claude-opus-5

'loss': 0.0105
'loss': 0.008
'loss': 0.0072
'loss': -0.0086
'loss': -0.0095
=== e2/e3 fmt vs baseline:
e1 47.5 fmt 99.72
e2 53.33 fmt 99.72
e3 45.83 fmt 99.44
None
README.md
logs

[stdout]
'loss': 0.0105
'loss': 0.008
'loss': 0.0072
'loss': -0.0086
'loss': -0.0095
=== e2/e3 fmt vs baseline:
e1 47.5 fmt 99.72
e2 53.33 fmt 99.72
e3 45.83 fmt 99.44
None
README.md
logs
[metadata] {"noOutputExpected": false}

e5 chained: paper recipe + mcl2048
=== [e4] train start 06:16:11  (remaining 12715s)
12143

[stdout]
e5 chained: paper recipe + mcl2048
=== [e4] train start 06:16:11  (remaining 12715s)
12143
[metadata] {"noOutputExpected": false}

=== [e4] train start 06:16:11  (remaining 12715s)
'loss': -0.0133, 'grad_norm': 0.038309067487716675
11542

[stdout]
=== [e4] train start 06:16:11  (remaining 12715s)
'loss': -0.0133, 'grad_norm': 0.038309067487716675
11542
[metadata] {"noOutputExpected": false}

=== [e4] train rc=0 06:36:03
/workspace/runs/e4/capped_seed42/checkpoint-100
=== [e4] eval start 06:36:03
11061

[stdout]
=== [e4] train rc=0 06:36:03
/workspace/runs/e4/capped_seed42/checkpoint-100
=== [e4] eval start 06:36:03
11061
[metadata] {"noOutputExpected": false}

=== [e4] train rc=0 06:36:03
/workspace/runs/e4/capped_seed42/checkpoint-100
=== [e4] eval start 06:36:03
---
10480

[stdout]
=== [e4] train rc=0 06:36:03
/workspace/runs/e4/capped_seed42/checkpoint-100
=== [e4] eval start 06:36:03
---
10480
[metadata] {"noOutputExpected": false}

=== [e4] train rc=0 06:36:03
/workspace/runs/e4/capped_seed42/checkpoint-100
=== [e4] eval start 06:36:03
---
10069

[stdout]
=== [e4] train rc=0 06:36:03
/workspace/runs/e4/capped_seed42/checkpoint-100
=== [e4] eval start 06:36:03
---
10069
[metadata] {"noOutputExpected": false}

=== [e4] eval rc=0 07:00:57
RESULT e4: avg@12=53.61 pass@12=80.00 fmt=98.3
=== [e4] done 07:00:57 (remaining 10029s)
---
[e5] NO CHECKPOINT

[stdout]
=== [e4] eval rc=0 07:00:57
RESULT e4: avg@12=53.61 pass@12=80.00 fmt=98.3
=== [e4] done 07:00:57 (remaining 10029s)
---
[e5] NO CHECKPOINT
[metadata] {"noOutputExpected": false}

154:
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   0%|          | 0/5 [00:00<?, ?it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  60%|██████    | 3/5 [00:00<00:00, 28.32it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 5/5 [00:00<00:00, 25.42it/s]
155:
Capturing CUDA graphs (decode, FULL):   0%|          | 0/4 [00:00<?, ?it/s]
Capturing CUDA graphs (decode, FULL):  75%|███████▌  | 3/4 [00:00<00:00, 28.47it/s]
Capturing CUDA graphs (decode, FULL): 100%|██████████| 4/4 [00:00<00:00, 31.46it/s]
160:[rank1]:W0803 07:02:01.643000 24134 torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. 
161:[rank1]:W0803 07:02:01.643000 24134 torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
193:[rank1]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.70 GiB. GPU 1 has a total capacity of 79.18 GiB of which 806.19 MiB is free. Process 1 has 78.38 GiB memory in use. Of the allocated memory 73.97 GiB is allocated by PyTorch, with 22.00 MiB allocated in private pools (e.g., CUDA Graphs), and 189.99 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
213:[rank3]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 3 has a total capacity of 79.18 GiB of which 1.15 GiB is free. Process 1 has 78.02 GiB memory in use. Of the allocated memory 73.60 GiB is allocated by PyTorch, with 22.00 MiB allocated in private pools (e.g., CUDA Graphs), and 187.17 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
...
  exitcode  : 1 (pid: 24134)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
TRAIN_CKPT=
[train_capped] FATAL: no checkpoint-100 produced
9769

