Release 2.4 windows wheels are not compatible with numpy 2.0 #131668

atalman · 2024-07-24T16:38:39Z

🐛 Describe the bug

When numpy not preinstalled on the machine, with latest torch install we get : https://github.com/pytorch/builder/actions/runs/10080038080/job/27868882276#step:9:438

++ python ./test/smoke_test/smoke_test.py

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.1 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

then later:
std::thread::hardware_concurrency() : 16
Environment variables:
	OMP_NUM_THREADS : [not set]
	MKL_NUM_THREADS : [not set]
ATen parallel backend: OpenMP

Testing smoke_test_conv2d
Testing smoke_test_linalg on cpu
  File "C:\actions-runner\_work\builder\builder\pytorch\builder\test\smoke_test\smoke_test.py", line 330, in main
    test_numpy()
  File "C:\actions-runner\_work\builder\builder\pytorch\builder\test\smoke_test\smoke_test.py", line 72, in test_numpy
    torch.tensor(x)
RuntimeError: Could not infer dtype of numpy.int64

Cause:
numpy release https://pypi.org/project/numpy/2.0.1/ is not compatible with torch
numpy dependency is coming from torchvision METADATA

Requires-Dist: numpy

Versions

2.4.0 - torchvision 0.19

cc @ezyang @gchanan @zou3519 @kadeng @msaroufim

The text was updated successfully, but these errors were encountered:

atalman · 2024-07-24T16:59:36Z

Looks like linux, macos are not affected

pip install torch torchvision
Collecting torch
  Downloading torch-2.4.0-cp310-cp310-manylinux1_x86_64.whl.metadata (26 kB)
Collecting torchvision
  Downloading torchvision-0.19.0-cp310-cp310-manylinux1_x86_64.whl.metadata (6.0 kB)
Collecting filelock (from torch)
  Using cached filelock-3.15.4-py3-none-any.whl.metadata (2.9 kB)
Collecting typing-extensions>=4.8.0 (from torch)
  Using cached typing_extensions-4.12.2-py3-none-any.whl.metadata (3.0 kB)
Collecting sympy (from torch)
  Downloading sympy-1.13.1-py3-none-any.whl.metadata (12 kB)
Collecting networkx (from torch)
  Using cached networkx-3.3-py3-none-any.whl.metadata (5.1 kB)
Collecting jinja2 (from torch)
  Using cached jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB)
Collecting fsspec (from torch)
  Using cached fsspec-2024.6.1-py3-none-any.whl.metadata (11 kB)
Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)
  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)
  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)
  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch)
  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cublas-cu12==12.1.3.1 (from torch)
  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)
  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-curand-cu12==10.3.2.106 (from torch)
  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)
  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)
  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-nccl-cu12==2.20.5 (from torch)
  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)
Collecting nvidia-nvtx-cu12==12.1.105 (from torch)
  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)
Collecting triton==3.0.0 (from torch)
  Downloading triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.3 kB)
Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)
  Using cached nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
Collecting numpy (from torchvision)
  Downloading numpy-2.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 60.9/60.9 kB 4.8 MB/s eta 0:00:00
Collecting pillow!=8.3.*,>=5.3.0 (from torchvision)
  Downloading pillow-10.4.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (9.2 kB)
Collecting MarkupSafe>=2.0 (from jinja2->torch)
  Using cached MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.0 kB)
Collecting mpmath<1.4,>=1.1.0 (from sympy->torch)
  Using cached mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)
Downloading torch-2.4.0-cp310-cp310-manylinux1_x86_64.whl (797.2 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 797.2/797.2 MB 3.6 MB/s eta 0:00:00
Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)
Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)
Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)
Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)
Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 664.8/664.8 MB 4.7 MB/s eta 0:00:00
Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)
Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)
Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)
Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)
Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)
Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)
Downloading triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (209.4 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 209.4/209.4 MB 18.3 MB/s eta 0:00:00
Downloading torchvision-0.19.0-cp310-cp310-manylinux1_x86_64.whl (7.0 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.0/7.0 MB 127.4 MB/s eta 0:00:00
Downloading pillow-10.4.0-cp310-cp310-manylinux_2_28_x86_64.whl (4.5 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.5/4.5 MB 120.4 MB/s eta 0:00:00
Using cached typing_extensions-4.12.2-py3-none-any.whl (37 kB)
Using cached filelock-3.15.4-py3-none-any.whl (16 kB)
Using cached fsspec-2024.6.1-py3-none-any.whl (177 kB)
Using cached jinja2-3.1.4-py3-none-any.whl (133 kB)
Using cached networkx-3.3-py3-none-any.whl (1.7 MB)
Downloading numpy-2.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (19.5 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 19.5/19.5 MB 96.4 MB/s eta 0:00:00
Downloading sympy-1.13.1-py3-none-any.whl (6.2 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.2/6.2 MB 120.9 MB/s eta 0:00:00
Using cached MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)
Using cached mpmath-1.3.0-py3-none-any.whl (536 kB)
Using cached nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)
Installing collected packages: mpmath, typing-extensions, sympy, pillow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, MarkupSafe, fsspec, filelock, triton, nvidia-cusparse-cu12, nvidia-cudnn-cu12, jinja2, nvidia-cusolver-cu12, torch, torchvision


