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Merge pull request #18 from janhq/fix_inference

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Fix inference service - ggml (LLM + SD)
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namvuong 2023-08-30 16:45:25 +07:00 committed by GitHub
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10 changed files with 101 additions and 229 deletions
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21
README.md
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@ -52,6 +52,15 @@ Jan is a free, source-available and [fair code licensed](https://faircode.io/) A
Jan offers an [Docker Compose](https://docs.docker.com/compose/) deployment that automates the setup process.
```bash
# Download models
# Runway SD 1.5
wget https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors -P jan-inference/sd/models
# Download LLM
wget https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q4_1.bin -P jan-inference/llm/models
``````
Run the following command to start all the services defined in the `docker-compose.yml`
```shell
@ -102,14 +111,4 @@ Jan is a monorepo that pulls in the following submodules
## Live Demo
You can access the live demo at https://cloud.jan.ai.
## Common Issues and Troubleshooting
**Error in `jan-inference` service** ![](images/download-model-error.png)
- Error: download model incomplete
- Solution:
- Manually download the LLM model using the URL specified in the environment variable `MODEL_URL` within the `.env` file. The URL is typically https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q4_1.bin
- Copy the downloaded file `llama-2-7b-chat.ggmlv3.q4_1.bin` to the folder `jan-inference/llm/models`
- Run `docker compose down` followed by `docker compose up -d` again to restart the services.
You can access the live demo at https://cloud.jan.ai.

99
docker-compose.yml
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@ -125,7 +125,6 @@ services:
timeout: 10s
retries: 5
start_period: 5s
networks:
jan_community:
ipv4_address: 172.20.0.14
@ -152,39 +151,9 @@ services:
jan_community:
ipv4_address: 172.20.0.15
# 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 ${MODEL_URL}; fi"
# Mount a local directory to store the downloaded model.
volumes:
- ./jan-inference/llm/models:/models
networks:
jan_community:
ipv4_address: 172.20.0.16
# Service to wait for the downloader service to finish downloading the model.
wait-for-downloader:
image: busybox
# The command waits until the model file (specified in MODEL_URL) exists.
command: /bin/sh -c "LLM_MODEL_FILE=$(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:
- ./jan-inference/llm/models:/models
networks:
jan_community:
ipv4_address: 172.20.0.17
# Service to run the Llama web application.
llm:
image: ghcr.io/abetlen/llama-cpp-python:latest
image: ghcr.io/abetlen/llama-cpp-python@sha256:b6d21ff8c4d9baad65e1fa741a0f8c898d68735fff3f3cd777e3f0c6a1839dd4
# Mount the directory that contains the downloaded model.
volumes:
- ./jan-inference/llm/models:/models
@ -192,20 +161,74 @@ services:
- 8000:8000
environment:
# Specify the path to the model for the web application.
MODEL: /models/llama-2-7b-chat.ggmlv3.q4_1.bin
MODEL: /models/${LLM_MODEL_FILE}
PYTHONUNBUFFERED: 1
# Restart policy configuration
restart: on-failure
# Specifies that this service should start only after wait-for-downloader has completed successfully.
depends_on:
wait-for-downloader:
condition: service_completed_successfully
# Connect this service to two networks: inference_net and traefik_public.
networks:
jan_community:
ipv4_address: 172.20.0.18
sd-downloader:
build:
context: ./jan-inference/sd/
dockerfile: compile.Dockerfile
# The command extracts the model filename from MODEL_URL and downloads it if it doesn't exist.
command: /bin/sh -c "if [ ! -f /models/*.bin ]; then python /sd.cpp/sd_cpp/models/convert.py --out_type q4_0 --out_file /models/${SD_MODEL_FILE}.q4_0.bin /models/${SD_MODEL_FILE}; fi"
# Mount a local directory to store the downloaded model.
volumes:
- ./jan-inference/sd/models:/models
networks:
jan_community:
ipv4_address: 172.20.0.19
# Service to run the SD web application.
sd:
build:
context: ./jan-inference/sd/
dockerfile: inference.Dockerfile
# Mount the directory that contains the downloaded model.
volumes:
- ./jan-inference/sd/models:/models
- ./jan-inference/sd/output/:/serving/output
command: /bin/bash -c "python -m uvicorn main:app --proxy-headers --host 0.0.0.0 --port 8000"
environment:
# Specify the path to the model for the web application.
BASE_URL: http://0.0.0.0:8000
MODEL_NAME: ${SD_MODEL_FILE}.q4_0.bin
MODEL_DIR: "/models"
SD_PATH: "/sd"
PYTHONUNBUFFERED: 1
ports:
- 8001:8000
# Restart policy configuration
restart: on-failure
# Specifies that this service should start only after wait-for-downloader has completed successfully.
depends_on:
sd-downloader:
condition: service_completed_successfully
networks:
jan_community:
ipv4_address: 172.20.0.21
# Service for Traefik, a modern HTTP reverse proxy and load balancer.
# traefik:
# image: traefik:v2.10
# ports:
# # Map port 80 in the container to port 80 on the host.
# - "80:80"
# # Map port 8080 in the container (Traefik's dashboard) to port 8080 on the host.
# - "8080:8080"
# # Mount the Docker socket to allow Traefik to listen to Docker's API.
# volumes:
# - /var/run/docker.sock:/var/run/docker.sock:ro
# - ./traefik/traefik.yml:/traefik.yml:ro
# - ./traefik/config.yml:/config.yml:ro
# networks:
# jan_community:
# ipv4_address: 172.20.0.22
networks:
jan_community:
driver: bridge

25
jan-inference/docker-compose.yml
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@ -1,25 +0,0 @@
version: '3'
services:
# Service for Traefik, a modern HTTP reverse proxy and load balancer.
traefik:
image: traefik:v2.10
ports:
# Map port 80 in the container to port 80 on the host.
- "80:80"
# Map port 8080 in the container (Traefik's dashboard) to port 8080 on the host.
- "8080:8080"
# Mount the Docker socket to allow Traefik to listen to Docker's API.
volumes:
- /var/run/docker.sock:/var/run/docker.sock:ro
- ./traefik/traefik.yml:/traefik.yml:ro
- ./traefik/config.yml:/config.yml:ro
# Connect this service to the traefik_public network.
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

8
jan-inference/llm/README.md
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@ -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
``````

