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{
"cells": [
{
"cell_type": "markdown",
"id": "db41d8ba-71c0-4951-9a88-e1ae01a282ec",
"metadata": {},
"source": [
"# Introduction\n",
"Please check out my [blog post](https://datavistics.github.io/posts/jais-inference-endpoints/) for more details!"
]
},
{
"cell_type": "markdown",
"id": "d2534669-003d-490c-9d7a-32607fa5f404",
"metadata": {},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"id": "3c830114-dd88-45a9-81b9-78b0e3da7384",
"metadata": {},
"source": [
"## Requirements"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "35386f72-32cb-49fa-a108-3aa504e20429",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.3.2\u001B[0m\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install -q \"huggingface-hub>=0.20\" ipywidgets"
]
},
{
"cell_type": "markdown",
"id": "b6f72042-173d-4a72-ade1-9304b43b528d",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99f60998-0490-46c6-a8e6-04845ddda7be",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from huggingface_hub import login, whoami, create_inference_endpoint\n",
"from getpass import getpass"
]
},
{
"cell_type": "markdown",
"id": "5eece903-64ce-435d-a2fd-096c0ff650bf",
"metadata": {},
"source": [
"## Config\n",
"Choose your `ENDPOINT_NAME` if you like."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "dcd7daed-6aca-4fe7-85ce-534bdcd8bc87",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ENDPOINT_NAME = \"jais13b-demo\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ca1140c-3fcc-4b99-9210-6da1505a27b7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"login()"
]
},
{
"cell_type": "markdown",
"id": "5f4ba0a8-0a6c-4705-a73b-7be09b889610",
"metadata": {},
"source": [
"Some users might have payment registered in an organization. This allows you to connect to an organization (that you are a member of) with a payment method.\n",
"\n",
"Leave it blank if you want to use your username."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "88cdbd73-5923-4ae9-9940-b6be935f70fa",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"What is your Hugging Face 馃 username or organization? (with an added payment method) 路路路路路路路路\n"
]
}
],
"source": [
"who = whoami()\n",
"organization = getpass(prompt=\"What is your Hugging Face 馃 username or organization? (with an added payment method)\")\n",
"\n",
"namespace = organization or who['name']"
]
},
{
"cell_type": "markdown",
"id": "93096cbc-81c6-4137-a283-6afb0f48fbb9",
"metadata": {},
"source": [
"# Inference Endpoints\n",
"## Create Inference Endpoint\n",
"We are going to use the [API](https://huggingface.co/docs/inference-endpoints/api_reference) to create an [Inference Endpoint](https://huggingface.co/inference-endpoints). This should provide a few main benefits:\n",
"- It's convenient (No clicking)\n",
"- It's repeatable (We have the code to run it easily)\n",
"- It's cheaper (No time spent waiting for it to load, and automatically shut it down)"
]
},
{
"cell_type": "markdown",
"id": "1cf8334d-6500-412e-9d6d-58990c42c110",
"metadata": {},
"source": [
"Here is a convenient table of instance details you can use when selecting a GPU. Once you have chosen a GPU in Inference Endpoints, you can use the corresponding `instanceType` and `instanceSize`.\n",
"\n",
"| hw_desc | instanceType | instanceSize | vRAM |\n",
"|---------------------|----------------|--------------|-------|\n",
"| 1x Nvidia Tesla T4 | g4dn.xlarge | small | 16GB |\n",
"| 4x Nvidia Tesla T4 | g4dn.12xlarge | large | 64GB |\n",
"| 1x Nvidia A10G | g5.2xlarge | medium | 24GB |\n",
"| 4x Nvidia A10G | g5.12xlarge | xxlarge | 96GB |\n",
"| 1x Nvidia A100 | p4de | xlarge | 80GB |\n",
"| 2x Nvidia A100 | p4de | 2xlarge | 160GB |\n",
"\n",
"Note: To use a node (multiple GPUs) you will need to use a sharded version of jais. I'm not sure if there is currently a version like this on the hub. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "89c7cc21-3dfe-40e6-80ff-1dcc8558859e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"hw_dict = dict(\n",
" accelerator=\"gpu\",\n",
" vendor=\"aws\",\n",
" region=\"us-east-1\",\n",
" type=\"protected\",\n",
" instance_type=\"p4de\",\n",
" instance_size=\"xlarge\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f4267bce-8516-4f3a-b1cc-8ccd6c14a9c7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"tgi_env = {\n",
