notebooks/3.0-ls-autogen-demo-retrieval-augmented-chat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install autogen\n",
    "# !pip install openai\n",
    "\n",
    "# or pip install -r requirements.txt (from the github repo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick explanation for the retrieval-augmented generation example:\n",
    "\n",
    "[source](https://microsoft.github.io/autogen/blog/2023/10/18/RetrieveChat#:~:text=Retrieval%20augmentation%20has,for%20the%20context.)\n",
    "\n",
    "![](2024-07-08-10-20-10.png)\n",
    "\n",
    "Retrieval augmentation has emerged as a practical and effective approach for mitigating the intrinsic limitations of LLMs by incorporating external documents. In this blog post, we introduce RAG agents of AutoGen that allows retrieval-augmented generation. The system consists of two agents: a Retrieval-augmented User Proxy agent, called RetrieveUserProxyAgent, and a Retrieval-augmented Assistant agent, called RetrieveAssistantAgent, both of which are extended from built-in agents from AutoGen. The overall architecture of the RAG agents is shown in the figure above.\n",
    "\n",
    "To use Retrieval-augmented Chat, one needs to initialize two agents including Retrieval-augmented User Proxy and Retrieval-augmented Assistant. Initializing the Retrieval-Augmented User Proxy necessitates specifying a path to the document collection. Subsequently, the Retrieval-Augmented User Proxy can download the documents, segment them into chunks of a specific size, compute embeddings, and store them in a vector database. Once a chat is initiated, the agents collaboratively engage in code generation or question-answering adhering to the procedures outlined below:\n",
    "\n",
    "The Retrieval-Augmented User Proxy retrieves document chunks based on the embedding similarity, and sends them along with the question to the Retrieval-Augmented Assistant.\n",
    "The Retrieval-Augmented Assistant employs an LLM to generate code or text as answers based on the question and context provided. If the LLM is unable to produce a satisfactory response, it is instructed to reply with “Update Context” to the Retrieval-Augmented User Proxy.\n",
    "If a response includes code blocks, the Retrieval-Augmented User Proxy executes the code and sends the output as feedback. If there are no code blocks or instructions to update the context, it terminates the conversation. Otherwise, it updates the context and forwards the question along with the new context to the Retrieval-Augmented Assistant. Note that if human input solicitation is enabled, individuals can proactively send any feedback, including Update Context”, to the Retrieval-Augmented Assistant.\n",
    "If the Retrieval-Augmented Assistant receives “Update Context”, it requests the next most similar chunks of documents as new context from the Retrieval-Augmented User Proxy. Otherwise, it generates new code or text based on the feedback and chat history. If the LLM fails to generate an answer, it replies with “Update Context” again. This process can be repeated several times. The conversation terminates if no more documents are available for the context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/greatmaster/miniconda3/envs/oreilly-autogen/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from autogen import config_list_from_json\n",
    "from autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgent\n",
    "from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_list = config_list_from_json(\"./OAI_CONFIG_LIST\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "assistant = RetrieveAssistantAgent(\n",
    "      name=\"assistant\",\n",
    "      system_message=\"You are a helpful assistant.\",\n",
    "      llm_config={\n",
    "          \"timeout\": 600,\n",
    "          \"cache_seed\": 42,\n",
    "          \"config_list\": config_list,\n",
    "      },\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<autogen.agentchat.contrib.retrieve_assistant_agent.RetrieveAssistantAgent at 0x32c999c10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assistant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "autogen.agentchat.contrib.retrieve_assistant_agent.RetrieveAssistantAgent"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "type(assistant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ragproxyagent = RetrieveUserProxyAgent(\n",
    "      name=\"ragproxyagent\",\n",
    "      human_input_mode=\"NEVER\",\n",
    "      max_consecutive_auto_reply=3,\n",
    "      retrieve_config={\n",
    "          \"task\": \"code\",\n",
    "          \"docs_path\": [\n",
    "              \"https://raw.githubusercontent.com/microsoft/FLAML/main/website/docs/Examples/Integrate%20-%20Spark.md\",\n",
    "              \"https://raw.githubusercontent.com/microsoft/FLAML/main/website/docs/Research.md\",\n",
    "              os.path.join(os.path.abspath(\"\"), \"..\", \"website\", \"docs\")],\n",
    "      },\n",
    "      code_execution_config=False,  # set to False if you don't want to execute the code\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "assistant.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mragproxyagent\u001b[0m (to assistant):\n",
      "\n",
      "How can I use FLAML to perform a classification task and use spark to do parallel training. Train 30 seconds and force cancel jobs if time limit is reached.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to ragproxyagent):\n",
      "\n",
      "FLAML (Fast Lightweight AutoML) is a Python library designed for efficient and effective AutoML, enabling automated machine learning. Integrating FLAML with Apache Spark for parallel training can speed up the process, especially when dealing with large datasets.\n",
      "\n",
      "Here's a step-by-step guide to integrate FLAML with Spark for a classification task with a 30-second time limit:\n",
      "\n",
      "### Step 1: Set up the environment\n",
      "Ensure you have the necessary libraries installed. You need `pyspark` and `flaml` at the very least. You can install them using pip if they are not already installed:\n",
