327 lines
8.3 KiB
Plaintext
327 lines
8.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Native APIs\n",
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"Apart from the OpenAI compatible APIs, the SGLang Runtime also provides its native server APIs. We introduce these following APIs:\n",
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"\n",
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"- `/generate`\n",
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"- `/get_server_args`\n",
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"- `/get_model_info`\n",
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"- `/health`\n",
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"- `/health_generate`\n",
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"- `/flush_cache`\n",
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"- `/get_memory_pool_size`\n",
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"- `/update_weights`\n",
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"- `/encode`\n",
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"\n",
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"We mainly use `requests` to test these APIs in the following examples. You can also use `curl`."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Launch A Server"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sglang.utils import (\n",
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" execute_shell_command,\n",
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" wait_for_server,\n",
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" terminate_process,\n",
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" print_highlight,\n",
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")\n",
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"\n",
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"server_process = execute_shell_command(\n",
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" \"\"\"\n",
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"python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --port=30010\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"wait_for_server(\"http://localhost:30010\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate\n",
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"Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](../references/sampling_params.html)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"\n",
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"url = \"http://localhost:30010/generate\"\n",
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"data = {\"text\": \"What is the capital of France?\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"print_highlight(response.json())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get Server Args\n",
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"Get the arguments of a server."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"http://localhost:30010/get_server_args\"\n",
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"\n",
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"response = requests.get(url)\n",
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"print_highlight(response.json())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get Model Info\n",
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"\n",
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"Get the information of the model.\n",
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"\n",
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"- `model_path`: The path/name of the model.\n",
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"- `is_generation`: Whether the model is used as generation model or embedding model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"http://localhost:30010/get_model_info\"\n",
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"\n",
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"response = requests.get(url)\n",
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"response_json = response.json()\n",
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"print_highlight(response_json)\n",
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"assert response_json[\"model_path\"] == \"meta-llama/Llama-3.2-1B-Instruct\"\n",
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"assert response_json[\"is_generation\"] is True\n",
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"assert response_json.keys() == {\"model_path\", \"is_generation\"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Health Check\n",
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"- `/health`: Check the health of the server.\n",
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"- `/health_generate`: Check the health of the server by generating one token."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"http://localhost:30010/health_generate\"\n",
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"\n",
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"response = requests.get(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"http://localhost:30010/health\"\n",
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"\n",
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"response = requests.get(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Flush Cache\n",
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"\n",
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"Flush the radix cache. It will be automatically triggered when the model weights are updated by the `/update_weights` API."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# flush cache\n",
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"\n",
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"url = \"http://localhost:30010/flush_cache\"\n",
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"\n",
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"response = requests.post(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get Memory Pool Size\n",
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"\n",
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"Get the memory pool size in number of tokens.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# get_memory_pool_size\n",
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"\n",
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"url = \"http://localhost:30010/get_memory_pool_size\"\n",
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"\n",
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"response = requests.get(url)\n",
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"print_highlight(response.text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Update Weights\n",
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"\n",
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"Update model weights without restarting the server. Use for continuous evaluation during training. Only applicable for models with the same architecture and parameter size."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# successful update with same architecture and size\n",
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"\n",
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"url = \"http://localhost:30010/update_weights\"\n",
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"data = {\"model_path\": \"meta-llama/Llama-3.2-1B\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"print_highlight(response.text)\n",
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"assert response.json()[\"success\"] == True\n",
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"assert response.json()[\"message\"] == \"Succeeded to update model weights.\"\n",
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"assert response.json().keys() == {\"success\", \"message\"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# failed update with different parameter size\n",
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"\n",
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"url = \"http://localhost:30010/update_weights\"\n",
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"data = {\"model_path\": \"meta-llama/Llama-3.2-3B\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"response_json = response.json()\n",
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"print_highlight(response_json)\n",
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"assert response_json[\"success\"] == False\n",
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"assert response_json[\"message\"] == (\n",
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" \"Failed to update weights: The size of tensor a (2048) must match \"\n",
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" \"the size of tensor b (3072) at non-singleton dimension 1.\\n\"\n",
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" \"Rolling back to original weights.\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Encode\n",
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"\n",
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"Encode text into embeddings. Note that this API is only available for [embedding models](openai_api_embeddings.html#openai-apis-embedding) and will raise an error for generation models.\n",
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"Therefore, we launch a new server to server an embedding model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"terminate_process(server_process)\n",
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"\n",
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"embedding_process = execute_shell_command(\n",
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" \"\"\"\n",
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"python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n",
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" --port 30020 --host 0.0.0.0 --is-embedding\n",
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"\"\"\"\n",
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")\n",
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"\n",
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"wait_for_server(\"http://localhost:30020\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# successful encode for embedding model\n",
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"\n",
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"url = \"http://localhost:30020/encode\"\n",
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"data = {\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"text\": \"Once upon a time\"}\n",
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"\n",
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"response = requests.post(url, json=data)\n",
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"response_json = response.json()\n",
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"print_highlight(f\"Text embedding (first 10): {response_json['embedding'][:10]}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {},
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"outputs": [],
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"source": [
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"terminate_process(embedding_process)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "AlphaMeemory",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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