Flask make_response(*args)

最新推荐文章于 2025-06-18 17:24:17 发布
原创 最新推荐文章于 2025-06-18 17:24:17 发布 · 3.4k 阅读 · 3 ·
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#flask

本文介绍了在Flask框架中如何使用make_response方法来创建并返回一个带有自定义头部信息的响应对象。通过示例展示了如何设置响应头部，以及make_response支持的不同参数类型。

视图本质上是返回一个response对象(也可以直接创建Response对象),但是在return后不方便设置头信息等,所以还是先封装成response对象,再返回比较全面,如下:

def index():
    return render_template('index.html')

make_response可以设置返回的其他信息

def index():
    response = make_response(render_template('index.html'))
    response.headers['X-Parachutes'] = 'parachutes are cool'
    return response
参数描述
str通过字符串创建response对象
bytes通过字节创建response对象
dict通过字典撞见response对象
tuple(body, status, headers) (body, status) (body, headers)

在使用tuple时:
body可以是str,bytes或dict
status可以是str或者integer
headers可以是dict或[(key,value),(key,value)]
如果body是一个response对象,其status会覆盖headers的

参考:
https://flask.palletsprojects.com/en/1.1.x/api/#flask.make_response
https://www.cnblogs.com/zhuchunyu/p/10466509.html

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视图函数的返回值会自动转换为一个响应对象。如果返回值是一个字典，那么会调用 jsonify() 来产生一个响应。
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C:\Users\ASUS\.conda\envs\wys\python.exe C:\Users\ASUS\Desktop\Pro\API\app.py Traceback (most recent call last): File "C:\Users\ASUS\Desktop\Pro\API\app.py", line 188, in <module> if not os.path.exists(app.config['UPLOAD_FOLDER']): KeyError: 'UPLOAD_FOLDER'from flask import Flask, request, jsonify, send_from_directory, make_response from flask_sqlalchemy import SQLAlchemy from flask_cors import CORS from functools import wraps # 新增 import os import uuid from datetime import datetime # 初始化Flask应用 app = Flask(__name__) # ================ CORS全局配置 ================ CORS(app, resources={ r"/*": { "origins": "http://localhost:5173", "methods": ["GET", "POST", "PUT", "DELETE", "OPTIONS"], "allow_headers": ["Content-Type", "Authorization", "X-Requested-With"], "supports_credentials": True, "expose_headers": ["Set-Cookie"] # 修改 } }) # ================ 新增认证中间件 ================ def login_required(f): @wraps(f) def decorated_function(*args, **kwargs): # 从cookie获取用户ID user_id = request.cookies.get('user_id') if not user_id: return jsonify({"message": "未认证"}), 401 # 验证用户是否存在 user = User.query.get(user_id) if not user: return jsonify({"message": "用户不存在"}), 401 return f(*args, **kwargs) return decorated_function # ================ 修改登录接口 ================ @app.route('/login', methods=['POST', 'OPTIONS']) def login(): try: if request.method == 'OPTIONS': return _build_cors_preflight_response() data = request.get_json() username = data.get('username', '').strip() password = data.get('password', '') user = User.query.filter_by(username=username).first() if not user or user.password != password: return jsonify({"message": "认证失败"}), 401 # 创建响应并设置cookie response = jsonify({ "message": "登录成功", "user_id": user.id, "profile_completed": user.profile_completed, "avatar_url": f"/uploads/{user.avatar}" }) # 设置HTTP-only Cookie response.set_cookie( key='user_id', value=str(user.id), httponly=True, samesite='Lax', max_age=86400, # 1天有效期 secure=False, # 开发环境关闭 domain='localhost' ) return response, 200 except Exception as e: return jsonify({"message": f"服务器错误: {str(e)}"}), 500 # ================ 修改用户资料接口 ================ @app.route('/user/<int:user_id>', methods=['GET', 