chore: Remove docker compose for inference

jan-inference/llm/README.md

@@ -1,8 +0,0 @@
-# Inference - LLM
-
-```bash
-docker network create traefik_public
-cp .env.example .env
-# -> Update MODEL_URL in `.env` file
-docker compose up -d --scale llm=2
-``````

jan-inference/llm/docker-compose.yml

@@ -1,60 +0,0 @@
-version: '3'
-
-services:
-
-  # Service to download a model file.
-  downloader:
-    image: busybox
-    # The command extracts the model filename from MODEL_URL and downloads it if it doesn't exist.
-    command: /bin/sh -c "LLM_MODEL_FILE=$(basename ${MODEL_URL}); if [ ! -f /models/$LLM_MODEL_FILE ]; then wget -O /models/$LLM_MODEL_FILE ${MODEL_URL}; fi"
-    # Mount a local directory to store the downloaded model.
-    volumes:
-      - ./models:/models
-
-  # Service to wait for the downloader service to finish downloading the model.
-  wait-for-downloader:
-    image: busybox
-    # The command waits until the model file (specified in MODEL_URL) exists.
-    command: /bin/sh -c "LLM_MODEL_FILE=$(basename ${MODEL_URL}); echo 'Waiting for downloader to finish'; while [ ! -f /models/$LLM_MODEL_FILE ]; do sleep 1; done; echo 'Model downloaded!'"
-    # Specifies that this service should start after the downloader service has started.
-    depends_on:
-      downloader:
-        condition: service_started
-    # Mount the same local directory to check for the downloaded model.
-    volumes:
-      - ./models:/models
-
-  # Service to run the Llama web application.
-  llm:
-    image: ghcr.io/abetlen/llama-cpp-python:latest
-    # Mount the directory that contains the downloaded model.
-    volumes:
-      - ./models:/models
-    ports:
-      - 8000:8000
-    environment:
-      # Specify the path to the model for the web application.
-      MODEL: /models/llama-2-7b-chat.ggmlv3.q4_1.bin
-      PYTHONUNBUFFERED: 1
-    # Health check configuration
-    # healthcheck:
-    #   test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:8000"]
-    #   interval: 30s
-    #   timeout: 10s
-    #   retries: 3
-    #   start_period: 30s
-    # Restart policy configuration
-    restart: on-failure
-    # Specifies that this service should start only after wait-for-downloader has completed successfully.
-    depends_on:
-      wait-for-downloader:
-        condition: service_completed_successfully
-    # Connect this service to two networks: inference_net and traefik_public.
-    networks:
-      - traefik_public
-
-# Define networks used in this docker-compose file.
-networks:
-  # Public-facing network that Traefik uses. Marked as external to indicate it may be defined outside this file.
-  traefik_public:
-    external: true

jan-inference/sd/docker-compose.yml

@@ -1,71 +0,0 @@
-version: '3'
-
-services:
-
-  # Service to download a model file.
-  downloader:
-    build:
-      context: ./
-      dockerfile: compile.Dockerfile
-    # platform: "linux/amd64"
-    # The command extracts the model filename from MODEL_URL and downloads it if it doesn't exist.
-    command: /bin/sh -c "SD_MODEL_FILE=$(basename ${MODEL_URL}); if [ ! -f /converted_models/*.bin ]; then wget -O /converted_models/$SD_MODEL_FILE ${MODEL_URL}; python /sd.cpp/models/convert.py --out_type q4_0 --out_file /converted_models/$SD_MODEL_FILE; fi"
-    # Mount a local directory to store the downloaded model.
-    volumes:
-      - ./models:/converted_models
-
-  # Service to wait for the downloader service to finish downloading the model.
-  wait-for-downloader:
-    image: busybox
-    # The command waits until the model file (specified in MODEL_URL) exists.
-    command: /bin/sh -c "SD_MODEL_FILE=$(basename ${MODEL_URL}); echo 'Waiting for downloader to finish'; while [ ! -f /models/*.bin ]; do sleep 1; done; echo 'Model downloaded and converted!'"
-    # Specifies that this service should start after the downloader service has started.
-    depends_on:
-      downloader:
-        condition: service_started
-    # Mount the same local directory to check for the downloaded model.
-    volumes:
-      - ./models:/models
-
-  # Service to run the SD web application.
-  sd:
-    build:
-      context: ./
-      dockerfile: inference.Dockerfile
-    # Mount the directory that contains the downloaded model.
-    volumes:
-      - ./models:/models
-      - ./output/:/serving/output
-    command:  /bin/bash -c "python -m uvicorn main:app --proxy-headers --host 0.0.0.0 --port 8000"
-    # platform: "linux/amd64"
-    environment:
-      # Specify the path to the model for the web application.
-      BASE_URL: http://0.0.0.0:8000
-      MODEL_NAME: "v1-5-pruned-emaonly-ggml-model-q5_0.bin"
-      MODEL_DIR: "/models"
-      SD_PATH: "/sd"
-      PYTHONUNBUFFERED: 1
-    ports:
-      - 8000:8000
-    # Health check configuration
-    # healthcheck:
-    #   test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:8000"]
-    #   interval: 30s
-    #   timeout: 10s
-    #   retries: 3
-    #   start_period: 30s
-    # Restart policy configuration
-    restart: on-failure
-    # Specifies that this service should start only after wait-for-downloader has completed successfully.
-    depends_on:
-      wait-for-downloader:
-        condition: service_completed_successfully
-    # Connect this service to two networks: inference_net and traefik_public.
-    networks:
-      - traefik_public
-
-# Define networks used in this docker-compose file.
-networks:
-  # Public-facing network that Traefik uses. Marked as external to indicate it may be defined outside this file.
-  traefik_public:
-    external: true