==================== Environment Info ====================
Dataset Path: /workspace/data
===========================================================

 Dataset found, listing contents:
total 12
drwxr-sr-x. 3 1000940000 1000200000 4096 Oct 30 19:50 .
drwxrwsrwx. 4         99 1000200000 4096 Oct 30 19:50 ..
drwxr-sr-x. 3 1000940000 1000200000 4096 Oct 30 19:50 FashionMNIST

 Using dependencies from shared volume: /workspace/lib

==================== Starting Distributed Training ====================
W1030 19:50:36.497000 3 torch/distributed/run.py:874] 
W1030 19:50:36.497000 3 torch/distributed/run.py:874] *****************************************
W1030 19:50:36.497000 3 torch/distributed/run.py:874] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1030 19:50:36.497000 3 torch/distributed/run.py:874] *****************************************
2026-10-30T19:50:41Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 0, LOCAL_RANK: 0.
2026-10-30T19:50:41Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 1, LOCAL_RANK: 1.
2026-10-30T19:50:41Z INFO     [CPU0] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-10-30T19:50:41Z INFO     [CPU1] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-10-30T19:50:42Z INFO     Train Epoch: 0 [0/60000 (0%)]	Loss: 2.300009
2026-10-30T19:50:43Z INFO     Train Epoch: 0 [2560/60000 (4%)]	Loss: 3.085467
2026-10-30T19:50:44Z INFO     Train Epoch: 0 [5120/60000 (9%)]	Loss: 1.369993
2026-10-30T19:50:45Z INFO     Train Epoch: 0 [7680/60000 (13%)]	Loss: 1.430857
2026-10-30T19:50:46Z INFO     Train Epoch: 0 [10240/60000 (17%)]	Loss: 1.426137
2026-10-30T19:50:47Z INFO     Train Epoch: 0 [12800/60000 (21%)]	Loss: 1.010547
2026-10-30T19:50:48Z INFO     Train Epoch: 0 [15360/60000 (26%)]	Loss: 0.677369
2026-10-30T19:50:49Z INFO     Train Epoch: 0 [17920/60000 (30%)]	Loss: 0.705096
2026-10-30T19:50:51Z INFO     Train Epoch: 0 [20480/60000 (34%)]	Loss: 0.677541
2026-10-30T19:50:52Z INFO     Train Epoch: 0 [23040/60000 (38%)]	Loss: 0.631126
2026-10-30T19:50:53Z INFO     Train Epoch: 0 [25600/60000 (43%)]	Loss: 0.440385
2026-10-30T19:50:54Z INFO     Train Epoch: 0 [28160/60000 (47%)]	Loss: 0.532594
2026-10-30T19:50:55Z INFO     Train Epoch: 0 [30720/60000 (51%)]	Loss: 0.436444
2026-10-30T19:50:57Z INFO     Train Epoch: 0 [33280/60000 (55%)]	Loss: 0.429890
2026-10-30T19:50:58Z INFO     Train Epoch: 0 [35840/60000 (60%)]	Loss: 0.734097
2026-10-30T19:50:59Z INFO     Train Epoch: 0 [38400/60000 (64%)]	Loss: 0.697388
2026-10-30T19:51:00Z INFO     Train Epoch: 0 [40960/60000 (68%)]	Loss: 0.283552
2026-10-30T19:51:01Z INFO     Train Epoch: 0 [43520/60000 (72%)]	Loss: 0.500900
2026-10-30T19:51:02Z INFO     Train Epoch: 0 [46080/60000 (77%)]	Loss: 0.629498
2026-10-30T19:51:04Z INFO     Train Epoch: 0 [48640/60000 (81%)]	Loss: 0.489288
2026-10-30T19:51:05Z INFO     Train Epoch: 0 [51200/60000 (85%)]	Loss: 0.526577
2026-10-30T19:51:06Z INFO     Train Epoch: 0 [53760/60000 (89%)]	Loss: 0.526942
2026-10-30T19:51:07Z INFO     Train Epoch: 0 [56320/60000 (94%)]	Loss: 0.793612
2026-10-30T19:51:08Z INFO     Train Epoch: 0 [58880/60000 (98%)]	Loss: 0.159489
2026-10-30T19:51:09Z INFO     [CPU0] Epoch 0 completed | Avg Loss: 0.813943 | Batches: 235
2026-10-30T19:51:09Z INFO     [CPU1] Epoch 0 completed | Avg Loss: 0.798166 | Batches: 235
2026-10-30T19:51:09Z INFO     [CPU1] Training is finished
2026-10-30T19:51:09Z INFO     [CPU0] Training is finished

==================== Training Complete ====================