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import json
import random
import time
import os

from spell.serving import BasePredictor
import spell.serving.metrics as m


class Predictor(BasePredictor):

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# import torch
from torch.utils.data import Dataset, DataLoader
import torchvision
import pandas as pd
from PIL import Image

import time
import argparse

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module go-azure

go 1.16

require (
	github.com/Azure/azure-sdk-for-go v55.4.0+incompatible // indirect
	github.com/Azure/go-autorest/autorest v0.11.19 // indirect
	github.com/Azure/go-autorest/autorest/azure/auth v0.5.7 // indirect
	github.com/Azure/go-autorest/autorest/to v0.4.0 // indirect
	github.com/Azure/go-autorest/autorest/validation v0.3.1 // indirect

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+ [[ ! -d /tmp/tvm ]]
+ git clone --recursive https://github.com/apache/tvm /tmp/tvm
Cloning into '/tmp/tvm'...
remote: Enumerating objects: 85, done.
remote: Counting objects: 100% (85/85), done.
remote: Compressing objects: 100% (76/76), done.
remote: Total 95489 (delta 26), reused 8 (delta 6), pack-reused 95404
Receiving objects: 100% (95489/95489), 36.67 MiB | 33.38 MiB/s, done.
Resolving deltas: 100% (69864/69864), done.
Submodule 'dlpack' (https://github.com/dmlc/dlpack) registered for path '3rdparty/dlpack'

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#!/bin/bash
set -ex
# https://tvm.apache.org/docs/install/from_source.html#install-from-source
if [[ ! -d "/tmp/tvm" ]]; then
    git clone --recursive https://github.com/apache/tvm /tmp/tvm
fi
apt-get update && \
    apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev \
        build-essential cmake libedit-dev libxml2-dev
if [[ ! -d "/tmp/tvm/build" ]]; then

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PandaModel(
  (enc): Sequential(
    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (4): Sequential(
      (0): Bottleneck(
        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

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import os
from pathlib import Path

import torch
from torchvision.datasets import ImageFolder
from torch.utils.data import Dataset, DataLoader
from torch import nn
from torchvision import transforms
import torch.optim as optim
import torch.quantization

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import click
import subprocess
import os

@click.group()
def cli():
    pass

@click.command(name="connect", short_help="Shell out to psql.")

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if __name__ == "__main__":
    from distributed import Client, LocalCluster
    import dask.dataframe as df
    import dask.array as da

    cluster = LocalCluster()
    client = Client(cluster)

    matches = da.from_npy_stack("/spell/data/")
    matches = df.from_array(matches)