stuck at building wheel

[neurosynapse]

pip install git+https://github.com/NVIDIA/TransformerEngine.git@stable
Defaulting to user installation because normal site-packages is not writeable
Collecting git+https://github.com/NVIDIA/TransformerEngine.git@stable
Cloning https://github.com/NVIDIA/TransformerEngine.git (to revision stable) to /tmp/pip-req-build-fa900tpa
Running command git clone --filter=blob:none --quiet https://github.com/NVIDIA/TransformerEngine.git /tmp/pip-req-build-fa900tpa
Running command git checkout -b stable --track origin/stable
Switched to a new branch 'stable'
Branch 'stable' set up to track remote branch 'stable' from 'origin'.
Resolved https://github.com/NVIDIA/TransformerEngine.git to commit 3ec998e
Running command git submodule update --init --recursive -q
Preparing metadata (setup.py) ... done
Requirement already satisfied: packaging in /usr/lib/python3/dist-packages (from transformer-engine==1.8.0+3ec998e) (21.3)
Collecting pydantic
Using cached pydantic-2.8.2-py3-none-any.whl (423 kB)
Requirement already satisfied: typing-extensions>=4.6.1 in /home/rob/.local/lib/python3.10/site-packages (from pydantic->transformer-engine==1.8.0+3ec998e) (4.12.2)
Collecting pydantic-core==2.20.1
Using cached pydantic_core-2.20.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)
Collecting annotated-types>=0.4.0
Using cached annotated_types-0.7.0-py3-none-any.whl (13 kB)
Building wheels for collected packages: transformer-engine
Building wheel for transformer-engine (setup.py) ... |

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Mon_Apr__3_17:16:06_PDT_2023
Cuda compilation tools, release 12.1, V12.1.105
Build cuda_12.1.r12.1/compiler.32688072_0

Ubuntu 22.04

Python 3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 15:12:24) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.

torch 2.4.0+cu121

[neurosynapse]

rtx 3090 ti

[timmoon10]

We use Ninja to parallelize the build process and I suspect it's overwhelming your system resources. Can you try running with MAX_JOBS=1 in your environment?

[1195343015]

          Hm, I'd expect most systems could handle building with `MAX_JOBS=1`. I wonder if we could get more clues if you build with verbose output (`pip install -v -v .`).

Originally posted by @timmoon10 in #976 (comment)

It's useful for me !
And you should wait for more time.

[timmoon10]

When building with minimal resource requirements, we now recommend setting MAX_JOBS=1 and NVTE_BUILD_THREADS_PER_JOB=1 in the environment. This will of course drastically slow down the build process. Setting NVTE_CUDA_ARCHS to your GPU compute capability (e.g. NVTE_CUDA_ARCHS=90 for H100) may help speed up building, but will run into problems if you run on the wrong GPU.