[stdout]
154:
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   0%|          | 0/5 [00:00<?, ?it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  60%|██████    | 3/5 [00:00<00:00, 28.32it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 5/5 [00:00<00:00, 25.42it/s]
155:
Capturing CUDA graphs (decode, FULL):   0%|          | 0/4 [00:00<?, ?it/s]
Capturing CUDA graphs (decode, FULL):  75%|███████▌  | 3/4 [00:00<00:00, 28.47it/s]
Capturing CUDA graphs (decode, FULL): 100%|██████████| 4/4 [00:00<00:00, 31.46it/s]
160:[rank1]:W0803 07:02:01.643000 24134 torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. 
161:[rank1]:W0803 07:02:01.643000 24134 torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
193:[rank1]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.70 GiB. GPU 1 has a total capacity of 79.18 GiB of which 806.19 MiB is free. Process 1 has 78.38 GiB memory in use. Of the allocated memory 73.97 GiB is allocated by PyTorch, with 22.00 MiB allocated in private pools (e.g., CUDA Graphs), and 189.99 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
213:[rank3]: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.54 GiB. GPU 3 has a total capacity of 79.18 GiB of which 1.15 GiB is free. Process 1 has 78.02 GiB memory in use. Of the allocated memory 73.60 GiB is allocated by PyTorch, with 22.00 MiB allocated in private pools (e.g., CUDA Graphs), and 187.17 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
...
  exitcode  : 1 (pid: 24134)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
TRAIN_CKPT=
[train_capped] FATAL: no checkpoint-100 produced
9769
[metadata] {"noOutputExpected": false}

[data] filter=hard_numeric: 29434 -> 9775 examples
'learning_rate': 9.9e-06
'learning_rate': 9.7e-06
9488

[stdout]
[data] filter=hard_numeric: 29434 -> 9775 examples
'learning_rate': 9.9e-06
'learning_rate': 9.7e-06
9488
[metadata] {"noOutputExpected": false}

1	import os
2	import wandb
3	
4	from datasets import load_dataset
5	from transformers import AutoTokenizer, GenerationConfig
6	
7	from trl import (
8	    LogCompletionsCallback,
9	    ModelConfig,
10	    ScriptArguments,
11	    TrlParser,
12	    get_kbit_device_map,
13	    get_peft_config,
14	    get_quantization_config,
15	)
16	from trl.experimental.gold import GOLDConfig
17	from opsd_trainer import OPSDTrainer
18	from dataclasses import dataclass, field
19	
20	# Enable logging in a Hugging Face Space
21	os.environ.setdefault("TRACKIO_SPACE_ID", "trl-trackio")
22

[metadata] {"type": "text", "file": {"filePath": "/workspace/repo/opsd_train.py", "content": "import os\nimport wandb\n\nfrom datasets import load_dataset\nfrom transformers import AutoTokenizer, GenerationConfig\n\nfrom trl import (\n    LogCompletionsCallback,\n    ModelConfig,\n    ScriptArguments,\n    TrlParser,\n    get_kbit_device_map,\n    get_peft_config,\n    get_quantization_config,\n)\nfrom trl.experimental.gold import GOLDConfig\nfrom opsd_trainer import OPSDTrainer\nfrom dataclasses import dataclass, field\n\n# Enable logging in a Hugging Face Space\nos.environ.setdefault(\"TRACKIO_SPACE_ID\", \"trl-trackio\")\n", "numLines": 22, "startLine": 1, "totalLines": 361}}

The file /workspace/repo/opsd_train.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/repo/opsd_train.py", "oldString": "import os\nimport wandb\n\nfrom datasets import load_dataset\nfrom transformers import AutoTokenizer, GenerationConfig", "newString": "import os\nimport torch\nimport wandb\n\nfrom datasets import load_dataset\nfrom transformers import AutoTokenizer, GenerationConfig, TrainerCallback", "originalFile": null, "structuredPatch": [{"oldStart": 1, "oldLines": 8, "newStart": 1, "newLines": 9, "lines": [" import os", "+import torch", " import wandb", " ", " from datasets import load_dataset", "-from transformers import AutoTokenizer, GenerationConfig", "+from transformers import AutoTokenizer, GenerationConfig, TrainerCallback", " ", " from trl import (", "     LogCompletionsCallback,"]}], "userModified": false, "replaceAll": false}

