== host: 169f95f090e6  2026-09-30T20:59:23+00:00
== GPU
NVIDIA GeForce RTX 5090, 32607 MiB, 580.82.09
| NVIDIA-SMI 580.82.09              Driver Version: 580.82.09      CUDA Version: 13.0     |
== disk (repo fs)
overlay         300G  250M  300G   1% /
== CPU/RAM
24
               total        used        free      shared  buff/cache   available
Mem:             125          10          81           0          34         113
python 3.10.13
torch 2.11.0+cu128 cuda build 12.8 cuda available True
  gpu0: NVIDIA GeForce RTX 5090 31.4 GiB sm_120
  matmul ok True
== batched equivalence tests (CPU + CUDA)
...........                                                              [100%]
=============================== warnings summary ===============================
test/test_batched_train.py::BatchIndependenceTests::test_b4_equals_four_b1
  /root/atom-ai/test/test_batched_train.py:235: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
  Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:836.)
    self.assertLessEqual(float((os_["byte_logits"][0] - o["byte_logits"][b]).abs().max()), TOL)

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
11 passed, 1 warning in 74.21s (0:01:14)
== short GPU benchmark (bytes/s vs B)
batched B=   64     88.3 ticks/s       5653 bytes/s wall (median 5583)       5654 bytes/cpu-s  peakRSS=1900MB cuda_peak=918MB
batched B=  256     87.6 ticks/s      22438 bytes/s wall (median 22351)      22445 bytes/cpu-s  peakRSS=1902MB cuda_peak=3604MB
batched B= 1024     83.9 ticks/s      85881 bytes/s wall (median 84573)      85908 bytes/cpu-s  peakRSS=1912MB cuda_peak=14383MB
== setup OK