diff --git "a/colab_benchmark.ipynb" "b/colab_benchmark.ipynb"
new file mode 100644--- /dev/null
+++ "b/colab_benchmark.ipynb"
@@ -0,0 +1,3641 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# SuiteSparse Benchmark — Remaining Matrices\n",
+ "\n",
+ "Run the remaining 86/867 SuiteSparse benchmarks on Colab.\n",
+ "\n",
+ "**Dataset:** `Csed-dev/matrixpfn-suitesparse` on HuggingFace \n",
+ "**Package:** `matrixpfn` from PyPI"
+ ],
+ "metadata": {
+ "id": "af7gDhN9vTlw"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install -q matrixpfn huggingface_hub"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gltIiauVvTly",
+ "outputId": "2d198ec4-b7d6-44ff-8c68-1f67d4e5c4c4"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/47.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.7/47.7 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.9 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.6/1.9 MB\u001b[0m \u001b[31m22.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m29.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/5.7 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m5.7/5.7 MB\u001b[0m \u001b[31m249.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.7/5.7 MB\u001b[0m \u001b[31m132.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from huggingface_hub import HfApi, hf_hub_download, snapshot_download\n",
+ "from pathlib import Path\n",
+ "\n",
+ "REPO_ID = \"Csed-dev/matrixpfn-suitesparse\"\n",
+ "DATA_DIR = Path(\"/content/data\")\n",
+ "SUITESPARSE_DIR = DATA_DIR / \"suitesparse\"\n",
+ "RESULTS_FILE = DATA_DIR / \"benchmark_gnp_paper.jsonl\"\n",
+ "\n",
+ "DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "hf_hub_download(\n",
+ " repo_id=REPO_ID,\n",
+ " repo_type=\"dataset\",\n",
+ " filename=\"benchmark_results/benchmark_gnp_paper.jsonl\",\n",
+ " local_dir=DATA_DIR,\n",
+ ")\n",
+ "\n",
+ "RESULTS_FILE = DATA_DIR / \"benchmark_results\" / \"benchmark_gnp_paper.jsonl\"\n",
+ "print(f\"Results file: {RESULTS_FILE}\")\n",
+ "print(f\"Exists: {RESULTS_FILE.exists()}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 212,
+ "referenced_widgets": [
+ "0d25683a2fa747eeb8a609a3dd0b8556",
+ "81330558260f4946bd6ea73dad91d711",
+ "66d02a9e81bb4dbbaf57afd360633cab",
+ "dd4700d6e2804f1b9378498fde4dafcb",
+ "c50a294b54f3445b87e3208a7238791d",
+ "809912ee439f47aca582c2171fdceeb3",
+ "098400d05167497f81e7f64e19ef527c",
+ "97654c5837944e8e97abf8d2cdf546e4",
+ "fed47810fd514658a84be72f3ec5ea3d",
+ "13b912c181614c88a1e34610180570a5",
+ "1923b818684f4f66a6a54b4b62f2b3b2"
+ ]
+ },
+ "id": "-Ucxg30VvTly",
+ "outputId": "dc6a3848-a6df-4ca3-ac59-ab7f22d34493"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "benchmark_results/benchmark_gnp_paper.js(…): 0%| | 0.00/12.8M [00:00 set[str]:\n",
+ " completed = set()\n",
+ " if results_file.exists():\n",
+ " for line in results_file.read_text().strip().split(\"\\n\"):\n",
+ " if line:\n",
+ " completed.add(json.loads(line)[\"matrix_name\"])\n",
+ " return completed\n",
+ "\n",
+ "def discover_matrices(suitesparse_dir: Path) -> list[Path]:\n",
+ " mtx_files = []\n",
+ " for matrix_dir in sorted(suitesparse_dir.iterdir()):\n",
+ " if not matrix_dir.is_dir():\n",
+ " continue\n",
+ " main_mtx = matrix_dir / f\"{matrix_dir.name}.mtx\"\n",
+ " if main_mtx.exists():\n",
+ " mtx_files.append(main_mtx)\n",
+ " if not mtx_files:\n",
+ " raise FileNotFoundError(f\"No matrix directories found in {suitesparse_dir}\")\n",
+ " return mtx_files\n",
+ "\n",
+ "all_matrices = discover_matrices(SUITESPARSE_DIR)\n",
+ "completed = load_completed(RESULTS_FILE)\n",
+ "pending = [p for p in all_matrices if p.stem not in completed]\n",
+ "\n",
+ "print(f\"Total: {len(all_matrices)}, Completed: {len(completed)}, Remaining: {len(pending)}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "OG0MsgcZvTlz",
+ "outputId": "e2668fcc-dd83-4f9b-b35a-2de6e76d5d1b"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Total: 867, Completed: 781, Remaining: 86\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Run benchmark\n",
+ "\n",
+ "Uses the same configuration as the local benchmark: `gnp_paper` config (restart=10, max_iters=100, rtol=1e-8), 60s timeout per preconditioner, sequential processing with hard kill via `multiprocessing.Process`."
+ ],
+ "metadata": {
+ "id": "ArU9DNXsvTlz"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import math\n",
+ "import time\n",
+ "import warnings\n",
+ "import multiprocessing\n",
+ "from dataclasses import dataclass, field, asdict\n",
+ "\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\", message=\"Sparse CSR tensor support is in beta state\")\n",
+ "from scipy.io import mmread\n",
+ "from scipy.sparse import issparse\n",
+ "\n",
+ "from matrixpfn.solver.fgmres import FGMRES, SolverResult\n",
+ "from matrixpfn.precond.jacobi import Jacobi\n",
+ "from matrixpfn.precond.ilu import ILU\n",
+ "from matrixpfn.precond.amg import AMG_AIR\n",
+ "from matrixpfn.precond.block_jacobi import BlockJacobi\n",
+ "from matrixpfn.precond.gmres_preconditioner import GMRESPreconditioner\n",
+ "\n",
+ "SOLVER_CONFIG = {\"restart\": 10, \"max_iters\": 100, \"rtol\": 1e-8}\n",
+ "PRECONDITIONERS = [\"none\", \"jacobi\", \"block_jacobi\", \"ilu\", \"amg\", \"gmres\"]\n",
+ "TIMEOUT = 60.0\n",
+ "\n",
+ "\n",
+ "@dataclass\n",
+ "class PrecondResult:\n",
+ " name: str\n",
+ " construction_time: float\n",
+ " construction_failed: bool\n",
+ " construction_error: str\n",
+ " converged: bool\n",
+ " iterations: int\n",
+ " final_residual: float\n",
+ " solution_error: float\n",
+ " solve_time: float\n",
+ " timed_out: bool\n",
+ " iter_auc: float = 0.0\n",
+ " time_auc: float = 0.0\n",
+ " history_rel_residual: list[float] = field(default_factory=list)\n",
+ " history_time: list[float] = field(default_factory=list)\n",
+ "\n",
+ "\n",
+ "@dataclass\n",
+ "class MatrixProperties:\n",
+ " is_symmetric: bool\n",
+ " density: float\n",
+ " scaling_factor: float\n",
+ " is_diagonally_dominant: bool\n",
+ " min_diag: float\n",
+ " max_diag: float\n",
+ " has_zero_diag: bool\n",
+ "\n",
+ "\n",
+ "@dataclass\n",
+ "class MatrixBenchmarkResult:\n",
+ " matrix_name: str\n",
+ " n: int\n",
+ " nnz: int\n",
+ " properties: MatrixProperties\n",
+ " preconditioner_results: list[PrecondResult] = field(default_factory=list)\n",
+ "\n",
+ "\n",
+ "def load_suitesparse_matrix(mtx_path: Path) -> tuple[torch.Tensor, int]:\n",
+ " raw = mmread(str(mtx_path))\n",
+ " if not issparse(raw):\n",
+ " raise ValueError(f\"Dense matrix not supported: {mtx_path}\")\n",
+ " scipy_matrix = raw.tocoo()\n",
+ " n = scipy_matrix.shape[0]\n",
+ " if scipy_matrix.shape[0] != scipy_matrix.shape[1]:\n",
+ " raise ValueError(f\"Non-square matrix: {mtx_path}\")\n",
+ " indices = torch.from_numpy(np.stack([scipy_matrix.row, scipy_matrix.col])).to(dtype=torch.long)\n",
+ " values = torch.from_numpy(scipy_matrix.data.astype(np.float64))\n",
+ " A = torch.sparse_coo_tensor(indices, values, (n, n)).coalesce().to_sparse_csc()\n",
+ " return A, len(scipy_matrix.data)\n",
+ "\n",
+ "\n",
+ "def _check_symmetry_sparse(A_coo: torch.Tensor, atol: float = 1e-12) -> bool:\n",
+ " indices = A_coo.indices()\n",
+ " values = A_coo.values()\n",
+ " rows, cols = indices[0], indices[1]\n",
+ " AT_coo = torch.sparse_coo_tensor(torch.stack([cols, rows]), values, A_coo.shape).coalesce()\n",
+ " diff = (A_coo - AT_coo).coalesce()\n",
+ " return bool(diff.values().abs().max() < atol) if diff._nnz() > 0 else True\n",
+ "\n",
+ "\n",
+ "def compute_matrix_properties(A_coo: torch.Tensor, scaling_factor: float) -> MatrixProperties:\n",
+ " indices = A_coo.indices()\n",
+ " values = A_coo.values()\n",
+ " n = A_coo.shape[0]\n",
+ " is_symmetric = _check_symmetry_sparse(A_coo) if n <= 10000 else False\n",
+ " density = A_coo._nnz() / (n * n)\n",
+ " diag_mask = indices[0] == indices[1]\n",
+ " diag_values = values[diag_mask]\n",
