mirror of https://github.com/microsoft/autogen.git
1501 lines
71 KiB
Plaintext
1501 lines
71 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_groupchat_RAG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Auto Generated Agent Chat: Group Chat with Retrieval Augmented Generation\n",
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"\n",
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"AutoGen supports conversable agents powered by LLMs, tools or humans, performing tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"## Requirements\n",
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"\n",
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"AutoGen requires `Python>=3.8`. To run this notebook example, please install:\n",
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"```bash\n",
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"pip install pyautogen\n",
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"```"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture --no-stderr\n",
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"# %pip install pyautogen[retrievechat]~=0.1.11"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Set your API Endpoint\n",
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"\n",
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"The [`config_list_from_json`](https://microsoft.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file."
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"LLM models: ['gpt-35-turbo', 'gpt-35-turbo-0613']\n"
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]
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}
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],
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"source": [
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"import autogen\n",
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"\n",
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"config_list = autogen.config_list_from_json(\n",
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" \"OAI_CONFIG_LIST\",\n",
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" file_location=\".\",\n",
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" filter_dict={\n",
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" \"model\": [\"gpt-3.5-turbo\", \"gpt-35-turbo\", \"gpt-35-turbo-0613\", \"gpt-4\", \"gpt4\", \"gpt-4-32k\"],\n",
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" },\n",
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")\n",
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"\n",
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"print(\"LLM models: \", [config_list[i][\"model\"] for i in range(len(config_list))])"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well).\n",
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"\n",
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"The config list looks like the following:\n",
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"```python\n",
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"config_list = [\n",
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" {\n",
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" \"model\": \"gpt-4\",\n",
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" \"api_key\": \"<your OpenAI API key>\",\n",
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" }, # OpenAI API endpoint for gpt-4\n",
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" {\n",
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" \"model\": \"gpt-35-turbo-0631\", # 0631 or newer is needed to use functions\n",
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" \"base_url\": \"<your Azure OpenAI API base>\", \n",
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" \"api_type\": \"azure\", \n",
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" \"api_version\": \"2023-08-01-preview\", # 2023-07-01-preview or newer is needed to use functions\n",
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" \"api_key\": \"<your Azure OpenAI API key>\"\n",
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" }\n",
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"]\n",
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"```\n",
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"\n",
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"You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb) for full code examples of the different methods."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Construct Agents"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent\n",
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"from autogen import AssistantAgent\n",
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"import chromadb\n",
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"\n",
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"llm_config = {\n",
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" \"timeout\": 60,\n",
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" \"seed\": 42,\n",
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" \"config_list\": config_list,\n",
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" \"temperature\": 0,\n",
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"}\n",
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"\n",
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"# autogen.ChatCompletion.start_logging()\n",
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"termination_msg = lambda x: isinstance(x, dict) and \"TERMINATE\" == str(x.get(\"content\", \"\"))[-9:].upper()\n",
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"\n",
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"boss = autogen.UserProxyAgent(\n",
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" name=\"Boss\",\n",
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" is_termination_msg=termination_msg,\n",
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" human_input_mode=\"TERMINATE\",\n",
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" system_message=\"The boss who ask questions and give tasks.\",\n",
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" code_execution_config=False, # we don't want to execute code in this case.\n",
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")\n",
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"\n",
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"boss_aid = RetrieveUserProxyAgent(\n",
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" name=\"Boss_Assistant\",\n",
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" is_termination_msg=termination_msg,\n",
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" system_message=\"Assistant who has extra content retrieval power for solving difficult problems.\",\n",
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" human_input_mode=\"TERMINATE\",\n",
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" max_consecutive_auto_reply=3,\n",
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" retrieve_config={\n",
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" \"task\": \"code\",\n",
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" \"docs_path\": \"https://raw.githubusercontent.com/microsoft/FLAML/main/website/docs/Examples/Integrate%20-%20Spark.md\",\n",
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" \"chunk_token_size\": 1000,\n",
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" \"model\": config_list[0][\"model\"],\n",
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" \"client\": chromadb.PersistentClient(path=\"/tmp/chromadb\"),\n",
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" \"collection_name\": \"groupchat\",\n",
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" \"get_or_create\": True,\n",
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" },\n",
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" code_execution_config=False, # we don't want to execute code in this case.\n",
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")\n",
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"\n",
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"coder = AssistantAgent(\n",
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" name=\"Senior_Python_Engineer\",\n",
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" is_termination_msg=termination_msg,\n",
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" system_message=\"You are a senior python engineer. Reply `TERMINATE` in the end when everything is done.\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"pm = autogen.AssistantAgent(\n",
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" name=\"Product_Manager\",\n",
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" is_termination_msg=termination_msg,\n",
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" system_message=\"You are a product manager. Reply `TERMINATE` in the end when everything is done.\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"reviewer = autogen.AssistantAgent(\n",
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" name=\"Code_Reviewer\",\n",
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" is_termination_msg=termination_msg,\n",
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" system_message=\"You are a code reviewer. Reply `TERMINATE` in the end when everything is done.\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"PROBLEM = \"How to use spark for parallel training in FLAML? Give me sample code.\"\n",
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"\n",
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"def _reset_agents():\n",
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" boss.reset()\n",
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" boss_aid.reset()\n",
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" coder.reset()\n",
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" pm.reset()\n",
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" reviewer.reset()\n",
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"\n",
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"def rag_chat():\n",
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" _reset_agents()\n",
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" groupchat = autogen.GroupChat(\n",
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" agents=[boss_aid, coder, pm, reviewer], messages=[], max_round=12\n",
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" )\n",
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" manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
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"\n",
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" # Start chatting with boss_aid as this is the user proxy agent.\n",
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" boss_aid.initiate_chat(\n",
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" manager,\n",
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" problem=PROBLEM,\n",
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" n_results=3,\n",
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" )\n",
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"\n",
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"def norag_chat():\n",
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" _reset_agents()\n",
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" groupchat = autogen.GroupChat(\n",
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" agents=[boss, coder, pm, reviewer], messages=[], max_round=12\n",
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" )\n",
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" manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
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"\n",
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" # Start chatting with boss as this is the user proxy agent.\n",
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" boss.initiate_chat(\n",
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" manager,\n",
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" message=PROBLEM,\n",
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" )\n",
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"\n",
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"def call_rag_chat():\n",
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" _reset_agents()\n",
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" # In this case, we will have multiple user proxy agents and we don't initiate the chat\n",
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" # with RAG user proxy agent.\n",
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" # In order to use RAG user proxy agent, we need to wrap RAG agents in a function and call\n",
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" # it from other agents.\n",
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" def retrieve_content(message, n_results=3):\n",
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" boss_aid.n_results = n_results # Set the number of results to be retrieved.\n",
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" # Check if we need to update the context.\n",
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" update_context_case1, update_context_case2 = boss_aid._check_update_context(message)\n",
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" if (update_context_case1 or update_context_case2) and boss_aid.update_context:\n",
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" boss_aid.problem = message if not hasattr(boss_aid, \"problem\") else boss_aid.problem\n",
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" _, ret_msg = boss_aid._generate_retrieve_user_reply(message)\n",
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" else:\n",
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" ret_msg = boss_aid.generate_init_message(message, n_results=n_results)\n",
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" return ret_msg if ret_msg else message\n",
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" \n",
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" boss_aid.human_input_mode = \"NEVER\" # Disable human input for boss_aid since it only retrieves content.\n",
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" \n",
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" llm_config = {\n",
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" \"functions\": [\n",
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" {\n",
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" \"name\": \"retrieve_content\",\n",
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" \"description\": \"retrieve content for code generation and question answering.\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"message\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"Refined message which keeps the original meaning and can be used to retrieve content for code generation and question answering.\",\n",
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" }\n",
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" },\n",
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" \"required\": [\"message\"],\n",
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" },\n",
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" },\n",
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" ],\n",
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" \"config_list\": config_list,\n",
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" \"timeout\": 60,\n",
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" \"seed\": 42,\n",
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" }\n",
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"\n",
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" for agent in [coder, pm, reviewer]:\n",
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" # update llm_config for assistant agents.\n",
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" agent.llm_config.update(llm_config)\n",
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"\n",
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" for agent in [boss, coder, pm, reviewer]:\n",
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" # register functions for all agents.\n",
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" agent.register_function(\n",
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" function_map={\n",
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" \"retrieve_content\": retrieve_content,\n",
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" }\n",
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" )\n",
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"\n",
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" groupchat = autogen.GroupChat(\n",
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" agents=[boss, coder, pm, reviewer], messages=[], max_round=12\n",
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" )\n",
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" manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
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"\n",
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" # Start chatting with boss as this is the user proxy agent.\n",
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" boss.initiate_chat(\n",
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" manager,\n",
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" message=PROBLEM,\n",
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" )"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Start Chat\n",
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"\n",
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"### UserProxyAgent doesn't get the correct code\n",
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"[FLAML](https://github.com/microsoft/FLAML) was open sourced in 2020, so ChatGPT is familiar with it. However, Spark-related APIs were added in 2022, so they were not in ChatGPT's training data. As a result, we end up with invalid code."