The file /workspace/repo/opsd_train.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/repo/opsd_train.py", "oldString": "@dataclass\nclass CustomScriptArguments(ScriptArguments):", "newString": "class TailWeightAverage(TrainerCallback):\n    \"\"\"Finish on the average of the last `last_k` adapter iterates instead of the last one.\n\n    Within this budget the trajectory is noise-dominated rather than steadily improving —\n    the paper's own step curves move non-monotonically between steps 50/75/100 — so the\n    step-100 weights are one noisy draw from a basin the run is rattling around in.\n    Uniformly averaging the tail of the trajectory keeps the progress made while cancelling\n    part of that per-iterate noise. It is pure post-processing of the same 100 steps: no\n    extra optimizer steps, no extra data, no change to the optimizer or to inference.\n    \"\"\"\n\n    def __init__(self, last_k, total_steps):\n        self.first_step = max(1, total_steps - last_k + 1)\n        self.total_steps = total_steps\n        self.running_sum = None\n        self.count = 0\n\n    def _trainable(self, model):\n        return [(name, p) for name, p in model.named_parameters() if p.requires_grad]\n\n    def on_step_end(self, args, state, control, model=None, **kwargs):\n        if model is None or state.global_step < self.first_step:\n            return\n        with torch.no_grad():\n            params = self._trainable(model)\n            if self.running_sum is None:\n                self.running_sum = {n: p.detach().float().clone() for n, p in params}\n            else:\n                for name, p in params:\n                    self.running_sum[name] += p.detach().float()\n            self.count += 1\n\n            if state.global_step >= self.total_steps:\n                # Swap the average in before the trainer writes checkpoint-<total_steps>.\n                for name, p in params:\n                    p.copy_((self.running_sum[name] / self.count).to(p.dtype))\n                print(f\"[wavg] final weights = mean of the last {self.count} iterates\", flush=True)\n\n\n@dataclass\nclass CustomScriptArguments(ScriptArguments):", "originalFile": null, "structuredPatch": [{"oldStart": 22, "oldLines": 6, "newStart": 22, "newLines": 45, "lines": [" os.environ.setdefault(\"TRACKIO_SPACE_ID\", \"trl-trackio\")", " ", " ", "+class TailWeightAverage(TrainerCallback):", "+    \"\"\"Finish on the average of the last `last_k` adapter iterates instead of the last one.", "+", "+    Within this budget the trajectory is noise-dominated rather than steadily improving —", "+    the paper's own step curves move non-monotonically between steps 50/75/100 — so the", "+    step-100 weights are one noisy draw from a basin the run is rattling around in.", "+    Uniformly averaging the tail of the trajectory keeps the progress made while cancelling", "+    part of that per-iterate noise. It is pure post-processing of the same 100 steps: no", "+    extra optimizer steps, no extra data, no change to the optimizer or to inference.", "+    \"\"\"", "+", "+    def __init__(self, last_k, total_steps):", "+        self.first_step = max(1, total_steps - last_k + 1)", "+        self.total_steps = total_steps", "+        self.running_sum = None", "+        self.count = 0", "+", "+    def _trainable(self, model):", "+        return [(name, p) for name, p in model.named_parameters() if p.requires_grad]", "+", "+    def on_step_end(self, args, state, control, model=None, **kwargs):", "+        if model is None or state.global_step < self.first_step:", "+            return", "+        with torch.no_grad():", "+            params = self._trainable(model)", "+            if self.running_sum is None:", "+                self.running_sum = {n: p.detach().float().clone() for n, p in params}", "+            else:", "+                for name, p in params:", "+                    self.running_sum[name] += p.detach().float()", "+            self.count += 1", "+", "+            if state.global_step >= self.total_steps:", "+                # Swap the average in before the trainer writes checkpoint-<total_steps>.", "+                for name, p in params:", "+                    p.copy_((self.running_sum[name] / self.count).to(p.dtype))", "+                print(f\"[wavg] final weights = mean of the last {self.count} iterates\", flush=True)", "+", "+", " @dataclass", " class CustomScriptArguments(ScriptArguments):", "     \"\"\"Extended script arguments with Thinking Machines loss option.\"\"\""]}], "userModified": false, "replaceAll": false}

