms

# Install NVIDIA drivers (replace with your card-supported driver if needed)
sudo apt install -y nvidia-driver-535

# Reboot to activate driver
sudo reboot

# Verify driver
nvidia-smi

# Install Docker
sudo apt install -y docker.io
sudo systemctl enable --now docker

# Install NVIDIA container toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) && \
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - && \
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt update
sudo apt install -y nvidia-container-toolkit

# Configure Docker to use NVIDIA runtime
sudo tee /etc/docker/daemon.json <<EOF
{
  "default-runtime": "nvidia",
  "runtimes": {
    "nvidia": {
      "path": "nvidia-container-runtime",
      "runtimeArgs": []
    }
  }
}
EOF

# Restart Docker
sudo systemctl restart docker

# Test Docker with GPU
docker run --rm --gpus all nvidia/cuda:12.3.2-base-ubuntu22.04 nvidia-smi

🛠 Deploy DeepKE (or any ML app) with GPU via Docker Compose¶

version: '3.9'
services:
  deepke:
    image: your-deepke-gpu-image
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    ports:
      - "8000:8000"
    volumes:
      - ./models:/app/models

Or more simply:

docker run --rm --gpus all -p 8000:8000 your-deepke-gpu-image

💡 Summary of why Ubuntu 22.04 is best:¶

Would you also like a ready-made DeepKE + FastAPI Dockerfile optimized for GPU inference, with a Clojure client to talk to it? If yes, just say “Yes, DeepKE GPU Dockerfile + Clojure client.”

DeepKE Dockerfile + Clojure Client¶

Yes, DeepKE GPU Dockerfile + Clojure client.

🔥 Great. Here’s a clean, production-grade example:

🚀 DeepKE GPU Inference with FastAPI via Docker¶

🐳 Dockerfile (DeepKE + FastAPI + GPU ready)¶

FROM pytorch/pytorch:2.2.2-cuda12.1-cudnn8-runtime

# Install OS-level deps
RUN apt update && apt install -y git python3-pip && rm -rf /var/lib/apt/lists/*

# Install DeepKE and FastAPI
WORKDIR /app
RUN git clone https://github.com/zjunlp/DeepKE.git && \
    cd DeepKE && \
    pip install -r requirements.txt && \
    pip install fastapi uvicorn pydantic

# Optional: preload model (adjust as needed)
RUN python -c "from deepke.re import Trainer; Trainer.from_pretrained('re_tacred_bert_softmax')"

# Copy app
COPY app /app/app

EXPOSE 8000

# Launch FastAPI (adjust DeepKE service call inside the app)
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

🗂 Example FastAPI App (app/main.py)¶

from fastapi import FastAPI
from pydantic import BaseModel
from deepke.re import Trainer

app = FastAPI()

# Load model at startup
model = Trainer.from_pretrained('re_tacred_bert_softmax')

class InferenceRequest(BaseModel):
    text: str

@app.post("/extract")
def extract_relations(request: InferenceRequest):
    result = model.predict([request.text])
    return {"relations": result}

✅ Example Docker Compose (GPU ready)¶

version: '3.9'
services:
  deepke-gpu:
    build: .
    ports:
      - "8000:8000"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

🛠 Build & Run¶

docker-compose build
docker-compose up -d

Test:

curl -X POST http://localhost:8000/extract -H "Content-Type: application/json" -d '{"text": "Apple acquired Beats Electronics for $3 billion."}'

💡 Minimal Clojure Client using clj-http¶

(ns deepke-client.core
  (:require [clj-http.client :as client]
            [cheshire.core :as json]))

(defn extract-relations [text]
  (let [response (client/post "http://localhost:8000/extract"
                              {:headers {"Content-Type" "application/json"}
                               :body (json/generate-string {:text text})
                               :as :json})]
    (:body response)))

;; Example
(println (extract-relations "Dr Nandipha Magudumana was deported from Tanzania."))

🧪 Notes¶

• The example uses DeepKE’s RE (Relation Extraction) pretrained model, but DeepKE also supports NER, event extraction, etc. Adjust Trainer.from_pretrained(...) accordingly.
• GPU acceleration works automatically inside the container (torch.cuda.is_available() will return True).
• This setup is clean, portable, and ready for local LAN, cloud, or internal API consumption.

