kvchen

import json

def main():
    with open('test.json', 'rb') as infile:
        test = json.load(infile)['result']
        print(test)

if __name__ == "__main__":
    main()

kvchen

Keybase proof

I hereby claim:

• I am kvchen on github.
• I am kevinchen (https://keybase.io/kevinchen) on keybase.
• I have a public key ASDOJ1rzklOEGkN0LP1t-vGd2Q3dQfON2di2vhNrDybITQo

To claim this, I am signing this object:

kvchen

@ray.remote
def lu_decomp_invert(lu_decomp):
    """Takes the inverse of each of the components in the P, L, U
    decomposition. Needed as a helper function for the block-level LU decomp.
    """
    return tuple(np.linalg.inv(x) for x in lu_decomp)


@ray.remote(num_return_vals=3)
def block_lu(a, block_size=100):

kvchen

import ray
import matplotlib.pyplot as plt
import numpy as np

import ray.array.distributed as rd
# from scipy.linalg import lu


def main():
    ray.init(start_ray_local=True, num_workers=10)

kvchen

@ray.remote
def lu_decomp_invert(lu_decomp):
    """Takes the inverse of each of the components in the P, L, U
    decomposition. Needed as a helper function for the block-level LU decomp.
    """
    return tuple(np.linalg.inv(x) for x in lu_decomp)


@ray.remote(num_return_vals=3)
def block_lu(a, block_size=100):

kvchen

@ray.remote
def lu_decomp_invert(lu_decomp):
    """Takes the inverse of each of the components in the P, L, U
    decomposition. Needed as a helper function for the block-level LU decomp.
    """
    return tuple(np.linalg.inv(x) for x in lu_decomp)


@ray.remote(num_return_vals=3)
def block_lu(a, block_size=100):

kvchen

@ray.remote
def lu_decomp_invert(lu_decomp):
    """Takes the inverse of each of the components in the P, L, U
    decomposition. Needed as a helper function for the block-level LU decomp.
    """
    return tuple(np.linalg.inv(x) for x in lu_decomp)


@ray.remote(num_return_vals=3)
def block_lu(a, block_size=100):

kvchen

@ray.remote(num_return_vals=3)
def block_lu(a, block_size=100):
    """Returns the LU decomposition of a square matrix.

    Parameters
    ----------
    a : array_like

    Returns
    -------

kvchen

def assemble(self):
    """Assemble an array on this node from a distributed array of object IDs."""
    first_block = ray.get(self.objectids[(0,) * self.ndim])
    dtype = first_block.dtype
    result = np.zeros(self.shape, dtype=dtype)

    indices = np.ndindex(*self.num_blocks)

    # Fetch all the blocks asynchronously
    blocks = ray.get([self.objectids[idx] for idx in indices])

kvchen

#!/usr/bin/env python

import click
import json
import mkl
import numpy as np
import ray

from ray.array.distributed import core as rdc
from timeit import default_timer as timer