+ " has_zero_diag = len(diag_values) < n or bool((diag_values.abs() < 1e-15).any())\n",
+ " min_diag = diag_values.abs().min().item() if len(diag_values) > 0 else 0.0\n",
+ " max_diag = diag_values.abs().max().item() if len(diag_values) > 0 else 0.0\n",
+ " offdiag_mask = ~diag_mask\n",
+ " offdiag_abs = values[offdiag_mask].abs()\n",
+ " row_offdiag_sums = torch.zeros(n, dtype=torch.float64)\n",
+ " row_offdiag_sums.scatter_add_(0, indices[0][offdiag_mask], offdiag_abs)\n",
+ " diag_abs = torch.zeros(n, dtype=torch.float64)\n",
+ " diag_abs.scatter_add_(0, indices[0][diag_mask], diag_values.abs())\n",
+ " is_diagonally_dominant = bool((diag_abs >= row_offdiag_sums).all())\n",
+ " return MatrixProperties(\n",
+ " is_symmetric=is_symmetric, density=density, scaling_factor=scaling_factor,\n",
+ " is_diagonally_dominant=is_diagonally_dominant, min_diag=min_diag,\n",
+ " max_diag=max_diag, has_zero_diag=has_zero_diag,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def scale_by_spectral_radius(A: torch.Tensor) -> tuple[torch.Tensor, float]:\n",
+ " A_coo = A.to_sparse_coo().coalesce() if A.layout == torch.sparse_csc else A.coalesce()\n",
+ " indices = A_coo.indices()\n",
+ " values = A_coo.values().abs()\n",
+ " n = A.shape[0]\n",
+ " row_sums = torch.zeros(n, dtype=torch.float64)\n",
+ " row_sums.scatter_add_(0, indices[0], values)\n",
+ " col_sums = torch.zeros(n, dtype=torch.float64)\n",
+ " col_sums.scatter_add_(0, indices[1], values)\n",
+ " gamma = min(row_sums.max().item(), col_sums.max().item())\n",
+ " scaled_values = A_coo.values() / gamma\n",
+ " A_scaled = torch.sparse_coo_tensor(indices, scaled_values, A.shape).coalesce().to_sparse_csc()\n",
+ " return A_scaled, gamma\n",
+ "\n",
+ "\n",
+ "def build_preconditioner(name: str, A: torch.Tensor):\n",
+ " if name == \"none\":\n",
+ " return None\n",
+ " if name == \"jacobi\":\n",
+ " return Jacobi(A)\n",
+ " if name == \"ilu\":\n",
+ " return ILU(A)\n",
+ " if name == \"amg\":\n",
+ " return AMG_AIR(A)\n",
+ " if name == \"block_jacobi\":\n",
+ " return BlockJacobi(A)\n",
+ " if name == \"gmres\":\n",
+ " return GMRESPreconditioner(A, inner_iters=10, inner_rtol=1e-6)\n",
+ " raise ValueError(f\"Unknown preconditioner: {name}\")\n",
+ "\n",
+ "\n",
+ "def compute_iter_auc(history_rel_residual: list[float], rtol: float) -> float:\n",
+ " log_rtol = math.log10(rtol)\n",
+ " total = 0.0\n",
+ " for r in history_rel_residual:\n",
+ " if r <= 0:\n",
+ " continue\n",
+ " total += math.log10(r) - log_rtol\n",
+ " return total\n",
+ "\n",
+ "\n",
+ "def compute_time_auc(history_rel_residual: list[float], history_time: list[float], rtol: float) -> float:\n",
+ " log_rtol = math.log10(rtol)\n",
+ " total = 0.0\n",
+ " for i in range(1, len(history_time)):\n",
+ " dt = history_time[i] - history_time[i - 1]\n",
+ " r_prev = max(history_rel_residual[i - 1], 1e-300)\n",
+ " r_curr = max(history_rel_residual[i], 1e-300)\n",
+ " avg_log = 0.5 * (math.log10(r_prev) + math.log10(r_curr)) - log_rtol\n",
+ " total += avg_log * dt\n",
+ " return total\n",
+ "\n",
+ "\n",
+ "def _make_solve_failure(precond_name, construction_time, error):\n",
+ " return PrecondResult(\n",
+ " name=precond_name, construction_time=construction_time,\n",
+ " construction_failed=True, construction_error=error,\n",
+ " converged=False, iterations=0, final_residual=float(\"inf\"),\n",
+ " solution_error=float(\"inf\"), solve_time=0.0, timed_out=False,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def run_single_preconditioner(A, b, x_true, precond_name, solver_config, timeout):\n",
+ " construction_error = \"\"\n",
+ " tic = time.perf_counter()\n",
+ " try:\n",
+ " M = build_preconditioner(precond_name, A)\n",
+ " construction_failed = False\n",
+ " except Exception as e:\n",
+ " construction_time = time.perf_counter() - tic\n",
+ " return _make_solve_failure(precond_name, construction_time, f\"{type(e).__name__}: {e}\")\n",
+ " construction_time = time.perf_counter() - tic\n",
+ "\n",
+ " remaining_timeout = max(0.1, timeout - construction_time)\n",
+ " solver = FGMRES(**solver_config, timeout=remaining_timeout)\n",
+ " tic = time.perf_counter()\n",
+ " try:\n",
+ " solver_result: SolverResult = solver.solve(A, b, M=M, progress_bar=False)\n",
+ " except Exception as e:\n",
+ " solve_time = time.perf_counter() - tic\n",
+ " return _make_solve_failure(precond_name, construction_time, f\"solve: {type(e).__name__}: {e}\")\n",
+ " solve_time = time.perf_counter() - tic\n",
+ " solution_error = (torch.linalg.norm(solver_result.solution - x_true) / torch.linalg.norm(x_true)).item()\n",
+ " timed_out = not solver_result.converged and solver_result.history_time[-1] >= remaining_timeout\n",
+ " rtol = solver_config[\"rtol\"]\n",
+ " iter_auc = compute_iter_auc(solver_result.history_rel_residual, rtol)\n",
+ " time_auc = compute_time_auc(solver_result.history_rel_residual, solver_result.history_time, rtol)\n",
+ " return PrecondResult(\n",
+ " name=precond_name, construction_time=construction_time,\n",
+ " construction_failed=False, construction_error=\"\",\n",
+ " converged=solver_result.converged, iterations=solver_result.iterations,\n",
+ " final_residual=solver_result.final_residual, solution_error=solution_error,\n",
+ " solve_time=solve_time, timed_out=timed_out,\n",
+ " iter_auc=iter_auc, time_auc=time_auc,\n",
+ " history_rel_residual=solver_result.history_rel_residual,\n",
+ " history_time=solver_result.history_time,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def process_matrix(mtx_path: Path, preconditioner_names: list[str], solver_config: dict, timeout: float) -> dict:\n",
+ " matrix_name = mtx_path.stem\n",
+ " A, nnz = load_suitesparse_matrix(mtx_path)\n",
+ " A, gamma = scale_by_spectral_radius(A)\n",
+ " A_coo = A.to_sparse_coo().coalesce()\n",
+ " properties = compute_matrix_properties(A_coo, gamma)\n",
+ " n = A.shape[0]\n",
+ " x_true = torch.ones(n, dtype=torch.float64)\n",
+ " b = A @ x_true\n",
+ " precond_results = []\n",
+ " for precond_name in preconditioner_names:\n",
+ " pr = run_single_preconditioner(A, b, x_true, precond_name, solver_config, timeout)\n",
+ " precond_results.append(pr)\n",
+ " result = MatrixBenchmarkResult(\n",
+ " matrix_name=matrix_name, n=n, nnz=nnz,\n",
+ " properties=properties, preconditioner_results=precond_results,\n",
+ " )\n",
+ " return asdict(result)\n",
+ "\n",
+ "\n",
+ "def _worker(result_queue, mtx_path, preconditioner_names, solver_config, timeout):\n",
+ " result_queue.put(process_matrix(mtx_path, preconditioner_names, solver_config, timeout))\n",
+ "\n",
+ "\n",
+ "print(\"Benchmark functions loaded.\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "OWdNmGp4vTl0",
+ "outputId": "ad1bb64f-2d91-48bd-c0d6-e4b533783747"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Benchmark functions loaded.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "MATRIX_TIMEOUT = TIMEOUT * len(PRECONDITIONERS) + 30\n",
+ "\n",
+ "done_count = len(completed)\n",
+ "total = len(all_matrices)\n",
+ "\n",
+ "for mtx_path in pending:\n",
+ " matrix_name = mtx_path.stem\n",
+ " done_count += 1\n",
+ "\n",
+ " result_queue = multiprocessing.Queue()\n",
+ " proc = multiprocessing.Process(\n",
+ " target=_worker,\n",
+ " args=(result_queue, mtx_path, PRECONDITIONERS, SOLVER_CONFIG, TIMEOUT),\n",
+ " )\n",
+ " proc.start()\n",
+ " proc.join(timeout=MATRIX_TIMEOUT)\n",
+ "\n",
+ " if proc.is_alive():\n",
+ " proc.kill()\n",
+ " proc.join()\n",
+ " print(f\"[{done_count}/{total}] {matrix_name}: KILLED (exceeded {MATRIX_TIMEOUT:.0f}s)\")\n",
+ " continue\n",
+ "\n",
+ " if proc.exitcode != 0:\n",
+ " print(f\"[{done_count}/{total}] {matrix_name}: PROCESS FAILED (exit code {proc.exitcode})\")\n",
+ " continue\n",
+ "\n",
+ " result_dict = result_queue.get_nowait()\n",
+ "\n",
+ " props = result_dict[\"properties\"]\n",
+ " print(f\"\\n[{done_count}/{total}] {matrix_name} \"\n",
+ " f\"n={result_dict['n']}, nnz={result_dict['nnz']}, \"\n",
