ahmedbesbes

{
  "transcript": "How much do you get paid? Don't answer that out loud. But put a number in your head. Now, how much do you think the person sitting next to you gets paid? It turns out that pay transparency, sharing salaries openly across a company, makes for a better workplace for both the employee and for the organization. You see, keeping salary secret leads to what economists call information asymmetry. This is a situation where in a negotiation, one party has loads more information than the other. And in hiring or promotion or annual raise discussions, an employer can use that secrecy to save a lot of money. Imagine how much better you could negotiate for a raise if you knew everybody's salary. Now, I realized that letting people know what you make might feel uncomfortable, but isn't it less uncomfortable than always wondering if you're being discriminated against, or if your wife or your daughter or your sister is being paid unfairly? Openness remains the best way to ensure fairness. And pay transparen

ahmedbesbes

import asyncio
import PIL.Image

import bentoml
from bentoml.io import Image, Text

preprocess_runner = bentoml.Runner(MyPreprocessRunnable)
model_a_runner = bentoml.xgboost.get('model_a:latest').to_runner()
model_b_runner = bentoml.pytorch.get('model_b:latest').to_runner()

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bentoml.pytorch.save_model(
    name="mnist",
    model=model,
    signature={
        "__call__": {
            "batchable": True,
            "batch_dim": (0, 0),
        },
    },
)

ahmedbesbes

import asyncio
import bentoml
import PIL.Image

import bentoml
from bentoml.io import Image, Text

transformers_runner = bentoml.transformers.get("sentiment_model:latest").to_runner()
ocr_runner = bentoml.easyocr.get("ocr_model:latest").to_runner()

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service: "service:svc"  # Same as the argument passed to `bentoml serve`
labels:
   owner: bentoml-team
   stage: dev
include:
- "*.py"  # A pattern for matching which files to include in the bento
python:
   packages:  # Additional pip packages required by the service
   - scikit-learn
   - pandas

ahmedbesbes

import requests
 
requests.post(
  "http://127.0.0.1:3000/classify",
  headers={"content-type": "application/json"},
  data="[[5.9, 3, 5.1, 1.8]]"
).text
  
'[2]'

ahmedbesbes

 import numpy as np
 import bentoml
 from bentoml.io import NumpyNdarray
 
 iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner()
 
 svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner])
 
 @svc.api(input=NumpyNdarray(), output=NumpyNdarray())
 def classify(input_series: np.ndarray) -> np.ndarray:

ahmedbesbes

from typing import Optional

def foo(format_layout: Optional[bool] = True):
    ...

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def load_model(filename: str, cache_folder: Union[str, Path]):
    if isinstance(cache_folder, Path):
        cache_folder = str(cache_folder)
    
    model_path = os.join(filename, cache_folder)
    model = torch.load(model_path)
    return model

ahmedbesbes

from typing import Tuple

# Fails the type check ❌
t: Tuple[int] = [1, 2, 3]

# Ok ✅
t: Tuple[int, int, int] = [1, 2, 3]