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https://github.com/open-webui/open-webui
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docker improvements & changed universal device type env for different models used
This commit is contained in:
Jannik Streidl
parent
132d741c55
commit
1f6739337b
37
Dockerfile
37
Dockerfile
@ -1,4 +1,7 @@
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# syntax=docker/dockerfile:1
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# Initialize device type args
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ARG USE_CUDA=false
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ARG USE_MPS=false
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######## WebUI frontend ########
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FROM node:21-alpine3.19 as build
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@ -23,6 +26,10 @@ RUN npm run build
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######## WebUI backend ########
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FROM python:3.11-slim-bookworm as base
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# Use args
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ARG USE_CUDA
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ARG USE_MPS
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## Basis ##
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ENV ENV=prod \
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PORT=8080
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@ -54,7 +61,8 @@ ENV RAG_EMBEDDING_MODEL="all-MiniLM-L6-v2" \
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# Important:
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# If you want to use CUDA you need to install the nvidia-container-toolkit (https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
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# you can set this to "cuda" but its recomended to use --build-arg CUDA_ENABLED=true flag when building the image
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RAG_EMBEDDING_MODEL_DEVICE_TYPE="cpu"
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RAG_EMBEDDING_MODEL_DEVICE_TYPE="cpu" \
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DEVICE_COMPUTE_TYPE="int8"
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# device type for whisper tts and embbeding models - "cpu" (default), "cuda" (nvidia gpu and CUDA required) or "mps" (apple silicon) - choosing this right can lead to better performance
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#### Preloaded models ##########################################################
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@ -62,19 +70,24 @@ WORKDIR /app/backend
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# install python dependencies
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COPY ./backend/requirements.txt ./requirements.txt
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RUN pip3 install -r requirements.txt --no-cache-dir
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RUN if [ "$RAG_EMBEDDING_MODEL_DEVICE_TYPE" = "cuda" ]; then \
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echo "CUDA enabled" && \
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 --no-cache-dir; \
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else \
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RUN if [ "$USE_CUDA" = "true" ]; then \
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export DEVICE_TYPE="cuda" && \
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 --no-cache-dir && \
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pip3 install -r requirements.txt --no-cache-dir; \
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elif [ "$USE_MPS" = "true" ]; then \
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export DEVICE_TYPE="mps" && \
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
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python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device=os.environ['RAG_EMBEDDING_MODEL_DEVICE_TYPE'])"; \
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pip3 install -r requirements.txt --no-cache-dir && \
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python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])" && \
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python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device=os.environ['DEVICE_TYPE'])"; \
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else \
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export DEVICE_TYPE="cpu" && \
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
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pip3 install -r requirements.txt --no-cache-dir && \
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python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])" && \
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python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device=os.environ['DEVICE_TYPE'])"; \
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fi
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# preload tts model
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RUN python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='auto', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"
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# install required packages
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RUN apt-get update \
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# Install pandoc and netcat
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@ -100,4 +113,4 @@ COPY ./backend .
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EXPOSE 8080
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CMD [ "bash", "start.sh"]
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CMD [ "bash", "start.sh"]
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@ -21,7 +21,11 @@ from utils.utils import (
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)
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from utils.misc import calculate_sha256
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from config import CACHE_DIR, UPLOAD_DIR, WHISPER_MODEL, WHISPER_MODEL_DIR
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from config import CACHE_DIR, UPLOAD_DIR, WHISPER_MODEL, WHISPER_MODEL_DIR, DEVICE_TYPE
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if DEVICE_TYPE != "cuda":
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whisper_device_type = "cpu"
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app = FastAPI()
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app.add_middleware(
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@ -56,7 +60,7 @@ def transcribe(
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model = WhisperModel(
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WHISPER_MODEL,
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device="auto",
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device=whisper_device_type,
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compute_type="int8",
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download_root=WHISPER_MODEL_DIR,
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)
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@ -57,7 +57,7 @@ from config import (
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UPLOAD_DIR,
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DOCS_DIR,
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RAG_EMBEDDING_MODEL,
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RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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DEVICE_TYPE,
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CHROMA_CLIENT,
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CHUNK_SIZE,
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CHUNK_OVERLAP,
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@ -87,7 +87,7 @@ app.state.TOP_K = 4
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app.state.sentence_transformer_ef = (
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embedding_functions.SentenceTransformerEmbeddingFunction(
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model_name=app.state.RAG_EMBEDDING_MODEL,
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device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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device=DEVICE_TYPE,
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)
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)
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@ -175,7 +175,7 @@ async def update_embedding_model(
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app.state.sentence_transformer_ef = (
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embedding_functions.SentenceTransformerEmbeddingFunction(
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model_name=app.state.RAG_EMBEDDING_MODEL,
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device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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device=DEVICE_TYPE,
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)
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)
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@ -330,8 +330,8 @@ CHROMA_DATA_PATH = f"{DATA_DIR}/vector_db"
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# this uses the model defined in the Dockerfile ENV variable. If you dont use docker or docker based deployments such as k8s, the default embedding model will be used (all-MiniLM-L6-v2)
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RAG_EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "all-MiniLM-L6-v2")
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# device type ebbeding models - "cpu" (default), "cuda" (nvidia gpu required) or "mps" (apple silicon) - choosing this right can lead to better performance
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RAG_EMBEDDING_MODEL_DEVICE_TYPE = os.environ.get(
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"RAG_EMBEDDING_MODEL_DEVICE_TYPE", "cpu"
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DEVICE_TYPE = os.environ.get(
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"DEVICE_TYPE", "cpu"
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)
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CHROMA_CLIENT = chromadb.PersistentClient(
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path=CHROMA_DATA_PATH,
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