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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
id: string
lane: string
tier: string
hypothesis: string
cmd: string
gpu_h_estimate: double
output_dir: string
validity_check: string
notes: string
to
{'id': Value('string'), 'lane': Value('string'), 'hypothesis': Value('string'), 'cmd': Value('string'), 'gpu_h_estimate': Value('float64'), 'output_dir': Value('string'), 'validity_check': Value('string'), 'notes': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              lane: string
              tier: string
              hypothesis: string
              cmd: string
              gpu_h_estimate: double
              output_dir: string
              validity_check: string
              notes: string
              to
              {'id': Value('string'), 'lane': Value('string'), 'hypothesis': Value('string'), 'cmd': Value('string'), 'gpu_h_estimate': Value('float64'), 'output_dir': Value('string'), 'validity_check': Value('string'), 'notes': Value('string')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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id
string
lane
string
hypothesis
string
cmd
string
gpu_h_estimate
float64
output_dir
string
validity_check
string
notes
string
1200_jacobian_train_flatten
jacobian_erank
Retrain the flatten readout (linear-in-Z baseline/control) to the exact positive-control recipe used by the ICML MI Workshop paper (icml-mi-workshop-2026, sections/05_positive_control.tex): gpt2-small, ProsQA, gamma=0, 6 latent positions, d=16, 25 epochs, batch 16, seed 1337, AdamW lr=1e-4. No checkpoint from the origi...
cd /home/nvidia/jacobian_erank_exp && PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True RANK=0 LOCAL_RANK=0 WORLD_SIZE=1 MASTER_ADDR=127.0.0.1 MASTER_PORT=29601 /home/nvidia/tdenv/bin/python3 run_matrix_codi.py --mode train_matrix --dataset prosqa --readout flatten --gamma 0.0 --epochs 25 --results-dir /home/nvidia/jaco...
3.5
/home/nvidia/jacobian_erank_exp/results/flatten
/home/nvidia/tdenv/bin/python3 -c "import json,os; rd='/home/nvidia/jacobian_erank_exp/results/flatten'; assert os.path.exists(rd+'/best_run_b_matrix.pt'), 'missing checkpoint'; d=json.load(open(rd+'/results.json')); assert 'train_matrix' in d, d.keys(); ba=d['train_matrix']['best_accuracy']; assert isinstance(ba,(int,...
LAUNCH-METHOD FIX (load-bearing, applies to all 4 training candidates in this lane): the script's own header docs 'torchrun --standalone --nproc_per_node=N ...', and torch.distributed.run/torchrun --standalone HANGS FOREVER on this box -- its c10d rendezvous TCPStore tries to bind a listener on whatever `socket.gethost...
1201_jacobian_train_bilinear_gelu
jacobian_erank
Retrain the bilinear+GELU readout (nonlinear-in-Z; phi(Z)=W*GELU(probes(Z)), probes_k(Z)=u_k^T Z v_k) to the exact SS5 positive-control recipe (icml-mi-workshop-2026): gpt2-small, ProsQA, gamma=0, 6 latents, d=16, 25 epochs, batch 16, seed 1337, lr=1e-4. This is one of the THREE readouts the paper's SS5.3 refined hypot...
cd /home/nvidia/jacobian_erank_exp && PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True RANK=0 LOCAL_RANK=0 WORLD_SIZE=1 MASTER_ADDR=127.0.0.1 MASTER_PORT=29602 /home/nvidia/tdenv/bin/python3 run_matrix_codi.py --mode train_matrix --dataset prosqa --readout bilinear_gelu --gamma 0.0 --epochs 25 --results-dir /home/nvidi...
3.5
/home/nvidia/jacobian_erank_exp/results/bilinear_gelu
/home/nvidia/tdenv/bin/python3 -c "import json,os; rd='/home/nvidia/jacobian_erank_exp/results/bilinear_gelu'; assert os.path.exists(rd+'/best_run_b_matrix.pt'), 'missing checkpoint'; d=json.load(open(rd+'/results.json')); assert 'train_matrix' in d, d.keys(); ba=d['train_matrix']['best_accuracy']; assert isinstance(ba...
