Single node on-prem deployment with TGI on Nvidia gpu

This deployment section covers single-node on-prem deployment of the ChatQnA example with OPEA comps to deploy using TGI service. There are several slice-n-dice ways to enable RAG with vectordb and LLM models, but here we will be covering one option of doing it for convenience : we will be showcasing how to build an e2e chatQnA with Redis VectorDB and meta-llama/Meta-Llama-3-8B-Instruct model, deployed on on-prem.

Overview

There are several ways to setup a ChatQnA use case. Here in this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node vLLM or TGI megaservice solution.

1. Data Prep

2. Embedding

3. Retriever

4. Reranking

5. LLM with TGI

The solution is aimed to show how to use Redis vectordb for RAG and meta-llama/Meta-Llama-3-8B-Instruct model on Nvidia GPU. We will go through how to setup docker container to start a microservices and megaservice . The solution will then utilize a sample Nike dataset which is in PDF format. Users can then ask a question about Nike and get a chat-like response by default for up to 1024 tokens. The solution is deployed with a UI. There are 2 modes you can use:

1. Basic UI

2. Conversational UI

Conversational UI is optional, but a feature supported in this example if you are interested to use.

Prerequisites

The first step is to clone the GenAIExamples and GenAIComps projects. GenAIComps are fundamental necessary components used to build the examples you find in GenAIExamples and deploy them as microservices. Set an environment variable for the desired release version with the number only (i.e. 1.0, 1.1, etc) and checkout using the tag with that version.

# Set workspace
export WORKSPACE=<path>
cd $WORKSPACE

# Set desired release version - number only
export RELEASE_VERSION=<insert-release-version>

# GenAIComps
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
git checkout tags/v${RELEASE_VERSION}
cd ..

# GenAIExamples
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples
git checkout tags/v${RELEASE_VERSION}
cd ..

The examples utilize model weights from HuggingFace and langchain.

Setup your HuggingFace account and generate user access token.

Setup the HuggingFace token

export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"

The example requires you to set the host_ip to deploy the microservices on endpoint enabled with ports. Set the host_ip env variable

export host_ip=$(hostname -I | awk '{print $1}')

Make sure to setup Proxies if you are behind a firewall

export no_proxy=${your_no_proxy},$host_ip
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy}

Prepare (Building / Pulling) Docker images

This step will involve building/pulling ( maybe in future) relevant docker images with step-by-step process along with sanity check in the end. For ChatQnA, the following docker images will be needed: embedding, retriever, rerank, LLM and dataprep. Additionally, you will need to build docker images for ChatQnA megaservice, and UI (conversational React UI is optional). In total, there are 8 required and 1 optional docker images.

Build/Pull Microservice images

Pull

If you decide to pull the docker containers and not build them locally, you can proceed to the next step where all the necessary containers will be pulled in from Docker Hub.

Build

Follow the steps below to build the docker images from within the GenAIComps folder. Note: For RELEASE_VERSIONS older than 1.0, you will need to add a ‘v’ in front of ${RELEASE_VERSION} to reference the correct image on Docker Hub.

cd $WORKSPACE/GenAIComps

Build Dataprep Image

docker build --no-cache -t opea/dataprep-redis:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain/Dockerfile .

Build Embedding Image

docker build --no-cache -t opea/embedding-tei:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/tei/langchain/Dockerfile .

Build Retriever Image

 docker build --no-cache -t opea/retriever-redis:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/redis/langchain/Dockerfile .

Build Rerank Image

docker build --no-cache -t opea/reranking-tei:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/reranks/tei/Dockerfile .

Build LLM Image

TGI

docker build --no-cache -t opea/llm-tgi:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .

Build Mega Service images

The Megaservice is a pipeline that channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the chatqna.py file, say in this example the output of embedding microservice will be the input of retrieval microservice which will in turn passes data to the reranking microservice and so on. You can also add newer or remove some microservices and customize the megaservice to suit the needs.

Build the megaservice image for this use case

cd $WORKSPACE/GenAIExamples/ChatQnA

docker build --no-cache -t opea/chatqna:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .

Build Other Service images

Build the UI Image

As mentioned, you can build 2 modes of UI

Basic UI

cd $WORKSPACE/GenAIExamples/ChatQnA/ui/
docker build --no-cache -t opea/chatqna-ui:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .

Conversation UI If you want a conversational experience with chatqna megaservice.

cd $WORKSPACE/GenAIExamples/ChatQnA/ui/
docker build --no-cache -t opea/chatqna-conversation-ui:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .

Sanity Check

Check if you have the below set of docker images, before moving on to the next step:

TGI

• opea/dataprep-redis:${RELEASE_VERSION}

• opea/embedding-tei:${RELEASE_VERSION}

• opea/retriever-redis:${RELEASE_VERSION}

• opea/reranking-tei:${RELEASE_VERSION}

• opea/chatqna:${RELEASE_VERSION}

• opea/chatqna-ui:${RELEASE_VERSION}

• opea/llm-tgi:${RELEASE_VERSION}

Use Case Setup

As mentioned the use case will use the following combination of the GenAIComps with the tools

TGI

use case components	Tools	Model	Service Type
Data Prep	LangChain	NA	OPEA Microservice
VectorDB	Redis	NA	Open source service
Embedding	TEI	BAAI/bge-base-en-v1.5	OPEA Microservice
Reranking	TEI	BAAI/bge-reranker-base	OPEA Microservice
LLM	TGI	meta-llama/Meta-Llama-3-8B-Instruct	OPEA Microservice
UI		NA	Gateway Service

Tools and models mentioned in the table are configurable either through the environment variable or compose.yaml file.

Set the necessary environment variables to setup the use case. If you want to swap out models, modify set_env.sh before running.

cd $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/nvidia/gpu
source ./set_env.sh

Deploy the use case

In this tutorial, we will be deploying via docker compose with the provided YAML file. The docker compose instructions should be starting all the above mentioned services as containers.

TGI

cd $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/nvidia/gpu/
docker compose -f compose.yaml up -d

Validate microservice

Check Env Variables

Check the start up log by docker compose -f ./compose.yaml logs. The warning messages print out the variables if they are NOT set.

TGI

ubuntu@nvidia-vm:~/GenAIExamples/ChatQnA/docker_compose/nvidia/gpu$ docker compose -f ./compose.yaml up -d
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string.
WARN[0000] /home/ubuntu/GenAIExamples/ChatQnA/docker_compose/nvidia/gpu/compose.yaml: `version` is obsolete

Check the container status

Check if all the containers launched via docker compose has started

For example, the ChatQnA example starts 11 docker (services), check these docker containers are all running, i.e, all the containers STATUS are Up To do a quick sanity check, try docker ps -a to see if all the containers are running

TGI

CONTAINER ID   IMAGE                                                   COMMAND                  CREATED        STATUS        PORTS                                                                                  NAMES
3b5fa9a722da   opea/chatqna-ui:${RELEASE_VERSION}                                  "docker-entrypoint.s…"   32 hours ago   Up 2 hours   0.0.0.0:5173->5173/tcp, :::5173->5173/tcp                                              chatqna-ui-server
d3b37f3d1faa   opea/chatqna:${RELEASE_VERSION}                                     "python chatqna.py"      32 hours ago   Up 2 hours   0.0.0.0:8888->8888/tcp, :::8888->8888/tcp                                              chatqna-backend-server
b3e1388fa2ca   opea/reranking-tei:${RELEASE_VERSION}                               "python reranking_te…"   32 hours ago   Up 2 hours   0.0.0.0:8000->8000/tcp, :::8000->8000/tcp                                              reranking-tei-server
24a240f8ad1c   opea/retriever-redis:${RELEASE_VERSION}                             "python retriever_re…"   32 hours ago   Up 2 hours   0.0.0.0:7000->7000/tcp, :::7000->7000/tcp                                              retriever-redis-server
9c0d2a2553e8   opea/embedding-tei:${RELEASE_VERSION}                               "python embedding_te…"   32 hours ago   Up 2 hours   0.0.0.0:6000->6000/tcp, :::6000->6000/tcp                                              embedding-tei-server
24cae0db1a70   opea/llm-tgi:${RELEASE_VERSION}                                    "bash entrypoint.sh"     32 hours ago   Up 2 hours   0.0.0.0:9000->9000/tcp, :::9000->9000/tcp                                              llm-tgi-server
ea3986c3cf82   opea/dataprep-redis:${RELEASE_VERSION}                              "python prepare_doc_…"   32 hours ago   Up 2 hours   0.0.0.0:6007->6007/tcp, :::6007->6007/tcp                                              dataprep-redis-server
e10dd14497a8   redis/redis-stack:7.2.0-v9                              "/entrypoint.sh"         32 hours ago   Up 2 hours   0.0.0.0:6379->6379/tcp, :::6379->6379/tcp, 0.0.0.0:8001->8001/tcp, :::8001->8001/tcp   redis-vector-db
79276cf45a47   ghcr.io/huggingface/text-embeddings-inference:cpu-1.2   "text-embeddings-rou…"   32 hours ago   Up 2 hours   0.0.0.0:8090->80/tcp, :::8090->80/tcp                                                  tei-embedding-server
4943e5f6cd80   ghcr.io/huggingface/text-embeddings-inference:cpu-1.2   "text-embeddings-rou…"   32 hours ago   Up 2 hours   0.0.0.0:8808->80/tcp, :::8808->80/tcp                                                  tei-reranking-server