      "\n",
      "```bash\n",
      "pip install pyspark flaml\n",
      "```\n",
      "\n",
      "### Step 2: Import the necessary libraries\n",
      "```python\n",
      "from pyspark.sql import SparkSession\n",
      "from flaml import AutoML\n",
      "from sklearn.datasets import load_iris\n",
      "from sklearn.model_selection import train_test_split\n",
      "import mlflow\n",
      "```\n",
      "\n",
      "### Step 3: Initialize Spark Session\n",
      "```python\n",
      "spark = SparkSession.builder \\\n",
      "    .master(\"local[*]\") \\\n",
      "    .appName(\"FLAML-Spark\") \\\n",
      "    .getOrCreate()\n",
      "```\n",
      "\n",
      "### Step 4: Load and process the dataset\n",
      "For demonstration purposes, let's use the Iris dataset.\n",
      "\n",
      "```python\n",
      "# Load the Iris dataset\n",
      "iris = load_iris()\n",
      "X, y = iris.data, iris.target\n",
      "\n",
      "# Split the dataset into training and testing\n",
      "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
      "```\n",
      "\n",
      "### Step 5: Set up FLAML's AutoML\n",
      "```python\n",
      "automl = AutoML()\n",
      "```\n",
      "\n",
      "### Step 6: Set up MLflow to track experiments\n",
      "For dataset tracking and logging:\n",
      "\n",
      "```python\n",
      "mlflow.set_experiment(\"iris_classification\")\n",
      "```\n",
      "\n",
      "### Step 7: Define a function to train the model in parallel\n",
      "To train the model in parallel using Spark, you'll need to distribute the task. Here, we define a function that will be executed in parallel:\n",
      "\n",
      "```python\n",
      "def train_model(X_train, y_train, X_test, y_test):\n",
      "    with mlflow.start_run():\n",
      "        automl.fit(X_train=X_train, y_train=y_train, task='classification', time_budget=30)\n",
      "        best_model = automl.model\n",
      "        mlflow.sklearn.log_model(best_model, \"model\")\n",
      "        mlflow.log_params(automl.best_config)\n",
      "        print(automl.best_estimator)\n",
      "```\n",
      "\n",
      "### Step 8: Parallelize the training process\n",
      "Use `spark.sparkContext` to parallelize the training task.\n",
      "\n",
      "```python\n",
      "from pyspark import SparkContext\n",
      "\n",
      "sc = SparkContext.getOrCreate()\n",
      "num_parallel_tasks = 5  # Define how many parallel tasks you want\n",
      "parallel_tasks = [sc.parallelize([i]).map(lambda x: train_model(X_train, y_train, X_test, y_test)) for i in range(num_parallel_tasks)]\n",
      "\n",
      "# Collect the result to force execution\n",
      "results = [task.collect() for task in parallel_tasks]\n",
      "```\n",
      "\n",
      "### Step 9: Stop the Spark context\n",
      "```python\n",
      "sc.stop()\n",
      "```\n",
      "\n",
      "### Step 10: Evaluate the trained model\n",
      "Finally, you can evaluate the trained model on the test set.\n",
      "\n",
      "```python\n",
      "# Make predictions on the test set\n",
      "y_pred = automl.predict(X_test)\n",
      "\n",
      "# Evaluate the model's performance\n",
      "accuracy = (y_pred == y_test).mean()\n",
      "print(f\"Accuracy: {accuracy}\")\n",
      "```\n",
      "\n",
      "### Important Notes:\n",
      "1. **Timeout and Job Cancellation:** FLAML will adhere to the time budget (`time_budget=30`), automatically canceling the training if the time limit is reached.\n",
      "2. **Resource Management:** Ensure your Spark cluster has enough resources (CPU, memory) to handle the parallel tasks.\n",
      "3. **MLflow Integration:** MLflow tracks experiment metrics and logs them. Ensure MLflow is set up if you need experiment tracking.\n",
      "\n",
      "By configuring these steps correctly, you can efficiently utilize FLAML with Spark to perform parallel training for a classification task within a specified time limit.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mragproxyagent\u001b[0m (to assistant):\n",
      "\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to ragproxyagent):\n",
      "\n",
      "To integrate FLAML with Apache Spark for a classification task and ensure that parallel training adheres to a 30-second time budget, follow the detailed guide below:\n",
      "\n",
      "### Step-by-Step Implementation\n",
      "\n",
      "### Step 1: Set Up the Environment\n",
      "\n",
      "Ensure you have the necessary libraries installed. You need `pyspark` and `flaml` installed.\n",
      "\n",
      "```bash\n",
      "pip install pyspark flaml\n",
      "```\n",
      "\n",
      "### Step 2: Import Required Libraries\n",
      "\n",
      "```python\n",
      "from pyspark.sql import SparkSession\n",
      "from flaml import AutoML\n",
      "from sklearn.datasets import load_iris\n",
      "from sklearn.model_selection import train_test_split\n",
      "import numpy as np\n",
      "```\n",
      "\n",
      "### Step 3: Initialize Spark Session\n",
      "\n",
      "```python\n",
      "spark = SparkSession.builder \\\n",
      "    .master(\"local[*]\") \\\n",
      "    .appName(\"FLAML-Spark\") \\\n",
      "    .config(\"spark.driver.memory\", \"8g\") \\\n",
      "    .getOrCreate()\n",
      "```\n",
      "\n",
      "### Step 4: Load and Process the Dataset\n",
      "\n",
      "For illustration, use the Iris dataset:\n",
      "\n",
      "```python\n",
      "# Load the Iris dataset\n",
      "iris = load_iris()\n",
      "X, y = iris.data, iris.target\n",
      "\n",
      "# Split the dataset into training and testing sets\n",
      "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
      "```\n",
      "\n",
      "### Step 5: Define a Function for Model Training\n",
      "\n",
      "This function will train the model using FLAML and can be parallelized.\n",
      "\n",
      "```python\n",
      "def train_model(X_train, y_train):\n",
      "    automl = AutoML()\n",
