Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems
Paper
• 2406.00809 • Published
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 91, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 193, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
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/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Executable Colab notebooks for MatrixPFN — Graph Neural Networks as learned preconditioners for sparse linear systems.
| Notebook | Description |
|---|---|
07_MatrixPFN_EndToEnd.ipynb |
Full pipeline: train ContextResGCN, benchmark against Jacobi, solve with FGMRES |
colab_benchmark.ipynb |
SuiteSparse benchmark: 838/867 matrices × 6 classical preconditioners (ILU, AMG, Jacobi, Block Jacobi, GMRES-Inner, None) |
ablation_edge_features_v3.ipynb |
Definitive GCN vs MPNN ablation: 5 seeds, 1000 epochs, 3 domains |
Open any notebook in Google Colab:
https://colab.research.google.com/github/...
Or download via the HuggingFace Hub:
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="Csed-dev/matrixpfn-notebooks",
repo_type="dataset",
filename="colab_benchmark.ipynb",
)