Would you also like me to show how to switch the model to event extraction or NER, and make the Clojure client support multiple modes? If yes, say “Yes, multi-mode DeepKE API + Clojure client.”

🔥 Here’s how to build a multi-mode DeepKE FastAPI service + Clojure client.

🚀 Multi-Mode DeepKE FastAPI (Relation, Event, NER in one API)¶

Updated app/main.py¶

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from deepke.re import Trainer as RE_Trainer
from deepke.ner import Trainer as NER_Trainer
from deepke.event import Trainer as Event_Trainer

app = FastAPI()

# Load models once at startup (choose your models)
re_model = RE_Trainer.from_pretrained('re_tacred_bert_softmax')
ner_model = NER_Trainer.from_pretrained('ner_conll03_bert')
event_model = Event_Trainer.from_pretrained('event_eventbert')

class InferenceRequest(BaseModel):
    text: str
    task: str  # 'relation', 'ner', or 'event'

@app.post("/extract")
def extract(request: InferenceRequest):
    if request.task == 'relation':
        result = re_model.predict([request.text])
    elif request.task == 'ner':
        result = ner_model.predict([request.text])
    elif request.task == 'event':
        result = event_model.predict([request.text])
    else:
        raise HTTPException(status_code=400, detail="Unknown task. Use 'relation', 'ner', or 'event'.")
    return {"result": result}

🧪 Docker Compose (unchanged)¶

Same as before:

version: '3.9'
services:
  deepke-gpu:
    build: .
    ports:
      - "8000:8000"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

✅ Build & Run¶

docker-compose build
docker-compose up -d

💡 Clojure Multi-Task Client (deepke-client.core)¶

(ns deepke-client.core
  (:require [clj-http.client :as client]
            [cheshire.core :as json]))

(defn extract [text task]
  (let [response (client/post "http://localhost:8000/extract"
                              {:headers {"Content-Type" "application/json"}
                               :body (json/generate-string {:text text :task task})
                               :as :json})]
    (:body response)))

;; Example Usage:
(println "Relation Extraction:")
(println (extract "Apple acquired Beats Electronics for $3 billion." "relation"))

(println "\nNamed Entity Recognition:")
(println (extract "Barack Obama was born in Hawaii." "ner"))

(println "\nEvent Extraction:")
(println (extract "Nandipha Magudumana was arrested in Tanzania." "event"))

🛠 Notes¶

• You can add validation, result formatting, or merge predictions in FastAPI later.
• Clojure client stays simple and only switches the task string.
• You can easily swap to other models by changing the .from_pretrained().

Would you also like a ready-made Docker Compose + Caddy reverse proxy + GPU monitoring dashboard (nvidia-smi) to deploy this in your home lab? If yes, say “Yes, home lab optimized stack.”

Home Lab Optimized Stack¶

🔥 Here’s your Home Lab Optimized Stack: DeepKE GPU FastAPI + Reverse Proxy + Monitoring

🖥 Components¶

🗂 Folder Structure¶

/home-lab-deepke/
├── docker-compose.yml
├── deepke-app/
│   ├── Dockerfile
│   ├── requirements.txt
│   └── app/
│       └── main.py
└── Caddyfile

🐳 Docker Compose (docker-compose.yml)¶

version: '3.9'

services:
  deepke-gpu:
    build: ./deepke-app
    container_name: deepke-gpu
    restart: unless-stopped
    networks:
      - backend
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

  caddy:
    image: caddy:2-alpine
    restart: unless-stopped
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./Caddyfile:/etc/caddy/Caddyfile
      - caddy_data:/data
      - caddy_config:/config
    networks:
      - backend

  nvidia-exporter:
    image: nvcr.io/nvidia/k8s/dcgm-exporter:latest
    restart: unless-stopped
    ports:
      - "9400:9400"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    networks:
      - backend

networks:
  backend:
    driver: bridge

volumes:
  caddy_data:
  caddy_config:

🌐 Caddyfile (Caddyfile)¶

deepke.local {
    reverse_proxy deepke-gpu:8000
}

nvidia.local {
    reverse_proxy nvidia-exporter:9400
}

You can use deepke.local and nvidia.local in your home LAN by adding them to your /etc/hosts or configuring local DNS.