      "    automl.fit(X_train=X_train, y_train=y_train, task='classification', time_budget=30)\n",
      "    return automl.best_estimator, automl.best_config, automl.best_loss\n",
      "```\n",
      "\n",
      "### Step 6: Parallelize the Training Task Using Spark\n",
      "\n",
      "You can use Spark's RDD to parallelize the task. Note that Spark functions require data to be in a distribule format:\n",
      "\n",
      "```python\n",
      "def parallel_train(partition_data):\n",
      "    X_train, X_test, y_train, y_test = partition_data\n",
      "    result = train_model(X_train, y_train)\n",
      "    return result\n",
      "\n",
      "# Here we create partitions of data for parallel processing.\n",
      "# For illustration, creating mock partitions as Spark RDDs\n",
      "\n",
      "num_partitions = 4  # Number of parallel tasks\n",
      "data_partitions = [(X_train, X_test, y_train, y_test)] * num_partitions\n",
      "\n",
      "rdd = spark.sparkContext.parallelize(data_partitions, num_partitions)\n",
      "result_rdd = rdd.map(parallel_train)\n",
      "\n",
      "results = result_rdd.collect()\n",
      "```\n",
      "\n",
      "### Step 7: Stop the Spark Session\n",
      "\n",
      "```python\n",
      "spark.stop()\n",
      "```\n",
      "\n",
      "### Step 8: Evaluate and Print Results\n",
      "\n",
      "Print the best estimators from each partition to observe the different model configurations and their performances:\n",
      "\n",
      "```python\n",
      "for i, (estimator, config, loss) in enumerate(results):\n",
      "    print(f\"Partition {i + 1}:\")\n",
      "    print(f\"Best Estimator: {estimator}\")\n",
      "    print(f\"Best Config: {config}\")\n",
      "    print(f\"Best Loss: {loss}\\n\")\n",
      "```\n",
      "\n",
      "### Important Notes:\n",
      "\n",
      "1. **Time Budget:** The `time_budget` parameter in FLAML ensures that the training process will be terminated if it exceeds 30 seconds.\n",
      "2. **Partitioning Strategy:** In a real-world scenario, ensure a meaningful partitioning of data. The current example uses mock partitions which simply duplicate the data for parallel processing.\n",
      "3. **Resource Allocation:** Ensure sufficient resources for each Spark worker node to handle the computation. Adjust `num_partitions` according to your cluster’s capacity.\n",
      "4. **Data Transfer:** Distribute data efficiently to avoid network bottlenecks if working with a large dataset in a distributed cluster environment.\n",
      "\n",
      "By adhering to these steps, you can perform classification tasks using FLAML in a parallelized manner with Apache Spark, respecting a strict 30-second time limit for each training task.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mragproxyagent\u001b[0m (to assistant):\n",
      "\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to ragproxyagent):\n",
      "\n",
      "To enhance the integration of FLAML and Apache Spark for executing parallelized training tasks with a strict 30-second time limit, follow this streamlined guide:\n",
      "\n",
      "### Step-by-Step Implementation\n",
      "\n",
      "### Step 1: Set Up the Environment\n",
      "\n",
      "Ensure you install the necessary libraries. You need `pyspark` and `flaml`:\n",
      "\n",
      "```bash\n",
      "pip install pyspark flaml\n",
      "```\n",
      "\n",
      "### Step 2: Import Required Libraries\n",
      "\n",
      "```python\n",
      "from pyspark.sql import SparkSession\n",
      "from flaml import AutoML\n",
      "from sklearn.datasets import load_iris\n",
      "from sklearn.model_selection import train_test_split\n",
      "from sklearn.metrics import accuracy_score\n",
      "import numpy as np\n",
      "```\n",
      "\n",
      "### Step 3: Initialize Spark Session\n",
      "\n",
      "```python\n",
      "spark = SparkSession.builder \\\n",
      "    .master(\"local[*]\") \\\n",
      "    .appName(\"FLAML-Spark\") \\\n",
      "    .config(\"spark.driver.memory\", \"8g\") \\\n",
      "    .getOrCreate()\n",
      "```\n",
      "\n",
      "### Step 4: Load and Process the Dataset\n",
      "\n",
      "For illustration, we'll use the Iris dataset:\n",
      "\n",
      "```python\n",
      "# Load the Iris dataset\n",
      "iris = load_iris()\n",
      "X, y = iris.data, iris.target\n",
      "\n",
      "# Split the dataset into training and testing sets\n",
      "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
      "```\n",
      "\n",
      "### Step 5: Define a Function for Model Training\n",
      "\n",
      "This function will train the model using FLAML and can be parallelized.\n",
      "\n",
      "```python\n",
      "def train_model(spark, X_train, y_train):\n",
      "    from flaml import AutoML\n",
      "    \n",
      "    automl = AutoML()\n",
      "    automl.fit(X_train=X_train, y_train=y_train, task='classification', time_budget=30)\n",
      "    return automl.best_estimator, automl.best_config, automl.best_loss\n",
      "\n",
      "def parallel_training(train_data):\n",
      "    X_train, y_train = train_data\n",
      "    try:\n",
      "        model, config, loss = train_model(spark, X_train, y_train)\n",
      "        return (model, config, loss)\n",
      "    except Exception as e:\n",
      "        return (None, None, str(e))\n",
      "```\n",
      "\n",
      "### Step 6: Prepare the Data for Parallel Processing\n",
      "\n",
      "Partition the data, distribute it across Spark workers, and initiate parallel training:\n",
      "\n",
      "```python\n",
      "num_partitions = 4  # Number of parallel tasks\n",
      "data_partitions = [(X_train, y_train)] * num_partitions\n",
      "\n",
      "rdd = spark.sparkContext.parallelize(data_partitions, num_partitions)\n",
      "result_rdd = rdd.map(parallel_training)\n",
      "\n",
      "results = result_rdd.collect()\n",
      "```\n",
      "\n",
      "### Step 7: Stop the Spark Session\n",
      "\n",
      "```python\n",
      "spark.stop()\n",
      "```\n",
      "\n",
