adding NVIDIA GPU Support with Stats
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17
Dockerfile.gpu
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17
Dockerfile.gpu
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FROM nvidia/cuda:12.1.1-devel-ubuntu20.04
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ENV DEBIAN_FRONTEND noninteractive
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WORKDIR /app
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RUN apt update
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RUN apt install sudo curl -y
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RUN curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash - && sudo apt-get install -y nodejs
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COPY package*.json ./
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RUN npm install --omit=dev
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COPY . .
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CMD node llamabot.js
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20
README.md
20
README.md
@ -78,4 +78,24 @@ This will automatically configure the API for you as well as the bot in two sepe
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5. `docker compose up -d`
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5. `docker compose up -d`
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# Docker Compose with GPU
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This will automatically configure the API that supports cuBLAS and GPU inference for you as well as the bot in two seperate containers within a stack.
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1. `git clone https://git.ssh.surf/snxraven/llama-cpp-python-djs-bot.git` - Clone the repo
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2. `mv docker-compose.yml docker-compose.nogpu.yml; mv docker-compose.gpu.yml docker-compose.yml;` - Move nongpu compose out of the way, Enable GPU Support
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3. `mv Dockerfile Dockerfile.nongpu; mv Dockerfile.gpu Dockerfile;` - Move nongpu Dockerfile out of the way, enable GPU Support
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3. `cp default.gpu.env .env` - Copy the default GPU .env to its proper location
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4. Set DATA_DIR in .env to the exact location of your model files.
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5. Edit docker-compose.yaml MODEL to ensure the correct model bin is set
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6. set N_GPU_LAYERS to the amount of layers you would like to export to GPU
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7. `docker compose up -d`
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Want to make this better? Issue a pull request!
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Want to make this better? Issue a pull request!
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54
default.gpu.env
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54
default.gpu.env
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# Discord Token
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THE_TOKEN = "DISCORD_TOKEN_HERE"
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# The Channel IDs the bot will operate in seperated by commas
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CHANNEL_IDS = 1094494101631680653,1094628334727614605
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# The INIT prompt for all conversations.
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INIT_PROMPT = "Assistant name: ChatAI. You code, write and provide any information without any mistakes."
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# Loading Emebed Refresh Timing
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REFRESH_INTERVAL=10
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# When a message is too large for discord we chunk the response into seperate messages.
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# To ensure we do not rate limit the bot we send these at a delay interval.
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# DEFAULT: 3 a good setting is between 3 and 7 seconds.
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OVERFLOW_DELAY=3
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# Max Content to fetch from given URLs
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MAX_CONTENT_LENGTH=2000
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# Max tokens for Generations
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MAX_TOKENS = 1024
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# ROOT_IP is only used when running the bot without docker compose
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ROOT_IP = 192.168.0.15
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# PORT is only used when running the bot without docker compose
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ROOT_PORT = 8000
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# Directory to your models (llama.cpp specfic settings)
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DATA_DIR = /home/USERNAME/weights
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# Enable Expirmental Message Caches (Limited to single session)
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# Cache will use ~1.4 GB or MORE of RAM. ONLY ENABLE IF YOUR SYSTEM CAN HANDLE THIS.
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CACHE = 1
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# Set number of threads to use, currently, a standard thread will utilize 1 whole core
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# I usually will set this between all cores I physcally have OR 2 cores less to allow for other processes.
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N_THREADS = 4
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# Always use MMAP unless you know what you are doing
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USE_MMAP=1
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# Only use MLOCK if you know what it does!
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USE_MLOCK=0
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# The higher the number the more hard core.
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REPEAT_PENALTY=1
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# GPU SPECIFIC SETTINGS BELOW
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GPU=1
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N_GPU_LAYERS=32
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34
docker-compose.gpu.yml
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34
docker-compose.gpu.yml
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version: '3.9'
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services:
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llama-python-server:
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container_name: llama-python-gpu-server
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restart: unless-stopped
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build:
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context: ./gpu-server
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env_file: .env
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volumes:
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- ${DATA_DIR}:/usr/src/app/models
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environment:
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- HOST=llama-python-gpu-server
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- MODEL=./models/ggml-vic7b-q5_1.bin.1
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- NVIDIA_VISIBLE_DEVICES=all
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runtime: nvidia
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llama-python-djs-bot:
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container_name: llama-python-djs-bot
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restart: unless-stopped
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build:
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context: .
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depends_on:
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- llama-python-server
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environment:
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- THE_TOKEN
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- REFRESH_INTERVAL
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- CHANNEL_IDS
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- GPU
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- ROOT_IP=llama-python-server
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- ROOT_PORT=8000
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- INIT_PROMPT='Assistant Name':' ChatAI. You code, write and provide any information without any mistakes.'
