Datasets:

codemaivanngu
/

simct-b200-portable-cu130

License:

Dataset Viewer

Split (1)

train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
kdflow: string
sglang-router: string
datasets: string
jedi: string
scipy: string
tensorboard: string
regex: string
executing: string
lark: string
pytest: string
prompt_toolkit: string
cuda-tile: string
msgpack: string
matplotlib-inline: string
setuptools: string
nvidia-cufile: string
airportsdata: string
grpcio-health-checking: string
pandas: string
tiktoken: string
pydantic-extra-types: string
cuda-core: string
torchvision: string
platformdirs: string
jsonschema-specifications: string
multidict: string
xxhash: string
loguru: string
torch_memory_saver: string
accelerate: string
starlette: string
nvidia-cusparse: string
compressed-tensors: string
partial-json-parser: string
pillow: string
nvidia-cudnn-cu13: string
lxml: string
aiosignal: string
llguidance: string
Markdown: string
traitlets: string
pexpect: string
opentelemetry-api: string
einops: string
httpcore: string
ring-flash-attn: string
absl-py: string
watchfiles: string
nvidia-cuda-runtime: string
Jinja2: string
Pygments: string
jiter: string
uvloop: string
httpx2: string
nvidia-cusparselt-cu13: string
pycparser: string
psutil: string
nvidia-nvjitlink: string
anthropic: string
grpcio: string
python-multipart: string
msgspec: string
ipython: string
certifi: string
tabulate: string
flashinfer-cubin: string
fastapi: string
py-spy: string
wandb: string
sglang-kernel: string
ptyprocess: string
peft: string
setproctitle: string
blobfile: string
mistral_common: string
timm: string
quack-kernels: string
pure_eval: string
typer
...
g
uvicorn: string
ninja: string
httpcore2: string
cuda-pathfinder: string
mdurl: string
typing-inspection: string
mpmath: string
nvidia-cudnn-frontend: string
torch: string
nvidia-cusolver: string
sentry-sdk: string
httpx: string
cbor2: string
hpack: string
hyperframe: string
h2: string
grpclib: string
image: string
files: struct<README.md: struct<sha256: string, bytes: int64>, install.sh: struct<sha256: string, bytes: in (... 315 chars omitted)
  child 0, README.md: struct<sha256: string, bytes: int64>
      child 0, sha256: string
      child 1, bytes: int64
  child 1, install.sh: struct<sha256: string, bytes: int64>
      child 0, sha256: string
      child 1, bytes: int64
  child 2, packages.json: struct<sha256: string, bytes: int64>
      child 0, sha256: string
      child 1, bytes: int64
  child 3, python-b200.sh: struct<sha256: string, bytes: int64>
      child 0, sha256: string
      child 1, bytes: int64
  child 4, runtime.tar.gz: struct<sha256: string, bytes: int64>
      child 0, sha256: string
      child 1, bytes: int64
  child 5, smoke.py: struct<sha256: string, bytes: int64>
      child 0, sha256: string
      child 1, bytes: int64
  child 6, source.tar: struct<sha256: string, bytes: int64>
      child 0, sha256: string
      child 1, bytes: int64
  child 7, targets.txt: struct<sha256: string, bytes: int64>
      child 0, sha256: string
      child 1, bytes: int64
target_gpu_test: string
targets: list<item: string>
  child 0, item: string
source_commit: string
to
{'image': Value('string'), 'source_commit': Value('string'), 'files': {'README.md': {'sha256': Value('string'), 'bytes': Value('int64')}, 'install.sh': {'sha256': Value('string'), 'bytes': Value('int64')}, 'packages.json': {'sha256': Value('string'), 'bytes': Value('int64')}, 'python-b200.sh': {'sha256': Value('string'), 'bytes': Value('int64')}, 'runtime.tar.gz': {'sha256': Value('string'), 'bytes': Value('int64')}, 'smoke.py': {'sha256': Value('string'), 'bytes': Value('int64')}, 'source.tar': {'sha256': Value('string'), 'bytes': Value('int64')}, 'targets.txt': {'sha256': Value('string'), 'bytes': Value('int64')}}, 'targets': List(Value('string')), 'target_gpu_test': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                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
              kdflow: string
              sglang-router: string
              datasets: string
              jedi: string
              scipy: string
              tensorboard: string
              regex: string
              executing: string
              lark: string
              pytest: string
              prompt_toolkit: string
              cuda-tile: string
              msgpack: string
              matplotlib-inline: string
              setuptools: string
              nvidia-cufile: string
              airportsdata: string
              grpcio-health-checking: string
              pandas: string
              tiktoken: string
              pydantic-extra-types: string
              cuda-core: string
              torchvision: string
              platformdirs: string
              jsonschema-specifications: string
              multidict: string
              xxhash: string
              loguru: string
              torch_memory_saver: string
              accelerate: string
              starlette: string
              nvidia-cusparse: string
              compressed-tensors: string
              partial-json-parser: string
              pillow: string
              nvidia-cudnn-cu13: string
              lxml: string
              aiosignal: string
              llguidance: string
              Markdown: string
              traitlets: string
              pexpect: string
              opentelemetry-api: string
              einops: string
              httpcore: string
              ring-flash-attn: string
              absl-py: string
              watchfiles: string
              nvidia-cuda-runtime: string
              Jinja2: string
              Pygments: string
              jiter: string
              uvloop: string
              httpx2: string
              nvidia-cusparselt-cu13: string
              pycparser: string
              psutil: string
              nvidia-nvjitlink: string
              anthropic: string
              grpcio: string
              python-multipart: string
              msgspec: string
              ipython: string
              certifi: string
              tabulate: string
              flashinfer-cubin: string
              fastapi: string
              py-spy: string
              wandb: string
              sglang-kernel: string
              ptyprocess: string
              peft: string
              setproctitle: string
              blobfile: string
              mistral_common: string
              timm: string
              quack-kernels: string
              pure_eval: string
              typer
              ...
              g
              uvicorn: string
              ninja: string
              httpcore2: string
              cuda-pathfinder: string
              mdurl: string
              typing-inspection: string
              mpmath: string
              nvidia-cudnn-frontend: string
              torch: string
              nvidia-cusolver: string
              sentry-sdk: string
              httpx: string
              cbor2: string
              hpack: string
              hyperframe: string
              h2: string
              grpclib: string
              image: string
              files: struct<README.md: struct<sha256: string, bytes: int64>, install.sh: struct<sha256: string, bytes: in (... 315 chars omitted)
                child 0, README.md: struct<sha256: string, bytes: int64>
                    child 0, sha256: string
                    child 1, bytes: int64
                child 1, install.sh: struct<sha256: string, bytes: int64>
                    child 0, sha256: string
                    child 1, bytes: int64
                child 2, packages.json: struct<sha256: string, bytes: int64>
                    child 0, sha256: string
                    child 1, bytes: int64
                child 3, python-b200.sh: struct<sha256: string, bytes: int64>
                    child 0, sha256: string
                    child 1, bytes: int64
                child 4, runtime.tar.gz: struct<sha256: string, bytes: int64>
                    child 0, sha256: string
                    child 1, bytes: int64
                child 5, smoke.py: struct<sha256: string, bytes: int64>
                    child 0, sha256: string
                    child 1, bytes: int64
                child 6, source.tar: struct<sha256: string, bytes: int64>
                    child 0, sha256: string
                    child 1, bytes: int64
                child 7, targets.txt: struct<sha256: string, bytes: int64>
                    child 0, sha256: string
                    child 1, bytes: int64
              target_gpu_test: string
              targets: list<item: string>
                child 0, item: string
              source_commit: string
              to
              {'image': Value('string'), 'source_commit': Value('string'), 'files': {'README.md': {'sha256': Value('string'), 'bytes': Value('int64')}, 'install.sh': {'sha256': Value('string'), 'bytes': Value('int64')}, 'packages.json': {'sha256': Value('string'), 'bytes': Value('int64')}, 'python-b200.sh': {'sha256': Value('string'), 'bytes': Value('int64')}, 'runtime.tar.gz': {'sha256': Value('string'), 'bytes': Value('int64')}, 'smoke.py': {'sha256': Value('string'), 'bytes': Value('int64')}, 'source.tar': {'sha256': Value('string'), 'bytes': Value('int64')}, 'targets.txt': {'sha256': Value('string'), 'bytes': Value('int64')}}, 'targets': List(Value('string')), 'target_gpu_test': Value('string')}
              because column names don't match