      "### Step 8: Evaluate and Print Results\n",
      "\n",
      "Print the best estimators from each partition to observe the different model configurations and their performances:\n",
      "\n",
      "```python\n",
      "for i, (estimator, config, loss) in enumerate(results):\n",
      "    print(f\"Partition {i + 1}:\")\n",
      "    if estimator is not None:\n",
      "        print(f\"Best Estimator: {estimator}\")\n",
      "        print(f\"Best Config: {config}\")\n",
      "        print(f\"Best Loss: {loss}\\n\")\n",
      "    else:\n",
      "        print(f\"Error: {loss}\\n\")\n",
      "```\n",
      "\n",
      "### Step 9: Evaluate the Best Model\n",
      "\n",
      "Find the best model among the partitions and evaluate it on the test set:\n",
      "\n",
      "```python\n",
      "best_model = None\n",
      "best_loss = float('inf')\n",
      "\n",
      "for estimator, config, loss in results:\n",
      "    if estimator is not None and loss < best_loss:\n",
      "        best_loss = loss\n",
      "        best_model = estimator\n",
      "\n",
      "if best_model is not None:\n",
      "    y_pred = best_model.predict(X_test)\n",
      "    accuracy = accuracy_score(y_test, y_pred)\n",
      "    print(f\"Best model accuracy on test set: {accuracy}\")\n",
      "else:\n",
      "    print(\"No valid model was found.\")\n",
      "```\n",
      "\n",
      "### Important Notes:\n",
      "\n",
      "1. **Time Budget:** The `time_budget` parameter in FLAML ensures that the training process will be terminated if it exceeds 30 seconds per task.\n",
      "2. **Partitioning Strategy:** In a real-world scenario, ensure that the data is partitioned meaningfully. This example uses mock partitions.\n",
      "3. **Resource Allocation:** Make sure there are enough resources for each Spark worker node to handle the computation. Adjust the `num_partitions` as per your cluster’s capacity.\n",
      "4. **Error Handling:** Handle exceptions during model training to avoid issues like out-of-memory errors or other exceptions. The provided example includes basic error handling.\n",
      "\n",
      "By following these steps, you can effectively utilize FLAML with Apache Spark for performing parallel training tasks with a strict time limit, ensuring efficient and scalable machine learning workflows.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33mragproxyagent\u001b[0m (to assistant):\n",
      "\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to ragproxyagent):\n",
      "\n",
      "To ensure clarity, here's an enhanced and more detailed guide on using FLAML with Apache Spark for performing parallelized training on a classification task while adhering to a 30-second time limit for each training job:\n",
      "\n",
      "### Step-by-Step Implementation\n",
      "\n",
      "### Step 1: Set Up the Environment\n",
      "\n",
      "Ensure you have the necessary libraries installed. You'll need `pyspark` and `flaml`:\n",
      "\n",
      "```bash\n",
      "pip install pyspark flaml\n",
      "```\n",
      "\n",
      "### Step 2: Import Required Libraries\n",
      "\n",
      "```python\n",
      "from pyspark.sql import SparkSession\n",
      "from flaml import AutoML\n",
      "from sklearn.datasets import load_iris\n",
      "from sklearn.model_selection import train_test_split\n",
      "from sklearn.metrics import accuracy_score\n",
      "import numpy as np\n",
      "```\n",
      "\n",
      "### Step 3: Initialize Spark Session\n",
      "\n",
      "```python\n",
      "spark = SparkSession.builder \\\n",
      "    .master(\"local[*]\") \\\n",
      "    .appName(\"FLAML-Spark\") \\\n",
      "    .config(\"spark.driver.memory\", \"8g\") \\\n",
      "    .getOrCreate()\n",
      "```\n",
      "\n",
      "### Step 4: Load and Process the Dataset\n",
      "\n",
      "For illustration, we'll use the Iris dataset:\n",
      "\n",
      "```python\n",
      "# Load the Iris dataset\n",
      "iris = load_iris()\n",
      "X, y = iris.data, iris.target\n",
      "\n",
      "# Split the dataset into training and testing sets\n",
      "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
      "```\n",
      "\n",
      "### Step 5: Define a Function for Model Training\n",
      "\n",
      "This function will train the model using FLAML and can be parallelized:\n",
      "\n",
      "```python\n",
      "def train_model(X_train, y_train):\n",
      "    automl = AutoML()\n",
      "    automl.fit(X_train=X_train, y_train=y_train, task='classification', time_budget=30)\n",
      "    return automl.best_estimator, automl.best_config, automl.best_loss\n",
      "\n",
      "def parallel_training(partition_data):\n",
      "    X_train, y_train = partition_data\n",
      "    try:\n",
      "        model, config, loss = train_model(X_train, y_train)\n",
      "        return (model, config, loss)\n",
      "    except Exception as e:\n",
      "        return (None, None, str(e))\n",
      "```\n",
      "\n",
      "### Step 6: Prepare Data for Parallel Processing\n",
      "\n",
      "Partition the data, distribute it across Spark workers, and initiate parallel training.\n",
      "\n",
      "```python\n",
      "num_partitions = 4  # Number of parallel tasks\n",
      "data_partitions = [(X_train, y_train)] * num_partitions\n",
      "\n",
      "# Create an RDD, each partition containing the same training data (for demo purposes)\n",
      "rdd = spark.sparkContext.parallelize(data_partitions, num_partitions)\n",
      "result_rdd = rdd.map(parallel_training)\n",
      "results = result_rdd.collect()\n",
      "```\n",
      "\n",
      "### Step 7: Stop the Spark Session\n",
      "\n",
      "```python\n",
      "spark.stop()\n",
      "```\n",
      "\n",
      "### Step 8: Evaluate and Print Results\n",
      "\n",
      "Print the results from each partition to observe the different model configurations and their performances:\n",
      "\n",
      "```python\n",
      "for i, (estimator, config, loss) in enumerate(results):\n",
      "    print(f\"Partition {i + 1}:\")\n",
