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

 Dataset found, listing contents:
total 12
drwxr-sr-x. 3 1000940000 1000200000 4096 Nov  2 23:41 .
drwxrwsrwx. 4         99 1000200000 4096 Nov  2 23:41 ..
drwxr-sr-x. 3 1000940000 1000200000 4096 Nov  2 23:41 FashionMNIST

 Using dependencies from shared volume: /workspace/lib

==================== Starting Distributed Training ====================
W1102 23:41:49.922000 3 torch/distributed/run.py:874] 
W1102 23:41:49.922000 3 torch/distributed/run.py:874] *****************************************
W1102 23:41:49.922000 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 23:41:49.922000 3 torch/distributed/run.py:874] *****************************************
2026-11-02T23:43:05Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 0, LOCAL_RANK: 0.
2026-11-02T23:43:05Z INFO     Distributed Training with WORLD_SIZE: 4, RANK: 1, LOCAL_RANK: 1.
2026-11-02T23:43:05Z INFO     [CPU0] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-11-02T23:43:05Z INFO     [CPU1] Epoch 0 | Batchsize: 64 | Steps: 235 | World Size: 4
2026-11-02T23:43:05Z INFO     Train Epoch: 0 [0/60000 (0%)]	Loss: 2.322829
2026-11-02T23:43:06Z INFO     Train Epoch: 0 [2560/60000 (4%)]	Loss: 2.943113
2026-11-02T23:43:08Z INFO     Train Epoch: 0 [5120/60000 (9%)]	Loss: 1.367669
2026-11-02T23:43:09Z INFO     Train Epoch: 0 [7680/60000 (13%)]	Loss: 0.981690
2026-11-02T23:43:10Z INFO     Train Epoch: 0 [10240/60000 (17%)]	Loss: 0.761314
2026-11-02T23:43:12Z INFO     Train Epoch: 0 [12800/60000 (21%)]	Loss: 0.654517
2026-11-02T23:43:13Z INFO     Train Epoch: 0 [15360/60000 (26%)]	Loss: 0.429687
2026-11-02T23:43:15Z INFO     Train Epoch: 0 [17920/60000 (30%)]	Loss: 0.503119
2026-11-02T23:43:16Z INFO     Train Epoch: 0 [20480/60000 (34%)]	Loss: 0.527988
2026-11-02T23:43:18Z INFO     Train Epoch: 0 [23040/60000 (38%)]	Loss: 0.537131
2026-11-02T23:43:19Z INFO     Train Epoch: 0 [25600/60000 (43%)]	Loss: 0.358959
2026-11-02T23:43:20Z INFO     Train Epoch: 0 [28160/60000 (47%)]	Loss: 0.427008
2026-11-02T23:43:22Z INFO     Train Epoch: 0 [30720/60000 (51%)]	Loss: 0.373469
2026-11-02T23:43:23Z INFO     Train Epoch: 0 [33280/60000 (55%)]	Loss: 0.321430
2026-11-02T23:43:25Z INFO     Train Epoch: 0 [35840/60000 (60%)]	Loss: 0.644619
2026-11-02T23:43:26Z INFO     Train Epoch: 0 [38400/60000 (64%)]	Loss: 0.696282
2026-11-02T23:43:27Z INFO     Train Epoch: 0 [40960/60000 (68%)]	Loss: 0.341748
2026-11-02T23:43:29Z INFO     Train Epoch: 0 [43520/60000 (72%)]	Loss: 0.475569
2026-11-02T23:43:30Z INFO     Train Epoch: 0 [46080/60000 (77%)]	Loss: 0.513672
2026-11-02T23:43:31Z INFO     Train Epoch: 0 [48640/60000 (81%)]	Loss: 0.432790
2026-11-02T23:43:33Z INFO     Train Epoch: 0 [51200/60000 (85%)]	Loss: 0.553397
2026-11-02T23:43:34Z INFO     Train Epoch: 0 [53760/60000 (89%)]	Loss: 0.461792
2026-11-02T23:43:36Z INFO     Train Epoch: 0 [56320/60000 (94%)]	Loss: 0.597376
2026-11-02T23:43:37Z INFO     Train Epoch: 0 [58880/60000 (98%)]	Loss: 0.202471
2026-11-02T23:43:37Z INFO     [CPU0] Epoch 0 completed | Avg Loss: 0.669231 | Batches: 235
2026-11-02T23:43:37Z INFO     [CPU1] Epoch 0 completed | Avg Loss: 0.652818 | Batches: 235
2026-11-02T23:43:37Z INFO     [CPU1] Training is finished
2026-11-02T23:43:37Z INFO     [CPU0] Training is finished

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