File size: 1,772 Bytes
9f1076a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | == 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
|