🚀 FastAPI Dockerfile (deepke-app/Dockerfile)¶

FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime

WORKDIR /app

COPY requirements.txt .
RUN pip install --upgrade pip && pip install -r requirements.txt

COPY app /app

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

📦 requirements.txt¶

fastapi
uvicorn
transformers
torch
deepke @ git+https://github.com/zjunlp/DeepKE.git

✅ Usage¶

# Build and deploy the whole stack
docker-compose up -d

# Check logs
docker-compose logs -f

# Visit
http://deepke.local  (API)
http://nvidia.local   (GPU metrics)

💡 Optional GUI: Portainer¶

Add this to your docker-compose.yml for an easy GUI to manage all containers:

  portainer:
    image: portainer/portainer-ce
    ports:
      - "9000:9000"
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
      - portainer_data:/data
    restart: unless-stopped
    networks:
      - backend

volumes:
  portainer_data:

⚡ Recommendation for your i3 + 16GB RAM + mid-tier GPU (e.g. RTX 3060):¶

• Use lightweight containers like above.
• No Kubernetes needed.
• Monitor GPU temps + VRAM usage (via NVIDIA exporter).
• Optimize FastAPI worker count (e.g. --workers 2 if your CPU is weak).
• Use LAN reverse proxy with Caddy.

Would you also like a fully pre-built Ansible playbook to set up the entire DeepKE + GPU Docker stack + monitoring on Ubuntu 22.04 headless? If yes, just say Yes, Ansible home lab playbook.

Multiple Computers¶

Absolutely! You can absolutely use multiple computers for this homelab setup, and it’s actually a great idea to distribute the workload, especially with resource-heavy tasks like DeepKE’s GPU inference and monitoring.

How to do it — Multi-Node Homelab with Docker:¶

1. Separate Roles by Machines¶

2. Networking¶

• Use a private network (VLAN or VPN) so containers across machines can talk.
• You can create a Docker Swarm cluster or Kubernetes if you want orchestration.
• Or use simple static IPs/DNS + configure Caddy on your gateway node to route requests between nodes.

3. Example Deployment Architecture¶

[ GPU Server ] -----------[ Monitoring Server ]------------[ Client Machine ]
     |                             |                              |
Docker: deepke-gpu            Docker: nvidia-exporter,         Your code runs here,
                             prometheus, grafana             sends requests to API

           \                                              /
            \--------------------------------------------/
                         Private Docker Network or VPN

4. How to share API endpoints¶

• On the gateway machine running Caddy, configure reverse proxy rules pointing to the GPU server IP/port.
• Or use a load balancer if you have multiple GPU nodes.

Example Caddy snippet routing requests:

deepke.mylab.local {
    reverse_proxy gpu-server.local:8000
}

metrics.mylab.local {
    reverse_proxy monitoring-server.local:9400
}

5. Notes¶

• The GPU server needs the Nvidia drivers + Docker Nvidia runtime installed.
• Monitoring and proxy servers don’t need GPUs, so can run on low-power machines.
• You can horizontally scale DeepKE by deploying multiple GPU servers behind a load balancer.
• Secure your network and API endpoints, especially if you expose outside your LAN.

TL;DR¶

• Yes, split services across multiple machines for performance and management.
• Use Docker networking or orchestration (Docker Swarm, Kubernetes) to connect containers.
• Use a reverse proxy on a gateway machine to unify access.
• Assign roles: GPU-heavy, proxy, monitoring, and client machines separately.

If you want, I can help you draft a multi-node Docker Swarm config or Kubernetes manifests for this! Just say: “Yes, multi-node orchestration config.”

Multi-Machine Networking¶

Would it be smartest to network traditionally or to use OpenShift/Kubernetes/Docker Swarm for this?

Great question! The choice between traditional networking (simple Docker + manual networking) versus container orchestration platforms like Kubernetes, OpenShift, or Docker Swarm depends on your homelab goals, scale, and how much complexity you want to manage.

