mGalarnyk

import math
import numpy as np
from timebudget import timebudget
from multiprocessing import Pool

iterations_count = round(1e7)

def complex_operation(input_index):
    print("Complex operation. Input index: {:2d}".format(input_index))

mGalarnyk

import math
import numpy as np
from timebudget import timebudget

iterations_count = round(1e7)

def complex_operation(input_index):
    print("Complex operation. Input index: {:2d}".format(input_index))

    [math.exp(i) * math.sinh(i) for i in [1] * iterations_count]

mGalarnyk

import math
import numpy as np
from timebudget import timebudget
import ray

iterations_count = round(1e7)

@ray.remote
def complex_operation(input_index):
    print("Complex operation. Input index: {:2d}".format(input_index))

mGalarnyk

import torch
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
import torchvision.transforms as transforms

import ray
from ray.util.sgd.torch import TorchTrainer
from ray.util.sgd.torch import TrainingOperator
# https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py
from ray.util.sgd.torch.resnet import ResNet18

mGalarnyk

from mars.session import new_session
ray_session = new_session(backend=’ray’).as_default() # Set Ray as the default backend.

import mars.dataframe as md
import mars.tensor as mt

t = mt.random.randint(100, size=(2**10, 2**8))
df = md.DataFrame(t)
print(df.head(10).execute())

mGalarnyk

import ray
# Modin defaults to backing Ray’s object store with disk.
# Start Ray before importing modin to use shared memory instead.
ray.init()

import modin.pandas as pd
import numpy as np

frame_data = np.random.randint(0, 100, size=(2**10, 2**8))
df = pd.DataFrame(frame_data)

mGalarnyk

import ray
from ray.util.dask import ray_dask_get

import dask
import dask.dataframe as dd 
import pandas as pd
import numpy as np

dask.config.set(scheduler=ray_dask_get) # Sets Ray as the default backend.

mGalarnyk

import ray
import raydp

ray.init()

@ray.remote
class PySparkDriver:
    def __init__(self):
        self.spark = raydp.init_spark(
            app_name='RayDP example',

mGalarnyk

import ray
from ray.util.sgd import TorchTrainer
from ray.util.sgd.torch import TrainingOperator
from ray.util.sgd.torch.examples.train_example import LinearDataset

import torch
from torch.utils.data import DataLoader

class CustomTrainingOperator(TrainingOperator):
    def setup(self, config):

mGalarnyk

import time
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# Set training and validation sets
X, y = make_classification(n_samples=1000000, n_features=1000, n_classes = 2)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=10000)

# Solvers