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
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"\n",
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"How to use spark for parallel training in FLAML? Give me sample code.\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"How to use spark for parallel training in FLAML? Give me sample code.\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mSenior_Python_Engineer\u001b[0m (to chat_manager):\n",
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"\n",
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"To use Spark for parallel training in FLAML, you can use the `SparkTrials` class provided by FLAML. Here is a sample code:\n",
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"\n",
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"```python\n",
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"from flaml import AutoML\n",
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"from flaml.data import load_credit\n",
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"from flaml.model import SparkTrials\n",
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"\n",
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"# Load data\n",
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"X_train, y_train, X_test, y_test = load_credit()\n",
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"\n",
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"# Define the search space\n",
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"search_space = {\n",
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" \"n_estimators\": {\"domain\": range(10, 100)},\n",
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" \"max_depth\": {\"domain\": range(6, 10)},\n",
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" \"learning_rate\": {\"domain\": (0.01, 0.1, 1)},\n",
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"}\n",
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"\n",
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"# Create an AutoML instance with SparkTrials\n",
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"automl = AutoML(\n",
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" search_space=search_space,\n",
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" task=\"classification\",\n",
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" n_jobs=1,\n",
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" ensemble_size=0,\n",
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" max_time=60,\n",
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" trials=SparkTrials(parallelism=2),\n",
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")\n",
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"\n",
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"# Train the model\n",
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"automl.fit(X_train=X_train, y_train=y_train)\n",
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"\n",
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"# Evaluate the model\n",
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"print(\"Best model:\", automl.best_model)\n",
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"print(\"Best hyperparameters:\", automl.best_config)\n",
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"print(\"Test accuracy:\", automl.score(X_test, y_test))\n",
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"\n",
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"# Terminate\n",
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"TERMINATE\n",
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"```\n",
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"\n",
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"In this code, we first load the credit dataset. Then, we define the search space for the hyperparameters. We create an `AutoML` instance with `SparkTrials` as the `trials` parameter. We set the `parallelism` parameter to 2 to use 2 Spark workers for parallel training. Finally, we fit the model and evaluate it.\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mCode_Reviewer\u001b[0m (to chat_manager):\n",
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"\n",
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"Great! This code looks good to me.\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mProduct_Manager\u001b[0m (to chat_manager):\n",
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"\n",
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"Thank you! Let me know if you have any other questions.\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[31m\n",
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">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
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"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
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"\n",
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"\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[31m\n",
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">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
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"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
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"\n",
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"\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[31m\n",
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">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
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"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
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"\n",
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"\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[31m\n",
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">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
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"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
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"\n",
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"\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[31m\n",
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">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
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"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
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"\n",
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"\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[31m\n",
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">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
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"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
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"\n",
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"\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[31m\n",
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">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
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"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
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"\n",
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"\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[31m\n",
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">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
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"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
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"\n",
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"\n",
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"\n",
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"--------------------------------------------------------------------------------\n"
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]
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}
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|
],
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"source": [
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"norag_chat()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### RetrieveUserProxyAgent get the correct code\n",
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"Since RetrieveUserProxyAgent can perform retrieval-augmented generation based on the given documentation file, ChatGPT can generate the correct code for us!"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Trying to create collection.\n"
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]
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},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"INFO:autogen.retrieve_utils:Found 2 chunks.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"doc_ids: [['doc_0', 'doc_1', 'doc_4']]\n",
|
|
"\u001b[32mAdding doc_id doc_0 to context.\u001b[0m\n",
|
|
"\u001b[32mAdding doc_id doc_1 to context.\u001b[0m\n",
|
|
"\u001b[32mAdding doc_id doc_4 to context.\u001b[0m\n",
|
|
"\u001b[33mBoss_Assistant\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"You're a retrieve augmented coding assistant. You answer user's questions based on your own knowledge and the\n",
|
|
"context provided by the user.\n",
|
|
"If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.\n",
|
|
"For code generation, you must obey the following rules:\n",
|
|
"Rule 1. You MUST NOT install any packages because all the packages needed are already installed.\n",
|
|
"Rule 2. You must follow the formats below to write your code:\n",
|
|
"```language\n",
|
|
"# your code\n",
|
|
"```\n",
|
|
"\n",
|
|
"User's question is: How to use spark for parallel training in FLAML? Give me sample code.\n",
|
|
"\n",
|
|
"Context is: # Integrate - Spark\n",
|
|
"\n",
|
|
"FLAML has integrated Spark for distributed training. There are two main aspects of integration with Spark:\n",
|
|
"- Use Spark ML estimators for AutoML.\n",
|
|
"- Use Spark to run training in parallel spark jobs.\n",
|
|
"\n",
|
|
"## Spark ML Estimators\n",
|
|
"\n",
|
|
"FLAML integrates estimators based on Spark ML models. These models are trained in parallel using Spark, so we called them Spark estimators. To use these models, you first need to organize your data in the required format.\n",
|
|
"\n",
|
|
"### Data\n",
|
|
"\n",
|
|
"For Spark estimators, AutoML only consumes Spark data. FLAML provides a convenient function `to_pandas_on_spark` in the `flaml.automl.spark.utils` module to convert your data into a pandas-on-spark (`pyspark.pandas`) dataframe/series, which Spark estimators require.\n",
|
|
"\n",
|
|
"This utility function takes data in the form of a `pandas.Dataframe` or `pyspark.sql.Dataframe` and converts it into a pandas-on-spark dataframe. It also takes `pandas.Series` or `pyspark.sql.Dataframe` and converts it into a [pandas-on-spark](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html) series. If you pass in a `pyspark.pandas.Dataframe`, it will not make any changes.\n",
|
|
"\n",
|
|
"This function also accepts optional arguments `index_col` and `default_index_type`.\n",
|
|
"- `index_col` is the column name to use as the index, default is None.\n",
|
|
"- `default_index_type` is the default index type, default is \"distributed-sequence\". More info about default index type could be found on Spark official [documentation](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/options.html#default-index-type)\n",
|
|
"\n",
|
|
"Here is an example code snippet for Spark Data:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import pandas as pd\n",
|
|
"from flaml.automl.spark.utils import to_pandas_on_spark\n",
|
|
"# Creating a dictionary\n",
|
|
"data = {\"Square_Feet\": [800, 1200, 1800, 1500, 850],\n",
|
|
" \"Age_Years\": [20, 15, 10, 7, 25],\n",
|
|
" \"Price\": [100000, 200000, 300000, 240000, 120000]}\n",
|
|
"\n",
|
|
"# Creating a pandas DataFrame\n",
|
|
"dataframe = pd.DataFrame(data)\n",
|
|
"label = \"Price\"\n",
|
|
"\n",
|
|
"# Convert to pandas-on-spark dataframe\n",
|
|
"psdf = to_pandas_on_spark(dataframe)\n",
|
|
"```\n",
|
|
"\n",
|
|
"To use Spark ML models you need to format your data appropriately. Specifically, use [`VectorAssembler`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html) to merge all feature columns into a single vector column.\n",
|
|
"\n",
|
|
"Here is an example of how to use it:\n",
|
|
"```python\n",
|
|
"from pyspark.ml.feature import VectorAssembler\n",
|
|
"columns = psdf.columns\n",
|
|
"feature_cols = [col for col in columns if col != label]\n",
|
|
"featurizer = VectorAssembler(inputCols=feature_cols, outputCol=\"features\")\n",
|
|
"psdf = featurizer.transform(psdf.to_spark(index_col=\"index\"))[\"index\", \"features\"]\n",
|
|
"```\n",
|
|
"\n",
|
|
"Later in conducting the experiment, use your pandas-on-spark data like non-spark data and pass them using `X_train, y_train` or `dataframe, label`.\n",
|
|
"\n",
|
|
"### Estimators\n",
|
|
"#### Model List\n",
|
|
"- `lgbm_spark`: The class for fine-tuning Spark version LightGBM models, using [SynapseML](https://microsoft.github.io/SynapseML/docs/features/lightgbm/about/) API.\n",
|
|
"\n",
|
|
"#### Usage\n",
|
|
"First, prepare your data in the required format as described in the previous section.\n",
|
|
"\n",
|
|
"By including the models you intend to try in the `estimators_list` argument to `flaml.automl`, FLAML will start trying configurations for these models. If your input is Spark data, FLAML will also use estimators with the `_spark` postfix by default, even if you haven't specified them.\n",
|
|
"\n",
|
|
"Here is an example code snippet using SparkML models in AutoML:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"# prepare your data in pandas-on-spark format as we previously mentioned\n",
|
|
"\n",
|
|
"automl = flaml.AutoML()\n",
|
|
"settings = {\n",
|
|
" \"time_budget\": 30,\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"estimator_list\": [\"lgbm_spark\"], # this setting is optional\n",
|
|
" \"task\": \"regression\",\n",
|
|
"}\n",
|
|
"\n",
|
|
"automl.fit(\n",
|
|
" dataframe=psdf,\n",
|
|
" label=label,\n",
|
|
" **settings,\n",
|
|
")\n",
|
|
"```\n",
|
|
"\n",
|
|
"\n",
|
|
"[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb)\n",
|
|
"\n",
|
|
"## Parallel Spark Jobs\n",
|
|
"You can activate Spark as the parallel backend during parallel tuning in both [AutoML](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning) and [Hyperparameter Tuning](/docs/Use-Cases/Tune-User-Defined-Function#parallel-tuning), by setting the `use_spark` to `true`. FLAML will dispatch your job to the distributed Spark backend using [`joblib-spark`](https://github.com/joblib/joblib-spark).\n",
|
|
"\n",
|
|
"Please note that you should not set `use_spark` to `true` when applying AutoML and Tuning for Spark Data. This is because only SparkML models will be used for Spark Data in AutoML and Tuning. As SparkML models run in parallel, there is no need to distribute them with `use_spark` again.\n",
|
|
"\n",
|
|
"All the Spark-related arguments are stated below. These arguments are available in both Hyperparameter Tuning and AutoML:\n",
|
|
"\n",
|
|
"\n",
|
|
"- `use_spark`: boolean, default=False | Whether to use spark to run the training in parallel spark jobs. This can be used to accelerate training on large models and large datasets, but will incur more overhead in time and thus slow down training in some cases. GPU training is not supported yet when use_spark is True. For Spark clusters, by default, we will launch one trial per executor. However, sometimes we want to launch more trials than the number of executors (e.g., local mode). In this case, we can set the environment variable `FLAML_MAX_CONCURRENT` to override the detected `num_executors`. The final number of concurrent trials will be the minimum of `n_concurrent_trials` and `num_executors`.\n",
|
|
"- `n_concurrent_trials`: int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, FLAML performes parallel tuning.\n",
|
|
"- `force_cancel`: boolean, default=False | Whether to forcely cancel Spark jobs if the search time exceeded the time budget. Spark jobs include parallel tuning jobs and Spark-based model training jobs.\n",
|
