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+                codePath: src/experiment/ssl_distil.py
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+                cpu_count_logical: 128
+                cudaVersion: "12.8"
+                disk:
+                    /:
+                        total: "53687091200"
+                        used: "12478357504"
+                email: acc.ggcolab@gmail.com
+                executable: /usr/bin/python3
+                git:
+                    commit: 901492890f0effd68ec8b452322c4eb112ba523d
+                    remote: https://github.com/PCTU-Deep-Medicine/ent-labotary.git
+                gpu: NVIDIA GeForce RTX 5090
+                gpu_count: 1
+                gpu_nvidia:
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+                      cudaCores: 21760
+                      memoryTotal: "34190917632"
+                      name: NVIDIA GeForce RTX 5090
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+                os: Linux-5.15.0-141-generic-x86_64-with-glibc2.39
+                program: /workspace/ent-labotary/src/experiment/ssl_distil.py
+                python: CPython 3.12.3
+                root: /workspace/ent-labotary/outputs/ssl_distil/vit16s
+                startedAt: "2025-08-15T17:12:07.817534Z"
+                writerId: q8pqinv2icrf8qd6bfrun4dsuq8ibotu
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+    value: highest
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+        n_projection_layers: 1
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+        teacher: dinov3/vitb16
+        teacher_url: https://dinov3.llamameta.net/dinov3_vitb16/dinov3_vitb16_pretrain_lvd1689m-73cec8be.pth?Policy=eyJTdGF0ZW1lbnQiOlt7InVuaXF1ZV9oYXNoIjoidHIwZnd1bWJpNXQ3MDN4cHR6ZnQwYWJvIiwiUmVzb3VyY2UiOiJodHRwczpcL1wvZGlub3YzLmxsYW1hbWV0YS5uZXRcLyoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE3NTU0NDQyOTJ9fX1dfQ__&Signature=My7izi9fGryWxHUZt0Ndjf%7EXL5DZqsEw7zj0srntCpnl-ef5RIXlC3GsiUhpPsk%7Ex1926VqFIZq1DopLq-qZLm8R%7E%7EMz50FnqqgmpTilHZ37qXXzfs5zKpoKtlREY4Zr7MY9mitysZM52qW-Ql59QnsPiw596ibmw9fuDo5%7EsVmD1mTJUaiY%7Eh2mwjTZIHDaBwCdyJMLcfjnd45ERNIJ3AmyNXxkZYSFjLBh%7E%7Erwo0fJStpUbbHlcgPp2y1qpKhkOb3GTPoqpPx9JGXAoRORBrnSfcG0O7vcP0I3b4gjqTNpmmtmUxkWytxM0uqw3wuanUucTg2a4oFx7XnkHrjyjQ__&Key-Pair-Id=K15QRJLYKIFSLZ&Download-Request-ID=1269751050845944
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ssl_distil/vit16s/wandb/run-20250815_171207-rj6obcl8/files/output.log

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+/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py:190: .fit(ckpt_path="last") is set, but there is no last checkpoint available. No checkpoint will be loaded. HINT: Set `ModelCheckpoint(..., save_last=True)`.
+
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+Loading `train_dataloader` to estimate number of stepping batches.
+Traceback (most recent call last):
+  File "/workspace/ent-labotary/src/experiment/ssl_distil.py", line 24, in <module>
+    lightly_train.train(
+  File "/usr/local/lib/python3.12/dist-packages/lightly_train/_commands/train.py", line 237, in train
+    train_from_config(config=config)
+  File "/usr/local/lib/python3.12/dist-packages/lightly_train/_commands/train.py", line 408, in train_from_config
+    trainer_instance.fit(
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 561, in fit
+    call._call_and_handle_interrupt(
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/call.py", line 49, in _call_and_handle_interrupt
+    return trainer_fn(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 599, in _fit_impl
+    self._run(model, ckpt_path=ckpt_path)
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 988, in _run
+    self.strategy.setup(self)
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/strategy.py", line 159, in setup
+    self.setup_optimizers(trainer)
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/strategy.py", line 139, in setup_optimizers
+    self.optimizers, self.lr_scheduler_configs = _init_optimizers_and_lr_schedulers(self.lightning_module)
+                                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/core/optimizer.py", line 180, in _init_optimizers_and_lr_schedulers
+    optim_conf = call._call_lightning_module_hook(model.trainer, "configure_optimizers", pl_module=model)
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/call.py", line 177, in _call_lightning_module_hook
+    output = fn(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/lightly_train/_methods/method.py", line 108, in configure_optimizers
+    int(self.trainer.estimated_stepping_batches),
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 1707, in estimated_stepping_batches
+    self.fit_loop.setup_data()
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/fit_loop.py", line 265, in setup_data
+    length = len(dl) if has_len_all_ranks(dl, trainer.strategy, allow_zero_length) else float("inf")
+                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/utilities/data.py", line 105, in has_len_all_ranks
+    if total_length == 0:
+       ^^^^^^^^^^^^^^^^^
+RuntimeError: CUDA error: no kernel image is available for execution on the device
+CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1
+Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