Successfully installed MarkupSafe-2.1.5 filelock-3.15.4 fsspec-2024.6.1 jinja2-3.1.4 mpmath-1.3.0 networkx-3.3 numpy-2.0.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 pillow-10.4.0 sympy-1.13.1 torch-2.4.0 torchvision-0.19.0 triton-3.0.0 typing-extensions-4.12.2
(rpy310) atalman@ip-10-200-84-248:~/temo$ cd builder/
(rpy310) atalman@ip-10-200-84-248:~/temo/builder$ cd test/smoke_test/
(rpy310) atalman@ip-10-200-84-248:~/temo/builder/test/smoke_test$ python smoke_test.py --package torchonly
torch: 2.4.0+cu121
ATen/Parallel:
        at::get_num_threads() : 48
        at::get_num_interop_threads() : 48
OpenMP 201511 (a.k.a. OpenMP 4.5)
        omp_get_max_threads() : 48
Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
        mkl_get_max_threads() : 48
Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67)
std::thread::hardware_concurrency() : 96
Environment variables:
        OMP_NUM_THREADS : [not set]
        MKL_NUM_THREADS : [not set]
ATen parallel backend: OpenMP

Skip version check for channel None as stable version is None
Testing smoke_test_conv2d
Testing smoke_test_linalg on cpu
Testing smoke_test_compile for cuda and torch.float16
Testing smoke_test_compile for cuda and torch.float32
Testing smoke_test_compile for cuda and torch.float64
Testing smoke_test_compile with mode 'max-autotune' for torch.float32
AUTOTUNE convolution(64x32x26x26, 64x32x3x3)
  triton_convolution_12 0.0512 ms 100.0%
  triton_convolution_9 0.0522 ms 98.0%
  triton_convolution_10 0.0553 ms 92.6%
  triton_convolution_7 0.0563 ms 90.9%
  triton_convolution_11 0.0594 ms 86.2%
  convolution 0.0737 ms 69.4%
  triton_convolution_6 0.0748 ms 68.5%
  triton_convolution_8 0.1526 ms 33.6%
SingleProcess AUTOTUNE benchmarking takes 3.5510 seconds and 0.4397 seconds precompiling
AUTOTUNE convolution(64x1x28x28, 32x1x3x3)
  convolution 0.0205 ms 100.0%
  triton_convolution_1 0.0215 ms 95.2%
  triton_convolution_5 0.0225 ms 90.9%
  triton_convolution_2 0.0236 ms 87.0%
  triton_convolution_0 0.0246 ms 83.3%
  triton_convolution_3 0.0266 ms 76.9%
  triton_convolution_4 0.0266 ms 76.9%
SingleProcess AUTOTUNE benchmarking takes 0.7732 seconds and 0.0005 seconds precompiling
AUTOTUNE addmm(64x1, 64x9216, 9216x1)
  addmm 0.0195 ms 100.0%
  triton_mm_17 0.0543 ms 35.8%
  triton_mm_23 0.0543 ms 35.8%
  triton_mm_24 0.0573 ms 33.9%
  triton_mm_15 0.0645 ms 30.2%
  triton_mm_16 0.0686 ms 28.4%
  triton_mm_20 0.0809 ms 24.1%
  triton_mm_14 0.0840 ms 23.2%
  triton_mm_22 0.0860 ms 22.6%
  triton_mm_21 0.1219 ms 16.0%

Validation workflows: https://github.com/pytorch/builder/actions/runs/10080038080

rgommers · 2024-07-24T18:18:50Z

From the linked CI log it seems likely indeed the 2.4.0 torch wheels on PyPI were built against numpy 1.x rather than 2.0.

The CI job confuses the matter slightly because:

numpy 1.26.4 is installed with conda
pip3 install --force-reinstall torch torchvision torchaudio ends up installing numpy 2.0.1 from PyPI

However, that shouldn't affect the result as far as I can tell. A plain pip install torch numpy on Windows in a clean environment should reproduce this problem.

atalman · 2024-07-24T19:44:22Z

To mitigate the issue torchvision windows binaries where published with numpy<2 constraint:
https://pypi.org/project/torchvision/#files

All the tests are successful now: https://github.com/pytorch/builder/actions/runs/10080038080/job/27877807547

nawfalhasan · 2024-07-29T08:22:20Z

@atalman I dont have torchvision. I was getting the error I quoted above with plain torch.

