使用 Qwen2.5-32B-Instruct 在所连接的 8xH100 AI 运行时上对 16,000 条多语言语音助手话语进行分类。 本笔记本介绍如何：
- 从MASSIVE 1.1构建一个平衡的多语言射线数据集。
- 在每个可用GPU上运行一个持久的vLLM模型副本。
- 用Ray仪表盘和MLflow系统指标监控工作负载。
- 将完整的预测结果保存为 Parquet 格式，存于 Unity 目录卷中。
注释
此示例需要Databricks AI环境版本5及以上。
连接到无服务器 GPU 计算
- 在笔记本计算选择器中，选择 无服务器GPU。
- 在 环境 面板中，选择 8xH100 加速器和 AI v5 环境。
- 点击 应用，然后确认环境。
Qwen模型是公开的，不需要Hugging Face认证。 笔记本从亚马逊的公开档案中下载了MASSIVE 1.1版本。
导入库文件
AI v5 包含本笔记本中使用的 Ray、vLLM、Hugging Face Datasets、Transformers、PyTorch 和 MLflow 包，因此无需安装包。
import json
import re
import time
from pathlib import Path
import mlflow
import pandas as pd
from datasets import DownloadConfig, DownloadManager, concatenate_datasets, load_dataset
from datasets.utils.logging import disable_progress_bar
from pyspark.sql import functions as F
from vllm import LLM, SamplingParams
from vllm.sampling_params import StructuredOutputsParams
配置工作负载
设置模型、地点、样本量和推理参数。
MODEL_NAME = "Qwen/Qwen2.5-32B-Instruct"
DATASET_NAME = "AmazonScience/massive"
MASSIVE_ARCHIVE_URL = "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.1.tar.gz"
LOCALES = ["en-US", "es-ES", "de-DE", "ar-SA", "hi-IN", "ja-JP", "sw-KE", "zh-CN"]
ROWS_PER_LOCALE = 2_000
BATCH_SIZE = 64
MAX_MODEL_LEN = 512
MAX_OUTPUT_TOKENS = 8
SEED = 42
配置 Unity 目录存储
使用小组件指定现有的 Unity Catalog 目录、架构和卷。 该 Notebook 将 MASSIVE 缓存和 Parquet 预测结果存储在此存储卷中。 你需要以下特权：
- 对目录具有
USE CATALOG权限，对架构具有USE SCHEMA权限。 - 对卷具有
READ VOLUME和WRITE VOLUME权限。
每个 MLflow 运行都会将预测结果写入已配置的 Parquet 输出根目录下各自的子目录。
widget_defaults = {
"uc_catalog": "main",
"uc_schema": "default",
"uc_volume": "ray_data",
}
for widget_name, default_value in widget_defaults.items():
dbutils.widgets.text(widget_name, default_value)
CATALOG = dbutils.widgets.get("uc_catalog")
SCHEMA = dbutils.widgets.get("uc_schema")
VOLUME = dbutils.widgets.get("uc_volume")
volume_path = f"/Volumes/{CATALOG}/{SCHEMA}/{VOLUME}"
parquet_output_root = f"{volume_path}/sgc-raydata-vllm-batch-inference"
massive_cache_path = f"{volume_path}/hf-cache/amazon-massive-1.1"
print(f"Parquet output root: {parquet_output_root}")
print(f"Dataset cache: {massive_cache_path}")
启动 Ray
ray_init() 会在所附加的计算资源上启动 Ray，并显示此笔记本的仪表板 URL。 只要笔记本保持连接状态，Ray 连接就会保持活动状态。 演员池使用Ray报告的GPU数量，因此每个可用GPU运行一个vLLM模型副本。
import ray
from serverless_gpu import ray_init
ray_context = ray_init()
ACTOR_COUNT = int(ray.cluster_resources().get("GPU", 0))
if ACTOR_COUNT < 1:
raise RuntimeError("Ray did not detect a GPU. Attach GPU compute and run the notebook again.")
print(f"Ray detected {ACTOR_COUNT} GPUs; using {ACTOR_COUNT} predictor actors.")
加载和采样 MASSIVE
下载MASSIVE 1.1到配置缓存中，然后每次运行时从每个区域选择相同的2000个训练样本。 第一个地点还提供了用于构建分类提示的场景名称和意图名称。
disable_progress_bar()
download_config = DownloadConfig(cache_dir=f"{massive_cache_path}/downloads")
download_manager = DownloadManager(download_config=download_config)
massive_archive_dir = Path(download_manager.download_and_extract(MASSIVE_ARCHIVE_URL))
massive_data_dir = massive_archive_dir / "1.1" / "data"
locale_datasets = []
scenario_names = None
scenario_intents = None
for locale in LOCALES:
locale_dataset = load_dataset(
"json",
data_files=str(massive_data_dir / f"{locale}.jsonl"),
split="train",
cache_dir=f"{massive_cache_path}/datasets",
)
locale_dataset = locale_dataset.filter(lambda row: row["partition"] == "train")
locale_scenarios = sorted(locale_dataset.unique("scenario"))
if scenario_names is not None and locale_scenarios != scenario_names:
raise ValueError(f"Scenario labels differ for locale {locale}.")