Traditional Networking (Docker Compose + manual IP/DNS)¶

Pros:

• Simple to set up and maintain — especially for 2-3 nodes.
• Low overhead — less CPU/memory consumed by orchestration agents.
• Easier to troubleshoot for smaller setups.
• Perfect for stable, static environments where you control the nodes.

Cons:

• No built-in service discovery or automatic failover.
• Manual management of container restarts, scaling, and networking.
• Harder to scale beyond a few machines.

Best for: Small home labs, fewer machines, mostly static workloads.

Docker Swarm¶

Pros:

• Built into Docker CLI — no extra tools needed.
• Easier learning curve than Kubernetes.
• Simple service discovery and scaling.
• Good for small to medium clusters (~up to a dozen nodes).
• Supports multi-host networking automatically.

Cons:

• Less ecosystem and community support than Kubernetes.
• Less powerful for complex deployments.
• Less flexible than Kubernetes/OpenShift.

Best for: Small to medium homelabs wanting lightweight orchestration.

Kubernetes (or OpenShift)¶

Pros:

• Industry standard for container orchestration.
• Highly scalable and flexible.
• Rich ecosystem: monitoring, logging, CI/CD integrations.
• Automated scaling, rolling updates, self-healing.
• OpenShift adds enterprise features on top of Kubernetes.

Cons:

• Higher complexity and learning curve.
• More resource overhead — your homelab machines need to be beefy enough.
• More complex setup and maintenance.
• Might be overkill for small personal projects.

Best for: Larger homelabs, experimentation with cloud-native tech, future-proofing.

What I’d recommend for your homelab (16GB RAM, Core i3, a few nodes)¶

• If you want simplicity and quick deployment, go traditional networking with Docker Compose + manual IP/DNS or a lightweight reverse proxy.
• If you want some orchestration but still easy setup, try Docker Swarm — good balance of features and simplicity.
• If you want to invest in learning Kubernetes/OpenShift and plan to scale or run many services, go for it — but expect a steeper setup curve.

Summary table¶

If you want, I can help you set up a Docker Swarm multi-node cluster for DeepKE and monitoring — easy and effective for your hardware specs. Just say: “Yes, Docker Swarm multi-node setup please.”

Docker Swarm Multi-Node Setup¶

Great! Here’s a step-by-step guide to set up a Docker Swarm multi-node cluster for your homelab running DeepKE (or similar containers). This will cover:

• Initializing the swarm
• Adding worker nodes
• Deploying services with a simple docker-compose.yml
• Networking considerations
• Basic monitoring

Step 1: Prepare your machines¶

• Make sure Docker is installed on all nodes (manager + workers).
• All nodes should be on the same network and able to communicate over ports 2377 (Swarm management), 7946 (container network discovery), 4789 (overlay network VXLAN).
• On your GPU machine(s), install Nvidia drivers and Docker Nvidia runtime if you want GPU acceleration.

Step 2: Initialize Docker Swarm on manager node¶

SSH to the machine you want as the manager (say, your main GPU box):

docker swarm init --advertise-addr <MANAGER_IP>

• <MANAGER_IP> is the IP reachable by all other nodes on your LAN.

This command outputs a join token command, something like:

docker swarm join --token SWMTKN-1-xxx-xxx <MANAGER_IP>:2377

Step 3: Join worker nodes¶

On every other machine (workers), run the join command from above:

docker swarm join --token SWMTKN-1-xxx-xxx <MANAGER_IP>:2377

Step 4: Verify the cluster¶

Back on the manager node, run:

docker node ls

You should see all nodes listed with status Ready.

Step 5: Create your docker-compose.yml for DeepKE + monitoring¶

Example docker-compose.yml adapted for swarm (use deploy section):

version: "3.8"

services:

  deepke:
    image: your-deepke-gpu-image:latest
    deploy:
      replicas: 1
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
      restart_policy:
        condition: on-failure
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
    runtime: nvidia
    ports:
      - "8000:8000"     # API port
    networks:
      - deepke-net

  prometheus:
    image: prom/prometheus:latest
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml:ro
    ports:
      - "9090:9090"
    deploy:
      replicas: 1
      restart_policy:
        condition: on-failure
    networks:
      - deepke-net

  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    deploy:
      replicas: 1
      restart_policy:
        condition: on-failure
    networks:
      - deepke-net

networks:
  deepke-net:
    driver: overlay

• Put your Prometheus config (prometheus.yml) in the same folder.
• This setup creates an overlay network so containers on different hosts can talk.