The file /workspace/repo/opsd_train.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/repo/opsd_train.py", "oldString": "    if training_args.eval_strategy != \"no\":", "newString": "    wavg_last = int(os.environ.get(\"OPSD_WAVG_LAST\", \"20\"))\n    if wavg_last > 0 and training_args.max_steps > 0:\n        trainer.add_callback(TailWeightAverage(wavg_last, training_args.max_steps))\n        print(f\"[wavg] tail-averaging the last {wavg_last} of {training_args.max_steps} iterates\")\n\n    if training_args.eval_strategy != \"no\":", "originalFile": null, "structuredPatch": [{"oldStart": 386, "oldLines": 6, "newStart": 386, "newLines": 11, "lines": ["         teacher_thinking=teacher_thinking,", "     )", " ", "+    wavg_last = int(os.environ.get(\"OPSD_WAVG_LAST\", \"20\"))", "+    if wavg_last > 0 and training_args.max_steps > 0:", "+        trainer.add_callback(TailWeightAverage(wavg_last, training_args.max_steps))", "+        print(f\"[wavg] tail-averaging the last {wavg_last} of {training_args.max_steps} iterates\")", "+", "     if training_args.eval_strategy != \"no\":", "         generation_config = GenerationConfig(", "             max_new_tokens=training_args.max_completion_length,"]}], "userModified": false, "replaceAll": false}

4.57.1
ng_rate = self._get_learning_rate()

                        if not self.accelerator.optimizer_step_was_skipped:
                            # Delay optimizer scheduling until metrics are generated
                            if not isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
                                self.lr_scheduler.step()

                        model.zero_grad()
                        self.state.global_step += 1
                        self.state.epoch = epoch + (step + 1) / steps_in_epoch
                        self.control = self.callback_handler.on_step_end(args, self.state, self.control)
                        self._maybe_log_save_evaluate(
                            tr_loss,
                            grad_norm,
                            model,
                            trial,
                            epoch,
                            ignore_keys_for_eval,
                            start_time,
                            learning_rate=learning_rate,
                        )
                    else:
                        self.control = self.callback_handler.on_substep_end(args, self.state, self.control)

                    # PyTorch/XLA relies on the data loader to insert the mark_step for
                    # ea

[stdout]
4.57.1
ng_rate = self._get_learning_rate()

                        if not self.accelerator.optimizer_step_was_skipped:
                            # Delay optimizer scheduling until metrics are generated
                            if not isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
                                self.lr_scheduler.step()

                        model.zero_grad()
                        self.state.global_step += 1
                        self.state.epoch = epoch + (step + 1) / steps_in_epoch
                        self.control = self.callback_handler.on_step_end(args, self.state, self.control)
                        self._maybe_log_save_evaluate(
                            tr_loss,
                            grad_norm,
                            model,
                            trial,
                            epoch,
                            ignore_keys_for_eval,
                            start_time,
                            learning_rate=learning_rate,
                        )
                    else:
                        self.control = self.callback_handler.on_substep_end(args, self.state, self.control)

                    # PyTorch/XLA relies on the data loader to insert the mark_step for
                    # ea
[metadata] {"noOutputExpected": false}

def call_event(self, event, args, state, control, **kwargs):
        for callback in self.callbacks:
            result = getattr(callback, event)(
                args,
                state,
                control,
                model=self.model,
                processing_class=self.processing_class,
                optimizer=self.optimizer,
                lr_scheduler=self.lr_scheduler,
                train_dataloader=self.train_dataloader,
                eval_dataloader=self.eval_dataloader,
                **kwargs,
            )
            # A Callback can skip the return of `control` if it doesn't change it.
            if result is not None:
                control = resu
92:    --save_steps 100 --logging_steps 2 --wandb_project OPSD \
100:    --output_dir "$OUT_DIR" 2>&1 | tee "$OUT_DIR/train_seed${SEED}.log"

[stdout]
    def call_event(self, event, args, state, control, **kwargs):
        for callback in self.callbacks:
            result = getattr(callback, event)(
                args,
                state,
                control,
                model=self.model,
                processing_class=self.processing_class,
                optimizer=self.optimizer,
                lr_scheduler=self.lr_scheduler,
                train_dataloader=self.train_dataloader,
                eval_dataloader=self.eval_dataloader,
                **kwargs,
            )
            # A Callback can skip the return of `control` if it doesn't change it.
            if result is not None:
                control = resu
92:    --save_steps 100 --logging_steps 2 --wandb_project OPSD \
100:    --output_dir "$OUT_DIR" 2>&1 | tee "$OUT_DIR/train_seed${SEED}.log"
[metadata] {"noOutputExpected": false}

