vllm - ✅(Solved) Fix [Bug]: Docker Build Failure for Dockerfile.nightly_pytorch [1 pull requests, 1 participants]

vllm2026-03-17 10:08:02

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vllm-project/vllm#37284•Fetched 2026-04-08 00:48:16

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Error Message

30.30 python3: can't open file `/vllm-workspace/flashinfer/setup.py`: [Error 2] No such file or directory

Fix Action

Fix / Workaround

============================== CPU Info

Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 48 On-line CPU(s) list: 0-47 Vendor ID: AuthenticAMD Model name: AMD Ryzen Threadripper 7960X 24-Cores CPU family: 25 Model: 24 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 1 Stepping: 1 Frequency boost: enabled CPU(s) scaling MHz: 74% CPU max MHz: 5364.3540 CPU min MHz: 420.1000 BogoMIPS: 8387.53 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 rep_good amd_lbr_v2 nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic 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_llc mwaitx cpuid_fault cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d debug_swap Virtualization: AMD-V L1d cache: 768 KiB (24 instances) L1i cache: 768 KiB (24 instances) L2 cache: 24 MiB (24 instances) L3 cache: 128 MiB (4 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-47 Vulnerability Gather data sampling: Not affected Vulnerability Ghostwrite: Not affected Vulnerability Indirect target selection: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Old microcode: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Mitigation; Safe RET Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsa: Vulnerable: No microcode Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

PR fix notes

PR #37286: [Bugfix] Migrate to python3 -m build from legacy setup.py in Dockerfile.nightly_torch

Repository: vllm-project/vllm
Author: mathivanansai
State: open | merged: False
Link: https://github.com/vllm-project/vllm/pull/37286

Description (problem / solution / changelog)

Fixes #37284

Purpose

File: docker/Dockerfile.nightly_torch FlashInfer removed setup.py from v0.4.0 and fully migrated to a modern build system using pyproject.toml. The existing Dockerfile.nightly_torch still uses the legacy python3 setup.py bdist_wheel command which causes the Docker build to fail at step [vllm-base 11/20].

This PR fixes the issue by replacing the legacy build command with python3 -m build, which is compatible with the pyproject.toml-based build system.

Test Plan

Test Result

<details> <summary> Essential Elements of an Effective PR Description Checklist </summary>

The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
[x ] The test results, such as pasting the results comparison before and after, or e2e results
(Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
(Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

</details>

Changed files

docker/Dockerfile.nightly_torch (modified, +2/-2)

Code Example

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.4 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version                : 18.1.3 (1ubuntu1)
CMake version                : version 3.28.3
Libc version                 : glibc-2.39
 
==============================
       PyTorch Info
==============================
PyTorch version              : 2.9.1+cu130
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A
 
==============================
      Python Environment
==============================
Python version               : 3.12.12 (main, Dec  9 2025, 19:02:36) [Clang 21.1.4 ] (64-bit runtime)
Python platform              : Linux-6.17.0-14-generic-x86_64-with-glibc2.39
 
==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 13.0.88
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : GPU 0: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
Nvidia driver version        : 590.48.01
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True
 
==============================
          CPU Info
==============================
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           52 bits physical, 57 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  48
On-line CPU(s) list:                     0-47
Vendor ID:                               AuthenticAMD
Model name:                              AMD Ryzen Threadripper 7960X 24-Cores
CPU family:                              25
Model:                                   24
Thread(s) per core:                      2
Core(s) per socket:                      24
Socket(s):                               1
Stepping:                                1
Frequency boost:                         enabled
CPU(s) scaling MHz:                      74%
CPU max MHz:                             5364.3540
CPU min MHz:                             420.1000
BogoMIPS:                                8387.53
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 rep_good amd_lbr_v2 nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic 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_llc mwaitx cpuid_fault cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d debug_swap
Virtualization:                          AMD-V
L1d cache:                               768 KiB (24 instances)
L1i cache:                               768 KiB (24 instances)
L2 cache:                                24 MiB (24 instances)
L3 cache:                                128 MiB (4 instances)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-47
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Not affected
Vulnerability Old microcode:             Not affected
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Not affected
Vulnerability Spec rstack overflow:      Mitigation; Safe RET
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Vulnerable: No microcode
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Mitigation; IBPB before exit to userspace
 