ssl_distil/vit16s/wandb/run-20250815_171207-rj6obcl8/files/requirements.txt

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+                gpu: NVIDIA GeForce RTX 5090
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Blackwell
+                      cudaCores: 21760
+                      memoryTotal: "34190917632"
+                      name: NVIDIA GeForce RTX 5090
+                      uuid: GPU-019f0e4f-54b5-a1a6-64dc-bd83ce0d3eba
+                host: c69022374a27
+                memory:
+                    total: "270281490432"
+                os: Linux-5.15.0-141-generic-x86_64-with-glibc2.39
+                program: /workspace/ent-labotary/src/experiment/ssl_distil.py
+                python: CPython 3.12.3
+                root: /workspace/ent-labotary/outputs/ssl_distil/vit16s
+                startedAt: "2025-08-15T17:14:41.349084Z"
+                writerId: xhdvp47ye1bexit3likqpljsmg0hhppn
+        m:
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+    value: CUDAAccelerator
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+    value: 32
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+    value:
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+        early_stopping:
+            check_finite: true
+            monitor: train_loss
+            patience: 1000000000000
+        model_checkpoint:
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+            save_last: true
+            save_top_k: 1
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+    value: data/kyucapsule
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+    value: 300
+float32_matmul_precision:
+    value: highest
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+    value: null
+loggers:
+    value:
+        jsonl:
+            flush_logs_every_n_steps: 100
+        mlflow: null
+        tensorboard:
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+            log_graph: false
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+            project: ent-endoscopy-ssl
+            version: null
+method:
+    value: distillation
+method_args:
+    value:
+        lr_scale_method: sqrt
+        n_projection_layers: 1
+        n_teacher_blocks: 2
+        projection_hidden_dim: 2048
+        reference_batch_size: 1536
+        teacher: dinov3/vitb16
+        teacher_url: https://dinov3.llamameta.net/dinov3_vitb16/dinov3_vitb16_pretrain_lvd1689m-73cec8be.pth?Policy=eyJTdGF0ZW1lbnQiOlt7InVuaXF1ZV9oYXNoIjoidHIwZnd1bWJpNXQ3MDN4cHR6ZnQwYWJvIiwiUmVzb3VyY2UiOiJodHRwczpcL1wvZGlub3YzLmxsYW1hbWV0YS5uZXRcLyoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE3NTU0NDQyOTJ9fX1dfQ__&Signature=My7izi9fGryWxHUZt0Ndjf%7EXL5DZqsEw7zj0srntCpnl-ef5RIXlC3GsiUhpPsk%7Ex1926VqFIZq1DopLq-qZLm8R%7E%7EMz50FnqqgmpTilHZ37qXXzfs5zKpoKtlREY4Zr7MY9mitysZM52qW-Ql59QnsPiw596ibmw9fuDo5%7EsVmD1mTJUaiY%7Eh2mwjTZIHDaBwCdyJMLcfjnd45ERNIJ3AmyNXxkZYSFjLBh%7E%7Erwo0fJStpUbbHlcgPp2y1qpKhkOb3GTPoqpPx9JGXAoRORBrnSfcG0O7vcP0I3b4gjqTNpmmtmUxkWytxM0uqw3wuanUucTg2a4oFx7XnkHrjyjQ__&Key-Pair-Id=K15QRJLYKIFSLZ&Download-Request-ID=1269751050845944
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+    value: VisionTransformer
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+    value: 64
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+    value: lars
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+        dampening: 0
+        eps: 1e-08
+        lr: 9
+        momentum: 0.9
+        nesterov: false
+        trust_coefficient: 0.001
+        weight_decay: 1e-06
+out:
+    value: outputs/ssl_distil/vit16s
+overwrite:
+    value: true
+precision:
+    value: 32-true
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+resume_interrupted:
+    value: true
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+strategy:
+    value: SingleDeviceStrategy
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+        gaussian_blur:
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ssl_distil/vit16s/wandb/run-20250815_171441-ek65ntv7/files/output.log