Step 6: Deploy the stack¶

On your manager node:

docker stack deploy -c docker-compose.yml deepke-stack

Check services:

docker service ls

Step 7: Access services¶

• DeepKE API: http://<MANAGER_IP>:8000
• Prometheus UI: http://<MANAGER_IP>:9090
• Grafana UI: http://<MANAGER_IP>:3000

Step 8: Optional: Add reverse proxy (Caddy/Traefik) on manager or gateway node¶

You can set up Caddy or Traefik container to proxy and secure your APIs with HTTPS and route multiple services.

Notes & Tips¶

• Use labels and placement constraints if you want to pin DeepKE service only to GPU nodes. Example:

deploy:
  placement:
    constraints:
      - node.labels.gpu==true

Set node label on manager:

docker node update --label-add gpu=true <NODE-ID>

• For advanced GPU scheduling, check Nvidia Docker + Swarm docs.

If you want, I can help you write the Prometheus config or Caddy reverse proxy too. Just say the word!

Prometheus and Grafana Config¶

Absolutely! Here are simple starter configs for Prometheus and Grafana tailored to monitor your Docker Swarm services like DeepKE.

Prometheus Config (prometheus.yml)¶

This config tells Prometheus to scrape metrics exposed by your services. If your services don’t expose Prometheus metrics natively, you can add exporters or enable metrics endpoints first.

global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  # Scrape Docker Swarm nodes using cAdvisor (if you deploy it)
  - job_name: 'cadvisor'
    static_configs:
      - targets: ['cadvisor:8080']

  # Scrape Docker Swarm services with metrics endpoint
  - job_name: 'deepke'
    static_configs:
      - targets: ['deepke:8000']

  # Docker Engine metrics (optional, via docker daemon or node-exporter)
  - job_name: 'docker-engine'
    static_configs:
      - targets: ['docker-host1:9323', 'docker-host2:9323']

Notes:¶

• You need to run cAdvisor or similar exporter to get container-level metrics.
• Your DeepKE API container must expose Prometheus metrics at the specified port (8000 here is the example API port). If DeepKE doesn’t expose Prometheus metrics, consider adding a Prometheus exporter sidecar or using a generic exporter.
• Replace docker-host1, docker-host2 with your actual node IPs and make sure the endpoints are reachable.

Simple cAdvisor Service (optional for container metrics)¶

Add this to your docker-compose.yml for monitoring:

  cadvisor:
    image: gcr.io/cadvisor/cadvisor:v0.47.0
    ports:
      - "8080:8080"
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock:ro
      - /sys:/sys:ro
      - /var/lib/docker/:/var/lib/docker:ro
    deploy:
      replicas: 1
      restart_policy:
        condition: on-failure
    networks:
      - deepke-net

Grafana Setup¶

Grafana config is mostly UI-driven but here’s what you do after launching Grafana (http://<MANAGER_IP>:3000):

1. Login (default: admin/admin) and change password.

2. Add Data Source:

   • Choose Prometheus
   • URL: http://prometheus:9090 (Docker service name inside the overlay network)

3. Import Dashboard:

   • Go to Dashboards > Manage > Import
   • Use a pre-built dashboard ID from Grafana Dashboards for Docker or cAdvisor, e.g. dashboard ID 893 (Docker monitoring).
   • Or build your own with metrics like container CPU, memory, network.

Summary¶

• Prometheus scrapes metrics (needs your services to expose or run exporters).
• cAdvisor can give you container-level metrics automatically.
• Grafana connects to Prometheus, visualize metrics, and create alerts.

If you want, I can generate a full sample docker-compose.yml with cAdvisor + Prometheus + Grafana plus a simple exporter for your DeepKE API in Clojure to expose metrics — just ask!