      "    if estimator is not None:\n",
      "        print(f\"Best Estimator: {estimator}\")\n",
      "        print(f\"Best Config: {config}\")\n",
      "        print(f\"Best Loss: {loss}\\n\")\n",
      "    else:\n",
      "        print(f\"Error: {loss}\\n\")\n",
      "```\n",
      "\n",
      "### Step 9: Evaluate the Best Model\n",
      "\n",
      "Find the best model among the partitions and evaluate it on the test set:\n",
      "\n",
      "```python\n",
      "best_model = None\n",
      "best_loss = float('inf')\n",
      "\n",
      "for estimator, config, loss in results:\n",
      "    if estimator is not None and loss < best_loss:\n",
      "        best_loss = loss\n",
      "        best_model = estimator\n",
      "\n",
      "if best_model is not None:\n",
      "    y_pred = best_model.predict(X_test)\n",
      "    accuracy = accuracy_score(y_test, y_pred)\n",
      "    print(f\"Best model accuracy on test set: {accuracy}\")\n",
      "else:\n",
      "    print(\"No valid model was found.\")\n",
      "```\n",
      "\n",
      "### Important Considerations:\n",
      "\n",
      "1. **Time Budget:** The `time_budget` parameter in FLAML ensures that the training process will be terminated if it exceeds 30 seconds per job.\n",
      "2. **Partitioning Strategy:** In a real-world scenario, the data should be partitioned meaningfully based on the nature and size of the dataset. The current example uses the same data in all partitions for demonstration purposes.\n",
      "3. **Resource Allocation:** Ensure there are sufficient resources for each Spark worker node to handle the computation. Adjust `num_partitions` based on your cluster’s capacity.\n",
      "4. **Error Handling:** Handle exceptions during model training to avoid issues like out-of-memory errors or other exceptions. Basic error handling is included in the example.\n",
      "\n",
      "By meticulously following these steps, you can effectively leverage FLAML with Apache Spark to perform parallelized classification tasks within a strict 30-second time limit, thereby achieving efficient and scalable machine learning workflows.\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ChatResult(chat_id=None, chat_history=[{'content': 'How can I use FLAML to perform a classification task and use spark to do parallel training. Train 30 seconds and force cancel jobs if time limit is reached.', 'role': 'assistant'}, {'content': 'FLAML (Fast Lightweight AutoML) is a Python library designed for efficient and effective AutoML, enabling automated machine learning. Integrating FLAML with Apache Spark for parallel training can speed up the process, especially when dealing with large datasets.\\n\\nHere\\'s a step-by-step guide to integrate FLAML with Spark for a classification task with a 30-second time limit:\\n\\n### Step 1: Set up the environment\\nEnsure you have the necessary libraries installed. You need `pyspark` and `flaml` at the very least. You can install them using pip if they are not already installed:\\n\\n```bash\\npip install pyspark flaml\\n```\\n\\n### Step 2: Import the necessary libraries\\n```python\\nfrom pyspark.sql import SparkSession\\nfrom flaml import AutoML\\nfrom sklearn.datasets import load_iris\\nfrom sklearn.model_selection import train_test_split\\nimport mlflow\\n```\\n\\n### Step 3: Initialize Spark Session\\n```python\\nspark = SparkSession.builder \\\\\\n    .master(\"local[*]\") \\\\\\n    .appName(\"FLAML-Spark\") \\\\\\n    .getOrCreate()\\n```\\n\\n### Step 4: Load and process the dataset\\nFor demonstration purposes, let\\'s use the Iris dataset.\\n\\n```python\\n# Load the Iris dataset\\niris = load_iris()\\nX, y = iris.data, iris.target\\n\\n# Split the dataset into training and testing\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\\n```\\n\\n### Step 5: Set up FLAML\\'s AutoML\\n```python\\nautoml = AutoML()\\n```\\n\\n### Step 6: Set up MLflow to track experiments\\nFor dataset tracking and logging:\\n\\n```python\\nmlflow.set_experiment(\"iris_classification\")\\n```\\n\\n### Step 7: Define a function to train the model in parallel\\nTo train the model in parallel using Spark, you\\'ll need to distribute the task. Here, we define a function that will be executed in parallel:\\n\\n```python\\ndef train_model(X_train, y_train, X_test, y_test):\\n    with mlflow.start_run():\\n        automl.fit(X_train=X_train, y_train=y_train, task=\\'classification\\', time_budget=30)\\n        best_model = automl.model\\n        mlflow.sklearn.log_model(best_model, \"model\")\\n        mlflow.log_params(automl.best_config)\\n        print(automl.best_estimator)\\n```\\n\\n### Step 8: Parallelize the training process\\nUse `spark.sparkContext` to parallelize the training task.\\n\\n```python\\nfrom pyspark import SparkContext\\n\\nsc = SparkContext.getOrCreate()\\nnum_parallel_tasks = 5  # Define how many parallel tasks you want\\nparallel_tasks = [sc.parallelize([i]).map(lambda x: train_model(X_train, y_train, X_test, y_test)) for i in range(num_parallel_tasks)]\\n\\n# Collect the result to force execution\\nresults = [task.collect() for task in parallel_tasks]\\n```\\n\\n### Step 9: Stop the Spark context\\n```python\\nsc.stop()\\n```\\n\\n### Step 10: Evaluate the trained model\\nFinally, you can evaluate the trained model on the test set.\\n\\n```python\\n# Make predictions on the test set\\ny_pred = automl.predict(X_test)\\n\\n# Evaluate the model\\'s performance\\naccuracy = (y_pred == y_test).mean()\\nprint(f\"Accuracy: {accuracy}\")\\n```\\n\\n### Important Notes:\\n1. **Timeout and Job Cancellation:** FLAML will adhere to the time budget (`time_budget=30`), automatically canceling the training if the time limit is reached.\\n2. **Resource Management:** Ensure your Spark cluster has enough resources (CPU, memory) to handle the parallel tasks.\\n3. **MLflow Integration:** MLflow tracks experiment metrics and logs them. Ensure MLflow is set up if you need experiment tracking.