Xiao-Chenguang · 2024-07-29T10:04:24Z

@atalman I dont have torchvision. I was getting the error I quoted above with plain torch.

same error here.

atalman · 2024-07-29T17:12:04Z

Todo: Add a smoke test https://github.com/pytorch/builder/blob/main/test/smoke_test/smoke_test.py for both numpy 1.x and 2.x install.

Ark-kun · 2024-08-13T07:39:13Z

JFYI: For older PyTorch versions (e.g. 2.3.1) this is still an issue (or at least a warning...).

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.1 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File exllamav2\exllamav2\ext.py", line 291, in <module>
    none_tensor = torch.empty((1, 1), device = "meta")
exllamav2\exllamav2\ext.py:291: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at ..\torch\csrc\utils\tensor_numpy.cpp:84.)
  none_tensor = torch.empty((1, 1), device = "meta")

Such breakages inevitably happen when a package (e.g. torch) does not specify the upper bound of a dependency (e.g. numpy). Eventually an incompatible version is released and all torch package versions become broken.

atalman · 2024-08-14T16:12:05Z

This issue is fixed in nightly build by:
pytorch/builder#1945

Test passing:

conda list | grep numpy
numpy                     2.0.0                    pypi_0    pypi

 python smoke_test.py --package torchonly
torch: 2.5.0.dev20240814+cpu
ATen/Parallel:
        at::get_num_threads() : 8
        at::get_num_interop_threads() : 8
OpenMP 2019
        omp_get_max_threads() : 8
Intel(R) oneAPI Math Kernel Library Version 2024.2.1-Product Build 20240722 for Intel(R) 64 architecture applications
        mkl_get_max_threads() : 8
Intel(R) MKL-DNN v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67)
std::thread::hardware_concurrency() : 16
Environment variables:
        OMP_NUM_THREADS : [not set]
        MKL_NUM_THREADS : [not set]
ATen parallel backend: OpenMP

Skip version check for channel None as stable version is None
Testing smoke_test_conv2d
Testing smoke_test_linalg on cpu

atalman · 2024-08-23T16:45:20Z

Fixed in rc 2.4.1. Validations with numpy 2.0.0 : https://github.com/pytorch/builder/actions/runs/10528142201/job/29172994583#step:9:486

- can be removed once pytorch 2.4.1 releases - see: pytorch/pytorch#131668 (comment)

PyTorch added support for numpy 2 starting with PyTorch 2.3.0. This allows for numpy 1 or numpy 2 to be used with torch>=2.3.0. A special case is being handled on Windows as PyTorch Windows binaries had compatibilities issues with numpy 2 that were fixed in torch 2.4.1 (see pytorch/pytorch#131668 (comment)). Signed-off-by: James Butler <james.butler@revvity.com>

…able numpy 2 compatibility (#8368) This is a follow-up to the comments made in #8296 (comment). ### Description This bumps the minimum required `torch` version from 1.13.1 to 2.2.0 in the first commit. See GHSA-5pcm-hx3q-hm94 and GHSA-pg7h-5qx3-wjr3 for more details regarding the "High" severity scoring. - https://nvd.nist.gov/vuln/detail/CVE-2024-31580 - https://nvd.nist.gov/vuln/detail/CVE-2024-31583 Additionally, PyTorch added support for numpy 2 starting with PyTorch 2.3.0. The second commit in this PR allows for numpy 1 or numpy 2 to be used with torch>=2.3.0. 628C I have included this commit in this PR as upgrading to torch 2.2 means you might as well update to 2.3 to get the numpy 2 compatibility. A special case is being handled on Windows as PyTorch Windows binaries had compatibilities issues with numpy 2 that were fixed in torch 2.4.1 (see pytorch/pytorch#131668 (comment)). Maintainers will need to update the required status checks for the [`dev`](https://github.com/Project-MONAI/MONAI/tree/dev) branch to: - Remove min-dep-pytorch (2.0.1) ### Types of changes  - [X] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. --------- Signed-off-by: James Butler <james.butler@revvity.com>

