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

Installing SQLite

The simplest way to start using SQLite is to download a precompiled binary from the

The latest version of SQLite at the time of writing is 3.8.9, but you can check for additional updates on the SQLite download page.

Windows

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

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])