ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter9/gaia-experience/AWorld/examples/parallel_run/README.md

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+# Parallel Run
+
+As the **first** agent framework that seamlessly supports running on different distributed computing engines, this README demonstrates how to perform parallel evaluation using **AWorld**.
+
+## Prerequisites
+
+- Python 3.11 or higher
+- Ray and PySpark require separate installation:
+  - `pip install ray` 
+  - `pip install pyspark==3.5.0` (requires JDK 1.8.0_441)
+
+## Setup
+
+### 1. Prepare the LLM Service
+
+```python
+import os
+
+# Set up LLM service using environment variables
+os.environ["LLM_PROVIDER"] = "openai"  # Choose from: openai, anthropic, azure_openai
+os.environ["LLM_MODEL_NAME"] = "gpt-4"
+os.environ["LLM_API_KEY"] = "your-api-key"
+os.environ["LLM_BASE_URL"] = "https://api.openai.com/v1"  # Optional for OpenAI
+```
+
+### 2. Prepare the Agent
+
+```python
+import os
+from aworld.agents.llm_agent import Agent
+from aworld.config.conf import AgentConfig
+
+agent_config = AgentConfig(
+    llm_provider=os.getenv("LLM_PROVIDER", "openai"),
+    llm_model_name=os.getenv("LLM_MODEL_NAME"),
+    llm_base_url=os.getenv("LLM_BASE_URL"),
+    llm_api_key=os.getenv("LLM_API_KEY"),
+)
+
+my_agent = Agent(name="my_agent", conf=agent_config)
+```
+
+### 3. Prepare the Tasks
+
+```python
+from aworld.core.task import Task
+
+tasks = [
+    Task(input="who are you?", agent=my_agent, id="abcd"),
+    Task(input="Hello World!", agent=my_agent, id="efgh"),
+    Task(input="Nice to meet you.", agent=my_agent, id="ijkl")
+]
+```
+
+## Running Tasks in Parallel
+
+AWorld supports three different parallel execution engines. Choose the one that best fits your needs:
+
+### Ray Engine (Recommended for distributed computing)
+
+```python
+from aworld.runner import Runners
+from aworld.config import RunConfig, EngineName
+
+res = Runners.sync_run_task(
+    task=tasks,
+    run_conf=RunConfig(
+        engine_name=EngineName.RAY, 
+        worker_num=len(tasks)
+    )
+)
+```
+
+### Spark Engine (For big data processing)
+
+```python
+res = Runners.sync_run_task(
+    task=tasks,
+    run_conf=RunConfig(
+        engine_name=EngineName.SPARK, 
+        in_local=True
+    )
+)
+```
+
+### Local Multiprocess Engine (For simple parallelization)
+
+```python
+res = Runners.sync_run_task(
+    task=tasks,
+    run_conf=RunConfig(
+        engine_name=EngineName.LOCAL, 
+        reuse_process=False
+    )
+)
+```
+
+## Complete Example
+
+Here's a complete working example that demonstrates parallel task execution:
+
+```python
+import os
+from aworld.agents.llm_agent import Agent
+from aworld.config.conf import AgentConfig
+from aworld.core.task import Task
+from aworld.runner import Runners
+from aworld.config import RunConfig, EngineName
+
+# Setup
+os.environ["LLM_PROVIDER"] = "openai"
+os.environ["LLM_MODEL_NAME"] = "gpt-4"
+os.environ["LLM_API_KEY"] = "your-api-key"
+
+# Create agent
+agent_config = AgentConfig(
+    llm_provider=os.getenv("LLM_PROVIDER", "openai"),
+    llm_model_name=os.getenv("LLM_MODEL_NAME"),
+    llm_api_key=os.getenv("LLM_API_KEY"),
+)
+my_agent = Agent(name="my_agent", conf=agent_config)
+
+# Create tasks
+tasks = [
+    Task(input="What is machine learning?", agent=my_agent, id="task1"),
+    Task(input="Explain neural networks", agent=my_agent, id="task2"),
+    Task(input="What is deep learning?", agent=my_agent, id="task3")
+]
+
+# Run in parallel
+results = Runners.sync_run_task(
+    task=tasks,
+    run_conf=RunConfig(
+        engine_name=EngineName.RAY,
+        worker_num=len(tasks)
+    )
+)
+
+# Process results
+for result in results:
+    print(f"Task {result.task_id}: {result.answer}")
+```
+
+## Engine Comparison
+
+| Engine | Use Case | Pros | Cons |
+|--------|----------|------|------|
+| **Ray** | Distributed computing, large-scale parallelization | Highly scalable, fault-tolerant | Requires Ray installation |
+| **Spark** | Big data processing, batch jobs | Excellent for large datasets | Requires Spark and JDK |
+| **Local** | Simple parallelization, development | No external dependencies | Limited to single machine |
+
+## Notes
+
+- The `worker_num` parameter should typically match the number of tasks for optimal performance
+- For Spark engine, set `in_local=True` to run locally without a Spark cluster
+- For Local engine, `reuse_process=False` creates new processes for each task, providing better isolation