@@ -0,0 +1,46 @@
+/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py:190: .fit(ckpt_path="last") is set, but there is no last checkpoint available. No checkpoint will be loaded. HINT: Set `ModelCheckpoint(..., save_last=True)`.
+
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+Loading `train_dataloader` to estimate number of stepping batches.
+Traceback (most recent call last):
+  File "/workspace/ent-labotary/src/experiment/ssl_distil.py", line 24, in <module>
+    lightly_train.train(
+  File "/usr/local/lib/python3.12/dist-packages/lightly_train/_commands/train.py", line 237, in train
+    train_from_config(config=config)
+  File "/usr/local/lib/python3.12/dist-packages/lightly_train/_commands/train.py", line 408, in train_from_config
+    trainer_instance.fit(
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 561, in fit
+    call._call_and_handle_interrupt(
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/call.py", line 49, in _call_and_handle_interrupt
+    return trainer_fn(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 599, in _fit_impl
+    self._run(model, ckpt_path=ckpt_path)
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 988, in _run
+    self.strategy.setup(self)
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/strategy.py", line 159, in setup
+    self.setup_optimizers(trainer)
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/strategy.py", line 139, in setup_optimizers
+    self.optimizers, self.lr_scheduler_configs = _init_optimizers_and_lr_schedulers(self.lightning_module)
+                                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/core/optimizer.py", line 180, in _init_optimizers_and_lr_schedulers
+    optim_conf = call._call_lightning_module_hook(model.trainer, "configure_optimizers", pl_module=model)
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/call.py", line 177, in _call_lightning_module_hook
+    output = fn(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/lightly_train/_methods/method.py", line 108, in configure_optimizers
+    int(self.trainer.estimated_stepping_batches),
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 1707, in estimated_stepping_batches
+    self.fit_loop.setup_data()
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/fit_loop.py", line 265, in setup_data
+    length = len(dl) if has_len_all_ranks(dl, trainer.strategy, allow_zero_length) else float("inf")
+                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/utilities/data.py", line 105, in has_len_all_ranks
+    if total_length == 0:
+       ^^^^^^^^^^^^^^^^^
+RuntimeError: CUDA error: no kernel image is available for execution on the device
+CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1
+Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

ssl_distil/vit16s/wandb/run-20250815_171441-ek65ntv7/files/requirements.txt

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ssl_distil/vit16s/wandb/run-20250815_171441-ek65ntv7/files/wandb-metadata.json

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+{
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+  "python": "CPython 3.12.3",
+  "startedAt": "2025-08-15T17:14:41.349084Z",
+  "program": "/workspace/ent-labotary/src/experiment/ssl_distil.py",
+  "codePath": "src/experiment/ssl_distil.py",
+  "codePathLocal": "src/experiment/ssl_distil.py",
+  "git": {
+    "remote": "https://github.com/PCTU-Deep-Medicine/ent-labotary.git",
+    "commit": "901492890f0effd68ec8b452322c4eb112ba523d"
+  },
+  "email": "acc.ggcolab@gmail.com",
+  "root": "/workspace/ent-labotary/outputs/ssl_distil/vit16s",
+  "host": "c69022374a27",
+  "executable": "/usr/bin/python3",
+  "cpu_count": 64,
+  "cpu_count_logical": 128,
+  "gpu": "NVIDIA GeForce RTX 5090",
+  "gpu_count": 1,
+  "disk": {
+    "/": {
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+      "used": "12478435328"
+    }
+  },
+  "memory": {
+    "total": "270281490432"
+  },
+  "gpu_nvidia": [
+    {
+      "name": "NVIDIA GeForce RTX 5090",
+      "memoryTotal": "34190917632",
+      "cudaCores": 21760,
+      "architecture": "Blackwell",
+      "uuid": "GPU-019f0e4f-54b5-a1a6-64dc-bd83ce0d3eba"
+    }
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ssl_distil/vit16s/wandb/run-20250815_171441-ek65ntv7/files/wandb-summary.json

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ssl_distil/vit16s/wandb/run-20250815_171441-ek65ntv7/logs/debug-core.log

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ssl_distil/vit16s/wandb/run-20250815_171634-0c5znv3v/files/output.log