[wavg] final weights = mean of the last 3 iterates
weights after: [4.0] expected mean of [3.0, 4.0, 5.0] = 4.0

[stdout]
[wavg] final weights = mean of the last 3 iterates
weights after: [4.0] expected mean of [3.0, 4.0, 5.0] = 4.0
[metadata] {"noOutputExpected": false}

=== [e6] train start 07:06:39  (remaining 9689s)
'loss': -0.0181, 'grad_norm': 0.050713010132312775
8747

[stdout]
=== [e6] train start 07:06:39  (remaining 9689s)
'loss': -0.0181, 'grad_norm': 0.050713010132312775
8747
[metadata] {"noOutputExpected": false}

=== [e6] train rc=0 07:23:57
/workspace/runs/e6/capped_seed42/checkpoint-100
=== [e6] eval start 07:23:57
8307

[stdout]
=== [e6] train rc=0 07:23:57
/workspace/runs/e6/capped_seed42/checkpoint-100
=== [e6] eval start 07:23:57
8307
[metadata] {"noOutputExpected": false}

e7 chained: paper recipe + tail weight averaging

[stdout]
e7 chained: paper recipe + tail weight averaging
[metadata] {"noOutputExpected": false}

=== [e6] train rc=0 07:23:57
/workspace/runs/e6/capped_seed42/checkpoint-100
=== [e6] eval start 07:23:57
---
7816

[stdout]
=== [e6] train rc=0 07:23:57
/workspace/runs/e6/capped_seed42/checkpoint-100
=== [e6] eval start 07:23:57
---
7816
[metadata] {"noOutputExpected": false}

=== [e6] train rc=0 07:23:57
/workspace/runs/e6/capped_seed42/checkpoint-100
=== [e6] eval start 07:23:57
---
7445

[stdout]
=== [e6] train rc=0 07:23:57
/workspace/runs/e6/capped_seed42/checkpoint-100
=== [e6] eval start 07:23:57
---
7445
[metadata] {"noOutputExpected": false}

=== [e6] eval rc=0 07:47:01
RESULT e6: avg@12=56.94 pass@12=76.67 fmt=98.6
=== [e6] done 07:47:01 (remaining 7265s)
---
=== [e7] train start 07:47:17  (remaining 7255s)

[stdout]
=== [e6] eval rc=0 07:47:01
RESULT e6: avg@12=56.94 pass@12=76.67 fmt=98.6
=== [e6] done 07:47:01 (remaining 7265s)
---
=== [e7] train start 07:47:17  (remaining 7255s)
[metadata] {"noOutputExpected": false}

1
1
1
1
---cycles:
30683 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785728893030-4i6hgj.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'sleep 10; nvidia-smi --query-compute-apps=pid --format=csv,noheader; echo "---cycles:"; pgrep -af "cycle.sh" | grep -v "^159 " | head; echo "---rc ok"' < /dev/null && pwd -P >| /tmp/claude-c79d-cwd
30700 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785728893030-4i6hgj.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'sleep 10; nvidia-smi --query-compute-apps=pid --format=csv,noheader; echo "---cycles:"; pgrep -af "cycle.sh" | grep -v "^159 " | head; echo "---rc ok"' < /dev/null && pwd -P >| /tmp/claude-c79d-cwd
---rc ok

[stdout]
1
1
1
1
---cycles:
30683 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785728893030-4i6hgj.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'sleep 10; nvidia-smi --query-compute-apps=pid --format=csv,noheader; echo "---cycles:"; pgrep -af "cycle.sh" | grep -v "^159 " | head; echo "---rc ok"' < /dev/null && pwd -P >| /tmp/claude-c79d-cwd
30700 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785728893030-4i6hgj.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'sleep 10; nvidia-smi --query-compute-apps=pid --format=csv,noheader; echo "---cycles:"; pgrep -af "cycle.sh" | grep -v "^159 " | head; echo "---rc ok"' < /dev/null && pwd -P >| /tmp/claude-c79d-cwd
---rc ok
[metadata] {"noOutputExpected": false}