" \"MAX_BATCH_PREFILL_TOKENS\": \"2048\",\n",
" \"MAX_INPUT_LENGTH\": \"2000\",\n",
" 'TRUST_REMOTE_CODE':'true',\n",
" \"QUANTIZE\": 'bitsandbytes', \n",
" \"MODEL_ID\": \"/repository\"\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "74fd83a0-fef0-4e47-8ff1-f4ba7aed131d",
"metadata": {},
"source": [
"A couple notes on my choices here:\n",
"- I used `derek-thomas/jais-13b-chat-hf` because that repo has SafeTensors merged which will lead to faster loading of the TGI container\n",
"- I'm using the latest TGI container as of the time of writing (1.3.4)\n",
"- `min_replica=0` allows [zero scaling](https://huggingface.co/docs/inference-endpoints/autoscaling#scaling-to-0) which is really useful for your wallet though think through if this makes sense for your use-case as there will be loading times\n",
"- `max_replica` allows you to handle high throughput. Make sure you read through the [docs](https://huggingface.co/docs/inference-endpoints/autoscaling#scaling-criteria) to understand how this scales"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9e59de46-26b7-4bb9-bbad-8bba9931bde7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"endpoint = create_inference_endpoint(\n",
" ENDPOINT_NAME,\n",
" repository=\"derek-thomas/jais-13b-chat-hf\", \n",
" framework=\"pytorch\",\n",
" task=\"text-generation\",\n",
" **hw_dict,\n",
" min_replica=0,\n",
" max_replica=1,\n",
" namespace=namespace,\n",
" custom_image={\n",
" \"health_route\": \"/health\",\n",
" \"env\": tgi_env,\n",
" \"url\": \"ghcr.io/huggingface/text-generation-inference:1.3.4\",\n",
" },\n",
")"
]
},
{
"cell_type": "markdown",
"id": "96d173b2-8980-4554-9039-c62843d3fc7d",
"metadata": {},
"source": [
"## Wait until its running"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f3a8bd2-753c-49a8-9452-899578beddc5",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"endpoint.wait()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "189b26f0-d404-4570-a1b9-e2a9d486c1f7",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'POSITIVE'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"endpoint.client.text_generation(\"\"\"\n",
"### Instruction: What is the sentiment of the input?\n",
"### Examples\n",
"I wish the screen was bigger - Negative\n",
"I hate the battery - Negative\n",
"I love the default appliations - Positive\n",
"### Input\n",
"I am happy with this purchase - \n",
"### Response\n",
"\"\"\",\n",
" do_sample=True,\n",
" repetition_penalty=1.2,\n",
" top_p=0.9,\n",
" temperature=0.3)"
]
},
{
"cell_type": "markdown",
"id": "bab97c7b-7bac-4bf5-9752-b528294dadc7",
"metadata": {},
"source": [
"## Pause Inference Endpoint\n",
"Now that we have finished, lets pause the endpoint so we don't incur any extra charges, this will also allow us to analyze the cost."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "540a0978-7670-4ce3-95c1-3823cc113b85",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Endpoint Status: paused\n"
]
}
],
"source": [
"endpoint = endpoint.pause()\n",
"\n",
"print(f\"Endpoint Status: {endpoint.status}\")"
]
},
{
"cell_type": "markdown",
"id": "41abea64-379d-49de-8d9a-355c2f4ce1ac",
"metadata": {},
"source": [
"## Analyze Usage\n",
"1. Go to your `dashboard_url` printed below\n",
"1. Check the dashboard\n",
"1. Analyze the Usage & Cost tab"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16815445-3079-43da-b14e-b54176a07a62",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"dashboard_url = f'https://ui.endpoints.huggingface.co/{namespace}/endpoints/{ENDPOINT_NAME}/analytics'\n",
"print(dashboard_url)"
]
},
{
"cell_type": "markdown",
"id": "b953d5be-2494-4ff8-be42-9daf00c99c41",
"metadata": {},
"source": [
"## Delete Endpoint"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c310c0f3-6f12-4d5c-838b-3a4c1f2e54ad",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Endpoint deleted successfully\n"
]
}
],
"source": [
"endpoint = endpoint.delete()\n",
"\n",
"if not endpoint:\n",
" print('Endpoint deleted successfully')\n",
"else:\n",
" print('Delete Endpoint in manually') "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "611e1345-8d8c-46b1-a9f8-cff27eecb426",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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