+ " f\"sym={props['is_symmetric']}, diag_dom={props['is_diagonally_dominant']}\")\n",
+ "\n",
+ " for pr in result_dict[\"preconditioner_results\"]:\n",
+ " if pr[\"construction_failed\"]:\n",
+ " status = f\"FAIL(build: {pr['construction_error'][:40]})\"\n",
+ " elif pr[\"timed_out\"]:\n",
+ " status = \"TIMEOUT\"\n",
+ " elif pr[\"converged\"]:\n",
+ " status = \"OK\"\n",
+ " else:\n",
+ " status = \"FAIL(solve)\"\n",
+ " print(f\" {pr['name']:>12s}: {status:>12s} iters={pr['iterations']:4d} \"\n",
+ " f\"residual={pr['final_residual']:.2e} sol_err={pr['solution_error']:.2e} \"\n",
+ " f\"build={pr['construction_time']:.3f}s solve={pr['solve_time']:.3f}s\")\n",
+ "\n",
+ " with open(RESULTS_FILE, \"a\") as f:\n",
+ " f.write(json.dumps(result_dict) + \"\\n\")\n",
+ "\n",
+ "print(f\"\\nBenchmark complete. Results in {RESULTS_FILE}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-fpm0qX_vTl0",
+ "outputId": "23be281c-f921-4de0-f96f-4c3e72ba5259"
+ },
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[782/867] EAT_RS: KILLED (exceeded 390s)\n",
+ "[783/867] EAT_SR: KILLED (exceeded 390s)\n",
+ "\n",
+ "[784/867] Wordnet3 n=82670, nnz=132964, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.88e-01 sol_err=7.93e-01 build=0.000s solve=1.060s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 81762 zero diagon) iters= 0 residual=inf sol_err=inf build=0.012s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.022s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: [0]: matrix is singular at) iters= 0 residual=inf sol_err=inf build=0.046s solve=0.000s\n",
+ " amg: FAIL(build: solve: ValueError: Indexing a matrix of ) iters= 0 residual=inf sol_err=inf build=0.011s solve=0.000s\n",
+ " gmres: FAIL(solve) iters= 100 residual=3.80e-01 sol_err=7.94e-01 build=0.000s solve=14.099s\n",
+ "[785/867] as-caida: KILLED (exceeded 390s)\n",
+ "[786/867] brainpc2: KILLED (exceeded 390s)\n",
+ "[787/867] c-55: KILLED (exceeded 390s)\n",
+ "[788/867] c-56: KILLED (exceeded 390s)\n",
+ "[789/867] c-58: KILLED (exceeded 390s)\n",
+ "[790/867] c-59: KILLED (exceeded 390s)\n",
+ "[791/867] c-62: KILLED (exceeded 390s)\n",
+ "[792/867] c-62ghs: KILLED (exceeded 390s)\n",
+ "[793/867] c-63: KILLED (exceeded 390s)\n",
+ "[794/867] c-64: KILLED (exceeded 390s)\n",
+ "[795/867] c-64b: KILLED (exceeded 390s)\n",
+ "[796/867] c-65: KILLED (exceeded 390s)\n",
+ "[797/867] c-66: KILLED (exceeded 390s)\n",
+ "[798/867] c-66b: KILLED (exceeded 390s)\n",
+ "[799/867] c-67: KILLED (exceeded 390s)\n",
+ "[800/867] c-67b: KILLED (exceeded 390s)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.12/dist-packages/scipy/sparse/linalg/_dsolve/linsolve.py:742: RuntimeWarning: overflow encountered in multiply\n",
+ " x = x * invdiag\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[801/867] c-68: KILLED (exceeded 390s)\n",
+ "[802/867] c-69: KILLED (exceeded 390s)\n",
+ "[803/867] c-70: KILLED (exceeded 390s)\n",
+ "[804/867] c-71: KILLED (exceeded 390s)\n",
+ "[805/867] c-72: KILLED (exceeded 390s)\n",
+ "[806/867] cage11: KILLED (exceeded 390s)\n",
+ "\n",
+ "[807/867] e40r0100 n=17281, nnz=553562, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=9.71e-01 sol_err=9.99e-01 build=0.000s solve=3.807s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 3744 zero diagona) iters= 0 residual=inf sol_err=inf build=0.078s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.046s solve=0.000s\n",
+ " ilu: FAIL(solve) iters= 100 residual=1.00e+00 sol_err=1.50e+01 build=1.300s solve=11.769s\n",
+ " amg: FAIL(build: solve: ValueError: array must not contai) iters= 0 residual=inf sol_err=inf build=3.302s solve=0.000s\n",
+ " gmres: FAIL(solve) iters= 100 residual=9.67e-01 sol_err=9.99e-01 build=0.000s solve=29.387s\n",
+ "\n",
+ "[808/867] mult_dcop_01 n=25187, nnz=193276, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.09e-04 sol_err=6.30e-01 build=0.000s solve=1.064s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 370 zero diagonal) iters= 0 residual=inf sol_err=inf build=0.015s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.011s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.131s solve=0.000s\n",
+ " amg: TIMEOUT iters= 1 residual=8.80e-01 sol_err=2.74e+00 build=84.139s solve=0.418s\n",
+ " gmres: FAIL(solve) iters= 100 residual=9.31e-05 sol_err=7.12e-01 build=0.000s solve=11.175s\n",
+ "[809/867] ns3Da: KILLED (exceeded 390s)\n",
+ "[810/867] soc-sign-Slashdot081106: KILLED (exceeded 390s)\n",
+ "[811/867] soc-sign-Slashdot090216: KILLED (exceeded 390s)\n",
+ "[812/867] soc-sign-Slashdot090221: KILLED (exceeded 390s)\n",
+ "[813/867] sx-mathoverflow: KILLED (exceeded 390s)\n",
+ "\n",
+ "[814/867] t2d_q4 n=9801, nnz=87025, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.94e-03 sol_err=3.91e-01 build=0.000s solve=0.413s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=3.69e-03 sol_err=3.65e-01 build=0.006s solve=0.404s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=3.70e-03 sol_err=3.66e-01 build=0.008s solve=0.603s\n",
+ " ilu: OK iters= 25 residual=5.81e-09 sol_err=2.49e-08 build=0.067s solve=0.349s\n",
+ " amg: OK iters= 11 residual=5.49e-09 sol_err=2.12e-08 build=0.325s solve=0.149s\n",
+ " gmres: OK iters= 23 residual=7.48e-09 sol_err=2.50e-07 build=0.000s solve=1.034s\n",
+ "\n",
+ "[815/867] t2d_q9 n=9801, nnz=87025, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.94e-03 sol_err=3.91e-01 build=0.000s solve=0.373s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=3.69e-03 sol_err=3.65e-01 build=0.007s solve=0.427s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=3.70e-03 sol_err=3.66e-01 build=0.008s solve=0.522s\n",
+ " ilu: OK iters= 25 residual=5.81e-09 sol_err=2.49e-08 build=0.067s solve=0.358s\n",
+ " amg: OK iters= 11 residual=5.49e-09 sol_err=2.12e-08 build=0.326s solve=0.151s\n",
+ " gmres: OK iters= 23 residual=7.48e-09 sol_err=2.50e-07 build=0.000s solve=1.135s\n",
+ "\n",
+ "[816/867] t2dah n=11445, nnz=176117, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=5.51e-02 sol_err=1.00e+00 build=0.000s solve=0.727s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=2.68e-02 sol_err=1.00e+00 build=0.013s solve=0.712s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=2.64e-02 sol_err=1.00e+00 build=0.009s solve=0.993s\n",
+ " ilu: FAIL(solve) iters= 100 residual=1.64e-01 sol_err=1.00e+00 build=0.082s solve=1.822s\n",
+ " amg: FAIL(solve) iters= 100 residual=5.54e-06 sol_err=4.24e-03 build=0.232s solve=5.582s\n",
+ " gmres: FAIL(solve) iters= 100 residual=3.66e-02 sol_err=1.00e+00 build=0.000s solve=8.063s\n",
+ "\n",
+ "[817/867] t2dah_a n=11445, nnz=176117, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=5.51e-02 sol_err=1.00e+00 build=0.000s solve=0.698s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=2.68e-02 sol_err=1.00e+00 build=0.010s solve=0.712s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=2.64e-02 sol_err=1.00e+00 build=0.010s solve=0.999s\n",
+ " ilu: FAIL(solve) iters= 100 residual=1.64e-01 sol_err=1.00e+00 build=0.085s solve=1.642s\n",
+ " amg: FAIL(solve) iters= 100 residual=5.54e-06 sol_err=4.24e-03 build=0.223s solve=6.034s\n",
+ " gmres: FAIL(solve) iters= 100 residual=3.66e-02 sol_err=1.00e+00 build=0.000s solve=8.062s\n",
+ "\n",
+ "[818/867] t2dal n=4257, nnz=37465, sym=True, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=6.56e-01 sol_err=1.00e+00 build=0.000s solve=0.296s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=3.06e-01 sol_err=1.00e+00 build=0.003s solve=0.247s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=3.05e-01 sol_err=1.00e+00 build=0.005s solve=0.366s\n",
+ " ilu: FAIL(solve) iters= 100 residual=8.84e-01 sol_err=1.00e+00 build=0.021s solve=0.525s\n",
+ " amg: FAIL(solve) iters= 100 residual=1.03e-04 sol_err=4.90e-06 build=0.047s solve=0.530s\n",
+ " gmres: FAIL(solve) iters= 100 residual=4.12e-01 sol_err=1.00e+00 build=0.000s solve=2.611s\n",
