mjm522

Steps

1. Install docker using instructions here. This installation instructions were tested on Ubuntu 18.04 with Docker version 19.03.3, build a872fc2f86.

2. To run docker as a non-root user, follow the post installation instruction here.

3. Install nvidia toolkit:Reference

$ sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit

mjm522

Note: To run a process dedicated on a CPU you can use the tasket command. For example

taskset -c 5,11 python -m timeit 'sum(range(10**7))'

However, this will not guarantee that the CPUs 5,11 will be used for that process alone. The same CPU can be interrupted by the scheduler and may not be properly isolated. So inorder to isolate the CPU the following steps are to be taken. Another feature that could be turned off for these CPUs are the interrupt feature 'IRQ'. However this is not covered here.

1. List all available cores and hyperthreads in your processor.

lscpu --all --extended

mjm522

from multiprocessing import Pool
import numpy as np
import time


def gen_data(s, e, N):
    q = np.linspace(s, e, N)
    dq = np.linspace(s, e, N)*2
    ddq = np.linspace(s, e, N)*3
    time.sleep(0.5)

mjm522

import ctypes
import mmap
import os
import stat
import numpy as np
from functools import reduce
from operator import mul
from typing import Any

rtld = ctypes.cdll.LoadLibrary("librt.so")

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T* constructOrFindInstance(const std::string& instance_name)
    {
        auto result = mSegment.find<T>(instance_name.c_str());
        if (result.first != nullptr) {
            return result.first;
        } else {
            return mSegment.construct<T>(instance_name.c_str())();
        }
    }

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import numpy as np

def two_d_array_compare(array_a, array_b, thresh):
    """
    this function does a pairwise comparision matching between array_b and array_a
    it returns all the row numbers in array_a that has a similar entry in array_b
    :param array_a: m x k
    :param array_b: n x k
    :thresh float difference
    :return: row numbers of similar rows

mjm522

#!/usr/bin/bash

input="full path to file"
while IFS= read -r line; do
  	arrIN=(${line// / }) ## to split with ; arrIN=(${line//;/ }) 
  	## do something with the words, words can be obtained by ${arrIN[i]} where i is the index
done < "${input}"

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from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

def plot_cuboid(sample_cuboid):  
  fig = plt.figure(figsize=(12,9))
  ax = fig.add_subplot(111, projection='3d')

  for pair in sampling_cuboid['plot_pairs']:
      start_point = sampling_cuboid['centre_point'] + sampling_cuboid[pair[0]]
      end_point =  sampling_cuboid['centre_point'] + sampling_cuboid[pair[1]]

mjm522

import numpy as np
import matplotlib.pyplot as plt

x_min = 0.51
x_max = 0.54
num_bins = 20
num_data=7000
x_mean = 0.53
plt.style.use("ggplot")

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import trimesh
from numpy.linalg import inv
import numpy as np

def pretty_print(array):
    print(np.round(array, 2))

def change_centroid(mesh_path, save_path, additional_T=None):
 mesh = trimesh.load_mesh(mesh_path)