|
"\n",
|
|
"An example code snippet for using parallel Spark jobs:\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"automl_experiment = flaml.AutoML()\n",
|
|
"automl_settings = {\n",
|
|
" \"time_budget\": 30,\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"task\": \"regression\",\n",
|
|
" \"n_concurrent_trials\": 2,\n",
|
|
" \"use_spark\": True,\n",
|
|
" \"force_cancel\": True, # Activating the force_cancel option can immediately halt Spark jobs once they exceed the allocated time_budget.\n",
|
|
"}\n",
|
|
"\n",
|
|
"automl.fit(\n",
|
|
" dataframe=dataframe,\n",
|
|
" label=label,\n",
|
|
" **automl_settings,\n",
|
|
")\n",
|
|
"```\n",
|
|
"\n",
|
|
"\n",
|
|
"[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb)\n",
|
|
"\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"# for flaml.tune\n",
|
|
"with mlflow.start_run(run_name=f\"spark_auto_trials_1686631558\"):\n",
|
|
" analysis = flaml.tune.run(\n",
|
|
" func_to_tune,\n",
|
|
" params,\n",
|
|
" metric=\"r2\",\n",
|
|
" mode=\"max\",\n",
|
|
" mlflow_exp_name=\"test_doc\",\n",
|
|
" use_spark=True,\n",
|
|
" )\n",
|
|
"\n",
|
|
"# for flaml.automl\n",
|
|
"automl_experiment = flaml.AutoML()\n",
|
|
"automl_settings = {\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"task\": \"regression\",\n",
|
|
" \"use_spark\": True,\n",
|
|
" \"mlflow_exp_name\": \"test_doc\",\n",
|
|
" \"estimator_list\": [\n",
|
|
" \"lgbm\",\n",
|
|
" \"rf\",\n",
|
|
" \"xgboost\",\n",
|
|
" \"extra_tree\",\n",
|
|
" \"xgb_limitdepth\",\n",
|
|
" ], # catboost does not yet support mlflow autologging\n",
|
|
"}\n",
|
|
"with mlflow.start_run(run_name=f\"automl_spark_trials_1686631579\"):\n",
|
|
" automl_experiment.fit(X_train=train_x, y_train=train_y, **automl_settings)\n",
|
|
"```\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"### Results\n",
|
|
"*Tune Autolog Trials on MLFlow UI*\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"*AutoML Autolog Trials on MLFlow UI*\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"### Differences Between Auto and Manual Logging\n",
|
|
"Autologging is managed by MLFlow, while manual logging is maintained by FLAML.\n",
|
|
"\n",
|
|
"\n",
|
|
"#### Details of Manual Logging\n",
|
|
"FLAML logs general artifacts for AutoML tasks. Specifically, we log these artifacts:\n",
|
|
"\n",
|
|
"**`flaml.tune`**\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"- We create a parent run to log the best metric and the best configuration for the entire tuning process.\n",
|
|
"- For each trial, we create a child run to log the metric specific to the tune function and the configuration for that trial.\n",
|
|
"\n",
|
|
"**`flaml.automl`**\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"- We create a parent run to log the results of the experiment. This includes:\n",
|
|
" - The configuration of this model.\n",
|
|
" - The `best_validation_loss` produced by this model.\n",
|
|
" - The `best_iteration` to identify the point at which this model was found.\n",
|
|
"- For each state (a specific learner with different hyperparameters), we record the best trial for this model. This includes:\n",
|
|
" - The configuration of the best trial.\n",
|
|
" - The `validation_loss` the best trial produces.\n",
|
|
" - The `iter_count` to identify how many trials we have conducted for this state.\n",
|
|
" - The `pred_time`, which is the time cost of predicting test data for this model.\n",
|
|
" - The `wall_clock_time`, which is the time cost of this state.\n",
|
|
" - The `sample_size` to show how much data we sampled in this state.\n",
|
|
"Note that we also added these information to autolog AutoML run.\n",
|
|
"\n",
|
|
"\n",
|
|
"#### Details of Autologging\n",
|
|
"Autolog artifacts typically include model parameters, model files, and runtime metrics like the following:\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"Artifacts can differ among various machine learning libraries. More detailed information can be found [here](https://mlflow.org/docs/latest/tracking.html#automatic-logging).\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"## Plot Experiment Result\n",
|
|
"The `flaml.visualization` module provides utility functions for plotting the optimization process using [plotly](https://plotly.com/python/). Leveraging `plotly`, users can interactively explore experiment results. To use these plotting functions, simply provide your optimized `flaml.AutoML` or `flaml.tune.tune.ExperimentAnalysis` object as input. Optional parameters can be added using keyword arguments.\n",
|
|
"\n",
|
|
"Avaliable plotting functions:\n",
|
|
"- `plot_optimization_history`: Plot optimization history of all trials in the experiment.\n",
|
|
"- `plot_feature_importance`: Plot importance for each feature in the dataset.\n",
|
|
"- `plot_parallel_coordinate`: Plot the high-dimensional parameter relationships in the experiment.\n",
|
|
"- `plot_contour`: Plot the parameter relationship as contour plot in the experiment.\n",
|
|
"- `plot_edf`: Plot the objective value EDF (empirical distribution function) of the experiment.\n",
|
|
"- `plot_timeline`: Plot the timeline of the experiment.\n",
|
|
"- `plot_slice`: Plot the parameter relationship as slice plot in a study.\n",
|
|
"\n",
|
|
"### Figure Examples\n",
|
|
"\n",
|
|
"\n",
|
|
"Check out our example [notebook](../../notebook/trident/automl_plot.ipynb) for a preview of all interactive plots.\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[32mAdding doc_id doc_1 to context.\u001b[0m\n",
|
|
"\u001b[32mAdding doc_id doc_4 to context.\u001b[0m\n",
|
|
"\u001b[33mBoss_Assistant\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"You're a retrieve augmented coding assistant. You answer user's questions based on your own knowledge and the\n",
|
|
"context provided by the user.\n",
|
|
"If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.\n",
|
|
"For code generation, you must obey the following rules:\n",
|
|
"Rule 1. You MUST NOT install any packages because all the packages needed are already installed.\n",
|
|
"Rule 2. You must follow the formats below to write your code:\n",
|
|
"```language\n",
|
|
"# your code\n",
|
|
"```\n",
|
|
"\n",
|
|
"User's question is: How to use spark for parallel training in FLAML? Give me sample code.\n",
|
|
"\n",
|
|
"Context is: # Integrate - Spark\n",
|
|
"\n",
|
|
"FLAML has integrated Spark for distributed training. There are two main aspects of integration with Spark:\n",
|
|
"- Use Spark ML estimators for AutoML.\n",
|
|
"- Use Spark to run training in parallel spark jobs.\n",
|
|
"\n",
|
|
"## Spark ML Estimators\n",
|
|
"\n",
|
|
"FLAML integrates estimators based on Spark ML models. These models are trained in parallel using Spark, so we called them Spark estimators. To use these models, you first need to organize your data in the required format.\n",
|
|
"\n",
|
|
"### Data\n",
|
|
"\n",
|
|
"For Spark estimators, AutoML only consumes Spark data. FLAML provides a convenient function `to_pandas_on_spark` in the `flaml.automl.spark.utils` module to convert your data into a pandas-on-spark (`pyspark.pandas`) dataframe/series, which Spark estimators require.\n",
|
|
"\n",
|
|
"This utility function takes data in the form of a `pandas.Dataframe` or `pyspark.sql.Dataframe` and converts it into a pandas-on-spark dataframe. It also takes `pandas.Series` or `pyspark.sql.Dataframe` and converts it into a [pandas-on-spark](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html) series. If you pass in a `pyspark.pandas.Dataframe`, it will not make any changes.\n",
|
|
"\n",
|
|
"This function also accepts optional arguments `index_col` and `default_index_type`.\n",
|
|
"- `index_col` is the column name to use as the index, default is None.\n",
|
|
"- `default_index_type` is the default index type, default is \"distributed-sequence\". More info about default index type could be found on Spark official [documentation](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/options.html#default-index-type)\n",
|
|
"\n",
|
|
"Here is an example code snippet for Spark Data:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import pandas as pd\n",
|
|