+ "\n",
+ "[819/867] t2dal_a n=4257, nnz=37465, sym=True, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=6.56e-01 sol_err=1.00e+00 build=0.000s solve=0.222s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=3.06e-01 sol_err=1.00e+00 build=0.003s solve=0.192s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=3.05e-01 sol_err=1.00e+00 build=0.005s solve=0.328s\n",
+ " ilu: FAIL(solve) iters= 100 residual=8.84e-01 sol_err=1.00e+00 build=0.021s solve=0.545s\n",
+ " amg: FAIL(solve) iters= 100 residual=1.03e-04 sol_err=4.90e-06 build=0.048s solve=0.548s\n",
+ " gmres: FAIL(solve) iters= 100 residual=4.12e-01 sol_err=1.00e+00 build=0.000s solve=2.415s\n",
+ "\n",
+ "[820/867] t2dal_bci n=4257, nnz=37465, sym=True, diag_dom=False\n",
+ " none: OK iters= 0 residual=0.00e+00 sol_err=1.00e+00 build=0.000s solve=0.000s\n",
+ " jacobi: OK iters= 0 residual=0.00e+00 sol_err=1.00e+00 build=0.002s solve=0.000s\n",
+ " block_jacobi: OK iters= 0 residual=0.00e+00 sol_err=1.00e+00 build=0.004s solve=0.000s\n",
+ " ilu: OK iters= 0 residual=0.00e+00 sol_err=1.00e+00 build=0.021s solve=0.000s\n",
+ " amg: OK iters= 0 residual=0.00e+00 sol_err=1.00e+00 build=0.045s solve=0.000s\n",
+ " gmres: OK iters= 0 residual=0.00e+00 sol_err=1.00e+00 build=0.000s solve=0.000s\n",
+ "\n",
+ "[821/867] t3dl n=20360, nnz=509866, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=6.56e-01 sol_err=1.00e+00 build=0.000s solve=2.458s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=2.61e-01 sol_err=1.00e+00 build=0.032s solve=2.276s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=2.63e-01 sol_err=1.00e+00 build=0.016s solve=2.755s\n",
+ " ilu: FAIL(solve) iters= 100 residual=4.87e-01 sol_err=1.00e+00 build=2.104s solve=7.975s\n",
+ " amg: FAIL(solve) iters= 100 residual=3.10e-05 sol_err=9.66e-07 build=0.759s solve=10.495s\n",
+ " gmres: FAIL(solve) iters= 100 residual=3.31e-01 sol_err=1.00e+00 build=0.000s solve=27.862s\n",
+ "\n",
+ "[822/867] t3dl_a n=20360, nnz=509866, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=6.56e-01 sol_err=1.00e+00 build=0.000s solve=2.182s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=2.61e-01 sol_err=1.00e+00 build=0.037s solve=2.294s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=2.63e-01 sol_err=1.00e+00 build=0.016s solve=2.717s\n",
+ " ilu: FAIL(solve) iters= 100 residual=4.87e-01 sol_err=1.00e+00 build=2.151s solve=7.802s\n",
+ " amg: FAIL(solve) iters= 100 residual=3.10e-05 sol_err=9.66e-07 build=0.763s solve=10.594s\n",
+ " gmres: FAIL(solve) iters= 100 residual=3.31e-01 sol_err=1.00e+00 build=0.000s solve=26.761s\n",
+ "\n",
+ "[823/867] t520 n=5563, nnz=286341, sym=True, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.43e-04 sol_err=1.19e-02 build=0.000s solve=1.115s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=3.43e-04 sol_err=1.19e-02 build=0.023s solve=1.097s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.008s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.079s solve=0.000s\n",
+ " amg: FAIL(solve) iters= 100 residual=7.26e-01 sol_err=2.51e+01 build=0.041s solve=1.365s\n",
+ " gmres: FAIL(solve) iters= 100 residual=3.14e-06 sol_err=1.08e-03 build=0.000s solve=12.239s\n",
+ "\n",
+ "[824/867] ted_A n=10605, nnz=424587, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=9.72e-01 sol_err=1.56e+01 build=0.000s solve=1.844s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 8484 zero diagona) iters= 0 residual=inf sol_err=inf build=0.028s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.008s solve=0.000s\n",
+ " ilu: FAIL(solve) iters= 100 residual=4.25e-05 sol_err=6.46e+02 build=0.052s solve=3.396s\n",
+ " amg: FAIL(solve) iters= 100 residual=3.88e-02 sol_err=1.52e+04 build=0.045s solve=11.159s\n",
+ " gmres: FAIL(solve) iters= 100 residual=8.61e-01 sol_err=9.98e+01 build=0.000s solve=20.563s\n",
+ "\n",
+ "[825/867] ted_A_unscaled n=10605, nnz=424587, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.14e-02 sol_err=7.80e+04 build=0.000s solve=1.808s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 8484 zero diagona) iters= 0 residual=inf sol_err=inf build=0.026s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.008s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=1.225s solve=0.000s\n",
+ " amg: FAIL(solve) iters= 100 residual=9.99e-01 sol_err=2.71e+07 build=0.521s solve=11.275s\n",
+ " gmres: FAIL(solve) iters= 100 residual=5.63e-04 sol_err=3.52e+03 build=0.000s solve=20.750s\n",
+ "\n",
+ "[826/867] thermal n=3456, nnz=66528, sym=False, diag_dom=False\n",
+ " none: OK iters= 27 residual=7.68e-09 sol_err=6.38e-09 build=0.000s solve=0.086s\n",
+ " jacobi: OK iters= 25 residual=9.88e-09 sol_err=1.07e-08 build=0.004s solve=0.079s\n",
+ " block_jacobi: OK iters= 19 residual=5.83e-09 sol_err=5.06e-09 build=0.006s solve=0.087s\n",
+ " ilu: OK iters= 3 residual=1.67e-11 sol_err=1.40e-11 build=0.022s solve=0.019s\n",
+ " amg: OK iters= 5 residual=4.53e-09 sol_err=3.81e-09 build=0.118s solve=0.055s\n",
+ " gmres: OK iters= 3 residual=9.24e-10 sol_err=9.13e-10 build=0.000s solve=0.097s\n",
+ "\n",
+ "[827/867] tols1090 n=1090, nnz=3546, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=7.63e-04 sol_err=3.77e+01 build=0.000s solve=0.074s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 218 zero diagonal) iters= 0 residual=inf sol_err=inf build=0.001s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.003s solve=0.000s\n",
+ " ilu: OK iters= 2 residual=3.18e-15 sol_err=3.13e-15 build=0.001s solve=0.006s\n",
+ " amg: FAIL(solve) iters= 100 residual=8.35e-06 sol_err=1.93e+00 build=0.004s solve=0.206s\n",
+ " gmres: FAIL(solve) iters= 100 residual=4.45e-05 sol_err=3.34e+00 build=0.000s solve=0.833s\n",
+ "\n",
+ "[828/867] tols2000 n=2000, nnz=5184, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=8.89e-04 sol_err=1.20e+02 build=0.000s solve=0.091s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 400 zero diagonal) iters= 0 residual=inf sol_err=inf build=0.001s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.003s solve=0.000s\n",
+ " ilu: OK iters= 2 residual=2.81e-14 sol_err=2.83e-14 build=0.001s solve=0.006s\n",
+ " amg: FAIL(solve) iters= 100 residual=1.92e-06 sol_err=1.46e+00 build=0.004s solve=0.237s\n",
+ " gmres: FAIL(solve) iters= 100 residual=1.21e-05 sol_err=1.67e-01 build=0.000s solve=1.020s\n",
+ "\n",
+ "[829/867] tols4000 n=4000, nnz=8784, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=8.70e-04 sol_err=4.34e+02 build=0.000s solve=0.127s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 800 zero diagonal) iters= 0 residual=inf sol_err=inf build=0.001s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.004s solve=0.000s\n",
+ " ilu: OK iters= 1 residual=5.79e-09 sol_err=5.36e-05 build=0.002s solve=0.006s\n",
+ " amg: FAIL(solve) iters= 100 residual=3.53e-07 sol_err=1.08e+00 build=0.005s solve=0.285s\n",
+ " gmres: FAIL(solve) iters= 100 residual=1.74e-05 sol_err=3.45e+00 build=0.000s solve=1.391s\n",
+ "\n",
+ "[830/867] tub1000 n=1000, nnz=3996, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=2.46e-01 sol_err=9.43e-01 build=0.000s solve=0.077s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=2.46e-01 sol_err=9.42e-01 build=0.001s solve=0.071s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=5.91e-01 sol_err=9.46e-01 build=0.004s solve=0.170s\n",
+ " ilu: OK iters= 6 residual=2.38e-10 sol_err=6.30e-09 build=0.001s solve=0.010s\n",
+ " amg: FAIL(solve) iters= 100 residual=1.53e-07 sol_err=7.70e-08 build=0.003s solve=0.333s\n",
+ " gmres: FAIL(solve) iters= 100 residual=2.44e-01 sol_err=1.26e+00 build=0.000s solve=0.831s\n",
+ "\n",
+ "[831/867] tube2 n=21498, nnz=897056, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.89e-04 sol_err=4.56e-02 build=0.000s solve=3.231s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=3.89e-04 sol_err=4.56e-02 build=0.045s solve=2.797s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.024s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.377s solve=0.000s\n",