\\n\\nBy configuring these steps correctly, you can efficiently utilize FLAML with Spark to perform parallel training for a classification task within a specified time limit.', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': 'To integrate FLAML with Apache Spark for a classification task and ensure that parallel training adheres to a 30-second time budget, follow the detailed guide below:\\n\\n### Step-by-Step Implementation\\n\\n### Step 1: Set Up the Environment\\n\\nEnsure you have the necessary libraries installed. You need `pyspark` and `flaml` installed.\\n\\n```bash\\npip install pyspark flaml\\n```\\n\\n### Step 2: Import Required Libraries\\n\\n```python\\nfrom pyspark.sql import SparkSession\\nfrom flaml import AutoML\\nfrom sklearn.datasets import load_iris\\nfrom sklearn.model_selection import train_test_split\\nimport numpy as np\\n```\\n\\n### Step 3: Initialize Spark Session\\n\\n```python\\nspark = SparkSession.builder \\\\\\n    .master(\"local[*]\") \\\\\\n    .appName(\"FLAML-Spark\") \\\\\\n    .config(\"spark.driver.memory\", \"8g\") \\\\\\n    .getOrCreate()\\n```\\n\\n### Step 4: Load and Process the Dataset\\n\\nFor illustration, use the Iris dataset:\\n\\n```python\\n# Load the Iris dataset\\niris = load_iris()\\nX, y = iris.data, iris.target\\n\\n# Split the dataset into training and testing sets\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\\n```\\n\\n### Step 5: Define a Function for Model Training\\n\\nThis function will train the model using FLAML and can be parallelized.\\n\\n```python\\ndef train_model(X_train, y_train):\\n    automl = AutoML()\\n    automl.fit(X_train=X_train, y_train=y_train, task=\\'classification\\', time_budget=30)\\n    return automl.best_estimator, automl.best_config, automl.best_loss\\n```\\n\\n### Step 6: Parallelize the Training Task Using Spark\\n\\nYou can use Spark\\'s RDD to parallelize the task. Note that Spark functions require data to be in a distribule format:\\n\\n```python\\ndef parallel_train(partition_data):\\n    X_train, X_test, y_train, y_test = partition_data\\n    result = train_model(X_train, y_train)\\n    return result\\n\\n# Here we create partitions of data for parallel processing.\\n# For illustration, creating mock partitions as Spark RDDs\\n\\nnum_partitions = 4  # Number of parallel tasks\\ndata_partitions = [(X_train, X_test, y_train, y_test)] * num_partitions\\n\\nrdd = spark.sparkContext.parallelize(data_partitions, num_partitions)\\nresult_rdd = rdd.map(parallel_train)\\n\\nresults = result_rdd.collect()\\n```\\n\\n### Step 7: Stop the Spark Session\\n\\n```python\\nspark.stop()\\n```\\n\\n### Step 8: Evaluate and Print Results\\n\\nPrint the best estimators from each partition to observe the different model configurations and their performances:\\n\\n```python\\nfor i, (estimator, config, loss) in enumerate(results):\\n    print(f\"Partition {i + 1}:\")\\n    print(f\"Best Estimator: {estimator}\")\\n    print(f\"Best Config: {config}\")\\n    print(f\"Best Loss: {loss}\\\\n\")\\n```\\n\\n### Important Notes:\\n\\n1. **Time Budget:** The `time_budget` parameter in FLAML ensures that the training process will be terminated if it exceeds 30 seconds.\\n2. **Partitioning Strategy:** In a real-world scenario, ensure a meaningful partitioning of data. The current example uses mock partitions which simply duplicate the data for parallel processing.\\n3. **Resource Allocation:** Ensure sufficient resources for each Spark worker node to handle the computation. Adjust `num_partitions` according to your cluster’s capacity.\\n4. **Data Transfer:** Distribute data efficiently to avoid network bottlenecks if working with a large dataset in a distributed cluster environment.\\n\\nBy adhering to these steps, you can perform classification tasks using FLAML in a parallelized manner with Apache Spark, respecting a strict 30-second time limit for each training task.', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': 'To enhance the integration of FLAML and Apache Spark for executing parallelized training tasks with a strict 30-second time limit, follow this streamlined guide:\\n\\n### Step-by-Step Implementation\\n\\n### Step 1: Set Up the Environment\\n\\nEnsure you install the necessary libraries. You need `pyspark` and `flaml`:\\n\\n```bash\\npip install pyspark flaml\\n```\\n\\n### Step 2: Import Required Libraries\\n\\n```python\\nfrom pyspark.sql import SparkSession\\nfrom flaml import AutoML\\nfrom sklearn.datasets import load_iris\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import accuracy_score\\nimport numpy as np\\n```\\n\\n### Step 3: Initialize Spark Session\\n\\n```python\\nspark = SparkSession.builder \\\\\\n    .master(\"local[*]\") \\\\\\n    .appName(\"FLAML-Spark\") \\\\\\n    .config(\"spark.driver.memory\", \"8g\") \\\\\\n    .getOrCreate()\\n```\\n\\n### Step 4: Load and Process the Dataset\\n\\nFor illustration, we\\'ll use the Iris dataset:\\n\\n```python\\n# Load the Iris dataset\\niris = load_iris()\\nX, y = iris.data, iris.target\\n\\n# Split the dataset into training and testing sets\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\\n```\\n\\n### Step 5: Define a Function for Model Training\\n\\nThis function will train the model using FLAML and can be parallelized.