'PUT', 'OPTIONS']) @login_required # 新增认证装饰器 def user_profile(user_id): try: if request.method == 'OPTIONS': return _build_cors_preflight_response() # 从cookie获取当前用户 current_user_id = int(request.cookies.get('user_id')) # 验证用户权限 if current_user_id != user_id: return jsonify({"message": "无权访问"}), 403 user = User.query.get_or_404(user_id) if request.method == 'GET': return jsonify({ "username": user.username, "shop_name": user.shop_name, "phone": user.phone, "email": user.email }), 200 if request.method == 'PUT': data = request.get_json() if not data: return jsonify({"message": "请求体不能为空"}), 400 update_fields = { 'shop_name': data.get('shop_name'), 'phone': data.get('phone'), 'email': data.get('email') } for field, value in update_fields.items(): if value is not None and getattr(user, field) != value: setattr(user, field, value) db.session.commit() return jsonify({"message": "更新成功"}), 200 except Exception as e: db.session.rollback() app.logger.error(f"用户资料操作失败: {str(e)}") return jsonify({"message": "服务器错误"}), 500 # ================ 修改密码接口 ================ @app.route('/user/<int:user_id>/password', methods=['PUT', 'OPTIONS']) @login_required # 新增认证装饰器 def update_password(user_id): try: # 权限验证 current_user_id = int(request.cookies.get('user_id')) if current_user_id != user_id: return jsonify({"message": "无权操作"}), 403 user = User.query.get_or_404(user_id) data = request.get_json() if 'old_password' not in data or 'new_password' not in data: return jsonify({"message": "需要提供原密码和新密码"}), 400 if user.password != data['old_password']: return jsonify({"message": "原密码错误"}), 401 if len(data['new_password']) < 6: return jsonify({"message": "密码至少6位"}), 400 user.password = data['new_password'] user.password_updated_at = datetime.utcnow() db.session.commit() return jsonify({"message": "密码更新成功"}), 200 except Exception as e: db.session.rollback() return jsonify({"message": f"服务器错误: {str(e)}"}), 500 # ================ 工具函数 ================ def allowed_file(filename): return '.' in filename and \ filename.rsplit('.', 1)[1].lower() in {'png', 'jpg', 'jpeg'} def _build_cors_preflight_response(): response = make_response() response.headers.add("Access-Control-Allow-Origin", "http://localhost:5173") response.headers.add("Access-Control-Allow-Headers", "Content-Type, Authorization, X-Requested-With") response.headers.add("Access-Control-Allow-Methods", "*") response.headers.add("Access-Control-Max-Age", "86400") response.headers.add("Access-Control-Allow-Credentials", "true") response.headers['Access-Control-Allow-Origin'] = 'http://localhost:5173' response.headers['Access-Control-Allow-Credentials'] = 'true' response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization' return response, 204 # ================ 启动应用 ================ if __name__ == '__main__': # 确保上传目录存在 if not os.path.exists(app.config['UPLOAD_FOLDER']): os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True) # 检查数据库连接 try: with app.app_context(): db.create_all() db.engine.connect() print("✅ 数据库连接成功") except Exception as e: print(f"❌ 数据库连接失败: {str(e)}") exit(1) app.run(host='0.0.0.0', port=5000, debug=True)
05-27
from flask import Flask, request, redirect, url_for, flash import os app = Flask(__name__) # 设置密钥以便支持闪现消息等功能 app.secret_key = 'your_secret_key_here' # 定义上传路径 UPLOAD_FOLDER = '/...