+ " amg: FAIL(solve) iters= 100 residual=9.51e-01 sol_err=3.82e+00 build=0.174s solve=10.154s\n",
+ " gmres: FAIL(solve) iters= 100 residual=1.04e-05 sol_err=5.03e-03 build=0.000s solve=36.308s\n",
+ "\n",
+ "[832/867] tuma1 n=22967, nnz=87760, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=8.77e-03 sol_err=5.90e-01 build=0.000s solve=0.419s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 9607 zero diagona) iters= 0 residual=inf sol_err=inf build=0.006s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.008s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.124s solve=0.000s\n",
+ " amg: FAIL(solve) iters= 100 residual=7.73e-01 sol_err=2.18e+00 build=0.173s solve=4.312s\n",
+ " gmres: FAIL(solve) iters= 100 residual=9.87e-04 sol_err=2.00e-01 build=0.000s solve=5.415s\n",
+ "\n",
+ "[833/867] tuma2 n=12992, nnz=49365, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=9.48e-03 sol_err=5.93e-01 build=0.000s solve=0.255s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 5477 zero diagona) iters= 0 residual=inf sol_err=inf build=0.003s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.006s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.048s solve=0.000s\n",
+ " amg: FAIL(solve) iters= 100 residual=9.88e-01 sol_err=9.97e-01 build=0.112s solve=2.003s\n",
+ " gmres: FAIL(solve) iters= 100 residual=1.04e-03 sol_err=2.10e-01 build=0.000s solve=3.561s\n",
+ "\n",
+ "[834/867] uni_chimera_i3 n=100000, nnz=499696, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.99e-02 sol_err=1.00e+00 build=0.000s solve=2.722s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=1.60e-02 sol_err=1.00e+00 build=0.069s solve=2.726s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=1.60e-02 sol_err=1.00e+00 build=0.034s solve=3.998s\n",
+ " ilu: FAIL(solve) iters= 100 residual=1.58e-02 sol_err=1.00e+00 build=1.794s solve=8.908s\n",
+ " amg: FAIL(solve) iters= 100 residual=1.28e-02 sol_err=1.00e+00 build=8.694s solve=23.422s\n",
+ " gmres: FAIL(solve) iters= 100 residual=7.49e-03 sol_err=1.00e+00 build=0.000s solve=30.536s\n",
+ "\n",
+ "[835/867] uni_chimera_i4 n=100000, nnz=814436, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=7.48e-03 sol_err=1.00e+00 build=0.000s solve=4.245s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=7.74e-03 sol_err=1.00e+00 build=0.125s solve=4.333s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=7.74e-03 sol_err=1.00e+00 build=0.027s solve=5.597s\n",
+ " ilu: TIMEOUT iters= 1 residual=1.99e-01 sol_err=1.00e+00 build=146.546s solve=0.251s\n",
+ " amg: TIMEOUT iters= 42 residual=1.16e-02 sol_err=1.00e+00 build=48.150s solve=11.953s\n",
+ " gmres: FAIL(solve) iters= 100 residual=6.84e-03 sol_err=1.00e+00 build=0.000s solve=47.050s\n",
+ "\n",
+ "[836/867] usps_norm_5NN n=11000, nnz=81112, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.70e-02 sol_err=1.84e-01 build=0.000s solve=0.470s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 11000 zero diagon) iters= 0 residual=inf sol_err=inf build=0.006s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.004s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.604s solve=0.000s\n",
+ " amg: OK iters= 1 residual=0.00e+00 sol_err=nan build=0.043s solve=0.055s\n",
+ " gmres: FAIL(solve) iters= 100 residual=7.42e-04 sol_err=6.46e-02 build=0.000s solve=4.732s\n",
+ "\n",
+ "[837/867] utm1700b n=1700, nnz=21509, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.92e-01 sol_err=2.31e+00 build=0.000s solve=0.238s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=9.24e-01 sol_err=1.17e+00 build=0.002s solve=0.238s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=9.39e-01 sol_err=1.44e+00 build=0.005s solve=0.380s\n",
+ " ilu: OK iters= 9 residual=2.52e-09 sol_err=1.19e-05 build=0.010s solve=0.038s\n",
+ " amg: FAIL(solve) iters= 100 residual=4.78e-01 sol_err=2.40e+00 build=0.014s solve=0.659s\n",
+ " gmres: FAIL(solve) iters= 100 residual=3.35e-01 sol_err=1.16e+01 build=0.000s solve=2.237s\n",
+ "\n",
+ "[838/867] utm3060 n=3060, nnz=42211, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=4.98e-01 sol_err=3.07e+00 build=0.000s solve=0.229s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=9.28e-01 sol_err=1.07e+00 build=0.003s solve=0.234s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=9.05e-01 sol_err=1.95e+00 build=0.006s solve=0.391s\n",
+ " ilu: OK iters= 18 residual=9.40e-09 sol_err=1.55e-04 build=0.029s solve=0.119s\n",
+ " amg: FAIL(solve) iters= 100 residual=3.51e-01 sol_err=2.17e+00 build=0.034s solve=0.976s\n",
+ " gmres: FAIL(solve) iters= 100 residual=4.49e-01 sol_err=1.94e+01 build=0.000s solve=2.648s\n",
+ "\n",
+ "[839/867] utm5940 n=5940, nnz=83842, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.45e-01 sol_err=2.64e+00 build=0.000s solve=0.403s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=9.30e-01 sol_err=1.04e+00 build=0.006s solve=0.371s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=8.85e-01 sol_err=2.02e+00 build=0.008s solve=0.658s\n",
+ " ilu: OK iters= 39 residual=4.58e-09 sol_err=1.72e-04 build=0.070s solve=0.479s\n",
+ " amg: FAIL(solve) iters= 100 residual=2.34e-01 sol_err=1.12e+00 build=0.043s solve=1.733s\n",
+ " gmres: FAIL(solve) iters= 100 residual=2.72e-01 sol_err=4.11e+00 build=0.000s solve=4.608s\n",
+ "\n",
+ "[840/867] venkat01 n=62424, nnz=1717792, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.20e-02 sol_err=9.25e-01 build=0.000s solve=6.227s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=3.55e-07 sol_err=2.76e-05 build=0.094s solve=5.992s\n",
+ " block_jacobi: OK iters= 96 residual=9.38e-09 sol_err=6.09e-07 build=0.034s solve=8.681s\n",
+ " ilu: OK iters= 3 residual=9.01e-09 sol_err=1.23e-07 build=1.809s solve=0.878s\n",
+ " amg: TIMEOUT iters= 30 residual=1.72e-08 sol_err=3.93e-06 build=37.620s solve=23.145s\n",
+ " gmres: TIMEOUT iters= 81 residual=1.78e-03 sol_err=6.16e-01 build=0.000s solve=60.507s\n",
+ "\n",
+ "[841/867] venkat25 n=62424, nnz=1717792, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.71e-02 sol_err=1.01e+00 build=0.000s solve=5.838s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=8.76e-02 sol_err=8.63e+00 build=0.091s solve=5.853s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=6.00e-03 sol_err=1.70e+00 build=0.033s solve=6.972s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=1.516s solve=0.000s\n",
+ " amg: TIMEOUT iters= 1 residual=9.73e-01 sol_err=2.57e+01 build=97.723s solve=1.132s\n",
+ " gmres: TIMEOUT iters= 63 residual=7.64e-03 sol_err=1.11e+00 build=0.000s solve=60.732s\n",
+ "\n",
+ "[842/867] venkat50 n=62424, nnz=1717792, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.76e-02 sol_err=1.01e+00 build=0.000s solve=5.899s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=7.59e-02 sol_err=3.02e+00 build=0.090s solve=5.817s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=6.45e-03 sol_err=1.89e+00 build=0.035s solve=8.364s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=1.564s solve=0.000s\n",
+ " amg: TIMEOUT iters= 1 residual=9.92e-01 sol_err=4.09e+01 build=98.585s solve=1.141s\n",
+ " gmres: TIMEOUT iters= 63 residual=7.79e-03 sol_err=1.12e+00 build=0.000s solve=61.000s\n",
+ "\n",
+ "[843/867] vibrobox n=12328, nnz=342828, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=5.91e-04 sol_err=6.71e-01 build=0.000s solve=1.583s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=4.33e-05 sol_err=2.16e+00 build=0.025s solve=1.626s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=2.71e-05 sol_err=1.98e+00 build=0.010s solve=1.956s\n",
+ " ilu: OK iters= 2 residual=nan sol_err=nan build=2.953s solve=0.141s\n",
+ " amg: FAIL(solve) iters= 100 residual=4.80e-08 sol_err=1.15e-01 build=4.569s solve=10.898s\n",
+ " gmres: FAIL(solve) iters= 100 residual=4.84e-06 sol_err=6.03e-01 build=0.000s solve=19.643s\n",
+ "\n",
