Skip to content

Report TBE data configuration with EEG-based indices estimation #10027

Report TBE data configuration with EEG-based indices estimation

Report TBE data configuration with EEG-based indices estimation #10027

You are viewing an older attempt in the history of this workflow run. View latest attempt.
Triggered via pull request April 25, 2025 18:51
Status Failure
Total duration 31m 49s
Artifacts

fbgemm_gpu_ci_rocm.yml

on: pull_request
Matrix: build_artifact
Matrix: test_and_publish_artifact
Fit to window
Zoom out
Zoom in

Annotations

1 error
build_artifact (x86, linux.24xlarge, ubuntu:22.04, 3.9, 6.3, clang)
Process completed with exit code 1.

Artifacts

Produced during runtime
Name Size Digest
fbgemm_gpu_nightly_rocm_x86_clang_py3.10_rocm6.2.4.whl
140 MB
sha256:41475a92d5971bca6afc97177e9bbe537545454d622ece67ff0a6c80fb65af41
fbgemm_gpu_nightly_rocm_x86_clang_py3.10_rocm6.3.whl
134 MB
sha256:f8bc1f0860adf9268073d493fb1974bf2ebb9711e2c619f7daa646cf9bebdcd3
fbgemm_gpu_nightly_rocm_x86_clang_py3.11_rocm6.2.4.whl
140 MB
sha256:df2c6582291f16db3c6373c66fdb8c7393a05a9e036ff4000b04d251f1888af3
fbgemm_gpu_nightly_rocm_x86_clang_py3.11_rocm6.3.whl
134 MB
sha256:0b7c947b204b856d6fde970d7db0b3a4438cef7a6ba4b830a188d9008a01be00
fbgemm_gpu_nightly_rocm_x86_clang_py3.12_rocm6.2.4.whl
140 MB
sha256:3ca3a566a06ce7fbde7d6935b04925904523df255f829054c345acb3a11767e4
fbgemm_gpu_nightly_rocm_x86_clang_py3.12_rocm6.3.whl
134 MB
sha256:f2077dd5e68ac5c32130928b2e30a1d16a19bacaee09dc5eb89342feb481da68
fbgemm_gpu_nightly_rocm_x86_clang_py3.13_rocm6.2.4.whl
140 MB
sha256:4a7f0ff3a76213f8b400f968cd65ac3fd54f77ae550f9b596b4d512776b7db6e
fbgemm_gpu_nightly_rocm_x86_clang_py3.13_rocm6.3.whl
134 MB
sha256:3741dfaccf0ea2e9e727ca4fa52b0e1289a292079a3c4509473df4013ac05193
fbgemm_gpu_nightly_rocm_x86_clang_py3.9_rocm6.2.4.whl
140 MB
sha256:9e1f02d267858e10881d2b30afb27ba0849acf1ef45b7cff5d17c57516287c87
fbgemm_gpu_nightly_rocm_x86_gcc_py3.10_rocm6.2.4.whl
140 MB
sha256:4a2378d67d22dafe3117525a422b3331e2bac45f31588daf7c01fc8cdba7d297
fbgemm_gpu_nightly_rocm_x86_gcc_py3.10_rocm6.3.whl
134 MB
sha256:85634a4e8f279867273b68b0a0b517a367c996668236f495869020782e55fcbe
fbgemm_gpu_nightly_rocm_x86_gcc_py3.11_rocm6.2.4.whl
140 MB
sha256:5a56889386390566bf2ea3a15dc33fdac328240f70c153cee099108d9ca69cea
fbgemm_gpu_nightly_rocm_x86_gcc_py3.11_rocm6.3.whl
134 MB
sha256:1ccc46d8a42e8fac02fff7a14a752a38953515d123c1e6460b74b76b3d094757
fbgemm_gpu_nightly_rocm_x86_gcc_py3.12_rocm6.2.4.whl
140 MB
sha256:96ad8915581a5d5cb482b8faecf6cc22a83c93ca0cb0a03cada0b07a2e7e45c3
fbgemm_gpu_nightly_rocm_x86_gcc_py3.12_rocm6.3.whl
134 MB
sha256:525e5a974b8a8d9ab4f4ef1dd492d3ac81661b562aff642cdb491f0baf1cccdb
fbgemm_gpu_nightly_rocm_x86_gcc_py3.13_rocm6.2.4.whl
140 MB
sha256:21e6232502f54a4d34b0834cf1227a23b4b307a23e388f6bbddd6448ee29dfb9
fbgemm_gpu_nightly_rocm_x86_gcc_py3.13_rocm6.3.whl
134 MB
sha256:44c5db72c8667948b415137555112678552c1e0f3d1fc350dcd7d08524f88d6f
fbgemm_gpu_nightly_rocm_x86_gcc_py3.9_rocm6.2.4.whl
140 MB
sha256:ac88cda231b6f9a50304e8407d6c36b2b6895eed9be19216e34bc50d0277cd95
fbgemm_gpu_nightly_rocm_x86_gcc_py3.9_rocm6.3.whl
134 MB
sha256:75a2229800842b2e35ceb1a66ab12d0483b226a80403a840b6fcf5332e20c9f0