Interacting with ChatQnA deployment

This section will walk you through what are the different ways to interact with the microservices deployed

Dataprep Microservice（Optional）

If you want to add/update the default knowledge base, you can use the following commands. The dataprep microservice extracts the texts from variety of data sources, chunks the data, embeds each chunk using embedding microservice and store the embedded vectors in the redis vector database.

nke-10k-2023.pdf is Nike’s annual report on a form 10-K. Run this command to get the file on a terminal:

wget https://github.com/opea-project/GenAIComps/blob/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf

Upload the file:

curl -X POST "http://${host_ip}:6007/v1/dataprep" \
     -H "Content-Type: multipart/form-data" \
     -F "files=@./nke-10k-2023.pdf"

This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.

Add Knowledge Base via HTTP Links:

curl -X POST "http://${host_ip}:6007/v1/dataprep" \
     -H "Content-Type: multipart/form-data" \
     -F 'link_list=["https://opea.dev"]'

This command updates a knowledge base by submitting a list of HTTP links for processing.

Also, you are able to get the file list that you uploaded:

curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
     -H "Content-Type: application/json"

To delete the file/link you uploaded you can use the following commands:

Delete link

# The dataprep service will add a .txt postfix for link file

curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
     -d '{"file_path": "https://opea.dev.txt"}' \
     -H "Content-Type: application/json"

Delete file

curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
     -d '{"file_path": "nke-10k-2023.pdf"}' \
     -H "Content-Type: application/json"

Delete all uploaded files and links

curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
     -d '{"file_path": "all"}' \
     -H "Content-Type: application/json"

TEI Embedding Service

The TEI embedding service takes in a string as input, embeds the string into a vector of a specific length determined by the embedding model and returns this embedded vector.

curl ${host_ip}:8090/embed \
    -X POST \
    -d '{"inputs":"What is Deep Learning?"}' \
    -H 'Content-Type: application/json'

In this example the embedding model used is “BAAI/bge-base-en-v1.5”, which has a vector size of 768. So the output of the curl command is a embedded vector of length 768.

Embedding Microservice

The embedding microservice depends on the TEI embedding service. In terms of input parameters, it takes in a string, embeds it into a vector using the TEI embedding service and pads other default parameters that are required for the retrieval microservice and returns it.

curl http://${host_ip}:6000/v1/embeddings\
  -X POST \
  -d '{"text":"hello"}' \
  -H 'Content-Type: application/json'

Retriever Microservice

To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector is determined by the embedding model. Here we use the model EMBEDDING_MODEL_ID=”BAAI/bge-base-en-v1.5”, which vector size is 768.

Check the vector dimension of your embedding model and set your_embedding dimension equal to it.

export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")

curl http://${host_ip}:7000/v1/retrieval \
  -X POST \
  -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
  -H 'Content-Type: application/json'

The output of the retriever microservice comprises of the a unique id for the request, initial query or the input to the retrieval microservice, a list of top n retrieved documents relevant to the input query, and top_n where n refers to the number of documents to be returned.

