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

 Dataset found, listing contents:
total 12
drwxr-sr-x. 3 1000950000 1000180000 4096 Oct 30 11:51 .
drwxrwsrwx. 4         99 1000180000 4096 Oct 30 11:51 ..
drwxr-sr-x. 3 1000950000 1000180000 4096 Oct 30 11:51 FashionMNIST

 Using dependencies from shared volume: /workspace/lib

==================== Starting Distributed Training ====================
W1030 11:52:04.904000 3 torch/distributed/run.py:874] 
W1030 11:52:04.904000 3 torch/distributed/run.py:874] *****************************************
W1030 11:52:04.904000 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 11:52:04.904000 3 torch/distributed/run.py:874] *****************************************
2026-10-30T11:53:05Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 0, LOCAL_RANK: 0.
2026-10-30T11:53:05Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 1, LOCAL_RANK: 1.
2026-10-30T11:53:05Z INFO     [CPU0] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-10-30T11:53:05Z INFO     [CPU1] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-10-30T11:53:05Z INFO     Train Epoch: 0 [0/60000 (0%)]	Loss: 2.296822
2026-10-30T11:53:06Z INFO     Train Epoch: 0 [2560/60000 (4%)]	Loss: 2.280551
2026-10-30T11:53:08Z INFO     Train Epoch: 0 [5120/60000 (9%)]	Loss: 1.161720
2026-10-30T11:53:09Z INFO     Train Epoch: 0 [7680/60000 (13%)]	Loss: 0.782881
2026-10-30T11:53:10Z INFO     Train Epoch: 0 [10240/60000 (17%)]	Loss: 0.576717
2026-10-30T11:53:11Z INFO     Train Epoch: 0 [12800/60000 (21%)]	Loss: 0.516113
2026-10-30T11:53:12Z INFO     Train Epoch: 0 [15360/60000 (26%)]	Loss: 0.442218
2026-10-30T11:53:13Z INFO     Train Epoch: 0 [17920/60000 (30%)]	Loss: 0.545243
2026-10-30T11:53:14Z INFO     Train Epoch: 0 [20480/60000 (34%)]	Loss: 0.550339
2026-10-30T11:53:16Z INFO     Train Epoch: 0 [23040/60000 (38%)]	Loss: 0.472355
2026-10-30T11:53:17Z INFO     Train Epoch: 0 [25600/60000 (43%)]	Loss: 0.336635
2026-10-30T11:53:18Z INFO     Train Epoch: 0 [28160/60000 (47%)]	Loss: 0.385827
2026-10-30T11:53:19Z INFO     Train Epoch: 0 [30720/60000 (51%)]	Loss: 0.414877
2026-10-30T11:53:20Z INFO     Train Epoch: 0 [33280/60000 (55%)]	Loss: 0.319647
2026-10-30T11:53:21Z INFO     Train Epoch: 0 [35840/60000 (60%)]	Loss: 0.631668
2026-10-30T11:53:23Z INFO     Train Epoch: 0 [38400/60000 (64%)]	Loss: 0.666155
2026-10-30T11:53:24Z INFO     Train Epoch: 0 [40960/60000 (68%)]	Loss: 0.290811
2026-10-30T11:53:25Z INFO     Train Epoch: 0 [43520/60000 (72%)]	Loss: 0.455164
2026-10-30T11:53:26Z INFO     Train Epoch: 0 [46080/60000 (77%)]	Loss: 0.491655
2026-10-30T11:53:27Z INFO     Train Epoch: 0 [48640/60000 (81%)]	Loss: 0.460820
2026-10-30T11:53:28Z INFO     Train Epoch: 0 [51200/60000 (85%)]	Loss: 0.464879
2026-10-30T11:53:30Z INFO     Train Epoch: 0 [53760/60000 (89%)]	Loss: 0.522570
2026-10-30T11:53:31Z INFO     Train Epoch: 0 [56320/60000 (94%)]	Loss: 0.476172
2026-10-30T11:53:32Z INFO     Train Epoch: 0 [58880/60000 (98%)]	Loss: 0.148861
2026-10-30T11:53:32Z INFO     [CPU0] Epoch 0 completed | Avg Loss: 0.625022 | Batches: 235
2026-10-30T11:53:32Z INFO     [CPU1] Epoch 0 completed | Avg Loss: 0.604351 | Batches: 235
2026-10-30T11:53:32Z INFO     [CPU0] Training is finished
2026-10-30T11:53:32Z INFO     [CPU1] Training is finished

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