"from flaml.automl.spark.utils import to_pandas_on_spark\n",
|
|
"# Creating a dictionary\n",
|
|
"data = {\"Square_Feet\": [800, 1200, 1800, 1500, 850],\n",
|
|
" \"Age_Years\": [20, 15, 10, 7, 25],\n",
|
|
" \"Price\": [100000, 200000, 300000, 240000, 120000]}\n",
|
|
"\n",
|
|
"# Creating a pandas DataFrame\n",
|
|
"dataframe = pd.DataFrame(data)\n",
|
|
"label = \"Price\"\n",
|
|
"\n",
|
|
"# Convert to pandas-on-spark dataframe\n",
|
|
"psdf = to_pandas_on_spark(dataframe)\n",
|
|
"```\n",
|
|
"\n",
|
|
"To use Spark ML models you need to format your data appropriately. Specifically, use [`VectorAssembler`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html) to merge all feature columns into a single vector column.\n",
|
|
"\n",
|
|
"Here is an example of how to use it:\n",
|
|
"```python\n",
|
|
"from pyspark.ml.feature import VectorAssembler\n",
|
|
"columns = psdf.columns\n",
|
|
"feature_cols = [col for col in columns if col != label]\n",
|
|
"featurizer = VectorAssembler(inputCols=feature_cols, outputCol=\"features\")\n",
|
|
"psdf = featurizer.transform(psdf.to_spark(index_col=\"index\"))[\"index\", \"features\"]\n",
|
|
"```\n",
|
|
"\n",
|
|
"Later in conducting the experiment, use your pandas-on-spark data like non-spark data and pass them using `X_train, y_train` or `dataframe, label`.\n",
|
|
"\n",
|
|
"### Estimators\n",
|
|
"#### Model List\n",
|
|
"- `lgbm_spark`: The class for fine-tuning Spark version LightGBM models, using [SynapseML](https://microsoft.github.io/SynapseML/docs/features/lightgbm/about/) API.\n",
|
|
"\n",
|
|
"#### Usage\n",
|
|
"First, prepare your data in the required format as described in the previous section.\n",
|
|
"\n",
|
|
"By including the models you intend to try in the `estimators_list` argument to `flaml.automl`, FLAML will start trying configurations for these models. If your input is Spark data, FLAML will also use estimators with the `_spark` postfix by default, even if you haven't specified them.\n",
|
|
"\n",
|
|
"Here is an example code snippet using SparkML models in AutoML:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"# prepare your data in pandas-on-spark format as we previously mentioned\n",
|
|
"\n",
|
|
"automl = flaml.AutoML()\n",
|
|
"settings = {\n",
|
|
" \"time_budget\": 30,\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"estimator_list\": [\"lgbm_spark\"], # this setting is optional\n",
|
|
" \"task\": \"regression\",\n",
|
|
"}\n",
|
|
"\n",
|
|
"automl.fit(\n",
|
|
" dataframe=psdf,\n",
|
|
" label=label,\n",
|
|
" **settings,\n",
|
|
")\n",
|
|
"```\n",
|
|
"\n",
|
|
"\n",
|
|
"[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb)\n",
|
|
"\n",
|
|
"## Parallel Spark Jobs\n",
|
|
"You can activate Spark as the parallel backend during parallel tuning in both [AutoML](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning) and [Hyperparameter Tuning](/docs/Use-Cases/Tune-User-Defined-Function#parallel-tuning), by setting the `use_spark` to `true`. FLAML will dispatch your job to the distributed Spark backend using [`joblib-spark`](https://github.com/joblib/joblib-spark).\n",
|
|
"\n",
|
|
"Please note that you should not set `use_spark` to `true` when applying AutoML and Tuning for Spark Data. This is because only SparkML models will be used for Spark Data in AutoML and Tuning. As SparkML models run in parallel, there is no need to distribute them with `use_spark` again.\n",
|
|
"\n",
|
|
"All the Spark-related arguments are stated below. These arguments are available in both Hyperparameter Tuning and AutoML:\n",
|
|
"\n",
|
|
"\n",
|
|
"- `use_spark`: boolean, default=False | Whether to use spark to run the training in parallel spark jobs. This can be used to accelerate training on large models and large datasets, but will incur more overhead in time and thus slow down training in some cases. GPU training is not supported yet when use_spark is True. For Spark clusters, by default, we will launch one trial per executor. However, sometimes we want to launch more trials than the number of executors (e.g., local mode). In this case, we can set the environment variable `FLAML_MAX_CONCURRENT` to override the detected `num_executors`. The final number of concurrent trials will be the minimum of `n_concurrent_trials` and `num_executors`.\n",
|
|
"- `n_concurrent_trials`: int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, FLAML performes parallel tuning.\n",
|
|
"- `force_cancel`: boolean, default=False | Whether to forcely cancel Spark jobs if the search time exceeded the time budget. Spark jobs include parallel tuning jobs and Spark-based model training jobs.\n",
|
|
"\n",
|
|
"An example code snippet for using parallel Spark jobs:\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"automl_experiment = flaml.AutoML()\n",
|
|
"automl_settings = {\n",
|
|
" \"time_budget\": 30,\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"task\": \"regression\",\n",
|
|
" \"n_concurrent_trials\": 2,\n",
|
|
" \"use_spark\": True,\n",
|
|
" \"force_cancel\": True, # Activating the force_cancel option can immediately halt Spark jobs once they exceed the allocated time_budget.\n",
|
|
"}\n",
|
|
"\n",
|
|
"automl.fit(\n",
|
|
" dataframe=dataframe,\n",
|
|
" label=label,\n",
|
|
" **automl_settings,\n",
|
|
")\n",
|
|
"```\n",
|
|
"\n",
|
|
"\n",
|
|
"[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb)\n",
|
|
"\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"# for flaml.tune\n",
|
|
"with mlflow.start_run(run_name=f\"spark_auto_trials_1686631558\"):\n",
|
|
" analysis = flaml.tune.run(\n",
|
|
" func_to_tune,\n",
|
|
" params,\n",
|
|
" metric=\"r2\",\n",
|
|
" mode=\"max\",\n",
|
|
" mlflow_exp_name=\"test_doc\",\n",
|
|
" use_spark=True,\n",
|
|
" )\n",
|
|
"\n",
|
|
"# for flaml.automl\n",
|
|
"automl_experiment = flaml.AutoML()\n",
|
|
"automl_settings = {\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"task\": \"regression\",\n",
|
|
" \"use_spark\": True,\n",
|
|
" \"mlflow_exp_name\": \"test_doc\",\n",
|
|
" \"estimator_list\": [\n",
|
|
" \"lgbm\",\n",
|
|
" \"rf\",\n",
|
|
" \"xgboost\",\n",
|
|
" \"extra_tree\",\n",
|
|
" \"xgb_limitdepth\",\n",
|
|
" ], # catboost does not yet support mlflow autologging\n",
|
|
"}\n",
|
|
"with mlflow.start_run(run_name=f\"automl_spark_trials_1686631579\"):\n",
|
|
" automl_experiment.fit(X_train=train_x, y_train=train_y, **automl_settings)\n",
|
|
"```\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"### Results\n",
|
|
"*Tune Autolog Trials on MLFlow UI*\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"*AutoML Autolog Trials on MLFlow UI*\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"### Differences Between Auto and Manual Logging\n",
|
|
"Autologging is managed by MLFlow, while manual logging is maintained by FLAML.\n",
|
|
"\n",
|
|
"\n",
|
|
"#### Details of Manual Logging\n",
|
|
"FLAML logs general artifacts for AutoML tasks. Specifically, we log these artifacts:\n",
|
|
"\n",
|
|
"**`flaml.tune`**\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"- We create a parent run to log the best metric and the best configuration for the entire tuning process.\n",
|
|
"- For each trial, we create a child run to log the metric specific to the tune function and the configuration for that trial.\n",
|
|
"\n",
|
|
"**`flaml.automl`**\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"- We create a parent run to log the results of the experiment. This includes:\n",
|
|
" - The configuration of this model.\n",
|
|
" - The `best_validation_loss` produced by this model.\n",
|
|
" - The `best_iteration` to identify the point at which this model was found.\n",
|
|
"- For each state (a specific learner with different hyperparameters), we record the best trial for this model. This includes:\n",
|
|
" - The configuration of the best trial.\n",
|
|
" - The `validation_loss` the best trial produces.\n",
|
|
" - The `iter_count` to identify how many trials we have conducted for this state.\n",
|
|
" - The `pred_time`, which is the time cost of predicting test data for this model.\n",
|
|
" - The `wall_clock_time`, which is the time cost of this state.\n",
|
|