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.3
[pip3] numpy==2.2.6
[pip3] nvidia-cublas==13.0.0.19
[pip3] nvidia-cuda-cupti==13.0.48
[pip3] nvidia-cuda-nvrtc==13.0.48
[pip3] nvidia-cuda-runtime==13.0.48
[pip3] nvidia-cudnn-cu13==9.13.0.50
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft==12.0.0.15
[pip3] nvidia-cufile==1.15.0.42
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.3.29
[pip3] nvidia-cusparse==12.6.2.49
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-cutlass-dsl==4.4.1
[pip3] nvidia-cutlass-dsl-libs-base==4.4.1
[pip3] nvidia-ml-py==13.590.48
[pip3] nvidia-nccl-cu13==2.27.7
[pip3] nvidia-nvjitlink==13.0.39
[pip3] nvidia-nvshmem-cu13==3.3.24
[pip3] nvidia-nvtx==13.0.39
[pip3] pyzmq==27.1.0
[pip3] torch==2.9.1+cu130
[pip3] torchaudio==2.9.1+cu130
[pip3] torchvision==0.24.1+cu130
[pip3] transformers==4.57.6
[pip3] triton==3.5.1
[conda] Could not collect
 
==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.16.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
  	[4mGPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID[0m
GPU0	 X 	0-47	0		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
 
==============================
     Environment Variables
==============================
LD_LIBRARY_PATH=/usr/local/cuda/lib64:
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1

---

30.30 python3: can't open file `/vllm-workspace/flashinfer/setup.py`: [Error 2] No such file or directory

RAW_BUFFERClick to expand / collapse

Your current environment

<details> <summary>The output of <code>python collect_env.py</code></summary>

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 24.04.4 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version                : 18.1.3 (1ubuntu1)
CMake version                : version 3.28.3
Libc version                 : glibc-2.39
 
==============================
       PyTorch Info
==============================
PyTorch version              : 2.9.1+cu130
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A
 
==============================
      Python Environment
==============================
Python version               : 3.12.12 (main, Dec  9 2025, 19:02:36) [Clang 21.1.4 ] (64-bit runtime)
Python platform              : Linux-6.17.0-14-generic-x86_64-with-glibc2.39
 
==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 13.0.88
CUDA_MODULE_LOADING set to   : 
GPU models and configuration : GPU 0: NVIDIA RTX PRO 6000 Blackwell Workstation Edition
Nvidia driver version        : 590.48.01
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True
 
==============================
          CPU Info
==============================
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           52 bits physical, 57 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  48
On-line CPU(s) list:                     0-47
Vendor ID:                               AuthenticAMD
Model name:                              AMD Ryzen Threadripper 7960X 24-Cores
CPU family:                              25
Model:                                   24
Thread(s) per core:                      2
Core(s) per socket:                      24
Socket(s):                               1
Stepping:                                1
Frequency boost:                         enabled
CPU(s) scaling MHz:                      74%
CPU max MHz:                             5364.3540
CPU min MHz:                             420.1000
BogoMIPS:                                8387.53
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 rep_good amd_lbr_v2 nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic 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_llc mwaitx cpuid_fault cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d debug_swap
Virtualization:                          AMD-V
L1d cache:                               768 KiB (24 instances)
L1i cache:                               768 KiB (24 instances)
L2 cache:                                24 MiB (24 instances)
L3 cache:                                128 MiB (4 instances)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-47
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Not affected
Vulnerability Old microcode:             Not affected
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Not affected
Vulnerability Spec rstack overflow:      Mitigation; Safe RET
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Vulnerable: No microcode
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Mitigation; IBPB before exit to userspace
 
