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import netket as nk | |
import jax | |
from jax.flatten_util import ravel_pytree | |
import numpy as np | |
import netket.nn as nknn | |
import sys | |
np.set_printoptions(edgeitems=30, linewidth=317, formatter=dict(float=lambda x: "%.3g" % x)) | |
hi =nk.hilbert.Spin(0.5, 4) |
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# This file is machine-generated - editing it directly is not advised | |
[[AbstractFFTs]] | |
deps = ["LinearAlgebra"] | |
git-tree-sha1 = "485ee0867925449198280d4af84bdb46a2a404d0" | |
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" | |
version = "1.0.1" | |
[[AbstractTrees]] | |
git-tree-sha1 = "03e0550477d86222521d254b741d470ba17ea0b5" |
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from flax import struct | |
import netket as nk | |
from netket.sampler.metropolis import MetropolisSampler | |
from netket.operator import AbstractOperator | |
from netket.jax import njit4jax | |
from netket.sampler.metropolis import MetropolisRule | |
from numba import jit |
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``` | |
➜ python -i Examples/Ising1d/ising1d.py | |
0%| | 0/30 [00:00<?, ?it/s]jambax: numba_fun_8774198495278 | |
encoding jambax gpu | |
With N_in=2 and n_out=2 | |
Fatal Python error: PyEval_SaveThread: NULL tstate | |
Python runtime state: initialized | |
Thread 0x00007fa82cbe9640 (most recent call first): | |
File "/usr/lib64/python3.8/threading.py", line 306 in wait |
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import netket as nk | |
import numpy as np | |
import json | |
L = 24 | |
g = nk.graph.Hypercube(length=L, n_dim=1, pbc=True) | |
hi = nk.hilbert.Spin(s=0.5, N=L) | |
ha = nk.operator.Ising(hilbert=hi, graph = g, h=0.9) |
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Using Pkg | |
Pkg.add("ProtoBuf") | |
Pkg.add("Glob") | |
Pkg.add("FilePaths") | |
using ProtoBuf | |
using Glob | |
using FilePaths; using FilePathsBase: / | |
pbpath =dirname(dirname(PosixPath(pathof(ProtoBuf))))/"gen" |
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import numpy as _np | |
import ctypes | |
from mpi4py import MPI as _MPI | |
from jax import core | |
from jax import abstract_arrays | |
from jax.lib import xla_client | |
from jax.interpreters import xla |
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from . import mpi_xla_bridge | |
from jax.lib import xla_client | |
for name, fn in mpi_xla_bridge.cpu_custom_call_targets.items(): | |
xla_client.register_cpu_custom_call_target(name, fn) |
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{"Output": [{"Sx": {"Mean": 0.9999999169282001, "Variance": 1.862586944649667e-07, "Sigma": 1.1098962439738887e-05, "R_hat": 0.9976736497590734, "TauCorr": 0.0}, "Sy": {"Mean": 5.077493691027381e-05, "Variance": 0.10370042335148308, "Sigma": 0.008281602265203468, "R_hat": 1.003089244834029, "TauCorr": 0.0}, "Sz": {"Mean": -0.0048941798941798936, "Variance": 0.10370620573332213, "Sigma": 0.00828183315490214, "R_hat": 1.0031086816827015, "TauCorr": 0.0}, "LdagL": {"Mean": 17.14925977446276, "Variance": 223.72984547380884, "Sigma": 0.2521810040464449, "R_hat": 1.0002016738591837, "TauCorr": 0.06963744988539322}, "Iteration": 0}, {"Sx": {"Mean": 0.8883244213563494, "Variance": 0.0073810615431987, "Sigma": 0.002209446641081766, "R_hat": 1.0020263155675062, "TauCorr": 0.0}, "Sy": {"Mean": 0.032671567827953656, "Variance": 0.08862480162138474, "Sigma": 0.0076559967859600175, "R_hat": 1.001262256781352, "TauCorr": 0.0}, "Sz": {"Mean": -0.1997354497354497, "Variance": 0.09846553847876598, "Sigma": 0.008069864350415346 |