collect-env：环境信息收集
collect-env 用于收集系统、GPU、深度学习框架及 FastDeploy 的相关环境信息。子命令没有额外参数，直接执行即可。
使用方式
fastdeploy collect-env
收集的信息
1. 系统信息
os：操作系统- Linux：
lsb_release -a或cat /etc/*-release - Windows：
wmic os get Caption -
macOS：
sw_vers -productVersion -
gcc_version：GCC 版本，通过gcc --version获取 clang_version：Clang 版本，通过clang --version获取cmake_version：CMake 版本，通过cmake --version获取libc_version：GNU C 库版本（仅 Linux），通过platform.libc_ver()获取
2. PyTorch 信息
torch_version：PyTorch 版本is_debug_build：是否为 Debug 模式cuda_compiled_version：编译 PyTorch 时的 CUDA 版本hip_compiled_version：编译 PyTorch 时的 HIP 版本（AMD GPU）
3. Paddle 信息
paddle_version：Paddle 版本paddle_compiled_version：编译 Paddle 时的 CUDA 版本
4. Python 环境
python_version：Python 版本python_platform：平台详细信息
5. CUDA / GPU 信息
is_cuda_available：CUDA 是否可用cuda_runtime_version：CUDA 运行时版本cuda_module_loading：CUDA 模块加载策略（环境变量CUDA_MODULE_LOADING）nvidia_gpu_models：GPU 型号nvidia_driver_version：NVIDIA 驱动版本cudnn_version：cuDNN 版本caching_allocator_config：CUDA 缓存分配器配置（环境变量PYTORCH_CUDA_ALLOC_CONF）is_xnnpack_available：XNNPACK 是否可用
6. CPU 信息
cpu_info：CPU 详细信息（通过lscpu或 Windows 系统命令获取）
7. 相关库版本
pip_packages：通过python -m pip list --format=freeze收集关键库版本conda_packages：通过conda list收集关键库版本
8. FastDeploy 特定信息
fastdeploy_version：FastDeploy 版本（开发版包含 Git 提交哈希）fastdeploy_build_flags：构建标志（显示fastdeploy针对的 CUDA 架构，环境变量FD_BUILDING_ARCS）gpu_topo：GPU 拓扑结构（通过nvidia-smi topo -m获取）
9. 环境变量
env_vars：收集以TORCH、CUDA、NCCL等开头，以及 FastDeploy 自定义的环境变量- 会过滤包含
secret、token等敏感信息
输出示例
==============================
System Info
==============================
OS : Ubuntu 20.04.6 LTS (x86_64)
GCC version : (GCC) 12.2.0
Clang version : 3.8.0 (tags/RELEASE_380/final)
CMake version : version 3.18.0
Libc version : glibc-2.31
==============================
PyTorch Info
==============================
PyTorch version : 2.5.1+cu118
Is debug build : False
CUDA used to build PyTorch : 11.8
==============================
Paddle Info
==============================
Paddle version : 3.1.0
CUDA used to build paddle : 12.6
==============================
Python Environment
==============================
Python version : 3.10.16 (main, Dec 11 2024, 16:24:50) [GCC 11.2.0] (64-bit runtime)
Python platform : Linux-5.10.0-1.0.0.28-x86_64-with-glibc2.31
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.3.103
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB
GPU 2: NVIDIA A100-SXM4-40GB
GPU 3: NVIDIA A100-SXM4-40GB
GPU 4: NVIDIA A100-SXM4-40GB
GPU 5: NVIDIA A100-SXM4-40GB
GPU 6: NVIDIA A100-SXM4-40GB
GPU 7: NVIDIA A100-SXM4-40GB
Nvidia driver version : 525.125.06
cuDNN version : Could not collect
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 46 bits physical, 48 bits virtual
CPU(s): 160
On-line CPU(s) list: 0-159
Thread(s) per core: 2
Core(s) per socket: 20
Socket(s): 4
NUMA node(s): 4
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz
Stepping: 7
CPU MHz: 3199.750
CPU max MHz: 3900.0000
CPU min MHz: 1000.0000