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SimCT B200 portable runtime

Private research runtime export, not a model or dataset. Source image contains Ubuntu22.04 userspace; target Ubuntu24.04 x86_64, NVIDIA580.105.08/CUDA13.0. This is a path-preserving runtime archive, not a relocatable virtualenv. Install requires permission to create the paths in targets.txt (usually root). Installer refuses existing paths and does not install/replace NVIDIA drivers. Default visible GPU is0; GPU1 on the supplied host is occupied. Download this pinned HF revision, then run bash install.sh in its directory. It verifies SHA256 before extracting and runs a small B200 CUDA test. Source is separate: source/kdflow, source/experiments. For source imports set PYTHONPATH to $PWD/source/experiments/modal/vendor:$PWD/source. Run Python with bash python-b200.sh ...; avoid relying on venv activation. Model weights, datasets, credentials, caches and training outputs are excluded. Host should provide bash/tar/gzip/sha256sum/find and gcc/g++ for JIT compilation. No training auto-starts. The CUDA smoke uses only GPU0, unless explicitly overridden. CPU packaging/import verification is not target GPU or full SGLang qualification. The existing Phi Modal launcher is provenance, NOT a Qwen training launcher. Licenses of bundled components remain in the runtime; consult them before reuse.

Downloads last month: 45

Total file size:

7.28 GB