MaxCompute:Desarrollo de funciones de IA en recursos GU

import os
import maxframe.dataframe as md
from maxframe import new_session
from maxframe.config import options
from maxframe.learn.contrib.llm.models.managed import ManagedTextGenLLM
from odps import ODPS

# Initialize session
o = ODPS(
    os.getenv('ALIBABA_CLOUD_ACCESS_KEY_ID'),
    os.getenv('ALIBABA_CLOUD_ACCESS_KEY_SECRET'),
    project='<your project>',
    endpoint='https://service.cn-<your region>.maxcompute.aliyun.com/api',
)
session = new_session(o)
options.session.gu_quota_name = "<your GU quota name>"  # Required to route tasks to GPU workers.

# Prepare input data
df = md.DataFrame({"query": ["What is the boiling point of water?"]})

# Initialize the managed LLM client
llm = ManagedTextGenLLM(name="Qwen3-4B-Instruct-2507-FP8")  # Name must be an exact match.

# Define the prompt template and run inference
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Please answer the following question: {query}"},
]
result_df = llm.generate(df, prompt_template=messages)
result_df.execute()

import os
import maxframe.dataframe as md
import numpy as np
from maxframe import new_session
from maxframe.config import options
from maxframe.udf import with_running_options  # Helper for attaching resource options to UDF-based tasks.
from odps import ODPS
import logging

# Route tasks to the DPE and MCSQL engines; disable SPE for GPU workloads.
options.dag.settings = {
    "engine_order": ["DPE", "MCSQL"],
    "unavailable_engines": ["SPE"],
}

logging.basicConfig(level=logging.INFO)

# Initialize the MaxFrame session with your project credentials.
o = ODPS(
    # Make sure the ALIBABA_CLOUD_ACCESS_KEY_ID environment variable is set to your AccessKey ID,
    # and the ALIBABA_CLOUD_ACCESS_KEY_SECRET environment variable is set to your AccessKey secret.
    # Do not use the AccessKey ID and AccessKey secret strings directly.
    os.getenv('ALIBABA_CLOUD_ACCESS_KEY_ID'),
    os.getenv('ALIBABA_CLOUD_ACCESS_KEY_SECRET'),
    project='<your project>',
    endpoint='https://service.cn-<your region>.maxcompute.aliyun.com/api',
)

session = new_session(o)

options.session.gu_quota_name = "xxxxx"  # Replace with your GU quota name.

print("LogView address:", session.get_logview_address())

Paso 1: Preparar los datos de entrada

import pandas as pd
from IPython.display import HTML

# Set display options for debugging.
pd.set_option("display.max_colwidth", None)
pd.set_option("display.max_columns", None)
HTML("<style>div.output_area pre {white-space: pre-wrap;}</style>")

# Create a query list.
query_list = [
    "What is the average distance between the Earth and the Sun?",
    "In what year did the American Revolutionary War begin?",
    "What is the boiling point of water?",
    "How can I quickly relieve a headache?",
    "Who is the main character in the Harry Potter series?",
]

# Convert to a MaxFrame DataFrame.
df = md.DataFrame({"query": query_list})
df.execute()

Paso 2: Inicializar la instancia de LLM

ManagedTextGenLLM es el cliente para los modelos de generación de texto administrados alojados en MaxCompute. Pase el nombre exacto del modelo para identificar cuál se utilizará.

from maxframe.learn.contrib.llm.models.managed import ManagedTextGenLLM

llm = ManagedTextGenLLM(
    name="Qwen3-4B-Instruct-2507-FP8"  # The model name must be an exact match.
)

Paso 3: Definir la plantilla de indicaciones

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Please answer the following question: {query}"},
]

Sintaxis de la plantilla:

• Utilice {column_name} como marcador de posición. Durante la ejecución, se reemplaza automáticamente por el valor de la columna correspondiente del DataFrame.

• Pase una lista messages para admitir conversaciones de varios turnos.

• Use el rol system para definir el comportamiento del modelo.

Paso 4: Ejecutar la tarea de generación

result_df = llm.generate(
    df,                          # Input DataFrame
    prompt_template=messages,
    running_options={
        "max_tokens": 4096,      # Maximum output length
        "verbose": True          # Enable verbose log output
    },
    params={"temperature": 0.7},
)

# Execute and get the result.
result_df.execute()

Optimización del rendimiento

Recomendaciones de depuración

Consultar los registros de ejecución

print(session.get_logview_address())  # Click the link to view real-time MaxFrame job logs.

Ejecutar una prueba a pequeña escala antes de la ejecución completa

df_sample = df.head(2)  # Use two rows for testing.
result_sample = llm.generate(df_sample, prompt_template=messages, running_options={"gu": 2})
result_sample.execute()

Comprobar el uso de recursos

Consulte el estado detallado de ejecución del trabajo en MaxFrame Logview.