if scenario_names is None:
scenario_names = locale_scenarios
label_frame = locale_dataset.select_columns(["scenario", "intent"]).to_pandas()
scenario_intents = {
scenario: sorted(group["intent"].unique())
for scenario, group in label_frame.groupby("scenario")
}
sample = locale_dataset.shuffle(seed=SEED).select(range(ROWS_PER_LOCALE))
locale_datasets.append(sample.select_columns(["id", "locale", "utt", "scenario"]))
创建射线数据集
合并局部样本，保留推理和评估所需的字段，并重新分区数据，使Ray能够保持所有预测变量演员的忙碌。
massive_sample = concatenate_datasets(locale_datasets)
records = [
{
"input_id": f"{row['locale']}:{row['id']}",
"locale": row["locale"],
"utterance": row["utt"],
"expected_scenario": row["scenario"],
}
for row in massive_sample
]
input_dataset = ray.data.from_items(records).repartition(ACTOR_COUNT * 8)
print(f"Prepared {len(records):,} records across {len(LOCALES)} locales and {len(scenario_names)} scenarios.")
定义vLLM预测变量
MASSIVE 将话语分为18个场景，例如 alarm、 weather和 music。 此笔记本根据数据集生成允许的标签和场景到意图的指导，而不是对它们进行硬编码。
情景到意图映射帮助Qwen区分具有相似意义的标签。 vLLM 返回允许的标签之一，最后的归一化步骤将其他响应标记为无效。
scenario_set = set(scenario_names)
scenario_guidance = "\n".join(
f"- {scenario}: {', '.join(scenario_intents[scenario])}"
for scenario in scenario_names
)
system_prompt = (
"Classify the user utterance into exactly one MASSIVE scenario. "
"Use these scenario-to-intent mappings to distinguish similar labels:\n"
f"{scenario_guidance}\n"
"Return only the scenario label."
)
def format_prompt(tokenizer, utterance: str) -> str:
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": utterance},
]
return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
def normalize_label(response: str) -> str | None:
normalized = re.sub(r"[^a-z]+", " ", response.lower()).strip()
return normalized if normalized in scenario_set else None
class VLLMPredictor:
def __init__(self):
gpu_ids = ray.get_runtime_context().get_accelerator_ids().get("GPU", [])
if len(gpu_ids) != 1:
raise RuntimeError(f"Expected one GPU per actor, but received {gpu_ids}.")
self.gpu_assignment = str(gpu_ids[0])
self.llm = LLM(
model=MODEL_NAME,
tensor_parallel_size=1,
dtype="bfloat16",
max_model_len=MAX_MODEL_LEN,
max_num_seqs=BATCH_SIZE,
gpu_memory_utilization=0.90,
enable_prefix_caching=True,
)
self.tokenizer = self.llm.get_tokenizer()
self.sampling_params = SamplingParams(
temperature=0.0,
max_tokens=MAX_OUTPUT_TOKENS,
structured_outputs=StructuredOutputsParams(choice=scenario_names),
)
def __call__(self, batch: pd.DataFrame) -> pd.DataFrame:
prompts = [format_prompt(self.tokenizer, utterance) for utterance in batch["utterance"]]
outputs = self.llm.generate(prompts, self.sampling_params, use_tqdm=False)
raw_responses = [output.outputs[0].text.strip() for output in outputs]
predicted_scenarios = [normalize_label(response) for response in raw_responses]
result = batch.copy()