BogoMIPS: 5000.00
Virtualization: VT-x
L1d cache: 2.5 MiB
L1i cache: 2.5 MiB
L2 cache: 80 MiB
L3 cache: 110 MiB
NUMA node0 CPU(s): 0-19,80-99
NUMA node1 CPU(s): 20-39,100-119
NUMA node2 CPU(s): 40-59,120-139
NUMA node3 CPU(s): 60-79,140-159
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku avx512_vnni md_clear flush_l1d arch_capabilities
==============================
Versions of relevant libraries
==============================
[pip3] aiozmq==1.0.0
[pip3] flake8==7.2.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu11==11.11.3.6
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cccl-cu12==12.6.77
[pip3] nvidia-cuda-cupti-cu11==11.8.87
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu11==11.8.89
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu11==11.8.89
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu11==9.1.0.70
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu11==10.9.0.58
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu11==10.3.0.86
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu11==11.4.1.48
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu11==11.7.5.86
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-nccl-cu11==2.21.5
[pip3] nvidia-nccl-cu12==2.25.1
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu11==11.8.86
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] onnx==1.18.0
[pip3] onnxoptimizer==0.3.13
[pip3] paddle2onnx==2.0.1
[pip3] pynvml==12.0.0
[pip3] pyzmq==26.4.0
[pip3] torch==2.5.1+cu118
[pip3] torchaudio==2.5.1+cu118
[pip3] torchvision==0.20.1+cu118
[pip3] transformers==4.55.4
[pip3] triton==3.3.0
[pip3] use_triton_in_paddle==0.1.0
[pip3] zmq==0.0.0
[conda] aiozmq 1.0.0 pypi_0 pypi
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu11 11.11.3.6 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi
[conda] nvidia-cuda-cccl-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu11 11.8.87 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu11 11.8.89 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu11 11.8.89 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cudnn-cu11 9.1.0.70 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi
[conda] nvidia-cufft-cu11 10.9.0.58 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi
[conda] nvidia-cufile-cu12 1.11.1.6 pypi_0 pypi
[conda] nvidia-curand-cu11 10.3.0.86 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi
[conda] nvidia-cusolver-cu11 11.4.1.48 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi
[conda] nvidia-cusparse-cu11 11.7.5.86 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi
[conda] nvidia-ml-py 12.575.51 pypi_0 pypi
[conda] nvidia-nccl-cu11 2.21.5 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.25.1 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi
[conda] nvidia-nvtx-cu11 11.8.86 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi
[conda] pynvml 12.0.0 pypi_0 pypi
