Gcav66

#!/bin/bash

set -e

# OVERVIEW
# This script installs a custom, persistent installation of conda on the Notebook Instance's EBS volume, and ensures
# that these custom environments are available as kernels in Jupyter.
# 
# The on-start script uses the custom conda environment created in the on-create script and uses the ipykernel package
# to add that as a kernel in Jupyter.

Gcav66

%%time
with afar.run, remotely:
    import dask_cudf

    df = dask_cudf.read_csv(
        "s3://nyc-tlc/trip data/yellow_tripdata_2019-*.csv",
        parse_dates=["tpep_pickup_datetime", "tpep_dropoff_datetime"],
        storage_options={"anon": True},
        assume_missing=True,
    ).persist()

Gcav66

from numba import cuda
import numpy as np
import cupy

def gather_from_detector():
    return cupy.random.random((1000, 1000), dtype="float32")


@cuda.jit
def smooth_gpu(x, out):

Gcav66

# USAGE
# Start the server:
# 	python app.py
# Submit a request via cURL:
# 	curl -X POST -F image=@dog.jpg 'http://localhost:5000/predict'

# import the necessary packages
from keras.applications import ResNet50
from keras.preprocessing.image import img_to_array
from keras.applications import imagenet_utils

Gcav66

unsorted_text = "uwlcnfcejefjudkmylhmmpjfkmhveozojepfvgboboxknkfdoghkvzxrkamhaghahpnnzh_figzhelkapd_dnxsiurbjgq_bcctqgigfksdrzgtxvihiqvfvinuqfzgohvilwyxpuqjolleuecpxcbisaptnoxbglpkhsvvwdgaminprfrha_uemwdepnngtxgkqx_flerkbnnzazcvnh_sealovrpuafumrmaizidjedvovhtbadjelfrbknpioqiindxqiozzpdrv_bwooblumjc_oqtcgrfmxkwkczhhqiwuqzoznqgfmbdxrxigvkebypbsybanuccpsezgv_dvteybbrbrbldbpiyhgzkptloxfktrzkjmdhppucsaohfnehdnlnhexwj_yhulslwpdreogjsffy_fkqcdkobywhqxpjvv_sqbscknxrew_ivgvpolmqfkyxoxzuyqctriyxcncwvvqhdupkagguee_zfbcrexbtkcsbnjcfzqoq_wrdktpsclzrmeybxpzrylfdydknd_zbjjqcnubpjbfaaecuxbeprjjjfcybvcghfbcvnjicpliwzkqvwo_hloynyjrmiqvubsyobetklnsgovesswqatrcrirsywdvbpwnhtjaug_nglxamsybkop_gnkdvgzp_cmyxclrtjtoohniuszzbnakknd_ahe_enbouxvpueotcjebex_vpqbwyjgczobcirmgfvwnlrxaryoltdlozwdgcp__iwu_vkod_kzkmeklloeixrxymlvyvtlfyydwtzxugrhxwqgmmsvtshrublypevlpglldlumsbscjuv_cbtfjpenrervyxtbrxaaqsqs__boiiubqmgwtvzxlnxxyskbwquztepk_uggukayehrifcrdpcnrfhmlucqzbsvoojsfexvbzsrccyqjuufbiae_siovpbhhqzkcemm_wtzio_d_emtkxpkpqftqjrhiiuvyijtwrmj_

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anonymous_id	dhsyear	latnum	longnum	alt_gps	alt_dem
1	2013	5.185091972	7.715280056	9999	97
2	2013	8.348506927	8.833737373	9999	180
3	2013	9.181493759	6.376670837	9999	127
4	2013	7.685192108	5.988824844	9999	370
5	2013	12.14755154	6.713367939	9999	478
6	2013	8.315398216	9.686684608	9999	147
7	2013	9.116501808	4.814712048	9999	97
8	2013	9.313765526	7.745061874	9999	454
9	2013	6.37945509	5.628859043	9999	102

Gcav66

import json
from requests_oauthlib import OAuth2
from oauthlib.oauth2 import BackendApplicationClient
from requests_oauthlib import OAuth2Session
from oauthlib.oauth2 import TokenExpiredError
import pandas as pd

class AWhereCall(object):
    """Use this joint to get you some weather data"""