" - The `sample_size` to show how much data we sampled in this state.\n",
|
|
"Note that we also added these information to autolog AutoML run.\n",
|
|
"\n",
|
|
"\n",
|
|
"#### Details of Autologging\n",
|
|
"Autolog artifacts typically include model parameters, model files, and runtime metrics like the following:\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"Artifacts can differ among various machine learning libraries. More detailed information can be found [here](https://mlflow.org/docs/latest/tracking.html#automatic-logging).\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"## Plot Experiment Result\n",
|
|
"The `flaml.visualization` module provides utility functions for plotting the optimization process using [plotly](https://plotly.com/python/). Leveraging `plotly`, users can interactively explore experiment results. To use these plotting functions, simply provide your optimized `flaml.AutoML` or `flaml.tune.tune.ExperimentAnalysis` object as input. Optional parameters can be added using keyword arguments.\n",
|
|
"\n",
|
|
"Avaliable plotting functions:\n",
|
|
"- `plot_optimization_history`: Plot optimization history of all trials in the experiment.\n",
|
|
"- `plot_feature_importance`: Plot importance for each feature in the dataset.\n",
|
|
"- `plot_parallel_coordinate`: Plot the high-dimensional parameter relationships in the experiment.\n",
|
|
"- `plot_contour`: Plot the parameter relationship as contour plot in the experiment.\n",
|
|
"- `plot_edf`: Plot the objective value EDF (empirical distribution function) of the experiment.\n",
|
|
"- `plot_timeline`: Plot the timeline of the experiment.\n",
|
|
"- `plot_slice`: Plot the parameter relationship as slice plot in a study.\n",
|
|
"\n",
|
|
"### Figure Examples\n",
|
|
"\n",
|
|
"\n",
|
|
"Check out our example [notebook](../../notebook/trident/automl_plot.ipynb) for a preview of all interactive plots.\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mSenior_Python_Engineer\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"To use Spark for parallel training in FLAML, you can activate Spark as the parallel backend during parallel tuning in both AutoML and Hyperparameter Tuning, by setting the `use_spark` to `true`. FLAML will dispatch your job to the distributed Spark backend using `joblib-spark`. \n",
|
|
"\n",
|
|
"Here is an example code snippet for using parallel Spark jobs:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"automl_experiment = flaml.AutoML()\n",
|
|
"automl_settings = {\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"task\": \"regression\",\n",
|
|
" \"use_spark\": True,\n",
|
|
" \"estimator_list\": [\n",
|
|
" \"lgbm\",\n",
|
|
" \"rf\",\n",
|
|
" \"xgboost\",\n",
|
|
" \"extra_tree\",\n",
|
|
" \"xgb_limitdepth\",\n",
|
|
" ],\n",
|
|
"}\n",
|
|
"automl_experiment.fit(X_train=train_x, y_train=train_y, **automl_settings)\n",
|
|
"```\n",
|
|
"\n",
|
|
"Note that you should not set `use_spark` to `true` when applying AutoML and Tuning for Spark Data. This is because only SparkML models will be used for Spark Data in AutoML and Tuning. As SparkML models run in parallel, there is no need to distribute them with `use_spark` again.\n",
|
|
"\n",
|
|
"You can also use Spark ML estimators for AutoML. FLAML integrates estimators based on Spark ML models. These models are trained in parallel using Spark, so we called them Spark estimators. To use these models, you first need to organize your data in the required format.\n",
|
|
"\n",
|
|
"Here is an example code snippet for Spark Data:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import pandas as pd\n",
|
|
"from flaml.automl.spark.utils import to_pandas_on_spark\n",
|
|
"# Creating a dictionary\n",
|
|
"data = {\"Square_Feet\": [800, 1200, 1800, 1500, 850],\n",
|
|
" \"Age_Years\": [20, 15, 10, 7, 25],\n",
|
|
" \"Price\": [100000, 200000, 300000, 240000, 120000]}\n",
|
|
"\n",
|
|
"# Creating a pandas DataFrame\n",
|
|
"dataframe = pd.DataFrame(data)\n",
|
|
"label = \"Price\"\n",
|
|
"\n",
|
|
"# Convert to pandas-on-spark dataframe\n",
|
|
"psdf = to_pandas_on_spark(dataframe)\n",
|
|
"```\n",
|
|
"\n",
|
|
"To use Spark ML models you need to format your data appropriately. Specifically, use `VectorAssembler` to merge all feature columns into a single vector column.\n",
|
|
"\n",
|
|
"Here is an example of how to use it:\n",
|
|
"```python\n",
|
|
"from pyspark.ml.feature import VectorAssembler\n",
|
|
"columns = psdf.columns\n",
|
|
"feature_cols = [col for col in columns if col != label]\n",
|
|
"featurizer = VectorAssembler(inputCols=feature_cols, outputCol=\"features\")\n",
|
|
"psdf = featurizer.transform(psdf.to_spark(index_col=\"index\"))[\"index\", \"features\"]\n",
|
|
"```\n",
|
|
"\n",
|
|
"Later in conducting the experiment, use your pandas-on-spark data like non-spark data and pass them using `X_train, y_train` or `dataframe, label`.\n",
|
|
"\n",
|
|
"You can also plot the optimization process using `plotly` by providing your optimized `flaml.AutoML` or `flaml.tune.tune.ExperimentAnalysis` object as input. Optional parameters can be added using keyword arguments. Available plotting functions include `plot_optimization_history`, `plot_feature_importance`, `plot_parallel_coordinate`, `plot_contour`, `plot_edf`, `plot_timeline`, and `plot_slice`.\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mProduct_Manager\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mCode_Reviewer\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"Is there anything else you need help with?\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
|
"\u001b[33mBoss_Assistant\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mProduct_Manager\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"No, that's all. Thank you for your help!\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mCode_Reviewer\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"You're welcome! Don't hesitate to ask if you have any more questions in the future. Have a great day!\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
|
"\u001b[33mBoss_Assistant\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mSenior_Python_Engineer\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"Have a great day too!\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mProduct_Manager\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"Thank you!\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mCode_Reviewer\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"You're welcome!\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
|
"\u001b[33mBoss_Assistant\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"rag_chat()\n",
|
|
"# type exit to terminate the chat"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Call RetrieveUserProxyAgent while init chat with another user proxy agent\n",
|
|
"Sometimes, there might be a need to use RetrieveUserProxyAgent in group chat without initializing the chat with it. In such scenarios, it becomes essential to create a function that wraps the RAG agents and allows them to be called from other agents."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"How to use spark for parallel training in FLAML? Give me sample code.\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"How to use spark for parallel training in FLAML? Give me sample code.\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mSenior_Python_Engineer\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"\u001b[32m***** Suggested function Call: retrieve_content *****\u001b[0m\n",
|
|
"Arguments: \n",
|
|
"{\n",
|
|
" \"message\": \"How to use spark for parallel training in FLAML?\"\n",
|
|
"}\n",
|
|
"\u001b[32m*****************************************************\u001b[0m\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[35m\n",
|
|
">>>>>>>> EXECUTING FUNCTION retrieve_content...\u001b[0m\n",
|
|
"doc_ids: [['doc_0', 'doc_1', 'doc_4']]\n",
|
|
"\u001b[32mAdding doc_id doc_0 to context.\u001b[0m\n",
|
|
"\u001b[32mAdding doc_id doc_1 to context.\u001b[0m\n",
|
|
"\u001b[32mAdding doc_id doc_4 to context.\u001b[0m\n",
|
|
"\u001b[33mSenior_Python_Engineer\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"\u001b[32m***** Response from calling function \"retrieve_content\" *****\u001b[0m\n",
