How to Check and Validate Micro Service in the GenAI Example¶
GenAI examples build mega-services on top of the micro-service and server. To make mega-service works, each micro service and server must work as expected.
Take the ChatQnA as an example, this document shows how to start a GenAI example.
Assumption: build all docker images already
Here are steps to check the micro service and server.
1. Check environment variables¶
Make sure environment variables are set
start the docker containers
cd ./GenAIExamples/ChatQnA/docker_compose/intel/hpu/gaudi
docker compose up -d
Check the start up log by docker compose -f ./docker_compose/intel/hpu/gaudi/compose.yaml logs.
Where the compose.yaml file is the mega service docker-compose configuration.
The warning messages point out the veriabls are NOT set.
ubuntu@gaudi-vm:~/GenAIExamples/ChatQnA/docker_compose/intel/hpu/gaudi$ docker compose -f ./compose.yaml up -d
WARN[0000] /home/ubuntu/GenAIExamples/ChatQnA/docker_compose/intel/hpu/gaudi/compose.yaml: `version` is obsolete
2. Check the docker container status¶
Check the docker containers are started
For example, the ChatQnA example starts 11 docker (services), check these docker containers are all running, i.e, all the contaniers STATUS are Up
run the command docker ps -a
Here is the output:
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
28d9a5570246 opea/chatqna-ui:latest "docker-entrypoint.s…" 2 minutes ago Up 2 minutes 0.0.0.0:5173->5173/tcp, :::5173->5173/tcp chatqna-gaudi-ui-server
bee1132464cd opea/chatqna:latest "python chatqna.py" 2 minutes ago Up 2 minutes 0.0.0.0:8888->8888/tcp, :::8888->8888/tcp chatqna-gaudi-backend-server
f810f3b4d329 opea/embedding-tei:latest "python embedding_te…" 2 minutes ago Up 2 minutes 0.0.0.0:6000->6000/tcp, :::6000->6000/tcp embedding-tei-server
325236a01f9b opea/llm-tgi:latest "python llm.py" 2 minutes ago Up 2 minutes 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp llm-tgi-gaudi-server
2fa17d84605f opea/dataprep-redis:latest "python prepare_doc_…" 2 minutes ago Up 2 minutes 0.0.0.0:6007->6007/tcp, :::6007->6007/tcp dataprep-redis-server
69e1fb59e92c opea/retriever-redis:latest "/home/user/comps/re…" 2 minutes ago Up 2 minutes 0.0.0.0:7000->7000/tcp, :::7000->7000/tcp retriever-redis-server
313b9d14928a opea/reranking-tei:latest "python reranking_te…" 2 minutes ago Up 2 minutes 0.0.0.0:8000->8000/tcp, :::8000->8000/tcp reranking-tei-gaudi-server
174bd43fa6b5 ghcr.io/huggingface/tei-gaudi:1.5.0 "text-embeddings-rou…" 2 minutes ago Up 2 minutes 0.0.0.0:8090->80/tcp, :::8090->80/tcp tei-embedding-gaudi-server
05c40b636239 ghcr.io/huggingface/tgi-gaudi:2.0.6 "text-generation-lau…" 2 minutes ago Exited (1) About a minute ago tgi-gaudi-server
74084469aa33 redis/redis-stack:7.2.0-v9 "/entrypoint.sh" 2 minutes ago Up 2 minutes 0.0.0.0:6379->6379/tcp, :::6379->6379/tcp, 0.0.0.0:8001->8001/tcp, :::8001->8001/tcp redis-vector-db
88399dbc9e43 ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 "text-embeddings-rou…" 2 minutes ago Up 2 minutes 0.0.0.0:8808->80/tcp, :::8808->80/tcp tei-reranking-gaudi-server
In this case, ghcr.io/huggingface/tgi-gaudi:2.0.6 Existed.
05c40b636239 ghcr.io/huggingface/tgi-gaudi:2.0.6 "text-generation-lau…" 2 minutes ago Exited (1) About a minute ago tgi-gaudi-server
Next we can check the container logs to get to know what happened during the docker start.
3. Check docker container log¶
Check the log of container by:
docker logs <CONTAINER ID> -t
View the logs of ghcr.io/huggingface/tgi-gaudi:2.0.6
docker logs 05c40b636239 -t
...