\\n\\n```python\\ndef train_model(spark, X_train, y_train):\\n    from flaml import AutoML\\n    \\n    automl = AutoML()\\n    automl.fit(X_train=X_train, y_train=y_train, task=\\'classification\\', time_budget=30)\\n    return automl.best_estimator, automl.best_config, automl.best_loss\\n\\ndef parallel_training(train_data):\\n    X_train, y_train = train_data\\n    try:\\n        model, config, loss = train_model(spark, X_train, y_train)\\n        return (model, config, loss)\\n    except Exception as e:\\n        return (None, None, str(e))\\n```\\n\\n### Step 6: Prepare the Data for Parallel Processing\\n\\nPartition the data, distribute it across Spark workers, and initiate parallel training:\\n\\n```python\\nnum_partitions = 4  # Number of parallel tasks\\ndata_partitions = [(X_train, y_train)] * num_partitions\\n\\nrdd = spark.sparkContext.parallelize(data_partitions, num_partitions)\\nresult_rdd = rdd.map(parallel_training)\\n\\nresults = result_rdd.collect()\\n```\\n\\n### Step 7: Stop the Spark Session\\n\\n```python\\nspark.stop()\\n```\\n\\n### Step 8: Evaluate and Print Results\\n\\nPrint the best estimators from each partition to observe the different model configurations and their performances:\\n\\n```python\\nfor i, (estimator, config, loss) in enumerate(results):\\n    print(f\"Partition {i + 1}:\")\\n    if estimator is not None:\\n        print(f\"Best Estimator: {estimator}\")\\n        print(f\"Best Config: {config}\")\\n        print(f\"Best Loss: {loss}\\\\n\")\\n    else:\\n        print(f\"Error: {loss}\\\\n\")\\n```\\n\\n### Step 9: Evaluate the Best Model\\n\\nFind the best model among the partitions and evaluate it on the test set:\\n\\n```python\\nbest_model = None\\nbest_loss = float(\\'inf\\')\\n\\nfor estimator, config, loss in results:\\n    if estimator is not None and loss < best_loss:\\n        best_loss = loss\\n        best_model = estimator\\n\\nif best_model is not None:\\n    y_pred = best_model.predict(X_test)\\n    accuracy = accuracy_score(y_test, y_pred)\\n    print(f\"Best model accuracy on test set: {accuracy}\")\\nelse:\\n    print(\"No valid model was found.\")\\n```\\n\\n### Important Notes:\\n\\n1. **Time Budget:** The `time_budget` parameter in FLAML ensures that the training process will be terminated if it exceeds 30 seconds per task.\\n2. **Partitioning Strategy:** In a real-world scenario, ensure that the data is partitioned meaningfully. This example uses mock partitions.\\n3. **Resource Allocation:** Make sure there are enough resources for each Spark worker node to handle the computation. Adjust the `num_partitions` as per your cluster’s capacity.\\n4. **Error Handling:** Handle exceptions during model training to avoid issues like out-of-memory errors or other exceptions. The provided example includes basic error handling.\\n\\nBy following these steps, you can effectively utilize FLAML with Apache Spark for performing parallel training tasks with a strict time limit, ensuring efficient and scalable machine learning workflows.', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': 'To ensure clarity, here\\'s an enhanced and more detailed guide on using FLAML with Apache Spark for performing parallelized training on a classification task while adhering to a 30-second time limit for each training job:\\n\\n### Step-by-Step Implementation\\n\\n### Step 1: Set Up the Environment\\n\\nEnsure you have the necessary libraries installed. You\\'ll need `pyspark` and `flaml`:\\n\\n```bash\\npip install pyspark flaml\\n```\\n\\n### Step 2: Import Required Libraries\\n\\n```python\\nfrom pyspark.sql import SparkSession\\nfrom flaml import AutoML\\nfrom sklearn.datasets import load_iris\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import accuracy_score\\nimport numpy as np\\n```\\n\\n### Step 3: Initialize Spark Session\\n\\n```python\\nspark = SparkSession.builder \\\\\\n    .master(\"local[*]\") \\\\\\n    .appName(\"FLAML-Spark\") \\\\\\n    .config(\"spark.driver.memory\", \"8g\") \\\\\\n    .getOrCreate()\\n```\\n\\n### Step 4: Load and Process the Dataset\\n\\nFor illustration, we\\'ll use the Iris dataset:\\n\\n```python\\n# Load the Iris dataset\\niris = load_iris()\\nX, y = iris.data, iris.target\\n\\n# Split the dataset into training and testing sets\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\\n```\\n\\n### Step 5: Define a Function for Model Training\\n\\nThis function will train the model using FLAML and can be parallelized:\\n\\n```python\\ndef train_model(X_train, y_train):\\n    automl = AutoML()\\n    automl.fit(X_train=X_train, y_train=y_train, task=\\'classification\\', time_budget=30)\\n    return automl.best_estimator, automl.best_config, automl.best_loss\\n\\ndef parallel_training(partition_data):\\n    X_train, y_train = partition_data\\n    try:\\n        model, config, loss = train_model(X_train, y_train)\\n        return (model, config, loss)\\n    except Exception as e:\\n        return (None, None, str(e))\\n```\\n\\n### Step 6: Prepare Data for Parallel Processing\\n\\nPartition the data, distribute it across Spark workers, and initiate parallel training.\\n\\n```python\\nnum_partitions = 4  # Number of parallel tasks\\ndata_partitions = [(X_train, y_train)] * num_partitions\\n\\n# Create an RDD, each partition containing the same training data (for demo purposes)\\nrdd = spark.sparkContext.parallelize(data_partitions, num_partitions)\\nresult_rdd = rdd.map(parallel_training)\\nresults = result_rdd.collect()\\n```\\n\\n### Step 7: Stop the Spark Session\\n\\n```python\\nspark.stop()\\n```\\n\\n### Step 8: Evaluate and Print Results\\n\\nPrint the results from each partition to observe the different model configurations and their performances:\\n\\n```python\\nfor i, (estimator, config, loss) in enumerate(results):\\n    print(f\"Partition {i + 1}:\")\\n    if estimator is not None:\\n        print(f\"Best Estimator: {estimator}\")\\n        print(f\"Best Config: {config}\")\\n        print(f\"Best Loss: {loss}\\\\n\")\\n    else:\\n        print(f\"Error: {loss}\\\\n\")\\n```\\n\\n### Step 9: Evaluate the Best Model\\n\\nFind the best model among the partitions and evaluate it on the test set:\\n\\n```python\\nbest_model = None\\nbest_loss = float(\\'inf\\')\\n\\nfor estimator, config, loss in results:\\n    if estimator is not None and loss < best_loss:\\n        best_loss = loss\\n        best_model = estimator\\n\\nif best_model is not None:\\n    y_pred = best_model.predict(X_test)\\n    accuracy = accuracy_score(y_test, y_pred)\\n    print(f\"Best model accuracy on test set: {accuracy}\")\\nelse:\\n    print(\"No valid model was found.\")\\n```\\n\\n### Important Considerations:\\n\\n1. **Time Budget:** The `time_budget` parameter in FLAML ensures that the training process will be terminated if it exceeds 30 seconds per job.\\n2. **Partitioning Strategy:** In a real-world scenario, the data should be partitioned meaningfully based on the nature and size of the dataset. The current example uses the same data in all partitions for demonstration purposes.