INFO 09-12 08:12:49 __init__.py:207] Automatically detected platform cuda. INFO 09-12 08:12:49 api_server.py:912] vLLM API server version 0.7.3 INFO 09-12 08:12:49 api_server.py:913] args: Namespace(host=None, port=8003, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, chat_template_content_format='auto', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_request_id_headers=False, enable_auto_tool_choice=False, enable_reasoning=False, reasoning_parser=None, tool_call_parser=None, tool_parser_plugin='', model='/models/DeepSeek-R1-Distill-Llama-70B', task='auto', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, allowed_local_media_path=None, download_dir=None, load_format='auto', config_format=<ConfigFormat.AUTO: 'auto'>, dtype='auto', kv_cache_dtype='auto', max_model_len=84320, guided_decoding_backend='xgrammar', logits_processor_pattern=None, model_impl='auto', distributed_executor_backend=None, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=None, enable_prefix_caching=None, disable_sliding_window=False, use_v2_block_manager=True, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.95, num_gpu_blocks_override=None, max_num_batched_tokens=512, max_num_partial_prefills=1, max_long_partial_prefills=1, long_prefill_token_threshold=0, max_num_seqs=1, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, hf_overrides=None, enforce_eager=True, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, disable_mm_preprocessor_cache=False, enable_lora=False, enable_lora_bias=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_disable_mqa_scorer=False, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=['DeepSeek-R1'], qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, scheduling_policy='fcfs', scheduler_cls='vllm.core.scheduler.Scheduler', override_neuron_config=None, override_pooler_config=None, compilation_config=None, kv_transfer_config=None, worker_cls='auto', generation_config=None, override_generation_config=None, enable_sleep_mode=False, calculate_kv_scales=False, additional_config=None, disable_log_requests=False, max_log_len=None, disable_fastapi_docs=False, enable_prompt_tokens_details=False) INFO 09-12 08:12:49 api_server.py:209] Started engine process with PID 76 INFO 09-12 08:12:53 __init__.py:207] Automatically detected platform cuda. INFO 09-12 08:12:55 config.py:549] This model supports multiple tasks: {'generate', 'score', 'reward', 'embed', 'classify'}. Defaulting to 'generate'. WARNING 09-12 08:12:55 arg_utils.py:1187] Chunked prefill is enabled by default for models with max_model_len > 32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False. INFO 09-12 08:12:55 config.py:1555] Chunked prefill is enabled with max_num_batched_tokens=512. WARNING 09-12 08:12:55 cuda.py:95] To see benefits of async output processing, enable CUDA graph. Since, enforce-eager is enabled, async output processor cannot be used WARNING 09-12 08:12:55 config.py:685] Async output processing is not supported on the current platform type cuda. INFO 09-12 08:12:58 config.py:549] This model supports multiple tasks: {'generate', 'embed', 'score', 'classify', 'reward'}. Defaulting to 'generate'. WARNING 09-12 08:12:58 arg_utils.py:1187] Chunked prefill is enabled by default for models with max_model_len > 32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False. INFO 09-12 08:12:58 config.py:1555] Chunked prefill is enabled with max_num_batched_tokens=512. WARNING 09-12 08:12:58 cuda.py:95] To see benefits of async output processing, enable CUDA graph. Since, enforce-eager is enabled, async output processor cannot be used WARNING 09-12 08:12:58 config.py:685] Async output processing is not supported on the current platform type cuda. INFO 09-12 08:12:58 llm_engine.py:234] Initializing a V0 LLM engine (v0.7.3) with config: model='/models/DeepSeek-R1-Distill-Llama-70B', speculative_config=None, tokenizer='/models/DeepSeek-R1-Distill-Llama-70B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=84320, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='xgrammar'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=DeepSeek-R1, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=False, chunked_prefill_enabled=True, use_async_output_proc=False, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[],"max_capture_size":0}, use_cached_outputs=True, INFO 09-12 08:12:59 cuda.py:229] Using Flash Attention backend. INFO 09-12 08:13:00 model_runner.py:1110] Starting to load model /models/DeepSeek-R1-Distill-Llama-70B... ERROR 09-12 08:13:00 engine.py:400] CUDA out of memory. Tried to allocate 896.00 MiB. GPU 0 has a total capacity of 23.64 GiB of which 214.81 MiB is free. Process 20061 has 23.43 GiB memory in use. Of the allocated memory 22.99 GiB is allocated by PyTorch, and 1.57 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) ERROR 09-12 08:13:00 engine.py:400] Traceback (most recent call last): Process SpawnProcess-1: ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 391, in run_mp_engine ERROR 09-12 08:13:00 engine.py:400] engine = MQLLMEngine.from_engine_args(engine_args=engine_args, ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 124, in from_engine_args ERROR 09-12 08:13:00 engine.py:400] return cls(ipc_path=ipc_path, ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 76, in __init__ ERROR 09-12 08:13:00 engine.py:400] self.engine = LLMEngine(*args, **kwargs) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/engine/llm_engine.py", line 273, in __init__ ERROR 09-12 08:13:00 engine.py:400] self.model_executor = executor_class(vllm_config=vllm_config, ) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/executor/executor_base.py", line 52, in __init__ ERROR 09-12 08:13:00 engine.py:400] self._init_executor() ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/executor/uniproc_executor.py", line 47, in _init_executor ERROR 09-12 08:13:00 engine.py:400] self.collective_rpc("load_model") ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/executor/uniproc_executor.py", line 56, in collective_rpc ERROR 09-12 08:13:00 engine.py:400] answer = run_method(self.driver_worker, method, args, kwargs) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/utils.py", line 2196, in run_method ERROR 09-12 08:13:00 engine.py:400] return func(*args, **kwargs) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/worker/worker.py", line 183, in load_model ERROR 09-12 08:13:00 engine.py:400] self.model_runner.load_model() ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/worker/model_runner.py", line 1112, in load_model ERROR 09-12 08:13:00 engine.py:400] self.model = get_model(vllm_config=self.vllm_config) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/model_loader/__init__.py", line 14, in get_model ERROR 09-12 08:13:00 engine.py:400] return loader.load_model(vllm_config=vllm_config) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/model_loader/loader.py", line 406, in load_model ERROR 09-12 08:13:00 engine.py:400] model = _initialize_model(vllm_config=vllm_config) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/model_loader/loader.py", line 125, in _initialize_model ERROR 09-12 08:13:00 engine.py:400] return model_class(vllm_config=vllm_config, prefix=prefix) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 496, in __init__ ERROR 09-12 08:13:00 engine.py:400] self.model = self._init_model(vllm_config=vllm_config, ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 533, in _init_model ERROR 09-12 08:13:00 engine.py:400] return LlamaModel(vllm_config=vllm_config, prefix=prefix) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/compilation/decorators.py", line 151, in __init__ ERROR 09-12 08:13:00 engine.py:400] old_init(self, vllm_config=vllm_config, prefix=prefix, **kwargs) ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 326, in __init__ ERROR 09-12 08:13:00 engine.py:400] self.start_layer, self.end_layer, self.layers = make_layers( ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/utils.py", line 558, in make_layers ERROR 09-12 08:13:00 engine.py:400] maybe_offload_to_cpu(layer_fn(prefix=f"{prefix}.{idx}")) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 328, in <lambda> ERROR 09-12 08:13:00 engine.py:400] lambda prefix: layer_type(config=config, ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 254, in __init__ ERROR 09-12 08:13:00 engine.py:400] self.mlp = LlamaMLP( ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 70, in __init__ ERROR 09-12 08:13:00 engine.py:400] self.gate_up_proj = MergedColumnParallelLinear( Traceback (most recent call last): ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/linear.py", line 441, in __init__ ERROR 09-12 08:13:00 engine.py:400] super().__init__(input_size=input_size, ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/linear.py", line 314, in __init__ ERROR 09-12 08:13:00 engine.py:400] self.quant_method.create_weights( ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/linear.py", line 129, in create_weights ERROR 09-12 08:13:00 engine.py:400] weight = Parameter(torch.empty(sum(output_partition_sizes), ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] File "/usr/local/lib/python3.12/dist-packages/torch/utils/_device.py", line 106, in __torch_function__ ERROR 09-12 08:13:00 engine.py:400] return func(*args, **kwargs) ERROR 09-12 08:13:00 engine.py:400] ^^^^^^^^^^^^^^^^^^^^^ ERROR 09-12 08:13:00 engine.py:400] torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 896.00 MiB. GPU 0 has a total capacity of 23.64 GiB of which 214.81 MiB is free. Process 20061 has 23.43 GiB memory in use. Of the allocated memory 22.99 GiB is allocated by PyTorch, and 1.57 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 402, in run_mp_engine raise e File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 391, in run_mp_engine engine = MQLLMEngine.from_engine_args(engine_args=engine_args, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 124, in from_engine_args return cls(ipc_path=ipc_path, ^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/engine/multiprocessing/engine.py", line 76, in __init__ self.engine = LLMEngine(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/engine/llm_engine.py", line 