2024-06-05T01:56:48.959581881Z File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 833, in _apply
2024-06-05T01:56:48.959583925Z param_applied = fn(param)
2024-06-05T01:56:48.959585811Z
2024-06-05T01:56:48.959587629Z File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1161, in convert
2024-06-05T01:56:48.959589733Z return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
2024-06-05T01:56:48.959591795Z
2024-06-05T01:56:48.959593607Z File "/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/core/weight_sharing.py", line 53, in __torch_function__
2024-06-05T01:56:48.959595769Z return super().__torch_function__(func, types, new_args, kwargs)
2024-06-05T01:56:48.959597800Z
2024-06-05T01:56:48.959599622Z RuntimeError: synStatus=9 [Device-type mismatch] Device acquire failed.
2024-06-05T01:56:48.959601665Z rank=0
2024-06-05T01:56:49.053352819Z 2024-06-05T01:56:49.053251Z ERROR text_generation_launcher: Shard 0 failed to start
2024-06-05T01:56:49.053373989Z 2024-06-05T01:56:49.053279Z INFO text_generation_launcher: Shutting down shards
2024-06-05T01:56:49.053385371Z Error: ShardCannotStart
The log shows RuntimeError: synStatus=9 [Device-type mismatch] Device acquire failed. This means the service fail to acquire the device.
So just make sure the devices are available.
Here is another failure example:
f7a08f9867f9 ghcr.io/huggingface/tgi-gaudi:2.0.6 "text-generation-lau…" 16 seconds ago Exited (2) 14 seconds ago tgi-gaudi-server
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 MODLE_ID is not set.
View the docker input parameters in ./ChatQnA/docker_compose/intel/hpu/gaudi/compose.yaml
tgi-service:
image: ghcr.io/huggingface/tgi-gaudi:2.0.6
container_name: tgi-gaudi-server
ports:
- "8008:80"
volumes:
- "./data:/data"
environment:
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
HUGGING_FACE_HUB_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
HABANA_VISIBLE_DEVICES: all
OMPI_MCA_btl_vader_single_copy_mechanism: none
ENABLE_HPU_GRAPH: true
LIMIT_HPU_GRAPH: true
USE_FLASH_ATTENTION: true
FLASH_ATTENTION_RECOMPUTE: true
runtime: habana
cap_add:
- SYS_NICE
ipc: host
command: --model-id ${LLM_MODEL_ID}
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.
docker compose -f ./docker-composer/gaudi/compose.yaml logs
4. Check each micro service used by the Mega Service¶
1 TEI Embedding Service¶
curl ${host_ip}:8090/embed \
-X POST \
-d '{"inputs":"What is Deep Learning?"}' \
-H 'Content-Type: application/json'
This test the embedding service. It sends “What is Deep Learning?” to the embedding service, the output is the embedding result of the sentences, it is a list of vector.
[[0.00030903306,-0.06356524,0.0025720573,-0.012404448,0.050649878, ... , -0.02776986,-0.0246678,0.03999176,0.037477136,-0.006806653,0.02261455,-0.04570737,-0.033122733,0.022785513,0.0160026,-0.021343587,-0.029969815,-0.0049176104]]
Note: The vector dimension are decided by the embedding model and the output value is dependent on model and input data.
2 Retriever Microservice¶
To consume the retriever microservice, you need to generate a mock embedding vector by Python script.
The length of embedding vector is determined by the embedding model.
Here we use the model EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", the model dimension is 768.
Check the vecotor dimension of your embedding model, set your_embedding dimension equals to it.
export your_embedding=$(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 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": ... }
3 TEI Reranking Service¶
Reranking service
curl http://${host_ip}:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H 'Content-Type: application/json'
Output is:
[{"index":1,"score":0.9988041},{"index":0,"score":0.022948774}]
It scores the input
4 TGI Service¶
curl http://${host_ip}:8008/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
-H 'Content-Type: application/json'
TGI service generate text for the input prompt. Here is the expected result from TGI output:
{"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 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 Intel/neural-chat-7b-v3-3
2024-06-05T05:45:27.867833811Z 2024-06-05T05:45:27.867759Z INFO text_generation_router: router/src/main.rs:221: Warming up model
5 MegaService¶
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"model": "Intel/neural-chat-7b-v3-3",
"messages": "What is the revenue of Nike in 2023?"
}'
Here it 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]
[Finished] Congratulation! All your services work as expected now!