[conda] pyzmq 26.4.0 pypi_0 pypi
[conda] torch 2.5.1+cu118 pypi_0 pypi
[conda] torchaudio 2.5.1+cu118 pypi_0 pypi
[conda] torchvision 0.20.1+cu118 pypi_0 pypi
[conda] transformers 4.55.4 pypi_0 pypi
[conda] triton 3.3.0 pypi_0 pypi
[conda] use-triton-in-paddle 0.1.0 pypi_0 pypi
[conda] zmq 0.0.0 pypi_0 pypi
==============================
FastDeploy Info
==============================
FastDeply Version : 2.0.0a0
FastDeply Build Flags:
CUDA Archs: [];
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 CPU Affinity NUMA Affinity
GPU0 X NV12 NV12 NV12 NV12 NV12 NV12 NV12 PXB SYS SYS 0-19,80-99 0
GPU1 NV12 X NV12 NV12 NV12 NV12 NV12 NV12 PXB SYS SYS 0-19,80-99 0
GPU2 NV12 NV12 X NV12 NV12 NV12 NV12 NV12 SYS NODE PXB 20-39,100-119 1
GPU3 NV12 NV12 NV12 X NV12 NV12 NV12 NV12 SYS NODE PXB 20-39,100-119 1
GPU4 NV12 NV12 NV12 NV12 X NV12 NV12 NV12 SYS SYS SYS 40-59,120-139 2
GPU5 NV12 NV12 NV12 NV12 NV12 X NV12 NV12 SYS SYS SYS 40-59,120-139 2
GPU6 NV12 NV12 NV12 NV12 NV12 NV12 X NV12 SYS SYS SYS 60-79,140-159 3
GPU7 NV12 NV12 NV12 NV12 NV12 NV12 NV12 X SYS SYS SYS 60-79,140-159 3
NIC0 PXB PXB SYS SYS SYS SYS SYS SYS X SYS SYS
NIC1 SYS SYS NODE NODE SYS SYS SYS SYS SYS X NODE
NIC2 SYS SYS PXB PXB SYS SYS SYS SYS SYS NODE X
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
NIC Legend:
NIC0: mlx5_0
NIC1: mlx5_1
NIC2: mlx5_2
==============================
Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=GPU-0fe14fa3-b286-3d79-b223-1912257b4d64,GPU-282b567f-d2c4-f472-5c0d-975a7d96e1a7,GPU-a9d7e24d-1bb2-eb83-63fb-40584754f4be,GPU-924f3dc2-1b05-c35d-12f5-53d9458a1bd2,GPU-57591c1d-c444-18b8-c29d-f44cbaae8142,GPU-a28a9121-042a-81cf-d759-83ce1e3b962a,GPU-c124b75e-2768-6b7d-41fa-46dbf0159c87,GPU-b196a47d-c21e-1ec3-8003-5d776173ec7c
NCCL_P2P_DISABLE=0
NVIDIA_REQUIRE_CUDA=cuda>=12.3 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536
NCCL_IB_CUDA_SUPPORT=0
NVIDIA_LIB=/usr/local/nvidia/lib64
NCCL_VERSION=2.19.3-1
NCCL_SOCKET_IFNAME=xgbe1
NVIDIA_GDRCOPY=enabled
NCCL_DEBUG_SUBSYS=INIT,ENV,GRAPH
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NCCL_DEBUG=INFO
NCCL_LIBRARY_PATH=/usr/local/nccl
NVIDIA_VISIBLE_GPUS_UUID=GPU-0fe14fa3-b286-3d79-b223-1912257b4d64,GPU-282b567f-d2c4-f472-5c0d-975a7d96e1a7,GPU-a9d7e24d-1bb2-eb83-63fb-40584754f4be,GPU-924f3dc2-1b05-c35d-12f5-53d9458a1bd2,GPU-57591c1d-c444-18b8-c29d-f44cbaae8142,GPU-a28a9121-042a-81cf-d759-83ce1e3b962a,GPU-c124b75e-2768-6b7d-41fa-46dbf0159c87,GPU-b196a47d-c21e-1ec3-8003-5d776173ec7c
NVIDIA_PRODUCT_NAME=CUDA
NCCL_IB_GID_INDEX=3
CUDA_VERSION=12.3.1
NVIDIA_TOOLS=/home/opt/cuda_tools
NCCL_DEBUG_FILE=/root/paddlejob/workspace/log/nccl.%h.%p.log
NCCL_IB_QPS_PER_CONNECTION=2
NCCL_IB_CONNECT_RETRY_CNT=15
NCCL_ERROR_FILE=/root/paddlejob/workspace/log/err.%h.%p.log
NCCL_IB_TIMEOUT=22
CUDNN_VERSION=9.0.0
NCCL_IB_DISABLE=0
NVIDIA_VISIBLE_GPUS_SLOT=6,7,0,1,2,3,4,5
NCCL_IB_ADAPTIVE_ROUTING=1
OMP_NUM_THREADS=1
CUDA_MODULE_LOADING=LAZY