60
jan-inference/llm/docker-compose.yml
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@ -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

71
jan-inference/sd/docker-compose.yml
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@ -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

38
jan-inference/sd/main.py
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@ -4,16 +4,26 @@ from fastapi.staticfiles import StaticFiles
import subprocess
import os
from uuid import uuid4
from pydantic import BaseModel
app = FastAPI()
OUTPUT_DIR = "output"
SD_PATH = os.environ.get("SD_PATH", "./sd")
MODEL_DIR = os.environ.get("MODEL_DIR", "./models")
BASE_URL = os.environ.get("BASE_URL", "http://localhost:8000")
MODEL_NAME = os.environ.get(
"MODEL_NAME", "v1-5-pruned-emaonly-ggml-model-q5_0.bin")
class Payload(BaseModel):
prompt: str
neg_prompt: str
seed: int
steps: int
width: int
height: int
# Create the OUTPUT_DIR directory if it does not exist
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
@ -26,33 +36,37 @@ if not os.path.exists(MODEL_DIR):
app.mount("/output", StaticFiles(directory=OUTPUT_DIR), name="output")
def run_command(prompt: str, filename: str):
def run_command(payload: Payload, filename: str):
# Construct the command based on your provided example
command = [SD_PATH,
"-m", os.path.join(MODEL_DIR, MODEL_NAME),
"-p", prompt,
"-o", os.path.join(OUTPUT_DIR, filename)
"--model", f'{os.path.join(MODEL_DIR, MODEL_NAME)}',
"--prompt", f'"{payload.prompt}"',
"--negative-prompt", f'"{payload.neg_prompt}"',
"--height", str(payload.height),
"--width", str(payload.width),
"--steps", str(payload.steps),
"--seed", str(payload.seed),
"--mode", 'txt2img',
"-o", f'{os.path.join(OUTPUT_DIR, filename)}',
]
try:
sub_output = subprocess.run(command, timeout=5*60, capture_output=True,
check=True, encoding="utf-8")
print(sub_output.stdout)
subprocess.run(command, timeout=5*60)
except subprocess.CalledProcessError:
raise HTTPException(
status_code=500, detail="Failed to execute the command.")
@app.post("/inference/")
async def run_inference(background_tasks: BackgroundTasks, prompt: str = Form()):
@app.post("/inferences/txt2img")
async def run_inference(background_tasks: BackgroundTasks, payload: Payload):
# Generate a unique filename using uuid4()
filename = f"{uuid4()}.png"
# We will use background task to run the command so it won't block
background_tasks.add_task(run_command, prompt, filename)
background_tasks.add_task(run_command, payload, filename)
# Return the expected path of the output file
return {"url": f'{BASE_URL}/serve/{filename}'}
return {"url": f'/serve/{filename}'}
@app.get("/serve/{filename}")

8
sample.env
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@ -9,9 +9,9 @@ KEYCLOAK_ADMIN_PASSWORD=admin
# Inference
## LLM
MODEL_URL=https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q4_1.bin
LLM_MODEL_FILE=$(basename $MODEL_URL)
LLM_MODEL_URL=https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q4_1.bin
LLM_MODEL_FILE=llama-2-7b-chat.ggmlv3.q4_1.bin
## SD
MODEL_URL=https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors
SD_MODEL_FILE=$(basename $MODEL_URL)
SD_MODEL_URL=https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors
SD_MODEL_FILE=v1-5-pruned-emaonly.safetensors

0
jan-inference/traefik/config.yml → traefik/config.yml
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0
jan-inference/traefik/traefik.yml → traefik/traefik.yml
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