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

 Dataset found, listing contents:
total 12
drwxr-sr-x. 3 1000970000 1000370000 4096 Nov  4 04:08 .
drwxrwsrwx. 4         99 1000370000 4096 Nov  4 04:08 ..
drwxr-sr-x. 3 1000970000 1000370000 4096 Nov  4 04:08 FashionMNIST

 Using dependencies from shared volume: /workspace/lib

==================== Starting Distributed Training ====================
W1104 04:08:15.129000 3 torch/distributed/run.py:874] 
W1104 04:08:15.129000 3 torch/distributed/run.py:874] *****************************************
W1104 04:08:15.129000 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. 
W1104 04:08:15.129000 3 torch/distributed/run.py:874] *****************************************
2026-11-04T04:08:19Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 1, LOCAL_RANK: 1.
2026-11-04T04:08:19Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 0, LOCAL_RANK: 0.
2026-11-04T04:08:20Z INFO     [CPU0] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-11-04T04:08:20Z INFO     [CPU1] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-11-04T04:08:20Z INFO     Train Epoch: 0 [0/60000 (0%)]	Loss: 2.299501
2026-11-04T04:08:21Z INFO     Train Epoch: 0 [2560/60000 (4%)]	Loss: 3.134786
2026-11-04T04:08:22Z INFO     Train Epoch: 0 [5120/60000 (9%)]	Loss: 1.320465
2026-11-04T04:08:23Z INFO     Train Epoch: 0 [7680/60000 (13%)]	Loss: 0.619038
2026-11-04T04:08:25Z INFO     Train Epoch: 0 [10240/60000 (17%)]	Loss: 0.558775
2026-11-04T04:08:26Z INFO     Train Epoch: 0 [12800/60000 (21%)]	Loss: 0.482427
2026-11-04T04:08:27Z INFO     Train Epoch: 0 [15360/60000 (26%)]	Loss: 0.479106
2026-11-04T04:08:28Z INFO     Train Epoch: 0 [17920/60000 (30%)]	Loss: 0.482458
2026-11-04T04:08:29Z INFO     Train Epoch: 0 [20480/60000 (34%)]	Loss: 0.562479
2026-11-04T04:08:31Z INFO     Train Epoch: 0 [23040/60000 (38%)]	Loss: 0.411700
2026-11-04T04:08:32Z INFO     Train Epoch: 0 [25600/60000 (43%)]	Loss: 0.344549
2026-11-04T04:08:33Z INFO     Train Epoch: 0 [28160/60000 (47%)]	Loss: 0.418998
2026-11-04T04:08:34Z INFO     Train Epoch: 0 [30720/60000 (51%)]	Loss: 0.339870
2026-11-04T04:08:36Z INFO     Train Epoch: 0 [33280/60000 (55%)]	Loss: 0.322785
2026-11-04T04:08:37Z INFO     Train Epoch: 0 [35840/60000 (60%)]	Loss: 0.678103
2026-11-04T04:08:38Z INFO     Train Epoch: 0 [38400/60000 (64%)]	Loss: 0.595431
2026-11-04T04:08:39Z INFO     Train Epoch: 0 [40960/60000 (68%)]	Loss: 0.303572
2026-11-04T04:08:41Z INFO     Train Epoch: 0 [43520/60000 (72%)]	Loss: 0.421570
2026-11-04T04:08:42Z INFO     Train Epoch: 0 [46080/60000 (77%)]	Loss: 0.497042
2026-11-04T04:08:43Z INFO     Train Epoch: 0 [48640/60000 (81%)]	Loss: 0.416092
2026-11-04T04:08:45Z INFO     Train Epoch: 0 [51200/60000 (85%)]	Loss: 0.426668
2026-11-04T04:08:46Z INFO     Train Epoch: 0 [53760/60000 (89%)]	Loss: 0.407581
2026-11-04T04:08:47Z INFO     Train Epoch: 0 [56320/60000 (94%)]	Loss: 0.421282
2026-11-04T04:08:48Z INFO     Train Epoch: 0 [58880/60000 (98%)]	Loss: 0.109286
2026-11-04T04:08:49Z INFO     [CPU1] Epoch 0 completed | Avg Loss: 0.577062 | Batches: 235
2026-11-04T04:08:49Z INFO     [CPU0] Epoch 0 completed | Avg Loss: 0.589879 | Batches: 235
2026-11-04T04:08:49Z INFO     [CPU1] Training is finished
2026-11-04T04:08:49Z INFO     [CPU0] Training is finished

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