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- NVIDIA_VISIBLE_DEVICES=all
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runtime: nvidia
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32
gpu-server/Dockerfile
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32
gpu-server/Dockerfile
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FROM nvidia/cuda:12.1.1-devel-ubuntu20.04
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# Install the deps
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ENV DEBIAN_FRONTEND=noninteractive
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ENV TZ=Etc/GMT
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RUN apt-get update && apt-get install -y --no-install-recommends python3 python3-pip git cmake
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# Get llama-cpp-python
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WORKDIR /usr/src
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RUN git clone https://github.com/abetlen/llama-cpp-python.git
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RUN mv llama-cpp-python app
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WORKDIR /usr/src/app
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#RUN git clone https://github.com/gjmulder/llama-cpp-python.git
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#RUN git checkout improved-unit-tests
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# Patch .gitmodules to use HTTPS
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RUN sed -i 's|git@github.com:ggerganov/llama.cpp.git|https://github.com/ggerganov/llama.cpp.git|' .gitmodules
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RUN git submodule update --init --recursive
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# Build llama-cpp-python w/CuBLAS
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RUN grep --colour "n_batch" ./llama_cpp/server/*.py
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RUN pip install scikit-build fastapi sse_starlette uvicorn && LLAMA_CUBLAS=1 python3 setup.py develop
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# We need to set the host to 0.0.0.0 to allow outside access
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ENV HOST 0.0.0.0
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# Run the server
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CMD python3 -m llama_cpp.server
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76
llamabot.js
76
llamabot.js
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import { errorMessages, busyResponses } from './assets/errorMessages.js';
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import { errorMessages, busyResponses } from './assets/errorMessages.js';
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import cpuStat from 'cpu-stat';
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import cpuStat from 'cpu-stat';
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import os from 'os';
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import os from 'os';
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import smi from 'node-nvidia-smi';
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import {
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import {
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Client,
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Client,
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@ -271,10 +273,7 @@ async function generateResponse(conversation, message) {
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const totalMemory = os.totalmem() / 1024 / 1024 / 1024;
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const totalMemory = os.totalmem() / 1024 / 1024 / 1024;
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const usedMemory = totalMemory - freeMemory;
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const usedMemory = totalMemory - freeMemory;
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const embedData = {
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let filedsData = [
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color: 0x0099ff,
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title: 'Please wait.. I am thinking...',
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fields: [
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{
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{
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name: 'System Load',
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name: 'System Load',
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value: `${systemLoad.toFixed(2)}%`,
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value: `${systemLoad.toFixed(2)}%`,
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name: 'Time',
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name: 'Time',
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value: `~${time} seconds.`,
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value: `~${time} seconds.`,
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},
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},
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],
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]
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if (process.env.GPU == 1) {
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smi(function (err, data) {
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if (err) {
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// Handle error if smi function fails
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console.error(err);
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return;
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}
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let utilization = data.nvidia_smi_log.gpu.utilization;
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let gpuUtilization = utilization.gpu_util;
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let memoryUtilization = utilization.memory_util;
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let gpuTemp = data.nvidia_smi_log.gpu.temperature.gpu_temp;
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// These are not used until nvidia-docker fixes their support
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let gpuTarget = data.nvidia_smi_log.gpu.temperature.gpu_target_temperature;
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let gpuFanSpeed = data.nvidia_smi_log.gpu.fan_speed;
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let filedsData = [
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{
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name: 'System Load',
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value: `${systemLoad.toFixed(2)}%`,
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},
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{
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name: 'Memory Usage',
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value: `${usedMemory.toFixed(2)} GB / ${totalMemory.toFixed(2)} GB`,
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},
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{
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name: 'GPU Utilization',
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value: `${gpuUtilization}`,
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},
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{
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name: 'Memory Utilization',
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value: `${memoryUtilization}`,
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},
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{
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name: 'GPU Temperature',
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value: `${gpuTemp}`,
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},
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{
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name: 'Time',
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value: `~${time} seconds.`,
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},
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];
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const embedData = {
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color: 0x0099ff,
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title: 'Please wait.. I am thinking...',
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fields: filedsData,
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};
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};
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// if the message object doesn't exist, create it
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// if the message object doesn't exist, create it
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@ -299,6 +347,24 @@ async function generateResponse(conversation, message) {
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botMessage.edit({ embeds: [embedData] }); // otherwise, update the message
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botMessage.edit({ embeds: [embedData] }); // otherwise, update the message
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}
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}
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});
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});
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} else {
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const embedData = {
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color: 0x0099ff,
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title: 'Please wait.. I am thinking...',
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fields: filedsData, // It seems like a typo, it should be `filedsData` instead of `filedsData`
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};
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// if the message object doesn't exist, create it
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if (!botMessage) {
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(async () => {
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botMessage = await message.channel.send({ embeds: [embedData] });
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})();
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} else {
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botMessage.edit({ embeds: [embedData] }); // otherwise, update the message
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}
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}
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});
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};
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};
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// call the function initially
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// call the function initially
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"dotenv": "^16.0.3",
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"dotenv": "^16.0.3",
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"node-fetch": "^3.3.1",
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"node-fetch": "^3.3.1",
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"os": "^0.1.2",
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"os": "^0.1.2",
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"cpu-stat": "^2.0.1"
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"cpu-stat": "^2.0.1",
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"node-nvidia-smi": "^1.0.0"
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}
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}
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}
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}
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