\\n3. **Resource Allocation:** Ensure there are sufficient resources for each Spark worker node to handle the computation. Adjust `num_partitions` based on your cluster’s capacity.\\n4. **Error Handling:** Handle exceptions during model training to avoid issues like out-of-memory errors or other exceptions. Basic error handling is included in the example.\\n\\nBy meticulously following these steps, you can effectively leverage FLAML with Apache Spark to perform parallelized classification tasks within a strict 30-second time limit, thereby achieving efficient and scalable machine learning workflows.', 'role': 'user'}], summary='To ensure clarity, here\\'s an enhanced and more detailed guide on using FLAML with Apache Spark for performing parallelized training on a classification task while adhering to a 30-second time limit for each training job:\\n\\n### Step-by-Step Implementation\\n\\n### Step 1: Set Up the Environment\\n\\nEnsure you have the necessary libraries installed. You\\'ll need `pyspark` and `flaml`:\\n\\n```bash\\npip install pyspark flaml\\n```\\n\\n### Step 2: Import Required Libraries\\n\\n```python\\nfrom pyspark.sql import SparkSession\\nfrom flaml import AutoML\\nfrom sklearn.datasets import load_iris\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import accuracy_score\\nimport numpy as np\\n```\\n\\n### Step 3: Initialize Spark Session\\n\\n```python\\nspark = SparkSession.builder \\\\\\n    .master(\"local[*]\") \\\\\\n    .appName(\"FLAML-Spark\") \\\\\\n    .config(\"spark.driver.memory\", \"8g\") \\\\\\n    .getOrCreate()\\n```\\n\\n### Step 4: Load and Process the Dataset\\n\\nFor illustration, we\\'ll use the Iris dataset:\\n\\n```python\\n# Load the Iris dataset\\niris = load_iris()\\nX, y = iris.data, iris.target\\n\\n# Split the dataset into training and testing sets\\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\\n```\\n\\n### Step 5: Define a Function for Model Training\\n\\nThis function will train the model using FLAML and can be parallelized:\\n\\n```python\\ndef train_model(X_train, y_train):\\n    automl = AutoML()\\n    automl.fit(X_train=X_train, y_train=y_train, task=\\'classification\\', time_budget=30)\\n    return automl.best_estimator, automl.best_config, automl.best_loss\\n\\ndef parallel_training(partition_data):\\n    X_train, y_train = partition_data\\n    try:\\n        model, config, loss = train_model(X_train, y_train)\\n        return (model, config, loss)\\n    except Exception as e:\\n        return (None, None, str(e))\\n```\\n\\n### Step 6: Prepare Data for Parallel Processing\\n\\nPartition the data, distribute it across Spark workers, and initiate parallel training.\\n\\n```python\\nnum_partitions = 4  # Number of parallel tasks\\ndata_partitions = [(X_train, y_train)] * num_partitions\\n\\n# Create an RDD, each partition containing the same training data (for demo purposes)\\nrdd = spark.sparkContext.parallelize(data_partitions, num_partitions)\\nresult_rdd = rdd.map(parallel_training)\\nresults = result_rdd.collect()\\n```\\n\\n### Step 7: Stop the Spark Session\\n\\n```python\\nspark.stop()\\n```\\n\\n### Step 8: Evaluate and Print Results\\n\\nPrint the results from each partition to observe the different model configurations and their performances:\\n\\n```python\\nfor i, (estimator, config, loss) in enumerate(results):\\n    print(f\"Partition {i + 1}:\")\\n    if estimator is not None:\\n        print(f\"Best Estimator: {estimator}\")\\n        print(f\"Best Config: {config}\")\\n        print(f\"Best Loss: {loss}\\\\n\")\\n    else:\\n        print(f\"Error: {loss}\\\\n\")\\n```\\n\\n### Step 9: Evaluate the Best Model\\n\\nFind the best model among the partitions and evaluate it on the test set:\\n\\n```python\\nbest_model = None\\nbest_loss = float(\\'inf\\')\\n\\nfor estimator, config, loss in results:\\n    if estimator is not None and loss < best_loss:\\n        best_loss = loss\\n        best_model = estimator\\n\\nif best_model is not None:\\n    y_pred = best_model.predict(X_test)\\n    accuracy = accuracy_score(y_test, y_pred)\\n    print(f\"Best model accuracy on test set: {accuracy}\")\\nelse:\\n    print(\"No valid model was found.\")\\n```\\n\\n### Important Considerations:\\n\\n1. **Time Budget:** The `time_budget` parameter in FLAML ensures that the training process will be terminated if it exceeds 30 seconds per job.\\n2. **Partitioning Strategy:** In a real-world scenario, the data should be partitioned meaningfully based on the nature and size of the dataset. The current example uses the same data in all partitions for demonstration purposes.\\n3. **Resource Allocation:** Ensure there are sufficient resources for each Spark worker node to handle the computation. Adjust `num_partitions` based on your cluster’s capacity.\\n4. **Error Handling:** Handle exceptions during model training to avoid issues like out-of-memory errors or other exceptions. Basic error handling is included in the example.\\n\\nBy meticulously following these steps, you can effectively leverage FLAML with Apache Spark to perform parallelized classification tasks within a strict 30-second time limit, thereby achieving efficient and scalable machine learning workflows.', cost={'usage_including_cached_inference': {'total_cost': 0.08141, 'gpt-4o-2024-05-13': {'cost': 0.08141, 'prompt_tokens': 5380, 'completion_tokens': 3634, 'total_tokens': 9014}}, 'usage_excluding_cached_inference': {'total_cost': 0.08141, 'gpt-4o-2024-05-13': {'cost': 0.08141, 'prompt_tokens': 5380, 'completion_tokens': 3634, 'total_tokens': 9014}}}, human_input=['How can I use FLAML to perform a classification task and use spark to do parallel training. Train 30 seconds and force cancel jobs if time limit is reached.'])"
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    "code_problem = \"How can I use FLAML to perform a classification task and use spark to do parallel training. Train 30 seconds and force cancel jobs if time limit is reached.\"\n",
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