DeepKE Prometheus Metrics¶

Sure! Here’s a full example docker-compose.yml that sets up:

• DeepKE API (simulated here with a basic example app exposing Prometheus metrics)
• cAdvisor for container metrics
• Prometheus to scrape metrics from DeepKE and cAdvisor
• Grafana for dashboards and visualization

docker-compose.yml¶

version: "3.8"

services:

  deepke:
    image: your-deepke-gpu-image:latest
    deploy:
      replicas: 1
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
      restart_policy:
        condition: on-failure
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
    runtime: nvidia
    ports:
      - "8000:8000"     # DeepKE API port
    networks:
      - monitoring-net

  # A simple Prometheus metrics exporter for DeepKE API.
  # If your actual DeepKE exposes /metrics, you can skip this service.
  deepke-exporter:
    image: your-deepke-exporter:latest
    depends_on:
      - deepke
    ports:
      - "9100:9100"
    networks:
      - monitoring-net

  cadvisor:
    image: gcr.io/cadvisor/cadvisor:v0.47.0
    ports:
      - "8080:8080"
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock:ro
      - /sys:/sys:ro
      - /var/lib/docker/:/var/lib/docker:ro
    deploy:
      replicas: 1
      restart_policy:
        condition: on-failure
    networks:
      - monitoring-net

  prometheus:
    image: prom/prometheus:latest
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml:ro
    ports:
      - "9090:9090"
    deploy:
      replicas: 1
      restart_policy:
        condition: on-failure
    networks:
      - monitoring-net

  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    deploy:
      replicas: 1
      restart_policy:
        condition: on-failure
    networks:
      - monitoring-net

networks:
  monitoring-net:
    driver: overlay

prometheus.yml¶

global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'cadvisor'
    static_configs:
      - targets: ['cadvisor:8080']

  - job_name: 'deepke-exporter'
    static_configs:
      - targets: ['deepke-exporter:9100']

Notes:¶

• Replace your-deepke-gpu-image:latest with your actual DeepKE GPU-enabled Docker image.
• deepke-exporter is a placeholder for a simple Prometheus exporter container. If your DeepKE API already exposes /metrics, you can expose that port directly and update Prometheus to scrape it, skipping this exporter service.
• All services share the monitoring-net overlay network so they can talk via service name.
• cadvisor scrapes Docker metrics from the host(s).
• prometheus scrapes cadvisor and deepke-exporter.
• grafana connects to Prometheus at http://prometheus:9090 when you set up the data source in the UI.

Simple DeepKE Prometheus Exporter (Clojure example snippet)¶

If you want a minimal Prometheus endpoint in your DeepKE app (using io.prometheus/simpleclient):

(ns deepke.metrics
  (:require [ring.adapter.jetty :refer [run-jetty]]
            [ring.util.response :refer [response]]
            [io.prometheus.simpleclient :as prom]))

(def registry (prom/collector-registry))

(def request-counter (prom/counter "deepke_requests_total" "Total API requests" [] registry))

(defn metrics-handler [_request]
  (response (prom/format-metrics registry)))

(defn wrap-count-requests [handler]
  (fn [req]
    (prom/inc! request-counter)
    (handler req)))

(defn -main []
  (run-jetty (-> your-app-handler
                 wrap-count-requests
                 (assoc :metrics metrics-handler))
             {:port 8000}))

If you want me to help build that exporter image or a full example with multi-node Swarm deploy, just ask!

Including an LLM in the Environment¶

Absolutely! To deploy a small LLM alongside your DeepKE + monitoring stack, you can add a lightweight LLM server container that exposes an HTTP API, and hook it into the same Docker Compose setup.

How to add a small LLM to your stack¶

1. Pick a lightweight LLM serving option¶

Here are some popular lightweight LLM frameworks you can run easily on a home lab or modest GPU:

• llama.cpp : CPU-optimized, very lightweight local LLM (7B, 13B).
• Text Generation Inference by Hugging Face: runs Hugging Face models with efficient GPU acceleration, supports small models.
• FastChat : for chat models, includes server.
• GPT4All : small models with API serving capabilities.

2. Example: Deploy llama.cpp HTTP server container¶

There are existing Docker containers for llama.cpp that serve a REST API.

One example: ghcr.io/corazzon/llama-cpp-python provides a simple FastAPI server wrapping llama.cpp.

