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conda now solves this without choking (it never used to work, and I presume that's why you added the nvidia channel at the top):
With this (flexible channel priority, didn't try strict):
name: aphrodite channels: - pytorch - nvidia/label/cuda-12.4.1 - conda-forge - defaults dependencies: - python=3.11.* - pytorch=2.4.0 - pytorch-cuda=12.4.* - cuda-nvcc=12.4.* - cuda-libraries-dev=12.4.* - cuda=12.4 - cuda-version=12.4.* - nccl - cxx-compiler - gxx=11.3 - pip - git=2.35.1 - ninja
I get:
cuda 12.4.1 0 nvidia/label/cuda-12.4.1 cuda-cccl 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-command-line-tools 12.4.1 0 nvidia/label/cuda-12.4.1 cuda-compiler 12.4.1 0 nvidia/label/cuda-12.4.1 cuda-cudart 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-cudart-dev 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-cudart-static 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-cuobjdump 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-cupti 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-cupti-static 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-cuxxfilt 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-demo-suite 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-documentation 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-driver-dev 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-gdb 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-libraries 12.4.1 0 nvidia/label/cuda-12.4.1 cuda-libraries-dev 12.4.1 0 nvidia/label/cuda-12.4.1 cuda-libraries-static 12.4.1 0 nvidia/label/cuda-12.4.1 cuda-nsight 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-nsight-compute 12.4.1 0 nvidia/label/cuda-12.4.1 cuda-nvcc 12.4.131 0 nvidia/label/cuda-12.4.1 cuda-nvdisasm 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-nvml-dev 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-nvprof 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-nvprune 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-nvrtc 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-nvrtc-dev 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-nvrtc-static 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-nvtx 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-nvvp 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-opencl 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-opencl-dev 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-profiler-api 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-runtime 12.4.1 0 nvidia/label/cuda-12.4.1 cuda-sanitizer-api 12.4.127 0 nvidia/label/cuda-12.4.1 cuda-toolkit 12.4.1 0 nvidia/label/cuda-12.4.1 cuda-tools 12.4.1 0 nvidia/label/cuda-12.4.1 cuda-version 12.4 h3060b56_3 conda-forge cuda-visual-tools 12.4.1 0 nvidia/label/cuda-12.4.1 gds-tools 1.9.1.3 0 nvidia/label/cuda-12.4.1 libcublas 12.4.5.8 0 nvidia/label/cuda-12.4.1 libcublas-dev 12.4.5.8 0 nvidia/label/cuda-12.4.1 libcublas-static 12.4.5.8 0 nvidia/label/cuda-12.4.1 libcufft 11.2.1.3 0 nvidia/label/cuda-12.4.1 libcufft-dev 11.2.1.3 0 nvidia/label/cuda-12.4.1 libcufft-static 11.2.1.3 0 nvidia/label/cuda-12.4.1 libcufile 1.9.1.3 0 nvidia/label/cuda-12.4.1 libcufile-dev 1.9.1.3 0 nvidia/label/cuda-12.4.1 libcufile-static 1.9.1.3 0 nvidia/label/cuda-12.4.1 libcurand 10.3.5.147 0 nvidia/label/cuda-12.4.1 libcurand-dev 10.3.5.147 0 nvidia/label/cuda-12.4.1 libcurand-static 10.3.5.147 0 nvidia/label/cuda-12.4.1 libcusolver 11.6.1.9 0 nvidia/label/cuda-12.4.1 libcusolver-dev 11.6.1.9 0 nvidia/label/cuda-12.4.1 libcusolver-static 11.6.1.9 0 nvidia/label/cuda-12.4.1 libcusparse 12.3.1.170 0 nvidia/label/cuda-12.4.1 libcusparse-dev 12.3.1.170 0 nvidia/label/cuda-12.4.1 libcusparse-static 12.3.1.170 0 nvidia/label/cuda-12.4.1 libnpp 12.2.5.30 0 nvidia/label/cuda-12.4.1 libnpp-dev 12.2.5.30 0 nvidia/label/cuda-12.4.1 libnpp-static 12.2.5.30 0 nvidia/label/cuda-12.4.1 libnvfatbin 12.4.127 0 nvidia/label/cuda-12.4.1 libnvfatbin-dev 12.4.127 0 nvidia/label/cuda-12.4.1 libnvjitlink 12.4.127 0 nvidia/label/cuda-12.4.1 libnvjitlink-dev 12.4.127 0 nvidia/label/cuda-12.4.1 libnvjpeg 12.3.1.117 0 nvidia/label/cuda-12.4.1 libnvjpeg-dev 12.3.1.117 0 nvidia/label/cuda-12.4.1 libnvjpeg-static 12.3.1.117 0 nvidia/label/cuda-12.4.1 nsight-compute 2024.1.1.4 0 nvidia/label/cuda-12.4.1 pytorch 2.4.0 py3.11_cuda12.4_cudnn9.1.0_0 pytorch pytorch-cuda 12.4 hc786d27_7 pytorch pytorch-mutex 1.0 cuda pytorch
Ain't life grand?
Collecting environment information... PyTorch version: 2.4.0 Is debug build: False CUDA used to build PyTorch: 12.4 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.4 LTS (x86_64) GCC version: (conda-forge gcc 11.3.0-19) 11.3.0 Clang version: Could not collect CMake version: version 3.26.4 Libc version: glibc-2.35 Python version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0] (64-bit runtime) Python platform: Linux-6.8.0-45-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.4.131 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H100 PCIe GPU 1: NVIDIA H100 PCIe Nvidia driver version: 560.35.03 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 Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 256 On-line CPU(s) list: 0-255 Vendor ID: AuthenticAMD Model name: AMD EPYC 9554 64-Core Processor CPU family: 25 Model: 17 Thread(s) per core: 2 Core(s) per socket: 64 Socket(s): 2 Stepping: 1 Frequency boost: enabled CPU max MHz: 3762.9880 CPU min MHz: 1500.0000 BogoMIPS: 6191.12 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 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 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 v_vmsave_vmload 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: 4 MiB (128 instances) L1i cache: 4 MiB (128 instances) L2 cache: 128 MiB (128 instances) L3 cache: 512 MiB (16 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-63,128-191 NUMA node1 CPU(s): 64-127,192-255 Vulnerability Gather data sampling: 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 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; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: 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; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] torch==2.4.0 [pip3] triton==3.0.0 [conda] blas 2.16 mkl conda-forge [conda] libblas 3.8.0 16_mkl conda-forge [conda] libcblas 3.8.0 16_mkl conda-forge [conda] liblapack 3.8.0 16_mkl conda-forge [conda] liblapacke 3.8.0 16_mkl conda-forge [conda] mkl 2020.2 256 [conda] nccl 2.23.4.1 h52f6c39_0 conda-forge [conda] pytorch 2.4.0 py3.11_cuda12.4_cudnn9.1.0_0 pytorch [conda] pytorch-cuda 12.4 hc786d27_7 pytorch [conda] pytorch-mutex 1.0 cuda pytorch [conda] torchtriton 3.0.0 py311 pytorch ROCM Version: Could not collect Neuron SDK Version: N/A Aphrodite Version: N/A Aphrodite Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: ESC[4mGPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA IDESC[0m GPU0 X NODE 64-127,192-255 1 N/A GPU1 NODE X 64-127,192-255 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
N/A
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Your current environment
conda now solves this without choking (it never used to work, and I presume that's why you added the nvidia channel at the top):
With this (flexible channel priority, didn't try strict):
I get:
Ain't life grand?
How did you install Aphrodite?
N/A
The text was updated successfully, but these errors were encountered: