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

 Dataset found, listing contents:
total 12
drwxr-sr-x. 3 1000960000 1000390000 4096 Nov  3 21:24 .
drwxrwsrwx. 4         99 1000390000 4096 Nov  3 21:24 ..
drwxr-sr-x. 3 1000960000 1000390000 4096 Nov  3 21:24 FashionMNIST

 Using dependencies from shared volume: /workspace/lib

==================== Starting Distributed Training ====================
W1103 21:24:54.594000 3 torch/distributed/run.py:874] 
W1103 21:24:54.594000 3 torch/distributed/run.py:874] *****************************************
W1103 21:24:54.594000 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. 
W1103 21:24:54.594000 3 torch/distributed/run.py:874] *****************************************
2026-11-03T21:25:00Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 0, LOCAL_RANK: 0.
2026-11-03T21:25:00Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 1, LOCAL_RANK: 1.
2026-11-03T21:25:00Z INFO     [CPU0] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-11-03T21:25:00Z INFO     [CPU1] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-11-03T21:25:00Z INFO     Train Epoch: 0 [0/60000 (0%)]	Loss: 2.298133
2026-11-03T21:25:02Z INFO     Train Epoch: 0 [2560/60000 (4%)]	Loss: 1.524599
2026-11-03T21:25:03Z INFO     Train Epoch: 0 [5120/60000 (9%)]	Loss: 1.453352
2026-11-03T21:25:05Z INFO     Train Epoch: 0 [7680/60000 (13%)]	Loss: 1.525208
2026-11-03T21:25:06Z INFO     Train Epoch: 0 [10240/60000 (17%)]	Loss: 0.742280
2026-11-03T21:25:08Z INFO     Train Epoch: 0 [12800/60000 (21%)]	Loss: 0.604724
2026-11-03T21:25:09Z INFO     Train Epoch: 0 [15360/60000 (26%)]	Loss: 0.516039
2026-11-03T21:25:10Z INFO     Train Epoch: 0 [17920/60000 (30%)]	Loss: 0.493729
2026-11-03T21:25:12Z INFO     Train Epoch: 0 [20480/60000 (34%)]	Loss: 0.599655
2026-11-03T21:25:13Z INFO     Train Epoch: 0 [23040/60000 (38%)]	Loss: 0.496400
2026-11-03T21:25:15Z INFO     Train Epoch: 0 [25600/60000 (43%)]	Loss: 0.362980
2026-11-03T21:25:16Z INFO     Train Epoch: 0 [28160/60000 (47%)]	Loss: 0.442423
2026-11-03T21:25:17Z INFO     Train Epoch: 0 [30720/60000 (51%)]	Loss: 0.374073
2026-11-03T21:25:19Z INFO     Train Epoch: 0 [33280/60000 (55%)]	Loss: 0.364565
2026-11-03T21:25:20Z INFO     Train Epoch: 0 [35840/60000 (60%)]	Loss: 0.675688
2026-11-03T21:25:22Z INFO     Train Epoch: 0 [38400/60000 (64%)]	Loss: 0.749283
2026-11-03T21:25:23Z INFO     Train Epoch: 0 [40960/60000 (68%)]	Loss: 0.294129
2026-11-03T21:25:24Z INFO     Train Epoch: 0 [43520/60000 (72%)]	Loss: 0.435661
2026-11-03T21:25:26Z INFO     Train Epoch: 0 [46080/60000 (77%)]	Loss: 0.598806
2026-11-03T21:25:27Z INFO     Train Epoch: 0 [48640/60000 (81%)]	Loss: 0.468880
2026-11-03T21:25:29Z INFO     Train Epoch: 0 [51200/60000 (85%)]	Loss: 0.395839
2026-11-03T21:25:30Z INFO     Train Epoch: 0 [53760/60000 (89%)]	Loss: 0.413048
2026-11-03T21:25:32Z INFO     Train Epoch: 0 [56320/60000 (94%)]	Loss: 0.499156
2026-11-03T21:25:33Z INFO     Train Epoch: 0 [58880/60000 (98%)]	Loss: 0.156661
2026-11-03T21:25:34Z INFO     [CPU0] Epoch 0 completed | Avg Loss: 0.682692 | Batches: 235
2026-11-03T21:25:34Z INFO     [CPU1] Epoch 0 completed | Avg Loss: 0.670935 | Batches: 235
2026-11-03T21:25:34Z INFO     [CPU1] Training is finished
2026-11-03T21:25:34Z INFO     [CPU0] Training is finished

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