3. Update your docker-compose.yml to include the LLM service¶

Here is an extended snippet with a llama.cpp-based LLM server added:

version: "3.8"

services:
  deepke:
    image: your-deepke-gpu-image:latest
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
    ports:
      - "8000:8000"
    networks:
      - monitoring-net

  deepke-exporter:
    image: your-deepke-exporter:latest
    depends_on:
      - deepke
    ports:
      - "9100:9100"
    networks:
      - monitoring-net

  cadvisor:
    image: gcr.io/cadvisor/cadvisor:v0.47.0
    ports:
      - "8080:8080"
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock:ro
      - /sys:/sys:ro
      - /var/lib/docker/:/var/lib/docker:ro
    networks:
      - monitoring-net

  prometheus:
    image: prom/prometheus:latest
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml:ro
    ports:
      - "9090:9090"
    networks:
      - monitoring-net

  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    networks:
      - monitoring-net

  llama-cpp:
    image: ghcr.io/corazzon/llama-cpp-python:latest
    command: >
      --model /models/ggml-model-q4_0.bin
      --host 0.0.0.0
      --port 5000      
    volumes:
      - ./models:/models
    ports:
      - "5000:5000"
    networks:
      - monitoring-net

networks:
  monitoring-net:
    driver: overlay

4. How to use this¶

• Put your quantized llama model (e.g. ggml-model-q4_0.bin) in the local folder ./models
• The LLM HTTP API will be available on port 5000 of the host.
• You can call it from your other services (or your client apps) via http://llama-cpp:5000 inside the Docker network.

5. Optionally: Add LLM metrics for Prometheus¶

If the LLM server exposes metrics, add a scrape job in prometheus.yml:

scrape_configs:
  - job_name: 'llama-cpp'
    static_configs:
      - targets: ['llama-cpp:5000']  # Adjust if metrics port differs

6. Minimal LLM API example request¶

You can POST a prompt like:

curl -X POST "http://localhost:5000/v1/completions" \
  -H "Content-Type: application/json" \
  -d '{"prompt":"Explain Docker Swarm in simple terms.","max_tokens":50}'

Summary¶

• Add a lightweight LLM container like llama-cpp-python to your Docker Compose.
• Make sure the model file is accessible (via mounted volume).
• Use the LLM HTTP API inside your Docker network for inference.
• Extend Prometheus to scrape metrics if available.

If you want, I can help you:

• Set up a simple client in Clojure to call the LLM API.
• Show how to link LLM responses with DeepKE extracted triples for enriched NLP workflows.
• Build a monitoring dashboard in Grafana combining DeepKE + LLM metrics.

Just ask!

Replacing Gemini¶

Yes, show me how to deploy a small GPU-enabled LLM to replace Gemini free in this environment please?

Got it! To replace Gemini Free with a small GPU-enabled LLM in your existing Docker Compose environment (with Prometheus, Grafana, DeepKE, etc.), here’s a practical step-by-step guide using Hugging Face’s text-generation-inference (TGI) server, which supports GPU acceleration out-of-the-box and is fairly lightweight for smaller models.

Step 1: Hardware & Prerequisites¶

• NVIDIA GPU with CUDA support (e.g., RTX 3060 or better recommended)
• NVIDIA drivers + CUDA toolkit installed on host
• NVIDIA Container Toolkit installed (nvidia-docker2)
• Docker version ≥ 19.03

Step 2: Prepare your model¶

Choose a small LLM compatible with TGI, e.g., tiiuae/falcon-7b-instruct or any smaller model from HF Hub.

Download or mount the model in your host’s ./models directory:

mkdir -p ./models
# Optionally use `git lfs` or huggingface CLI to download the model

Step 3: Add llm service to your docker-compose.yml¶

version: '3.8'

services:
  # Your existing services here: cadvisor, prometheus, grafana, deepke...

  llm:
    image: ghcr.io/huggingface/text-generation-inference:latest-gpu
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=0  # Use GPU 0
    volumes:
      - ./models:/models
    command: >
      --model /models/falcon-7b-instruct
      --port 5000
      --trust-remote-code      
    ports:
      - "5000:5000"
    networks:
      - monitoring-net

networks:
  monitoring-net:
    external: true

Step 4: Notes on the Compose config¶

• runtime: nvidia + NVIDIA_VISIBLE_DEVICES=0 enables GPU passthrough.
• --trust-remote-code needed for some models with custom code.
• Expose port 5000 (adjust as needed).
• Assumes monitoring-net is your existing overlay network.
• Make sure ./models/falcon-7b-instruct contains your model files.