result["raw_response"] = raw_responses
# Preserve invalid responses as nulls with a stable string type across batches.
result["predicted_scenario"] = pd.array(predicted_scenarios, dtype="string")
result["valid_prediction"] = result["predicted_scenario"].notna()
result["correct"] = (result["predicted_scenario"] == result["expected_scenario"]).fillna(False)
result["model_name"] = MODEL_NAME
result["ray_gpu_assignment"] = self.gpu_assignment
return result
运行并监控批次推理
VLLMPredictor 每个角色启动时加载一次Qwen，然后每次收到批次都会重复使用该模型。 Ray Data 会为每个检测到的 GPU 启动一个 actor，并将每个批次调度到下一个可用的 actor。
在推理运行期间，打开由 ray_init() 在单元 10 中打印的 Ray 仪表板 URL。 使用仪表盘检查八个预测角色、GPU预留、任务进度、日志和落后者。
predictions = input_dataset.map_batches(
VLLMPredictor,
batch_format="pandas",
batch_size=BATCH_SIZE,
compute=ray.data.ActorPoolStrategy(size=ACTOR_COUNT),
num_gpus=1,
)
落实并跟踪结果
Ray Data 以惰性方式构建此流水线，因此 write_parquet() 会在一个步骤中运行推理并保存结果。 Spark随后读取Parquet文件进行评估，无需重新运行模型。 相关的 MLflow 运行会记录工作负载参数、质量指标、耗时、吞吐量和系统指标，而 Databricks 会在其完成后在单元格下方添加一个可点击的 (1 MLflow run) 链接。
mlflow.set_system_metrics_sampling_interval(2)
with mlflow.start_run(run_name="raydata-massive-qwen25-32b", log_system_metrics=True) as active_run:
parquet_output_path = f"{parquet_output_root}/{active_run.info.run_id}"
print(f"Parquet output: {parquet_output_path}")
mlflow.log_params(
{
"model": MODEL_NAME,
"dataset": DATASET_NAME,
"dataset_version": "1.1",
"locales": json.dumps(LOCALES),
"record_count": len(records),
"actor_count": ACTOR_COUNT,
"batch_size": BATCH_SIZE,
"max_model_len": MAX_MODEL_LEN,
"max_output_tokens": MAX_OUTPUT_TOKENS,
"temperature": 0.0,
"output_constraint": "scenario_choices",
"system_metrics_interval_seconds": 2,
"gpu_memory_utilization": 0.90,
}
)
mlflow.set_tags(
{
"dataset_source": MASSIVE_ARCHIVE_URL,
"parquet_output_path": parquet_output_path,
}
)
start_time = time.perf_counter()
predictions.write_parquet(parquet_output_path)
cold_start_inclusive_duration_seconds = time.perf_counter() - start_time
results_df = spark.read.parquet(parquet_output_path)
aggregate = results_df.agg(
F.count("*").alias("record_count"),
F.avg(F.col("correct").cast("double")).alias("overall_accuracy"),
F.avg(F.col("valid_prediction").cast("double")).alias("valid_prediction_rate"),
F.countDistinct("ray_gpu_assignment").alias("unique_gpu_assignments"),
).first()
scenario_accuracy_df = results_df.groupBy("expected_scenario").agg(
F.count("*").alias("record_count"),
F.avg(F.col("correct").cast("double")).alias("accuracy"),
F.avg(F.col("valid_prediction").cast("double")).alias("valid_prediction_rate"),
).orderBy("expected_scenario")
macro_scenario_accuracy = scenario_accuracy_df.agg(F.avg("accuracy")).first()[0]
cold_start_inclusive_records_per_second = (
aggregate["record_count"] / cold_start_inclusive_duration_seconds
)
mlflow.log_metrics(
{
"overall_accuracy": aggregate["overall_accuracy"],
"macro_scenario_accuracy": macro_scenario_accuracy,
"valid_prediction_rate": aggregate["valid_prediction_rate"],
"cold_start_inclusive_duration_seconds": cold_start_inclusive_duration_seconds,
"cold_start_inclusive_records_per_second": cold_start_inclusive_records_per_second,
}
)
mlflow_run_id = active_run.info.run_id
print(f"MLflow run ID: {mlflow_run_id}")
print("Open the '(1 MLflow run)' link attached to this cell for parameters and metrics.")
验证结果
以下检查确认输出每个输入包含一行，且每个预测变量至少处理了一批。
计时在 Ray 创建 Actor 并加载模型之前就已开始，因此报告的时长和吞吐量包含冷启动时间。
if aggregate["record_count"] != len(records):
raise RuntimeError("The persisted result count does not match the input count.")
if aggregate["unique_gpu_assignments"] != ACTOR_COUNT:
raise RuntimeError(f"Expected results from {ACTOR_COUNT} Ray GPU assignments.")
print(f"Records: {aggregate['record_count']:,}")
print(f"Overall accuracy: {aggregate['overall_accuracy']:.2%}")
print(f"Macro scenario accuracy: {macro_scenario_accuracy:.2%}")
print(f"Valid prediction rate: {aggregate['valid_prediction_rate']:.2%}")
print(f"Inference duration including actor and model cold start: {cold_start_inclusive_duration_seconds:.1f} seconds")
print(f"Throughput including actor and model cold start: {cold_start_inclusive_records_per_second:.1f} records/second")
print(f"Unique GPU assignments: {aggregate['unique_gpu_assignments']}")
分析预测质量
按区域、预测样本以及GPU参与者间记录分布显示准确性。
locale_accuracy_df = (
results_df.groupBy("locale")
.agg(
F.count("*").alias("record_count"),
F.avg(F.col("correct").cast("double")).alias("accuracy"),
F.avg(F.col("valid_prediction").cast("double")).alias("valid_prediction_rate"),
)
.orderBy("locale")
)
print("Accuracy by locale:")
locale_accuracy_df.show(truncate=False)
prediction_columns = [
"locale", "utterance", "expected_scenario", "predicted_scenario",
"correct", "ray_gpu_assignment",
]
sample_predictions_df = (
results_df.select(prediction_columns)
.orderBy(F.rand(SEED))
.limit(16)
)
actor_distribution_df = (
results_df.groupBy("ray_gpu_assignment")
.agg(F.count("*").alias("record_count"))
.orderBy("ray_gpu_assignment")
)
print("Sample predictions:")
sample_predictions_df.show(truncate=80)
print("Records by Ray GPU assignment:")
actor_distribution_df.show(truncate=False)