+ "[844/867] viscoplastic1 n=4326, nnz=61166, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=7.87e-04 sol_err=4.69e+00 build=0.000s solve=0.273s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=2.30e-04 sol_err=7.44e-01 build=0.005s solve=0.304s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=2.30e-04 sol_err=7.44e-01 build=0.004s solve=0.428s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.121s solve=0.000s\n",
+ " amg: FAIL(solve) iters= 100 residual=9.99e-01 sol_err=7.82e+01 build=0.162s solve=0.999s\n",
+ " gmres: FAIL(solve) iters= 100 residual=9.35e-05 sol_err=7.52e-01 build=0.000s solve=3.431s\n",
+ "\n",
+ "[845/867] viscoplastic2 n=32769, nnz=381326, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=7.48e-03 sol_err=1.48e+01 build=0.000s solve=1.609s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=5.99e-04 sol_err=3.68e+00 build=0.022s solve=1.418s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=5.99e-04 sol_err=3.68e+00 build=0.011s solve=1.650s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=4.552s solve=0.000s\n",
+ " amg: FAIL(solve) iters= 100 residual=1.00e+00 sol_err=7.80e+01 build=1.468s solve=11.899s\n",
+ " gmres: FAIL(solve) iters= 100 residual=6.08e-06 sol_err=1.12e-02 build=0.000s solve=16.901s\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.12/dist-packages/scipy/sparse/linalg/_dsolve/linsolve.py:742: RuntimeWarning: overflow encountered in multiply\n",
+ " x = x * invdiag\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "[846/867] viscorocks n=37762, nnz=1162244, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=2.77e-03 sol_err=8.79e-01 build=0.000s solve=4.251s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=4.05e-04 sol_err=9.90e-01 build=0.070s solve=4.329s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=2.75e-05 sol_err=7.70e-01 build=0.032s solve=4.863s\n",
+ " ilu: FAIL(solve) iters= 100 residual=nan sol_err=nan build=0.767s solve=14.571s\n",
+ " amg: FAIL(solve) iters= 100 residual=1.47e-06 sol_err=7.20e-01 build=1.701s solve=14.251s\n",
+ " gmres: FAIL(solve) iters= 100 residual=3.08e-06 sol_err=8.73e-01 build=0.000s solve=46.932s\n",
+ "\n",
+ "[847/867] vt2010 n=32580, nnz=155598, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.60e-02 sol_err=5.69e-01 build=0.000s solve=0.936s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 32580 zero diagon) iters= 0 residual=inf sol_err=inf build=0.013s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.008s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: [0]: matrix is singular at) iters= 0 residual=inf sol_err=inf build=0.033s solve=0.000s\n",
+ " amg: OK iters= 1 residual=0.00e+00 sol_err=nan build=0.500s solve=0.189s\n",
+ " gmres: FAIL(solve) iters= 100 residual=2.41e-03 sol_err=3.65e-01 build=0.000s solve=9.185s\n",
+ "\n",
+ "[848/867] wang1 n=2903, nnz=19093, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.30e-03 sol_err=1.07e+00 build=0.000s solve=0.171s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=4.08e-04 sol_err=1.12e-01 build=0.002s solve=0.170s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=6.29e-06 sol_err=1.52e-03 build=0.005s solve=0.302s\n",
+ " ilu: OK iters= 57 residual=9.19e-09 sol_err=7.19e-07 build=0.027s solve=0.265s\n",
+ " amg: OK iters= 28 residual=6.65e-09 sol_err=4.64e-07 build=0.254s solve=0.360s\n",
+ " gmres: OK iters= 92 residual=8.97e-09 sol_err=6.90e-07 build=0.000s solve=1.857s\n",
+ "\n",
+ "[849/867] wang2 n=2903, nnz=19093, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.83e-03 sol_err=9.11e-01 build=0.000s solve=0.172s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=1.12e-03 sol_err=4.33e-01 build=0.002s solve=0.170s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=1.85e-04 sol_err=5.43e-02 build=0.004s solve=0.292s\n",
+ " ilu: OK iters= 65 residual=8.56e-09 sol_err=1.64e-06 build=0.027s solve=0.315s\n",
+ " amg: OK iters= 24 residual=8.42e-09 sol_err=6.65e-07 build=0.238s solve=0.267s\n",
+ " gmres: FAIL(solve) iters= 100 residual=1.05e-07 sol_err=3.32e-05 build=0.000s solve=1.980s\n",
+ "\n",
+ "[850/867] wang3 n=26064, nnz=177168, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=2.25e-03 sol_err=6.69e-01 build=0.000s solve=0.778s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=7.79e-04 sol_err=2.47e-01 build=0.012s solve=0.768s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=6.99e-04 sol_err=2.06e-01 build=0.020s solve=1.138s\n",
+ " ilu: FAIL(solve) iters= 100 residual=7.25e-06 sol_err=1.20e-03 build=0.719s solve=3.011s\n",
+ " amg: OK iters= 41 residual=8.84e-09 sol_err=4.89e-07 build=20.422s solve=4.231s\n",
+ " gmres: OK iters= 45 residual=6.15e-09 sol_err=1.12e-06 build=0.000s solve=4.270s\n",
+ "\n",
+ "[851/867] wang4 n=26068, nnz=177196, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.43e-02 sol_err=6.04e+00 build=0.000s solve=0.716s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=1.40e-02 sol_err=9.70e+01 build=0.010s solve=0.751s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=7.67e-03 sol_err=1.16e+01 build=0.014s solve=1.310s\n",
+ " ilu: FAIL(solve) iters= 100 residual=9.99e-03 sol_err=4.09e+01 build=0.747s solve=2.647s\n",
+ " amg: FAIL(solve) iters= 100 residual=7.62e-05 sol_err=1.33e-01 build=3.383s solve=18.668s\n",
+ " gmres: FAIL(solve) iters= 100 residual=1.10e-03 sol_err=6.85e+00 build=0.000s solve=9.082s\n",
+ "\n",
+ "[852/867] watt_1 n=1856, nnz=11360, sym=False, diag_dom=False\n",
+ " none: OK iters= 6 residual=9.83e-09 sol_err=9.40e-01 build=0.000s solve=0.009s\n",
+ " jacobi: OK iters= 5 residual=8.65e-09 sol_err=9.26e-01 build=0.001s solve=0.007s\n",
+ " block_jacobi: OK iters= 3 residual=8.88e-09 sol_err=9.31e-01 build=0.004s solve=0.010s\n",
+ " ilu: OK iters= 2 residual=4.21e-09 sol_err=7.76e-01 build=0.011s solve=0.008s\n",
+ " amg: OK iters= 1 residual=1.97e-09 sol_err=4.82e-02 build=0.016s solve=0.110s\n",
+ " gmres: OK iters= 2 residual=5.44e-09 sol_err=9.20e-01 build=0.000s solve=0.101s\n",
+ "\n",
+ "[853/867] watt_2 n=1856, nnz=11550, sym=False, diag_dom=False\n",
+ " none: OK iters= 7 residual=9.63e-09 sol_err=9.69e-01 build=0.000s solve=0.010s\n",
+ " jacobi: OK iters= 6 residual=9.10e-09 sol_err=9.61e-01 build=0.001s solve=0.008s\n",
+ " block_jacobi: OK iters= 4 residual=8.10e-09 sol_err=9.63e-01 build=0.004s solve=0.012s\n",
+ " ilu: OK iters= 2 residual=1.80e-09 sol_err=8.28e-01 build=0.011s solve=0.008s\n",
+ " amg: OK iters= 3 residual=9.98e-09 sol_err=9.76e-01 build=0.016s solve=0.010s\n",
+ " gmres: OK iters= 2 residual=6.66e-09 sol_err=9.63e-01 build=0.000s solve=0.019s\n",
+ "\n",
+ "[854/867] west1505 n=1505, nnz=5445, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=8.19e-01 sol_err=2.02e+01 build=0.000s solve=0.093s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 1500 zero diagona) iters= 0 residual=inf sol_err=inf build=0.001s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.003s solve=0.000s\n",
+ " ilu: FAIL(solve) iters= 100 residual=1.15e+01 sol_err=2.51e+06 build=0.002s solve=0.211s\n",
+ " amg: FAIL(solve) iters= 100 residual=9.98e-01 sol_err=2.38e+04 build=0.007s solve=0.387s\n",
+ " gmres: FAIL(solve) iters= 100 residual=7.81e-01 sol_err=4.04e+02 build=0.000s solve=1.042s\n",
+ "\n",
+ "[855/867] west2021 n=2021, nnz=7353, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=8.82e-01 sol_err=1.62e+02 build=0.000s solve=0.107s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 2016 zero diagona) iters= 0 residual=inf sol_err=inf build=0.001s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.003s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.002s solve=0.000s\n",
+ " amg: FAIL(solve) iters= 100 residual=9.99e-01 sol_err=9.18e+03 build=0.008s solve=0.458s\n",
+ " gmres: FAIL(solve) iters= 100 residual=8.09e-01 sol_err=3.03e+03 build=0.000s solve=1.197s\n",
+ "\n",