Step 5: Launch & verify¶

docker-compose up -d llm
docker logs -f <llm_container_id>

Look for:

INFO:     Started server process [pid]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:5000

Step 6: Test the LLM endpoint¶

Example using curl:

curl -X POST http://localhost:5000/generate -H "Content-Type: application/json" -d '{
  "inputs": "What is the meaning of life?",
  "parameters": {
    "max_new_tokens": 50
  }
}'

Response will be a JSON with generated text.

Step 7: Integrate with your Clojure client or API stack¶

Use this endpoint as your new LLM service, replacing Gemini Free. The API is RESTful and documented here .

Optional: Resource optimization¶

• For your 16GB RAM + mid-tier GPU home lab, Falcon 7B may be a bit heavy but can work with mixed precision.
• Alternatively, pick a smaller model like EleutherAI/gpt-neo-125m or facebook/opt-1.3b to reduce resource load.
• Monitor GPU/CPU usage with your existing Prometheus + cAdvisor stack.

Summary¶

• Use Hugging Face’s text-generation-inference GPU Docker image.
• Deploy with GPU support via Docker Compose (runtime: nvidia).
• Mount a small HF model and expose port 5000.
• Replace Gemini free with this endpoint in your app.

If you want, I can also help you with Clojure client code to query this new LLM endpoint!

Great question! Running a reasonably powerful LLM at home for inference and Retrieval-Augmented Generation (RAG) means balancing hardware cost, power, and responsiveness.

Here’s a breakdown of what you’d ideally want:

1. GPU¶

• Minimum: NVIDIA RTX 3060 (12GB VRAM)

  • Can handle models like Falcon 7B (quantized, mixed precision).
  • Good for moderate batch sizes and latency.

• Recommended: NVIDIA RTX 4080 or RTX 3090/3090 Ti (16–24GB VRAM)

  • Supports larger models (13B+ or even 33B with quantization).
  • Faster inference, multi-user capacity.
  • Better for production-grade RAG pipelines.

• Why GPU? LLM inference and embedding generation for RAG are heavy on matrix multiplications; GPUs massively speed this up.

2. CPU¶

• A modern 6-8 core CPU (e.g., AMD Ryzen 5/7 or Intel i5/i7)

  • Manages data preprocessing, retrieval, orchestrating pipelines.
  • A bottleneck if too weak, but less critical than GPU for inference.

3. RAM¶

• Minimum: 32 GB RAM

  • Loading models, caching embeddings, running retrieval indices.

• Recommended: 64 GB RAM+

  • Smooth multitasking, handling larger indices (e.g., FAISS or other vector stores).
  • Run multiple services simultaneously (RAG + embedding generation + API servers).

4. Storage¶

• Fast NVMe SSD (1 TB+)

  • Quick loading of models (several GB each).
  • Fast retrieval index reads and writes.
  • OS, logs, and datasets.

5. Network¶

• Gigabit Ethernet or fast Wi-Fi for LAN communication between services/nodes.
• Optional: setup a mini cluster for distributed inference or multi-node RAG.

6. Software Setup¶

• Linux-based OS (Ubuntu 22.04 LTS preferred) with NVIDIA drivers + CUDA + Docker + NVIDIA Container Toolkit.
• Containerized model serving (e.g., Hugging Face TGI, DeepKE, or custom).
• Vector search engine (FAISS, Milvus) for RAG.

Summary Example Build for a Good Home LLM+RAG Box¶

Lower budget option¶

Final Thoughts¶

• Bigger GPU VRAM = bigger models / faster inference / more users.
• RAM affects retrieval & embedding workflows more than inference itself.
• RAG needs both fast vector search and quick LLM calls—optimize both.

If you want, I can help you design a custom home lab config, including recommended parts and cost estimates! Would you like that?