Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Column() changed from object to string in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 174, in _generate_tables
df = pandas_read_json(f)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1391, in _parse
self.obj = DataFrame(
^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/core/frame.py", line 778, in __init__
mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
index = _extract_index(arrays)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/core/internals/construction.py", line 677, in _extract_index
raise ValueError("All arrays must be of the same length")
ValueError: All arrays must be of the same length
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 177, in _generate_tables
raise e
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 151, in _generate_tables
pa_table = paj.read_json(
^^^^^^^^^^^^^^
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
🔬 Distributed Muon: Field Notes & Reproducibility Artifacts
Code, Performance Traces, and Analysis Logs
This repository contains the raw engineering artifacts for the deep-dive investigation: "Reproducing and Validating Distributed Muon".
It serves as the proof of work for the performance claims regarding the Muon optimizer's communication efficiency and computational overhead in a distributed setting (Data Parallel + Tensor Parallel).
📄 Read the Full Report: Reproducing and Validating Distributed Muon 🐢✨: A Practical Verification of Communication Efficiency Claims 🛠️ Get the Tutorial Code: bird-of-paradise/muon-distributed
📂 Repository Structure
traces/: Raw Chrome Trace (.json) files generated by PyTorch Profiler. You can load these intochrome://tracingor ui.perfetto.dev to visualize the exact CPU/GPU execution timeline.comparison/: Side-by-side traces of AdamW vs. Muon (Hybrid DP=2/TP=2).distributed_muon/: Scaling traces for DP=4, TP=4, and Hybrid configurations.
analysis_scripts/: The exact Python scripts used to generate the traces and parse the performance metrics.figures/: High-resolution charts and trace visualizations used in the report.report/: A PDF archive of the full technical investigation.
🔍 Key Findings (Verified in Traces)
The traces in this repository provide empirical evidence for the following:
- Communication Efficiency: Muon (Hybrid DP2/TP2) demonstrates 0.57x the communication overhead of AdamW on a bandwidth-constrained cluster (PCIe Gen4 x4).
- Evidence: Compare
traces/comparison/adamw_fullstep_rank0.jsonvsmuon_fullstep_dp2_tp2_rank0.json.
- Evidence: Compare
- Optimizer Latency: The Muon step accounts for ~1.1% of total training time, validating the paper's "negligible overhead" claim.
- Hybrid Scaling: The
DP=2, TP=2configuration outperforms pure DP or pure TP on 4 GPUs, balancing memory bandwidth with communication overhead.
🛠️ How to Reproduce
To run these benchmarks yourself on a 4-GPU cluster:
- Clone this repository.
- Install dependencies:
torch. - Run the benchmark script:
# This will generate new JSON traces in your local directory
python analysis_scripts/muon_vs_adam.py
- Run the performance analysis on included trace files
python analysis_scripts/performance_comparison.py
🙏 Acknowledgments
- Mahdi Chaker for generously providing GPU cluster access
- MoonShot AI team for open-sourcing their PoC implementation
📖 Citation If you use these traces or analysis in your work, please cite:
@misc{wei2025muoneproducibility, author = {Wei, Jen}, title = {Distributed Muon: Performance Artifacts and Benchmarks}, year = {2025}, publisher = {Hugging Face}, journal = {Hugging Face Datasets}, howpublished = {\url{https://huggingface.co/datasets/bird-of-paradise/muon-distributed-reproducibility}} }
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