+ "[856/867] wiki-RfA n=11380, nnz=188077, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.60e-01 sol_err=1.08e+00 build=0.000s solve=1.007s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 11287 zero diagon) iters= 0 residual=inf sol_err=inf build=0.016s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.005s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: [0]: matrix is singular at) iters= 0 residual=inf sol_err=inf build=0.078s solve=0.000s\n",
+ " amg: OK iters= 1 residual=1.74e-14 sol_err=8.33e-01 build=0.006s solve=247.790s\n",
+ " gmres: FAIL(solve) iters= 100 residual=1.49e-01 sol_err=1.12e+00 build=0.000s solve=10.680s\n",
+ "\n",
+ "[857/867] windtunnel_evap2d n=8256, nnz=233468, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.48e-02 sol_err=2.30e+02 build=0.000s solve=1.028s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 1024 zero diagona) iters= 0 residual=inf sol_err=inf build=0.017s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.007s solve=0.000s\n",
+ " ilu: FAIL(solve) iters= 100 residual=2.90e+01 sol_err=5.37e+09 build=0.222s solve=2.010s\n",
+ " amg: FAIL(solve) iters= 100 residual=9.83e-01 sol_err=1.93e+07 build=0.043s solve=2.802s\n",
+ " gmres: FAIL(solve) iters= 100 residual=7.34e-04 sol_err=8.42e+02 build=0.000s solve=11.625s\n",
+ "\n",
+ "[858/867] windtunnel_evap3d n=40816, nnz=2730600, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.18e-01 sol_err=7.75e+02 build=0.000s solve=12.115s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 6240 zero diagona) iters= 0 residual=inf sol_err=inf build=0.188s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.038s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=30.610s solve=0.000s\n",
+ " amg: FAIL(solve) iters= 100 residual=9.97e-01 sol_err=7.04e+01 build=2.258s solve=50.101s\n",
+ " gmres: TIMEOUT iters= 42 residual=2.75e-02 sol_err=1.39e+03 build=0.000s solve=61.255s\n",
+ "\n",
+ "[859/867] worms20_10NN n=20055, nnz=240826, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.56e-03 sol_err=7.61e-02 build=0.000s solve=1.145s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 20055 zero diagon) iters= 0 residual=inf sol_err=inf build=0.018s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.010s solve=0.000s\n",
+ " ilu: OK iters= 5 residual=9.48e-10 sol_err=1.22e-06 build=0.224s solve=0.204s\n",
+ " amg: OK iters= 1 residual=0.00e+00 sol_err=nan build=0.139s solve=0.113s\n",
+ " gmres: FAIL(solve) iters= 100 residual=5.76e-05 sol_err=2.90e-02 build=0.000s solve=13.541s\n",
+ "\n",
+ "[860/867] ww_vref_6405 n=13251, nnz=48737, sym=False, diag_dom=False\n",
+ " none: OK iters= 1 residual=0.00e+00 sol_err=1.74e+18 build=0.000s solve=0.009s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 13251 zero diagon) iters= 0 residual=inf sol_err=inf build=0.005s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.006s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.013s solve=0.000s\n",
+ " amg: OK iters= 1 residual=2.23e-14 sol_err=1.97e+04 build=0.022s solve=0.036s\n",
+ " gmres: OK iters= 5 residual=5.56e-11 sol_err=7.88e+06 build=0.000s solve=0.128s\n",
+ "\n",
+ "[861/867] wy2010 n=86204, nnz=427586, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=8.64e-02 sol_err=7.40e-01 build=0.000s solve=2.153s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 86204 zero diagon) iters= 0 residual=inf sol_err=inf build=0.033s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.015s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: [0]: matrix is singular at) iters= 0 residual=inf sol_err=inf build=0.085s solve=0.000s\n",
+ " amg: OK iters= 1 residual=0.00e+00 sol_err=nan build=0.167s solve=0.160s\n",
+ " gmres: FAIL(solve) iters= 100 residual=1.17e-02 sol_err=5.16e-01 build=0.000s solve=26.011s\n",
+ "\n",
+ "[862/867] xenon1 n=48600, nnz=1181120, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=6.36e-04 sol_err=8.86e-01 build=0.000s solve=4.702s\n",
+ " jacobi: FAIL(solve) iters= 100 residual=6.47e-04 sol_err=8.74e-01 build=0.072s solve=4.864s\n",
+ " block_jacobi: FAIL(solve) iters= 100 residual=6.18e-04 sol_err=8.60e-01 build=0.029s solve=5.914s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=3.358s solve=0.000s\n",
+ " amg: FAIL(solve) iters= 100 residual=2.68e-07 sol_err=2.92e-04 build=2.354s solve=18.418s\n",
+ " gmres: FAIL(solve) iters= 100 residual=1.21e-06 sol_err=3.18e-03 build=0.000s solve=55.011s\n",
+ "\n",
+ "[863/867] xingo3012 n=20944, nnz=74386, sym=False, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=3.30e-01 sol_err=9.54e+09 build=0.000s solve=0.461s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 781 zero diagonal) iters= 0 residual=inf sol_err=inf build=0.007s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.011s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.019s solve=0.000s\n",
+ " amg: OK iters= 1 residual=4.08e-09 sol_err=1.99e+01 build=0.075s solve=0.160s\n",
+ " gmres: FAIL(solve) iters= 100 residual=1.71e-03 sol_err=4.18e+07 build=0.000s solve=4.866s\n",
+ "\n",
+ "[864/867] xingo_afonso_itaipu n=13250, nnz=48735, sym=False, diag_dom=False\n",
+ " none: OK iters= 1 residual=0.00e+00 sol_err=1.74e+18 build=0.000s solve=0.010s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 13250 zero diagon) iters= 0 residual=inf sol_err=inf build=0.004s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.007s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.012s solve=0.000s\n",
+ " amg: OK iters= 1 residual=2.23e-14 sol_err=1.97e+04 build=0.022s solve=0.038s\n",
+ " gmres: OK iters= 5 residual=5.56e-11 sol_err=7.88e+06 build=0.000s solve=0.135s\n",
+ "\n",
+ "[865/867] yeast_30NN n=1484, nnz=62350, sym=True, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=1.13e-03 sol_err=7.80e-02 build=0.000s solve=0.293s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 1484 zero diagona) iters= 0 residual=inf sol_err=inf build=0.005s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.003s solve=0.000s\n",
+ " ilu: FAIL(solve) iters= 100 residual=9.72e-01 sol_err=3.34e+00 build=0.192s solve=1.084s\n",
+ " amg: OK iters= 1 residual=0.00e+00 sol_err=nan build=0.038s solve=0.010s\n",
+ " gmres: FAIL(solve) iters= 100 residual=4.92e-05 sol_err=3.40e-02 build=0.000s solve=3.272s\n",
+ "\n",
+ "[866/867] zenios n=2873, nnz=27191, sym=True, diag_dom=False\n",
+ " none: FAIL(solve) iters= 100 residual=5.81e-03 sol_err=9.60e-01 build=0.000s solve=0.281s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 2873 zero diagona) iters= 0 residual=inf sol_err=inf build=0.003s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.003s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: [0]: matrix is singular at) iters= 0 residual=inf sol_err=inf build=0.002s solve=0.000s\n",
+ " amg: OK iters= 1 residual=0.00e+00 sol_err=nan build=0.006s solve=0.007s\n",
+ " gmres: FAIL(solve) iters= 100 residual=2.18e-04 sol_err=9.59e-01 build=0.000s solve=3.166s\n",
+ "\n",
+ "[867/867] zeros_nopss_13k n=13296, nnz=48827, sym=False, diag_dom=False\n",
+ " none: OK iters= 1 residual=0.00e+00 sol_err=1.73e+18 build=0.000s solve=0.008s\n",
+ " jacobi: FAIL(build: ValueError: Matrix has 13296 zero diagon) iters= 0 residual=inf sol_err=inf build=0.004s solve=0.000s\n",
+ " block_jacobi: FAIL(build: RuntimeError: failed to factorize matrix) iters= 0 residual=inf sol_err=inf build=0.006s solve=0.000s\n",
+ " ilu: FAIL(build: RuntimeError: Factor is exactly singular) iters= 0 residual=inf sol_err=inf build=0.011s solve=0.000s\n",
+ " amg: OK iters= 1 residual=2.23e-14 sol_err=1.97e+04 build=0.021s solve=0.038s\n",
+ " gmres: OK iters= 5 residual=5.56e-11 sol_err=7.87e+06 build=0.000s solve=0.119s\n",
+ "\n",
+ "Benchmark complete. Results in /content/data/benchmark_results/benchmark_gnp_paper.jsonl\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Upload updated results to HuggingFace\n",
+ "\n",
+ "Requires a HuggingFace token with write access. Run `huggingface-cli login` or set `HF_TOKEN`."