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+/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py:190: .fit(ckpt_path="last") is set, but there is no last checkpoint available. No checkpoint will be loaded. HINT: Set `ModelCheckpoint(..., save_last=True)`.
+
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+Loading `train_dataloader` to estimate number of stepping batches.
+Traceback (most recent call last):
+  File "/workspace/ent-labotary/src/experiment/ssl_distil.py", line 24, in <module>
+    lightly_train.train(
+  File "/usr/local/lib/python3.12/dist-packages/lightly_train/_commands/train.py", line 237, in train
+    train_from_config(config=config)
+  File "/usr/local/lib/python3.12/dist-packages/lightly_train/_commands/train.py", line 408, in train_from_config
+    trainer_instance.fit(
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 561, in fit
+    call._call_and_handle_interrupt(
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/call.py", line 49, in _call_and_handle_interrupt
+    return trainer_fn(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 599, in _fit_impl
+    self._run(model, ckpt_path=ckpt_path)
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 988, in _run
+    self.strategy.setup(self)
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/strategy.py", line 159, in setup
+    self.setup_optimizers(trainer)
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/strategy.py", line 139, in setup_optimizers
+    self.optimizers, self.lr_scheduler_configs = _init_optimizers_and_lr_schedulers(self.lightning_module)
+                                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/core/optimizer.py", line 180, in _init_optimizers_and_lr_schedulers
+    optim_conf = call._call_lightning_module_hook(model.trainer, "configure_optimizers", pl_module=model)
+                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/call.py", line 177, in _call_lightning_module_hook
+    output = fn(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/lightly_train/_methods/method.py", line 108, in configure_optimizers
+    int(self.trainer.estimated_stepping_batches),
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 1707, in estimated_stepping_batches
+    self.fit_loop.setup_data()
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/fit_loop.py", line 265, in setup_data
+    length = len(dl) if has_len_all_ranks(dl, trainer.strategy, allow_zero_length) else float("inf")
+                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/utilities/data.py", line 105, in has_len_all_ranks
+    if total_length == 0:
+       ^^^^^^^^^^^^^^^^^
+RuntimeError: CUDA error: no kernel image is available for execution on the device
+CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
+For debugging consider passing CUDA_LAUNCH_BLOCKING=1
+Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

ssl_distil/vit16s/wandb/run-20250815_171634-0c5znv3v/files/requirements.txt

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ssl_distil/vit16s/wandb/run-20250815_172544-nw3nbw83/files/output.log

@@ -0,0 +1,58 @@
+/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py:190: .fit(ckpt_path="last") is set, but there is no last checkpoint available. No checkpoint will be loaded. HINT: Set `ModelCheckpoint(..., save_last=True)`.
+
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+Loading `train_dataloader` to estimate number of stepping batches.
+
+  | Name                    | Type                  | Params | Mode
+--------------------------------------------------------------------------
+0 | teacher_embedding_model | DinoVisionTransformer | 85.7 M | eval
+1 | student_embedding_model | EmbeddingModel        | 30.1 M | train
+2 | student_projection_head | DistillationV2Head    | 591 K  | train
+3 | criterion               | DistillationV2Loss    | 0      | train
+--------------------------------------------------------------------------
+116 M     Trainable params
+0         Non-trainable params
+116 M     Total params
+465.345   Total estimated model params size (MB)
+284       Modules in train mode
+188       Modules in eval mode
+Epoch 299: 100%|██████████████████████████████████████████████████████| 577/577 [00:57<00:00,  9.97it/s, v_num=bw83, train_loss=0.0762, data_wait=13.4%]
+`Trainer.fit` stopped: `max_epochs=300` reached.
+Training completed.
+Example: How to use the exported model
+----------------------------------------------------------------------------------------
+import timm
+
+# Load the pretrained model
+model = timm.create_model(
+    model_name='vit_small_patch16_224',
+    checkpoint_path='/workspace/ent-labotary/outputs/ssl_distil/vit16s/exported_models/exported_last.pt',
+)
+
+# Finetune or evaluate the model
+...
+----------------------------------------------------------------------------------------
+
+Model exported.
+Args: {
+    "checkpoint": "outputs/ssl_distil/vit16s/checkpoints/last.ckpt",
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+    "out": "outputs/ssl_distil/vit16s/vit16s_patch16_224_distil.pth",
+    "overwrite": false,
+    "part": "model"
+}
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+Exporting model to '/workspace/ent-labotary/outputs/ssl_distil/vit16s/vit16s_patch16_224_distil.pth'
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