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.3
[pip3] numpy==2.2.6
[pip3] nvidia-cublas==13.0.0.19
[pip3] nvidia-cuda-cupti==13.0.48
[pip3] nvidia-cuda-nvrtc==13.0.48
[pip3] nvidia-cuda-runtime==13.0.48
[pip3] nvidia-cudnn-cu13==9.13.0.50
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft==12.0.0.15
[pip3] nvidia-cufile==1.15.0.42
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.3.29
[pip3] nvidia-cusparse==12.6.2.49
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-cutlass-dsl==4.4.1
[pip3] nvidia-cutlass-dsl-libs-base==4.4.1
[pip3] nvidia-ml-py==13.590.48
[pip3] nvidia-nccl-cu13==2.27.7
[pip3] nvidia-nvjitlink==13.0.39
[pip3] nvidia-nvshmem-cu13==3.3.24
[pip3] nvidia-nvtx==13.0.39
[pip3] pyzmq==27.1.0
[pip3] torch==2.9.1+cu130
[pip3] torchaudio==2.9.1+cu130
[pip3] torchvision==0.24.1+cu130
[pip3] transformers==4.57.6
[pip3] triton==3.5.1
[conda] Could not collect
 
==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.16.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
  	[4mGPU0	CPU Affinity	NUMA Affinity	GPU NUMA ID[0m
GPU0	 X 	0-47	0		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
 
==============================
     Environment Variables
==============================
LD_LIBRARY_PATH=/usr/local/cuda/lib64:
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1

</details>

🐛 Describe the bug

The docker build fails during the FlashInfer build step in Dockerfile.nightly_torch. The Dockerfile attempts to build FlashInfer using the legacy setup.py approach, but FlashInfer no longer ships setup.py from v0.4.0 - it has migrated to a modern build system (pyproject.toml).

Error

30.30 python3: can't open file `/vllm-workspace/flashinfer/setup.py`: [Error 2] No such file or directory

Steps to Reproduce

Build Docker image using Dockerfile.nightly_torch
Build proceeds through [vllm-base 10/20] successfully
Failed at [vllm-base 11/20] during FlashInfer wheel build

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extent analysis

Fix Plan

To fix the issue, we need to update the Dockerfile to use the modern build system for FlashInfer. Here are the steps:

Update the Dockerfile.nightly_torch to use pyproject.toml instead of setup.py for building FlashInfer.
Install the required build tools, such as pip and setuptools, to support the new build system.
Modify the RUN command to use pip install . instead of python setup.py install.

Example code:

# Install required build tools
RUN pip install --upgrade pip setuptools

# Build FlashInfer using pyproject.toml
RUN pip install .

Alternatively, you can use the following command to build FlashInfer:

RUN pip install --no-deps .

This will install FlashInfer without installing its dependencies.

Verification

To verify that the fix worked, you can rebuild the Docker image using the updated Dockerfile.nightly_torch and check if the FlashInfer build step is successful.

Extra Tips

Make sure to update the Dockerfile.nightly_torch to use the latest version of FlashInfer.
If you encounter any issues during the build process, you can try to install the required dependencies manually using pip install -r requirements.txt.
You can also try to use a different version of FlashInfer that still uses the legacy setup.py approach, but this is not recommended as it may not be compatible with the latest versions of other dependencies.

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vllm - ✅(Solved) Fix [Bug]: Docker Build Failure for Dockerfile.nightly_pytorch [1 pull requests, 1 participants]

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============================== CPU Info

PR fix notes

PR #37286: [Bugfix] Migrate to python3 -m build from legacy setup.py in Dockerfile.nightly_torch

Description (problem / solution / changelog)

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Changed files

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vllm - ✅(Solved) Fix [Bug]: Docker Build Failure for Dockerfile.nightly_pytorch [1 pull requests, 1 participants]

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============================== CPU Info

PR fix notes

PR #37286: [Bugfix] Migrate to python3 -m build from legacy setup.py in Dockerfile.nightly_torch

Description (problem / solution / changelog)

Purpose

Test Plan

Test Result

Changed files

Code Example

Your current environment

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Before submitting a new issue...

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