+ ],
+ "metadata": {
+ "id": "txHYdKDwvTl0"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "notebook_login()"
+ ],
+ "metadata": {
+ "id": "rzfLeq0yvTl0",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 17,
+ "referenced_widgets": [
+ "4092598ad4ca4ce6aa43109449572c3c",
+ "5daa7d83d21f4b57b61b38df401a2a07",
+ "6f6851c49410450395f075906e2ae810",
+ "42bc97cf6ab1408f8da350b338b7aa0c",
+ "c31ac75bc0a74bbd8c36c6556d5fe883",
+ "e9cd28ec0f45480dab1e12b663b64a8b",
+ "c99bdb044a934920b77548d6e758ab47",
+ "9d5e5e1238784cda8089fd369577a537",
+ "a30735a217304b17bb03080312edc90a",
+ "01139f11b8a8412eb086c94f9d08b619",
+ "a997c12db85f4343bbcbf556bdef0adc",
+ "a2f324ac52784767b545c11d28d7c3c2",
+ "8f7f3184230445b2935db5edeac2dd5e",
+ "114b282b1444421bae4961bc0f5e5583",
+ "39f8013eb4084427b2836364d795ca1d",
+ "6a0c318e5d1140e58d6657e8980563e1",
+ "756bef726efc456b86c162c2535ba743",
+ "d0a9187031f64fb7ba95f73c79082c1c",
+ "3b20008a528c4ca7aad27a2c0f2ca0b7",
+ "e6500eee0b7e42aa975416e1cd34728c"
+ ]
+ },
+ "outputId": "cfdda7be-b068-4c99-8d89-4d460d7730fa"
+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='
Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. "
+ }
+ },
+ "6f6851c49410450395f075906e2ae810": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "PasswordModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "PasswordModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "PasswordView",
+ "continuous_update": true,
+ "description": "Token:",
+ "description_tooltip": null,
+ "disabled": false,
+ "layout": "IPY_MODEL_01139f11b8a8412eb086c94f9d08b619",
+ "placeholder": "",
+ "style": "IPY_MODEL_a997c12db85f4343bbcbf556bdef0adc",
+ "value": ""
+ }
+ },
+ "42bc97cf6ab1408f8da350b338b7aa0c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "CheckboxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "CheckboxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "CheckboxView",
+ "description": "Add token as git credential?",
+ "description_tooltip": null,
+ "disabled": false,
+ "indent": true,
+ "layout": "IPY_MODEL_a2f324ac52784767b545c11d28d7c3c2",
+ "style": "IPY_MODEL_8f7f3184230445b2935db5edeac2dd5e",
+ "value": false
+ }
+ },
+ "c31ac75bc0a74bbd8c36c6556d5fe883": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ButtonModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ButtonModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ButtonView",
+ "button_style": "",
+ "description": "Login",
+ "disabled": false,
+ "icon": "",
+ "layout": "IPY_MODEL_114b282b1444421bae4961bc0f5e5583",
+ "style": "IPY_MODEL_39f8013eb4084427b2836364d795ca1d",
+ "tooltip": ""
+ }
+ },
+ "e9cd28ec0f45480dab1e12b663b64a8b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_6a0c318e5d1140e58d6657e8980563e1",
+ "placeholder": "",
+ "style": "IPY_MODEL_756bef726efc456b86c162c2535ba743",
+ "value": "\nPro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. "
+ }
+ },
+ "c99bdb044a934920b77548d6e758ab47": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": "center",
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": "flex",
+ "flex": null,
+ "flex_flow": "column",
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": "50%"
+ }
+ },
+ "9d5e5e1238784cda8089fd369577a537": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "a30735a217304b17bb03080312edc90a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "01139f11b8a8412eb086c94f9d08b619": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "a997c12db85f4343bbcbf556bdef0adc": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "a2f324ac52784767b545c11d28d7c3c2": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "8f7f3184230445b2935db5edeac2dd5e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "114b282b1444421bae4961bc0f5e5583": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "39f8013eb4084427b2836364d795ca1d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ButtonStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ButtonStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "button_color": null,
+ "font_weight": ""
+ }
+ },
+ "6a0c318e5d1140e58d6657e8980563e1": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "756bef726efc456b86c162c2535ba743": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "d0a9187031f64fb7ba95f73c79082c1c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "LabelModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "LabelModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "LabelView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_3b20008a528c4ca7aad27a2c0f2ca0b7",
+ "placeholder": "",
+ "style": "IPY_MODEL_e6500eee0b7e42aa975416e1cd34728c",
+ "value": "Connecting..."
+ }
+ },
+ "3b20008a528c4ca7aad27a2c0f2ca0b7": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "e6500eee0b7e42aa975416e1cd34728c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "cac40f4c44f2485487da4154e4c789ff": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_cbf2a31238b744ed9ab5ec7dad8f4995",
+ "IPY_MODEL_700bc50a27314e29bd35fb5db46ce161",
+ "IPY_MODEL_1cab9a89f23646ceb677b9977f4e73f4"
+ ],
+ "layout": "IPY_MODEL_c7683f961f884a0b87a3de14c18de50e"
+ }
+ },
+ "cbf2a31238b744ed9ab5ec7dad8f4995": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_85efb27b79384e0b9de56d62d08f8157",
+ "placeholder": "",
+ "style": "IPY_MODEL_77e6ce8af20048b49d2d4fd3f403872f",
+ "value": "Processing Files (1 / 1) : 100%"
+ }
+ },
+ "700bc50a27314e29bd35fb5db46ce161": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_803c6f9f8ef2491d9cd135fb0627bf26",
+ "max": 1,
+ "min": 0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_8d5a762b38a24a788b3135cecd351dd1",
+ "value": 1
+ }
+ },
+ "1cab9a89f23646ceb677b9977f4e73f4": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_a2af605425b943248eaf97a377ff8fa9",
+ "placeholder": "",
+ "style": "IPY_MODEL_8f3ca459fd544774a604effe0580c381",
+ "value": " 13.8MB / 13.8MB, 23.1MB/s "
+ }
+ },
+ "c7683f961f884a0b87a3de14c18de50e": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "85efb27b79384e0b9de56d62d08f8157": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "77e6ce8af20048b49d2d4fd3f403872f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "803c6f9f8ef2491d9cd135fb0627bf26": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": "20px"
+ }
+ },
+ "8d5a762b38a24a788b3135cecd351dd1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "a2af605425b943248eaf97a377ff8fa9": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "8f3ca459fd544774a604effe0580c381": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "5aa3c965c54f49db9072d59f66c205cf": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_7bc1247346104353ba547a94d1d2bfdb",
+ "IPY_MODEL_fd93865b3777421893bc2226c7ae5c32",
+ "IPY_MODEL_5500072039ca4ea9b6a9fb72241a8ae4"
+ ],
+ "layout": "IPY_MODEL_c0964b3364fb4936828be7751fb40789"
+ }
+ },
+ "7bc1247346104353ba547a94d1d2bfdb": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_86698e6cd09646c5bb46899a4f640793",
+ "placeholder": "",
+ "style": "IPY_MODEL_ee65576015344e2a8875b3be8cddd96f",
+ "value": "New Data Upload : 100%"
+ }
+ },
+ "fd93865b3777421893bc2226c7ae5c32": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_38821c25bf234e5cb2acb8c2c39c36a8",
+ "max": 1,
+ "min": 0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_e5fe411293884f4da658d6c9e7254c35",
+ "value": 1
+ }
+ },
+ "5500072039ca4ea9b6a9fb72241a8ae4": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_8fbd2d78ecd34a048e0595262ee663da",
+ "placeholder": "",
+ "style": "IPY_MODEL_a50eeca275ec46a597ece51f37cb6def",
+ "value": " 1.08MB / 1.08MB, 1.79MB/s "
+ }
+ },
+ "c0964b3364fb4936828be7751fb40789": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "86698e6cd09646c5bb46899a4f640793": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "ee65576015344e2a8875b3be8cddd96f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "38821c25bf234e5cb2acb8c2c39c36a8": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": "20px"
+ }
+ },
+ "e5fe411293884f4da658d6c9e7254c35": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "8fbd2d78ecd34a048e0595262ee663da": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "a50eeca275ec46a597ece51f37cb6def": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "1d1daaf38ee548d79d1978e62c884c7b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HBoxModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_988a5285b74e41c58967f1ab46027a11",
+ "IPY_MODEL_a2843f2b72a7422eacfdd267d5148310",
+ "IPY_MODEL_22bad9b2890946d385abbcc4ea44a088"
+ ],
+ "layout": "IPY_MODEL_aa3dd3385ef24f27b52bba01a2a2402b"
+ }
+ },
+ "988a5285b74e41c58967f1ab46027a11": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_82798b7d4b8a43a2b60737cab9255680",
+ "placeholder": "",
+ "style": "IPY_MODEL_8d21d751b0a247889ccc2fb68e6ebbab",
+ "value": " ...benchmark_gnp_paper.jsonl: 100%"
+ }
+ },
+ "a2843f2b72a7422eacfdd267d5148310": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "FloatProgressModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_7ac7411ad2074e3c95abcc1a92fad83e",
+ "max": 13832051,
+ "min": 0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_06314c2ebb4c4fa28517c015432c381d",
+ "value": 13832051
+ }
+ },
+ "22bad9b2890946d385abbcc4ea44a088": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "HTMLModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_34dedee7e81f41ac9c862e8167513771",
+ "placeholder": "",
+ "style": "IPY_MODEL_ab97a7df663c440b8385392fcf1a6d53",
+ "value": " 13.8MB / 13.8MB "
+ }
+ },
+ "aa3dd3385ef24f27b52bba01a2a2402b": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "82798b7d4b8a43a2b60737cab9255680": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "8d21d751b0a247889ccc2fb68e6ebbab": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "7ac7411ad2074e3c95abcc1a92fad83e": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "06314c2ebb4c4fa28517c015432c381d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "ProgressStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "34dedee7e81f41ac9c862e8167513771": {
+ "model_module": "@jupyter-widgets/base",
+ "model_name": "LayoutModel",
+ "model_module_version": "1.2.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
+ }
+ },
+ "ab97a7df663c440b8385392fcf1a6d53": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_name": "DescriptionStyleModel",
+ "model_module_version": "1.5.0",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ }
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file