Collecting environment information...
PyTorch version: 2.1.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: CentOS Linux 7 (Core) (x86_64)
GCC version: (GCC) 10.1.0
Clang version: Could not collect
CMake version: version 3.29.2
Libc version: glibc-2.17
Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-3.10.0-862.el7.x86_64-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 11.6.124
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090
Nvidia driver version: 535.54.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 64
On-line CPU(s) list: 0-63
Thread(s) per core: 2
Core(s) per socket: 16
座: 2
NUMA 节点: 2
厂商 ID: AuthenticAMD
CPU 系列: 23
型号: 49
型号名称: AMD EPYC 7302 16-Core Processor
步进: 0
CPU MHz: 1500.000
CPU max MHz: 3000.0000
CPU min MHz: 1500.0000
BogoMIPS: 5989.04
虚拟化: AMD-V
L1d 缓存: 32K
L1i 缓存: 32K
L2 缓存: 512K
L3 缓存: 16384K
NUMA 节点0 CPU: 0-15,32-47
NUMA 节点1 CPU: 16-31,48-63
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc art rep_good nopl nonstop_tsc extd_apicid aperfmperf eagerfpu pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_l2 cpb cat_l3 cdp_l3 hw_pstate sme retpoline_amd vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr ibpb ibrs stibp arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca
Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.2
[pip3] nvidia-nccl-cu12==2.18.1
[pip3] sentence-transformers==2.2.2
[pip3] torch==2.1.2
[pip3] torchvision==0.16.2
[pip3] transformers==4.37.0
[pip3] transformers-stream-generator==0.0.4
[pip3] triton==2.1.0
[conda] numpy 1.26.2 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.18.1 pypi_0 pypi
[conda] sentence-transformers 2.2.2 pypi_0 pypi
[conda] torch 2.1.2 pypi_0 pypi
[conda] torchvision 0.16.2 pypi_0 pypi
[conda] transformers 4.37.0 pypi_0 pypi
[conda] transformers-stream-generator 0.0.4 pypi_0 pypi
[conda] triton 2.1.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.3.0
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PIX SYS SYS 0-15,32-47 0 N/A
GPU1 PIX X SYS SYS 0-15,32-47 0 N/A
GPU2 SYS SYS X PIX 16-31,48-63 1 N/A
GPU3 SYS SYS PIX X 16-31,48-63 1 N/A
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
When I start the vllm inference service in openai format from the command line “”“CUDA_VISIBLE_DEVICES=1 python -m vllm.entrypoints.openai.api_server
--model /date/pretrained_models/Qwen1.5-14B-Chat
--trust-remote-code
--served-model-name qwen7b
--api-key sk-abcd
--port 8005
--gpu-memory-utilization 0.8
--max-model-len 6832
--tensor-parallel-size 2
--disable-log-requests
--disable-log-stats”“”,How can I adjust the issue of continuously generating relevant log files under temp/ray/, which takes up a lot of disk space, to prevent LLM from generating any log files through this command line to save space? I really don't want to record any log files
Your current environment
🐛 Describe the bug
When I start the vllm inference service in openai format from the command line “”“CUDA_VISIBLE_DEVICES=1 python -m vllm.entrypoints.openai.api_server
--model /date/pretrained_models/Qwen1.5-14B-Chat
--trust-remote-code
--served-model-name qwen7b
--api-key sk-abcd
--port 8005
--gpu-memory-utilization 0.8
--max-model-len 6832
--tensor-parallel-size 2
--disable-log-requests
--disable-log-stats”“”,How can I adjust the issue of continuously generating relevant log files under temp/ray/, which takes up a lot of disk space, to prevent LLM from generating any log files through this command line to save space? I really don't want to record any log files