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

 Dataset found, listing contents:
total 12
drwxr-sr-x. 3 1000950000 1000540000 4096 Nov  2 23:09 .
drwxrwsrwx. 4         99 1000540000 4096 Nov  2 23:09 ..
drwxr-sr-x. 3 1000950000 1000540000 4096 Nov  2 23:09 FashionMNIST

 Using dependencies from shared volume: /workspace/lib

==================== Starting Distributed Training ====================
W1102 23:09:14.682000 3 torch/distributed/run.py:874] 
W1102 23:09:14.682000 3 torch/distributed/run.py:874] *****************************************
W1102 23:09:14.682000 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. 
W1102 23:09:14.682000 3 torch/distributed/run.py:874] *****************************************
2026-11-02T23:09:20Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 0, LOCAL_RANK: 0.
2026-11-02T23:09:20Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 1, LOCAL_RANK: 1.
2026-11-02T23:09:20Z INFO     [CPU1] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-11-02T23:09:20Z INFO     [CPU0] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-11-02T23:09:21Z INFO     Train Epoch: 0 [0/60000 (0%)]	Loss: 2.305437
2026-11-02T23:09:22Z INFO     Train Epoch: 0 [2560/60000 (4%)]	Loss: 2.751881
2026-11-02T23:09:24Z INFO     Train Epoch: 0 [5120/60000 (9%)]	Loss: 1.562921
2026-11-02T23:09:25Z INFO     Train Epoch: 0 [7680/60000 (13%)]	Loss: 1.844730
2026-11-02T23:09:26Z INFO     Train Epoch: 0 [10240/60000 (17%)]	Loss: 0.911992
2026-11-02T23:09:28Z INFO     Train Epoch: 0 [12800/60000 (21%)]	Loss: 0.599048
2026-11-02T23:09:29Z INFO     Train Epoch: 0 [15360/60000 (26%)]	Loss: 0.450076
2026-11-02T23:09:31Z INFO     Train Epoch: 0 [17920/60000 (30%)]	Loss: 0.480931
2026-11-02T23:09:32Z INFO     Train Epoch: 0 [20480/60000 (34%)]	Loss: 0.560084
2026-11-02T23:09:34Z INFO     Train Epoch: 0 [23040/60000 (38%)]	Loss: 0.508433
2026-11-02T23:09:35Z INFO     Train Epoch: 0 [25600/60000 (43%)]	Loss: 0.327606
2026-11-02T23:09:36Z INFO     Train Epoch: 0 [28160/60000 (47%)]	Loss: 0.447438
2026-11-02T23:09:38Z INFO     Train Epoch: 0 [30720/60000 (51%)]	Loss: 0.367471
2026-11-02T23:09:39Z INFO     Train Epoch: 0 [33280/60000 (55%)]	Loss: 0.325807
2026-11-02T23:09:40Z INFO     Train Epoch: 0 [35840/60000 (60%)]	Loss: 0.602129
2026-11-02T23:09:42Z INFO     Train Epoch: 0 [38400/60000 (64%)]	Loss: 0.637312
2026-11-02T23:09:43Z INFO     Train Epoch: 0 [40960/60000 (68%)]	Loss: 0.332923
2026-11-02T23:09:45Z INFO     Train Epoch: 0 [43520/60000 (72%)]	Loss: 0.455416
2026-11-02T23:09:46Z INFO     Train Epoch: 0 [46080/60000 (77%)]	Loss: 0.553323
2026-11-02T23:09:47Z INFO     Train Epoch: 0 [48640/60000 (81%)]	Loss: 0.444628
2026-11-02T23:09:49Z INFO     Train Epoch: 0 [51200/60000 (85%)]	Loss: 0.470808
2026-11-02T23:09:50Z INFO     Train Epoch: 0 [53760/60000 (89%)]	Loss: 0.447906
2026-11-02T23:09:52Z INFO     Train Epoch: 0 [56320/60000 (94%)]	Loss: 0.525470
2026-11-02T23:09:53Z INFO     Train Epoch: 0 [58880/60000 (98%)]	Loss: 0.194747
2026-11-02T23:09:54Z INFO     [CPU0] Epoch 0 completed | Avg Loss: 0.683609 | Batches: 235
2026-11-02T23:09:54Z INFO     [CPU1] Epoch 0 completed | Avg Loss: 0.670276 | Batches: 235
2026-11-02T23:09:54Z INFO     [CPU1] Training is finished
2026-11-02T23:09:54Z INFO     [CPU0] Training is finished

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