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

 Dataset found, listing contents:
total 12
drwxr-sr-x. 3 1000940000 1000170000 4096 Oct 30 07:54 .
drwxrwsrwx. 4         99 1000170000 4096 Oct 30 07:54 ..
drwxr-sr-x. 3 1000940000 1000170000 4096 Oct 30 07:54 FashionMNIST

 Using dependencies from shared volume: /workspace/lib

==================== Starting Distributed Training ====================
W1030 07:54:12.230000 3 torch/distributed/run.py:874] 
W1030 07:54:12.230000 3 torch/distributed/run.py:874] *****************************************
W1030 07:54:12.230000 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 07:54:12.230000 3 torch/distributed/run.py:874] *****************************************
2026-10-30T07:54:17Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 0, LOCAL_RANK: 0.
2026-10-30T07:54:17Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 1, LOCAL_RANK: 1.
2026-10-30T07:54:17Z INFO     [CPU0] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-10-30T07:54:17Z INFO     [CPU1] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-10-30T07:54:17Z INFO     Train Epoch: 0 [0/60000 (0%)]	Loss: 2.295491
2026-10-30T07:54:18Z INFO     Train Epoch: 0 [2560/60000 (4%)]	Loss: 3.757230
2026-10-30T07:54:20Z INFO     Train Epoch: 0 [5120/60000 (9%)]	Loss: 1.058433
2026-10-30T07:54:21Z INFO     Train Epoch: 0 [7680/60000 (13%)]	Loss: 1.100611
2026-10-30T07:54:22Z INFO     Train Epoch: 0 [10240/60000 (17%)]	Loss: 0.792665
2026-10-30T07:54:23Z INFO     Train Epoch: 0 [12800/60000 (21%)]	Loss: 0.592141
2026-10-30T07:54:24Z INFO     Train Epoch: 0 [15360/60000 (26%)]	Loss: 0.425056
2026-10-30T07:54:25Z INFO     Train Epoch: 0 [17920/60000 (30%)]	Loss: 0.480940
2026-10-30T07:54:26Z INFO     Train Epoch: 0 [20480/60000 (34%)]	Loss: 0.569043
2026-10-30T07:54:27Z INFO     Train Epoch: 0 [23040/60000 (38%)]	Loss: 0.438187
2026-10-30T07:54:29Z INFO     Train Epoch: 0 [25600/60000 (43%)]	Loss: 0.350992
2026-10-30T07:54:30Z INFO     Train Epoch: 0 [28160/60000 (47%)]	Loss: 0.484495
2026-10-30T07:54:31Z INFO     Train Epoch: 0 [30720/60000 (51%)]	Loss: 0.342585
2026-10-30T07:54:32Z INFO     Train Epoch: 0 [33280/60000 (55%)]	Loss: 0.328917
2026-10-30T07:54:33Z INFO     Train Epoch: 0 [35840/60000 (60%)]	Loss: 0.663749
2026-10-30T07:54:34Z INFO     Train Epoch: 0 [38400/60000 (64%)]	Loss: 0.742799
2026-10-30T07:54:35Z INFO     Train Epoch: 0 [40960/60000 (68%)]	Loss: 0.314006
2026-10-30T07:54:37Z INFO     Train Epoch: 0 [43520/60000 (72%)]	Loss: 0.472554
2026-10-30T07:54:38Z INFO     Train Epoch: 0 [46080/60000 (77%)]	Loss: 0.501093
2026-10-30T07:54:39Z INFO     Train Epoch: 0 [48640/60000 (81%)]	Loss: 0.463974
2026-10-30T07:54:40Z INFO     Train Epoch: 0 [51200/60000 (85%)]	Loss: 0.444290
2026-10-30T07:54:41Z INFO     Train Epoch: 0 [53760/60000 (89%)]	Loss: 0.389926
2026-10-30T07:54:42Z INFO     Train Epoch: 0 [56320/60000 (94%)]	Loss: 0.490629
2026-10-30T07:54:44Z INFO     Train Epoch: 0 [58880/60000 (98%)]	Loss: 0.113994
2026-10-30T07:54:44Z INFO     [CPU0] Epoch 0 completed | Avg Loss: 0.644156 | Batches: 235
2026-10-30T07:54:44Z INFO     [CPU1] Epoch 0 completed | Avg Loss: 0.628160 | Batches: 235
2026-10-30T07:54:44Z INFO     [CPU1] Training is finished
2026-10-30T07:54:44Z INFO     [CPU0] Training is finished

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