|
|
"You're a retrieve augmented coding assistant. You answer user's questions based on your own knowledge and the\n",
|
|
"context provided by the user.\n",
|
|
"If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.\n",
|
|
"For code generation, you must obey the following rules:\n",
|
|
"Rule 1. You MUST NOT install any packages because all the packages needed are already installed.\n",
|
|
"Rule 2. You must follow the formats below to write your code:\n",
|
|
"```language\n",
|
|
"# your code\n",
|
|
"```\n",
|
|
"\n",
|
|
"User's question is: How to use spark for parallel training in FLAML?\n",
|
|
"\n",
|
|
"Context is: # Integrate - Spark\n",
|
|
"\n",
|
|
"FLAML has integrated Spark for distributed training. There are two main aspects of integration with Spark:\n",
|
|
"- Use Spark ML estimators for AutoML.\n",
|
|
"- Use Spark to run training in parallel spark jobs.\n",
|
|
"\n",
|
|
"## Spark ML Estimators\n",
|
|
"\n",
|
|
"FLAML integrates estimators based on Spark ML models. These models are trained in parallel using Spark, so we called them Spark estimators. To use these models, you first need to organize your data in the required format.\n",
|
|
"\n",
|
|
"### Data\n",
|
|
"\n",
|
|
"For Spark estimators, AutoML only consumes Spark data. FLAML provides a convenient function `to_pandas_on_spark` in the `flaml.automl.spark.utils` module to convert your data into a pandas-on-spark (`pyspark.pandas`) dataframe/series, which Spark estimators require.\n",
|
|
"\n",
|
|
"This utility function takes data in the form of a `pandas.Dataframe` or `pyspark.sql.Dataframe` and converts it into a pandas-on-spark dataframe. It also takes `pandas.Series` or `pyspark.sql.Dataframe` and converts it into a [pandas-on-spark](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html) series. If you pass in a `pyspark.pandas.Dataframe`, it will not make any changes.\n",
|
|
"\n",
|
|
"This function also accepts optional arguments `index_col` and `default_index_type`.\n",
|
|
"- `index_col` is the column name to use as the index, default is None.\n",
|
|
"- `default_index_type` is the default index type, default is \"distributed-sequence\". More info about default index type could be found on Spark official [documentation](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/options.html#default-index-type)\n",
|
|
"\n",
|
|
"Here is an example code snippet for Spark Data:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import pandas as pd\n",
|
|
"from flaml.automl.spark.utils import to_pandas_on_spark\n",
|
|
"# Creating a dictionary\n",
|
|
"data = {\"Square_Feet\": [800, 1200, 1800, 1500, 850],\n",
|
|
" \"Age_Years\": [20, 15, 10, 7, 25],\n",
|
|
" \"Price\": [100000, 200000, 300000, 240000, 120000]}\n",
|
|
"\n",
|
|
"# Creating a pandas DataFrame\n",
|
|
"dataframe = pd.DataFrame(data)\n",
|
|
"label = \"Price\"\n",
|
|
"\n",
|
|
"# Convert to pandas-on-spark dataframe\n",
|
|
"psdf = to_pandas_on_spark(dataframe)\n",
|
|
"```\n",
|
|
"\n",
|
|
"To use Spark ML models you need to format your data appropriately. Specifically, use [`VectorAssembler`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html) to merge all feature columns into a single vector column.\n",
|
|
"\n",
|
|
"Here is an example of how to use it:\n",
|
|
"```python\n",
|
|
"from pyspark.ml.feature import VectorAssembler\n",
|
|
"columns = psdf.columns\n",
|
|
"feature_cols = [col for col in columns if col != label]\n",
|
|
"featurizer = VectorAssembler(inputCols=feature_cols, outputCol=\"features\")\n",
|
|
"psdf = featurizer.transform(psdf.to_spark(index_col=\"index\"))[\"index\", \"features\"]\n",
|
|
"```\n",
|
|
"\n",
|
|
"Later in conducting the experiment, use your pandas-on-spark data like non-spark data and pass them using `X_train, y_train` or `dataframe, label`.\n",
|
|
"\n",
|
|
"### Estimators\n",
|
|
"#### Model List\n",
|
|
"- `lgbm_spark`: The class for fine-tuning Spark version LightGBM models, using [SynapseML](https://microsoft.github.io/SynapseML/docs/features/lightgbm/about/) API.\n",
|
|
"\n",
|
|
"#### Usage\n",
|
|
"First, prepare your data in the required format as described in the previous section.\n",
|
|
"\n",
|
|
"By including the models you intend to try in the `estimators_list` argument to `flaml.automl`, FLAML will start trying configurations for these models. If your input is Spark data, FLAML will also use estimators with the `_spark` postfix by default, even if you haven't specified them.\n",
|
|
"\n",
|
|
"Here is an example code snippet using SparkML models in AutoML:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"# prepare your data in pandas-on-spark format as we previously mentioned\n",
|
|
"\n",
|
|
"automl = flaml.AutoML()\n",
|
|
"settings = {\n",
|
|
" \"time_budget\": 30,\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"estimator_list\": [\"lgbm_spark\"], # this setting is optional\n",
|
|
" \"task\": \"regression\",\n",
|
|
"}\n",
|
|
"\n",
|
|
"automl.fit(\n",
|
|
" dataframe=psdf,\n",
|
|
" label=label,\n",
|
|
" **settings,\n",
|
|
")\n",
|
|
"```\n",
|
|
"\n",
|
|
"\n",
|
|
"[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb)\n",
|
|
"\n",
|
|
"## Parallel Spark Jobs\n",
|
|
"You can activate Spark as the parallel backend during parallel tuning in both [AutoML](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning) and [Hyperparameter Tuning](/docs/Use-Cases/Tune-User-Defined-Function#parallel-tuning), by setting the `use_spark` to `true`. FLAML will dispatch your job to the distributed Spark backend using [`joblib-spark`](https://github.com/joblib/joblib-spark).\n",
|
|
"\n",
|
|
"Please note that you should not set `use_spark` to `true` when applying AutoML and Tuning for Spark Data. This is because only SparkML models will be used for Spark Data in AutoML and Tuning. As SparkML models run in parallel, there is no need to distribute them with `use_spark` again.\n",
|
|
"\n",
|
|
"All the Spark-related arguments are stated below. These arguments are available in both Hyperparameter Tuning and AutoML:\n",
|
|
"\n",
|
|
"\n",
|
|
"- `use_spark`: boolean, default=False | Whether to use spark to run the training in parallel spark jobs. This can be used to accelerate training on large models and large datasets, but will incur more overhead in time and thus slow down training in some cases. GPU training is not supported yet when use_spark is True. For Spark clusters, by default, we will launch one trial per executor. However, sometimes we want to launch more trials than the number of executors (e.g., local mode). In this case, we can set the environment variable `FLAML_MAX_CONCURRENT` to override the detected `num_executors`. The final number of concurrent trials will be the minimum of `n_concurrent_trials` and `num_executors`.\n",
|
|
"- `n_concurrent_trials`: int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, FLAML performes parallel tuning.\n",
|
|
"- `force_cancel`: boolean, default=False | Whether to forcely cancel Spark jobs if the search time exceeded the time budget. Spark jobs include parallel tuning jobs and Spark-based model training jobs.\n",
|
|
"\n",
|
|
"An example code snippet for using parallel Spark jobs:\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"automl_experiment = flaml.AutoML()\n",
|
|
"automl_settings = {\n",
|
|
" \"time_budget\": 30,\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"task\": \"regression\",\n",
|
|
" \"n_concurrent_trials\": 2,\n",
|
|
" \"use_spark\": True,\n",
|
|
" \"force_cancel\": True, # Activating the force_cancel option can immediately halt Spark jobs once they exceed the allocated time_budget.\n",
|
|
"}\n",
|
|
"\n",
|
|
"automl.fit(\n",
|
|
" dataframe=dataframe,\n",
|
|
" label=label,\n",
|
|
" **automl_settings,\n",
|
|
")\n",
|
|
"```\n",
|
|
"\n",
|
|
"\n",
|
|
"[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb)\n",
|
|
"\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"# for flaml.tune\n",
|
|
"with mlflow.start_run(run_name=f\"spark_auto_trials_1686631558\"):\n",
|
|
" analysis = flaml.tune.run(\n",
|
|
" func_to_tune,\n",
|