…able numpy 2 compatibility (Project-MONAI#8368) This is a follow-up to the comments made in Project-MONAI#8296 (comment). ### Description This bumps the minimum required `torch` version from 1.13.1 to 2.2.0 in the first commit. See GHSA-5pcm-hx3q-hm94 and GHSA-pg7h-5qx3-wjr3 for more details regarding the "High" severity scoring. - https://nvd.nist.gov/vuln/detail/CVE-2024-31580 - https://nvd.nist.gov/vuln/detail/CVE-2024-31583 Additionally, PyTorch added support for numpy 2 starting with PyTorch 2.3.0. The second commit in this PR allows for numpy 1 or numpy 2 to be used with torch>=2.3.0. I have included this commit in this PR as upgrading to torch 2.2 means you might as well update to 2.3 to get the numpy 2 compatibility. A special case is being handled on Windows as PyTorch Windows binaries had compatibilities issues with numpy 2 that were fixed in torch 2.4.1 (see pytorch/pytorch#131668 (comment)). Maintainers will need to update the required status checks for the [`dev`](https://github.com/Project-MONAI/MONAI/tree/dev) branch to: - Remove min-dep-pytorch (2.0.1) ### Types of changes  - [X] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. --------- Signed-off-by: James Butler <james.butler@revvity.com> Signed-off-by: Can-Zhao <volcanofly@gmail.com>

atalman added ciflow/binaries Trigger all binary build and upload jobs on the PR high priority labels Jul 24, 2024

pytorch-bot bot added the triage review label Jul 24, 2024

atalman changed the title ~~Release 2.4 not compatible with numpy 2.0~~ Release 2.4 windows wheels are not compatible with numpy 2.0 Jul 24, 2024

malfet added this to the 2.4.1 milestone Jul 24, 2024

grimulkan mentioned this issue Jul 25, 2024

Quantizing Llama 3.1 405B turboderp-org/exllamav2#565

Closed

malfet mentioned this issue Jul 29, 2024

NumPy 2.0 Support #107302

Closed

3 tasks

atalman self-assigned this Jul 29, 2024

jbschlosser added triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module and removed triage review labels Jul 29, 2024

This was referenced Jul 29, 2024

Use numpy 2.0.0rc1 for windows builds pytorch/builder#1941

Merged

Use numpy 2.0.0rc1 for windows builds pytorch/builder#1945

Merged

nawfalhasan mentioned this issue Aug 2, 2024

OSError: [WinError 126] The specified module could not be found. #131662

Closed

Balandat mentioned this issue Aug 4, 2024

Unpin numpy from <2.0 pytorch/botorch#2459

Closed

NicolasHug mentioned this issue Aug 7, 2024

Support NumPy 2.0 pytorch/vision#8460

Closed

rgommers mentioned this issue Aug 11, 2024

Ecosystem compatibility with numpy 2.0 numpy/numpy#26191

Open

This was referenced Aug 14, 2024

[Release 2.4] cherry pick of Use numpy 2.0.0rc1 for windows builds #1945 pytorch/builder#1957

Merged

[v2.4.1] Release Tracker #132400

Closed

IgorTatarnikov mentioned this issue Aug 20, 2024

Unpin numpy brainglobe/cellfinder#452

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5 tasks

JacksonBurns mentioned this issue Aug 20, 2024

[v2]: Acceptance Testing for Numpy v2.0 chemprop/chemprop#733

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joshjkim mentioned this issue Aug 21, 2024

Compatibility issues with NumPy 2.0.0 microsoft/DirectML#608

Open

IgorTatarnikov mentioned this issue Aug 22, 2024

[BUG] Keras's incompatibility with numpy>=2 breaks cellfinder's model training brainglobe/cellfinder#446

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atalman closed this as completed Aug 23, 2024

atalman mentioned this issue Aug 28, 2024

Release 2.4.1 validations checklist and cherry-picks #134694

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40 tasks

robinjhuang mentioned this issue Aug 30, 2024

ERRO : "A module that was compiled using NumPy 1.x cannot be run" Comfy-Org/comfy-cli#163

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telamonian added a commit to Comfy-Org/comfy-cli that referenced this issue Aug 30, 2024

add a temporary override that limits numpy<2

846a5c5

- can be removed once pytorch 2.4.1 releases - see: pytorch/pytorch#131668 (comment)

telamonian mentioned this issue Aug 30, 2024

numpy<2 override Comfy-Org/comfy-cli#170

Closed

jakirkham mentioned this issue Sep 25, 2024

Remove NumPy <2 pin rapidsai/cugraph#4615

Merged

marbre mentioned this issue Nov 13, 2024

[sharktank] Unpin NumPy on non-Windows nod-ai/shark-ai#496

Merged

jamesobutler mentioned this issue Feb 26, 2025

Bump torch minimum to mitigate CVE-2024-31580 & CVE-2024-31583 and enable numpy 2 compatibility Project-MONAI/MONAI#8368

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3 tasks

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Release 2.4 windows wheels are not compatible with numpy 2.0 #131668

Release 2.4 windows wheels are not compatible with numpy 2.0 #131668

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Release 2.4 windows wheels are not compatible with numpy 2.0 #131668

Release 2.4 windows wheels are not compatible with numpy 2.0 #131668

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