LAUNCH-METHOD FIX: see job 1200's notes for the full writeup -- torchrun/torch.distributed.run --standalone HANGS FOREVER on this box (c10d rendezvous binds to a bogus public-resolving hostname IP, not localhost, even with --master-addr passed explicitly). This cmd instead invokes run_matrix_codi.py directly as a plain...
1202_jacobian_train_svd_aug
jacobian_erank
Retrain the svd-augmented readout (nonlinear-in-Z; phi(Z)=W_down@vec(Z) + MLP(svdvals(Z))) to the exact SS5 positive-control recipe (icml-mi-workshop-2026): gpt2-small, ProsQA, gamma=0, 6 latents, d=16, 25 epochs, batch 16, seed 1337, lr=1e-4. One of the THREE readouts the paper's SS5.3 refined hypothesis predicts eran...
cd /home/nvidia/jacobian_erank_exp && PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True RANK=0 LOCAL_RANK=0 WORLD_SIZE=1 MASTER_ADDR=127.0.0.1 MASTER_PORT=29603 /home/nvidia/tdenv/bin/python3 run_matrix_codi.py --mode train_matrix --dataset prosqa --readout svd_aug --gamma 0.0 --epochs 25 --results-dir /home/nvidia/jaco...
3.5
/home/nvidia/jacobian_erank_exp/results/svd_aug
/home/nvidia/tdenv/bin/python3 -c "import json,os; rd='/home/nvidia/jacobian_erank_exp/results/svd_aug'; assert os.path.exists(rd+'/best_run_b_matrix.pt'), 'missing checkpoint'; d=json.load(open(rd+'/results.json')); assert 'train_matrix' in d, d.keys(); ba=d['train_matrix']['best_accuracy']; assert isinstance(ba,(int,...
LAUNCH-METHOD FIX: see job 1200's notes for the full writeup -- torchrun/torch.distributed.run --standalone HANGS FOREVER on this box (c10d rendezvous binds to a bogus public-resolving hostname IP, not localhost, even with --master-addr passed explicitly). This cmd instead invokes run_matrix_codi.py directly as a plain...
1203_jacobian_train_quadratic
jacobian_erank
Retrain the quadratic readout (nonlinear-in-Z; phi(Z)=W_down_quad@concat(vec(Z Z^T), vec(Z^T Z))) to the exact SS5 positive-control recipe (icml-mi-workshop-2026): gpt2-small, ProsQA, gamma=0, 6 latents, d=16, 25 epochs, batch 16, seed 1337, lr=1e-4. One of the THREE readouts the paper's SS5.3 refined hypothesis predic...
cd /home/nvidia/jacobian_erank_exp && PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True RANK=0 LOCAL_RANK=0 WORLD_SIZE=1 MASTER_ADDR=127.0.0.1 MASTER_PORT=29604 /home/nvidia/tdenv/bin/python3 run_matrix_codi.py --mode train_matrix --dataset prosqa --readout quadratic --gamma 0.0 --epochs 25 --results-dir /home/nvidia/ja...
3.5
/home/nvidia/jacobian_erank_exp/results/quadratic
/home/nvidia/tdenv/bin/python3 -c "import json,os; rd='/home/nvidia/jacobian_erank_exp/results/quadratic'; assert os.path.exists(rd+'/best_run_b_matrix.pt'), 'missing checkpoint'; d=json.load(open(rd+'/results.json')); assert 'train_matrix' in d, d.keys(); ba=d['train_matrix']['best_accuracy']; assert isinstance(ba,(in...
LAUNCH-METHOD FIX: see job 1200's notes for the full writeup -- torchrun/torch.distributed.run --standalone HANGS FOREVER on this box (c10d rendezvous binds to a bogus public-resolving hostname IP, not localhost, even with --master-addr passed explicitly). This cmd instead invokes run_matrix_codi.py directly as a plain...

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