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@@ -20,18 +20,9 @@ crewai train -n <n_iterations>
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Replace `<n_iterations>` with the desired number of training iterations. This determines how many times the agents will go through the training process.
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Replace `<n_iterations>` with the desired number of training iterations. This determines how many times the agents will go through the training process.
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Remember to also replace the placeholder inputs with the actual values you want to use on the main.py file in the `train` function.
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### Key Points to Note:
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- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
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```python
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- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
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def train():
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"""
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Train the crew for a given number of iterations.
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"""
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inputs = {"topic": "AI LLMs"}
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try:
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ProjectCreationCrew().crew().train(n_iterations=int(sys.argv[1]), inputs=inputs)
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...
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```
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It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
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It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
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@@ -0,0 +1,40 @@
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---
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title: Kickoff Async
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description: Kickoff a Crew Asynchronously
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---
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## Introduction
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CrewAI provides the ability to kickoff a crew asynchronously, allowing you to start the crew execution in a non-blocking manner. This feature is particularly useful when you want to run multiple crews concurrently or when you need to perform other tasks while the crew is executing.
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## Asynchronous Crew Execution
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To kickoff a crew asynchronously, use the `kickoff_async()` method. This method initiates the crew execution in a separate thread, allowing the main thread to continue executing other tasks.
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Here's an example of how to kickoff a crew asynchronously:
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```python
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from crewai import Crew, Agent, Task
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# Create an agent with code execution enabled
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coding_agent = Agent(
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role="Python Data Analyst",
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goal="Analyze data and provide insights using Python",
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backstory="You are an experienced data analyst with strong Python skills.",
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allow_code_execution=True
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)
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# Create a task that requires code execution
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data_analysis_task = Task(
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description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
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agent=coding_agent
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)
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# Create a crew and add the task
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analysis_crew = Crew(
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agents=[coding_agent],
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tasks=[data_analysis_task]
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)
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# Execute the crew
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result = analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
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```
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@@ -0,0 +1,45 @@
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---
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title: Kickoff For Each
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description: Kickoff a Crew for a List
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---
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## Introduction
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CrewAI provides the ability to kickoff a crew for each item in a list, allowing you to execute the crew for each item in the list. This feature is particularly useful when you need to perform the same set of tasks for multiple items.
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## Kicking Off a Crew for Each Item
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To kickoff a crew for each item in a list, use the `kickoff_for_each()` method. This method executes the crew for each item in the list, allowing you to process multiple items efficiently.
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Here's an example of how to kickoff a crew for each item in a list:
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```python
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from crewai import Crew, Agent, Task
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# Create an agent with code execution enabled
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coding_agent = Agent(
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role="Python Data Analyst",
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goal="Analyze data and provide insights using Python",
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backstory="You are an experienced data analyst with strong Python skills.",
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allow_code_execution=True
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)
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# Create a task that requires code execution
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data_analysis_task = Task(
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description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
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agent=coding_agent
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)
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# Create a crew and add the task
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analysis_crew = Crew(
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agents=[coding_agent],
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tasks=[data_analysis_task]
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)
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datasets = [
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{ "ages": [25, 30, 35, 40, 45] },
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{ "ages": [20, 25, 30, 35, 40] },
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{ "ages": [30, 35, 40, 45, 50] }
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]
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# Execute the crew
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result = analysis_crew.kickoff_for_each(inputs=datasets)
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```
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