273, in __init__ self.model_executor = executor_class(vllm_config=vllm_config, ) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/executor/executor_base.py", line 52, in __init__ self._init_executor() File "/usr/local/lib/python3.12/dist-packages/vllm/executor/uniproc_executor.py", line 47, in _init_executor self.collective_rpc("load_model") File "/usr/local/lib/python3.12/dist-packages/vllm/executor/uniproc_executor.py", line 56, in collective_rpc answer = run_method(self.driver_worker, method, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/utils.py", line 2196, in run_method return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/worker/worker.py", line 183, in load_model self.model_runner.load_model() File "/usr/local/lib/python3.12/dist-packages/vllm/worker/model_runner.py", line 1112, in load_model self.model = get_model(vllm_config=self.vllm_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/model_loader/__init__.py", line 14, in get_model return loader.load_model(vllm_config=vllm_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/model_loader/loader.py", line 406, in load_model model = _initialize_model(vllm_config=vllm_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/model_loader/loader.py", line 125, in _initialize_model return model_class(vllm_config=vllm_config, prefix=prefix) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 496, in __init__ self.model = self._init_model(vllm_config=vllm_config, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 533, in _init_model return LlamaModel(vllm_config=vllm_config, prefix=prefix) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/compilation/decorators.py", line 151, in __init__ old_init(self, vllm_config=vllm_config, prefix=prefix, **kwargs) File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 326, in __init__ self.start_layer, self.end_layer, self.layers = make_layers( ^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/utils.py", line 558, in make_layers maybe_offload_to_cpu(layer_fn(prefix=f"{prefix}.{idx}")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 328, in <lambda> lambda prefix: layer_type(config=config, ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 254, in __init__ self.mlp = LlamaMLP( ^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/llama.py", line 70, in __init__ self.gate_up_proj = MergedColumnParallelLinear( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/linear.py", line 441, in __init__ super().__init__(input_size=input_size, File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/linear.py", line 314, in __init__ self.quant_method.create_weights( File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/linear.py", line 129, in create_weights weight = Parameter(torch.empty(sum(output_partition_sizes), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/torch/utils/_device.py", line 106, in __torch_function__ return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 896.00 MiB. GPU 0 has a total capacity of 23.64 GiB of which 214.81 MiB is free. Process 20061 has 23.43 GiB memory in use. Of the allocated memory 22.99 GiB is allocated by PyTorch, and 1.57 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) [rank0]:[W912 08:13:00.032028245 ProcessGroupNCCL.cpp:1250] Warning: WARNING: process group has NOT been destroyed before we destruct ProcessGroupNCCL. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL operations have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present, but this warning has only been added since PyTorch 2.4 (function operator()) Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 991, in <module> uvloop.run(run_server(args)) File "/usr/local/lib/python3.12/dist-packages/uvloop/__init__.py", line 109, in run return __asyncio.run( ^^^^^^^^^^^^^^ File "/usr/lib/python3.12/asyncio/runners.py", line 195, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/usr/lib/python3.12/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete File "/usr/local/lib/python3.12/dist-packages/uvloop/__init__.py", line 61, in wrapper return await main ^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 947, in run_server async with build_async_engine_client(args) as engine_client: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.12/contextlib.py", line 210, in __aenter__ return await anext(self.gen) ^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 139, in build_async_engine_client async with build_async_engine_client_from_engine_args( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.12/contextlib.py", line 210, in __aenter__ return await anext(self.gen) ^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 233, in build_async_engine_client_from_engine_args raise RuntimeError( RuntimeError: Engine process failed to start. See stack trace for the root cause. ubuntu docker部署DeepSeek-R1-Distill-Llama-70B 模型 报错
最新发布
09-13
我们遇到的问题是：在Ubuntu的Docker环境中部署DeepSeek-R1-Distill-Llama-70B模型时出现了CUDA内存不足的错误。 错误信息显示：`torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 896.00 MiB....
