fernandocamargoai

'use strict'

// Must come before importing any instrumented module.
import tracer from 'dd-trace';
tracer.init();

import { PubSub } from '@google-cloud/pubsub'
import { Storage } from "@google-cloud/storage"
import { status as gRPCStatus } from '@grpc/grpc-js'

fernandocamargoai

'use strict'

// Must come before importing any instrumented module.
import tracer from 'dd-trace';
tracer.init();

import { PubSub } from '@google-cloud/pubsub'
import { Storage } from "@google-cloud/storage"
import { status as gRPCStatus } from '@grpc/grpc-js'

fernandocamargoai

'use strict'

// Must come before importing any instrumented module.
import tracer from 'dd-trace';
tracer.init();

import { PubSub } from '@google-cloud/pubsub'
import { Storage } from "@google-cloud/storage"
import { status as gRPCStatus } from '@grpc/grpc-js'

fernandocamargoai

import os
from typing import Callable, Tuple

import numpy as np
import pandas as pd
import tensorflow as tf
from imgaug import augmenters as iaa


def rescale(images: tf.Tensor) -> tf.Tensor:

fernandocamargoai

import functools
import os
import warnings
from glob import glob
from multiprocessing import Pool
from pathlib import Path
from typing import Optional, Iterator, List

import numpy as np
import ujson as json

fernandocamargoai

import json
from typing import NamedTuple, Tuple

import PIL.Image
import numpy as np
from bentoml import BentoService, api, env, ver, artifacts
from bentoml.artifact import KerasModelArtifact, TextFileArtifact, TensorflowSavedModelArtifact
from bentoml.handlers import ImageHandler
import tensorflow as tf
from tensorflow.keras import Model

fernandocamargoai

import imgaug.augmenters as iaa
import numpy as np


class Conditional(iaa.Augmenter):
    """Apply child augmenter(s) for images wich a given conditional comes true.

    Let ``C`` be one or more child augmenters given to
    :class:`~imgaug.augmenters.meta.Conditional`.
    Let ``p`` be the fraction of images (or other data) to augment.

fernandocamargoai

INFO: [pid 14484] Worker Worker(salt=140992192, workers=1, host=ufgdeepfood.RadSquare.cloud, username=fernando, pid=14484) running   EvaluateIfoodModel(model_module=recommendation.task.model.m
atrix_factorization, model_cls=MatrixFactorizationTraining, model_task_id=MatrixFactorizationTraining____500_False_4bb5a61c77, limit_list_size=50, nofilter_iteractions_test=False, task_hash=no
ne, num_processes=16, bandit_policy=none, bandit_policy_params={}, bandit_weights=none, batch_size=100000, plot_histogram=False, no_offpolicy_eval=False)
2020-01-23 13:36:38,335 : INFO : [pid 14484] Worker Worker(salt=140992192, workers=1, host=ufgdeepfood.RadSquare.cloud, username=fernando, pid=14484) running   EvaluateIfoodModel(model_module=
recommendation.task.model.matrix_factorization, model_cls=MatrixFactorizationTraining, model_task_id=MatrixFactorizationTraining____500_False_4bb5a61c77, limit_list_size=50, nofilter_iteractio
ns_test=False, task_hash=none, num_processes=16, bandit_policy=none, bandit_policy_params={}, band

fernandocamargoai

import numpy as np
from typing import List, Callable, Tuple
import math

def shuffle(a: np.ndarray, b: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
    assert len(a) == len(b)
    p = np.random.permutation(len(a))
    return a[p], b[p]

fernandocamargoai

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