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#PyData Berlin Program, 2014

Friday, July 25

Time	Beginner/Intermediate Tutorial	Intermediate/Advanced Tutorial
12:00-12:45	Registration	Registration
-- --	-- -- -- --	-- -- -- --
12:45-14:05	Frank Kaufer (bakdata)	Bryan Van De Ven (Continuum Analytics)
	Python and Big Data Framework	Interactive Plots using Bokeh

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import pandas as pd
import numpy as np

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Network Analysis Tutorial using Python & networkx

MSCX Ph.D. Summer School, Salina (ME) - Italy

Author: Valerio Maggio

PostDoc Data Scientist @ FBK/MPBA

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import pandas as pd
import numpy as np
from IPython.display import HTML 

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Kubeflow Kale: from Jupyter Notebook to Complex Pipelines

Abstract

In this talk I will present a new solution to automatically scale Jupyter notebooks to complex and reproducibility pipelines based on Kubernetes and KubeFlow.

Description

Nowadays, most of the High Performance Computing (HPC) tasks are carried out in the Cloud, and this is as much

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import pandas as pd
import numpy as np

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import umap
import numpy as np
import scipy as sp

print('umap: ', umap.__version__)
print('Numpy: ', np.__version__)
print('Scipy: ', sp.__version__)

# umap:  0.4.0
# Numpy:  1.17.5

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#----------------------------------------------------------------------------------------
# model.py
#----------------------------------------------------------------------------------------
from django.db import models
from numpy.random import random_sample

class CustomModel(models.Model):
    # Put your fields here

    def get_data(self):

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from typing import Tuple, List, Optional, Union, NewType
from typing import Protocol, runtime_checkable
from abc import ABC, abstractmethod
import pandas as pd
try:
    import torch as t
    from torch import Tensor as Tensor
except ImportError: # fallback to Numpy, if torch not available - this is just as a POP!
    import numpy as t
    from numpy import ndarray as Tensor

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import numpy as np
import multiprocessing as mp
from argparse import ArgumentParser


def get_current_seed(wid):
    s = np.random.get_state()[1][0]
    print(f"Pool Worker {wid}: Random State {s}")