|
" params,\n",
|
|
" metric=\"r2\",\n",
|
|
" mode=\"max\",\n",
|
|
" mlflow_exp_name=\"test_doc\",\n",
|
|
" use_spark=True,\n",
|
|
" )\n",
|
|
"\n",
|
|
"# for flaml.automl\n",
|
|
"automl_experiment = flaml.AutoML()\n",
|
|
"automl_settings = {\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"task\": \"regression\",\n",
|
|
" \"use_spark\": True,\n",
|
|
" \"mlflow_exp_name\": \"test_doc\",\n",
|
|
" \"estimator_list\": [\n",
|
|
" \"lgbm\",\n",
|
|
" \"rf\",\n",
|
|
" \"xgboost\",\n",
|
|
" \"extra_tree\",\n",
|
|
" \"xgb_limitdepth\",\n",
|
|
" ], # catboost does not yet support mlflow autologging\n",
|
|
"}\n",
|
|
"with mlflow.start_run(run_name=f\"automl_spark_trials_1686631579\"):\n",
|
|
" automl_experiment.fit(X_train=train_x, y_train=train_y, **automl_settings)\n",
|
|
"```\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"### Results\n",
|
|
"*Tune Autolog Trials on MLFlow UI*\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"*AutoML Autolog Trials on MLFlow UI*\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"### Differences Between Auto and Manual Logging\n",
|
|
"Autologging is managed by MLFlow, while manual logging is maintained by FLAML.\n",
|
|
"\n",
|
|
"\n",
|
|
"#### Details of Manual Logging\n",
|
|
"FLAML logs general artifacts for AutoML tasks. Specifically, we log these artifacts:\n",
|
|
"\n",
|
|
"**`flaml.tune`**\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"- We create a parent run to log the best metric and the best configuration for the entire tuning process.\n",
|
|
"- For each trial, we create a child run to log the metric specific to the tune function and the configuration for that trial.\n",
|
|
"\n",
|
|
"**`flaml.automl`**\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"- We create a parent run to log the results of the experiment. This includes:\n",
|
|
" - The configuration of this model.\n",
|
|
" - The `best_validation_loss` produced by this model.\n",
|
|
" - The `best_iteration` to identify the point at which this model was found.\n",
|
|
"- For each state (a specific learner with different hyperparameters), we record the best trial for this model. This includes:\n",
|
|
" - The configuration of the best trial.\n",
|
|
" - The `validation_loss` the best trial produces.\n",
|
|
" - The `iter_count` to identify how many trials we have conducted for this state.\n",
|
|
" - The `pred_time`, which is the time cost of predicting test data for this model.\n",
|
|
" - The `wall_clock_time`, which is the time cost of this state.\n",
|
|
" - The `sample_size` to show how much data we sampled in this state.\n",
|
|
"Note that we also added these information to autolog AutoML run.\n",
|
|
"\n",
|
|
"\n",
|
|
"#### Details of Autologging\n",
|
|
"Autolog artifacts typically include model parameters, model files, and runtime metrics like the following:\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"Artifacts can differ among various machine learning libraries. More detailed information can be found [here](https://mlflow.org/docs/latest/tracking.html#automatic-logging).\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"## Plot Experiment Result\n",
|
|
"The `flaml.visualization` module provides utility functions for plotting the optimization process using [plotly](https://plotly.com/python/). Leveraging `plotly`, users can interactively explore experiment results. To use these plotting functions, simply provide your optimized `flaml.AutoML` or `flaml.tune.tune.ExperimentAnalysis` object as input. Optional parameters can be added using keyword arguments.\n",
|
|
"\n",
|
|
"Avaliable plotting functions:\n",
|
|
"- `plot_optimization_history`: Plot optimization history of all trials in the experiment.\n",
|
|
"- `plot_feature_importance`: Plot importance for each feature in the dataset.\n",
|
|
"- `plot_parallel_coordinate`: Plot the high-dimensional parameter relationships in the experiment.\n",
|
|
"- `plot_contour`: Plot the parameter relationship as contour plot in the experiment.\n",
|
|
"- `plot_edf`: Plot the objective value EDF (empirical distribution function) of the experiment.\n",
|
|
"- `plot_timeline`: Plot the timeline of the experiment.\n",
|
|
"- `plot_slice`: Plot the parameter relationship as slice plot in a study.\n",
|
|
"\n",
|
|
"### Figure Examples\n",
|
|
"\n",
|
|
"\n",
|
|
"Check out our example [notebook](../../notebook/trident/automl_plot.ipynb) for a preview of all interactive plots.\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\u001b[32m*************************************************************\u001b[0m\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mProduct_Manager\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"To use Spark for parallel training in FLAML, you can follow these steps:\n",
|
|
"\n",
|
|
"1. Prepare your data in the required format. FLAML only consumes Spark data for Spark estimators. You can use the `to_pandas_on_spark` function from the `flaml.automl.spark.utils` module to convert your data into a pandas-on-spark dataframe. Here's an example:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import pandas as pd\n",
|
|
"from flaml.automl.spark.utils import to_pandas_on_spark\n",
|
|
"\n",
|
|
"# Create a dictionary\n",
|
|
"data = {\n",
|
|
" \"Square_Feet\": [800, 1200, 1800, 1500, 850],\n",
|
|
" \"Age_Years\": [20, 15, 10, 7, 25],\n",
|
|
" \"Price\": [100000, 200000, 300000, 240000, 120000]\n",
|
|
"}\n",
|
|
"\n",
|
|
"# Create a pandas DataFrame\n",
|
|
"dataframe = pd.DataFrame(data)\n",
|
|
"label = \"Price\"\n",
|
|
"\n",
|
|
"# Convert to pandas-on-spark dataframe\n",
|
|
"psdf = to_pandas_on_spark(dataframe)\n",
|
|
"```\n",
|
|
"\n",
|
|
"2. Use the Spark ML estimators in FLAML. FLAML integrates estimators based on Spark ML models. You can include the models you want to try in the `estimator_list` argument when creating an instance of `flaml.AutoML`. Here's an example:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"\n",
|
|
"automl = flaml.AutoML()\n",
|
|
"settings = {\n",
|
|
" \"time_budget\": 30,\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"estimator_list\": [\"lgbm_spark\"], # Optional: specify the Spark ML estimator\n",
|
|
" \"task\": \"regression\"\n",
|
|
"}\n",
|
|
"\n",
|
|
"automl.fit(\n",
|
|
" dataframe=psdf,\n",
|
|
" label=label,\n",
|
|
" **settings\n",
|
|
")\n",
|
|
"```\n",
|
|
"\n",
|
|
"3. Activate Spark as the parallel backend. You can set the `use_spark` parameter to `True` to activate Spark as the parallel backend during parallel tuning. FLAML will dispatch your job to the distributed Spark backend using `joblib-spark`. Here's an example:\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import flaml\n",
|
|
"\n",
|
|
"automl_experiment = flaml.AutoML()\n",
|
|
"automl_settings = {\n",
|
|
" \"time_budget\": 30,\n",
|
|
" \"metric\": \"r2\",\n",
|
|
" \"task\": \"regression\",\n",
|
|
" \"n_concurrent_trials\": 2,\n",
|
|
" \"use_spark\": True,\n",
|
|
" \"force_cancel\": True # Optional: force cancel Spark jobs if time budget is exceeded\n",
|
|
"}\n",
|
|
"\n",
|
|
"automl_experiment.fit(\n",
|
|
" dataframe=dataframe,\n",
|
|
" label=label,\n",
|
|
" **automl_settings\n",
|
|
")\n",
|
|
"```\n",
|
|
"\n",
|
|
"These are the steps to use Spark for parallel training in FLAML. Let me know if you need any further assistance!\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mCode_Reviewer\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"Great! You now have the steps to use Spark for parallel training in FLAML. If you have any more questions, feel free to ask.\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
|
|
"\u001b[33mBoss\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[33mSenior_Python_Engineer\u001b[0m (to chat_manager):\n",
|
|
"\n",
|
|
"TERMINATE\n",
|
|
"\n",
|
|
"--------------------------------------------------------------------------------\n",
|
|
"\u001b[31m\n",
|
|
">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"call_rag_chat()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "flaml",
|
|
"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": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|