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import json | |
import os | |
from functools import partial | |
import flytekit | |
from flytekit import ImageSpec, Resources, Secret, map_task, task, workflow | |
embed_image = ImageSpec( | |
name="langchain-flyte-vectordb", | |
packages=[ |
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import json | |
import os | |
from functools import partial | |
import flytekit | |
from flytekit import ImageSpec, Resources, Secret, dynamic, map_task, task, workflow | |
load_data_image = ImageSpec( | |
name="langchain-flyte-load-data", | |
packages=[ |
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from dataclasses import dataclass | |
from typing import Dict, List | |
import flytekit | |
import matplotlib.pyplot as plt | |
import mpld3 | |
import numpy as np | |
import pandas as pd | |
import pyarrow as pa | |
import vaex |
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def to_literal( | |
self, | |
ctx: FlyteContext, | |
python_val: T, | |
python_type: Type[T], | |
expected: LiteralType, | |
) -> Literal: | |
meta = BlobMetadata( | |
type=_core_types.BlobType( | |
format=self.TENSORFLOW_FORMAT, |
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... | |
ONNX_MODEL = "onnx_geolocator" | |
VERSION = "latest" | |
class GeoLocatorRunnable(bentoml.Runnable): | |
""" | |
Custom BentoML runner to fetch multiple ONNX outputs | |
""" |
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