The output is retrieved text that relevant to the input data:

{"id":"27210945c7c6c054fa7355bdd4cde818","retrieved_docs":[{"id":"0c1dd04b31ab87a5468d65f98e33a9f6","text":"Company: Nike. financial instruments are subject to master netting arrangements that allow for the offset of assets and liabilities in the event of default or early termination of the contract.\nAny amounts of cash collateral received related to these instruments associated with the Company's credit-related contingent features are recorded in Cash and\nequivalents and Accrued liabilities, the latter of which would further offset against the Company's derivative asset balance. Any amounts of cash collateral posted related\nto these instruments associated with the Company's credit-related contingent features are recorded in Prepaid expenses and other current assets, which would further\noffset against the Company's derivative liability balance. Cash collateral received or posted related to the Company's credit-related contingent features is presented in the\nCash provided by operations component of the Consolidated Statements of Cash Flows. The Company does not recognize amounts of non-cash collateral received, such\nas securities, on the Consolidated Balance Sheets. For further information related to credit risk, refer to Note 12 — Risk Management and Derivatives.\n2023 FORM 10-K 68Table of Contents\nThe following tables present information about the Company's derivative assets and liabilities measured at fair value on a recurring basis and indicate the level in the fair\nvalue hierarchy in which the Company classifies the fair value measurement:\nMAY 31, 2023\nDERIVATIVE ASSETS\nDERIVATIVE LIABILITIES"},{"id":"1d742199fb1a86aa8c3f7bcd580d94af","text": ... }

TEI Reranking Service

The TEI Reranking Service reranks the documents returned by the retrieval service. It consumes the query and list of documents and returns the document index based on decreasing order of the similarity score. The document corresponding to the returned index with the highest score is the most relevant document for the input query.

curl http://${host_ip}:8808/rerank \
    -X POST \
    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
    -H 'Content-Type: application/json'

Output is: [{"index":1,"score":0.9988041},{"index":0,"score":0.022948774}]

Reranking Microservice

The reranking microservice consumes the TEI Reranking service and pads the response with default parameters required for the llm microservice.

curl http://${host_ip}:8000/v1/reranking\
  -X POST \
  -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": \
     [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
  -H 'Content-Type: application/json'

The input to the microservice is the initial_query and a list of retrieved documents and it outputs the most relevant document to the initial query along with other default parameter such as temperature, repetition_penalty, chat_template and so on. We can also get top n documents by setting top_n as one of the input parameters. For example:

curl http://${host_ip}:8000/v1/reranking\
  -X POST \
  -d '{"initial_query":"What is Deep Learning?" ,"top_n":2, "retrieved_docs": \
     [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
  -H 'Content-Type: application/json'

Here is the output:

{"id":"e1eb0e44f56059fc01aa0334b1dac313","query":"Human: Answer the question based only on the following context:\n    Deep learning is...\n    Question: What is Deep Learning?","max_new_tokens":1024,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}

You may notice reranking microservice are with state (‘ID’ and other meta data), while reranking service are not.

TGI Service

TGI

curl http://${host_ip}:8008/generate \
  -X POST \
  -d '{"inputs":"What is Deep Learning?", \
     "parameters":{"max_new_tokens":17, "do_sample": true}}' \
  -H 'Content-Type: application/json'

TGI service generates text for the input prompt. Here is the expected result from TGI:

{"generated_text":"We have all heard the buzzword, but our understanding of it is still growing. It’s a sub-field of Machine Learning, and it’s the cornerstone of today’s Machine Learning breakthroughs.\n\nDeep Learning makes machines act more like humans through their ability to generalize from very large"}

NOTE: After launch the TGI, it takes few minutes for TGI server to load LLM model and warm up.

If you get

curl: (7) Failed to connect to 100.81.104.168 port 8008 after 0 ms: Connection refused

and the log shows model warm up, please wait for a while and try it later.

2024-06-05T05:45:27.707509646Z 2024-06-05T05:45:27.707361Z  WARN text_generation_router: router/src/main.rs:357: `--revision` is not set
2024-06-05T05:45:27.707539740Z 2024-06-05T05:45:27.707379Z  WARN text_generation_router: router/src/main.rs:358: We strongly advise to set it to a known supported commit.
2024-06-05T05:45:27.852525522Z 2024-06-05T05:45:27.852437Z  INFO text_generation_router: router/src/main.rs:379: Serving revision bdd31cf498d13782cc7497cba5896996ce429f91 of model meta-llama/Meta-Llama-3-8B-Instruct
2024-06-05T05:45:27.867833811Z 2024-06-05T05:45:27.867759Z  INFO text_generation_router: router/src/main.rs:221: Warming up model