2025-06-20 22:58:46.696914: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. WARNING:tensorflow:From E:\Miniconda3\envs\openvla\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead. E:\Miniconda3\envs\openvla\lib\site-packages\huggingface_hub\file_download.py:943: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. warnings.warn( E:\Miniconda3\envs\openvla\lib\site-packages\huggingface_hub\file_download.py:943: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. warnings.warn( Traceback (most recent call last): File "C:\Users\wyzzw\Desktop\华迪\代码.py", line 93, in <module> deploy() File "E:\Miniconda3\envs\openvla\lib\site-packages\draccus\argparsing.py", line 203, in wrapper_inner response = fn(cfg, *args, **kwargs) File "C:\Users\wyzzw\Desktop\华迪\代码.py", line 88, in deploy server = OpenVLAServer(cfg.openvla_path) File "C:\Users\wyzzw\Desktop\华迪\代码.py", line 39, in __init__ self.vla = AutoModelForVision2Seq.from_pretrained( File "E:\Miniconda3\envs\openvla\lib\site-packages\transformers\models\auto\auto_factory.py", line 558, in from_pretrained return model_class.from_pretrained( File "E:\Miniconda3\envs\openvla\lib\site-packages\transformers\modeling_utils.py", line 3544, in from_pretrained config = cls._autoset_attn_implementation( File "E:\Miniconda3\envs\openvla\lib\site-packages\transformers\modeling_utils.py", line 1454, in _autoset_attn_implementation cls._check_and_enable_flash_attn_2( File "E:\Miniconda3\envs\openvla\lib\site-packages\transformers\modeling_utils.py", line 1591, in _check_and_enable_flash_attn_2 raise ValueError( ValueError: You are attempting to use Flash Attention 2.0 with a model not initialized on GPU and with no GPU available. This is not supported yet. Please make sure to have access to a GPU and either initialise the model on a GPU by passing a device_map or initialising the model on CPU and then moving it to GPU.
06-21
### Flash Attention 2.0 在无 GPU 环境下的解决方案 Flash Attention 2.0 是一种优化的注意力机制实现，旨在通过减少内存占用和加速计算来提高 Transformer 模型的性能[^3]。然而，该技术通常依赖于 GPU 的特定...
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本文深入解析Flask中make_response与直接返回字符串的区别与适用场景。文中还包含常见问题解决方案和高级应用技巧，帮助开发者根据具体需求做出最佳选择。
一分钟搞懂Flask中的Response响应
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在flask中，想要给前端页面返回数据，必须是Response的对象 1.Response 1.视图函数中return字符串 from flask import Flask app = Flask(__name__) @app.route("/index/") def index(): return "index" if __name__ == "__main__": app.run() 2.return多个值 @app.route("/many/") def many(): return "index", "200 Ok", {"name": "abin"} index:网页
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记录一次数据节点太多，发送数据缓慢的问题 flask中make_response()可以生成接口响应 res = make_response() res.response = Data res.response 数据放在res.response中时，接口发送数据时不会一次性全部发送，而是一点一点发送。 app_iter中为数据的单个字节的列表 flask会使用yield item单个字节发送res.response数据的内容 # flask中的源码 app_iter = self.app(environ,
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RESTful请求 REST全称是Representational State Transfer，如果一个架构符合REST的约束条件和原则，我们就称它为RESTful架构。可以唯一标识和定位资源，对于该URL标识的资源做何种操作是由Http方法决定的。请求方法有4种，get、post、put、delete分别对应获取资源，添加资源，更新资源及删除资源。 每个网址代表一种资源（resource），所以网址中不能有动词，只能有名词，而且所用的名词往往与数据库的表格名对应。一般来说，数据库中的表都是同种记录的"集
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Response-响应对象(Server返回值) from flask import Flask, make_response app = Flask(__name__) @app.route('/response') def res(): content = ''' <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>用户登录&.
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