LLM Microservice

curl http://${host_ip}:9000/v1/chat/completions\
  -X POST \
  -d '{"query":"What is Deep Learning?","max_new_tokens":17,"top_k":10,"top_p":0.95,\
     "typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
  -H 'Content-Type: application/json'

You will get generated text from LLM:

data: b'\n'
data: b'\n'
data: b'Deep'
data: b' learning'
data: b' is'
data: b' a'
data: b' subset'
data: b' of'
data: b' machine'
data: b' learning'
data: b' that'
data: b' uses'
data: b' algorithms'
data: b' to'
data: b' learn'
data: b' from'
data: b' data'
data: [DONE]

MegaService

curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
     "model": "meta-llama/Meta-Llama-3-8B-Instruct",
     "messages": "What is the revenue of Nike in 2023?"
     }'

Here is the output for your reference:

data: b'\n'
data: b'An'
data: b'swer'
data: b':'
data: b' In'
data: b' fiscal'
data: b' '
data: b'2'
data: b'0'
data: b'2'
data: b'3'
data: b','
data: b' N'
data: b'I'
data: b'KE'
data: b','
data: b' Inc'
data: b'.'
data: b' achieved'
data: b' record'
data: b' Rev'
data: b'en'
data: b'ues'
data: b' of'
data: b' $'
data: b'5'
data: b'1'
data: b'.'
data: b'2'
data: b' billion'
data: b'.'
data: b'</s>'
data: [DONE]

Check docker container log

Check the log of container by:

docker logs <CONTAINER ID> -t

Check the log by docker logs f7a08f9867f9 -t.

2024-06-05T01:30:30.695934928Z error: a value is required for '--model-id <MODEL_ID>' but none was supplied
2024-06-05T01:30:30.697123534Z
2024-06-05T01:30:30.697148330Z For more information, try '--help'.

The log indicates the MODEL_ID is not set.

TGI

View the docker input parameters in $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/nvidia/gpu/compose.yaml

 tgi-service:
    image: ghcr.io/huggingface/text-generation-inference:2.2.0
    container_name: tgi-service
    ports:
      - "9009:80"
    volumes:
      - "./data:/data"
    shm_size: 1g
    environment:
      no_proxy: ${no_proxy}
      http_proxy: ${http_proxy}
      https_proxy: ${https_proxy}
      HF_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
      HF_HUB_DISABLE_PROGRESS_BARS: 1
      HF_HUB_ENABLE_HF_TRANSFER: 0
    command: --model-id ${LLM_MODEL_ID} --cuda-graphs 0

The input MODEL_ID is ${LLM_MODEL_ID}

Check environment variable LLM_MODEL_ID is set correctly, spelled correctly. Set the LLM_MODEL_ID then restart the containers.

Also you can check overall logs with the following command, where the compose.yaml is the mega service docker-compose configuration file.

TGI

docker compose -f ./docker_compose/nvidia/gpu/compose.yaml logs

Launch UI

Basic UI

To access the frontend, open the following URL in your browser: http://{host_ip}:5173. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the compose.yaml file as shown below:

  chaqna-ui-server:
    image: opea/chatqna-ui:${TAG:-latest}
    ...
    ports:
      - "5173:5173"

Conversational UI

To access the Conversational UI (react based) frontend, modify the UI service in the compose.yaml file. Replace chaqna-ui-server service with the chatqna-conversation-ui-server service as per the config below:

chaqna-conversation-ui-server:
  image: opea/chatqna-conversation-ui:${TAG:-latest}
  container_name: chatqna-conversation-ui-server
  environment:
    - APP_BACKEND_SERVICE_ENDPOINT=${BACKEND_SERVICE_ENDPOINT}
    - APP_DATA_PREP_SERVICE_URL=${DATAPREP_SERVICE_ENDPOINT}
  ports:
    - "5174:5174"
  depends_on:
    - chaqna-backend-server
  ipc: host
  restart: always

Once the services are up, open the following URL in your browser: http://{host_ip}:5174. By default, the UI runs on port 80 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the compose.yaml file as shown below:

  chaqna-conversation-ui-server:
    image: opea/chatqna-conversation-ui:${TAG:-latest}
    ...
    ports:
      - "80:80"

Stop the services

Once you are done with the entire pipeline and wish to stop and remove all the containers, use the command below:

TGI

docker compose -f compose.yaml down