Akashdesarda

# Stage 1: Builder/Compiler
FROM python:3.8-slim as builder
RUN apt update && \
    apt install --no-install-recommends -y build-essential gcc
COPY req.txt /req.txt

RUN pip install --no-cache-dir --user -r /req.txt

# Stage 2: Runtime
FROM nvidia/cuda:10.1-cudnn7-runtime

Akashdesarda

FROM nvidia/cuda:10.1-cudnn7-runtime

RUN apt update && \
    apt install --no-install-recommends -y build-essential software-properties-common && \
    add-apt-repository -y ppa:deadsnakes/ppa && \
    apt install --no-install-recommends -y python3.8 python3-pip python3-setuptools python3-distutils && \
    apt clean && rm -rf /var/lib/apt/lists/*

COPY req.txt /req.txt
COPY ./src /src

Akashdesarda

# Stage 1: Builder/Compiler
FROM python:3.7-slim as builder
RUN apt update && \
    apt install --no-install-recommends -y build-essential gcc
COPY req.txt /req.txt

RUN pip install --no-cache-dir --user -r /req.txt

# Stage 2: Runtime
FROM debian:buster-slim

Akashdesarda

FROM python:3.8-slim

RUN apt update && \
    apt install --no-install-recommends -y build-essential gcc && \
    apt clean && rm -rf /var/lib/apt/lists/*
COPY ./req.txt /req.txt
COPY ./src /src

RUN pip3 install --no-cache-dir -r /req.txt
CMD ['python3', '/src/app.py']

Akashdesarda

distil_bert = 'distilbert-base-uncased'

config = DistilBertConfig(dropout=0.2, attention_dropout=0.2)
config.output_hidden_states = False
transformer_model = TFDistilBertModel.from_pretrained(distil_bert, config = config)

input_ids_in = tf.keras.layers.Input(shape=(128,), name='input_token', dtype='int32')
input_masks_in = tf.keras.layers.Input(shape=(128,), name='masked_token', dtype='int32') 

embedding_layer = transformer_model(input_ids_in, attention_mask=input_masks_in)[0]

Akashdesarda

distil_bert = 'distilbert-base-uncased'

config = DistilBertConfig(dropout=0.2, attention_dropout=0.2)
config.output_hidden_states = False
transformer_model = TFDistilBertModel.from_pretrained(distil_bert, config = config)

input_ids_in = tf.keras.layers.Input(shape=(128,), name='input_token', dtype='int32')
input_masks_in = tf.keras.layers.Input(shape=(128,), name='masked_token', dtype='int32') 

embedding_layer = transformer_model(input_ids_in, attention_mask=input_masks_in)[0]

Akashdesarda

from transformers import TFDistilBertForSequenceClassification, DistilBertConfig
import tensorflow as tf

distil_bert = 'distilbert-base-uncased'

config = DistilBertConfig(num_labels=6)
config.output_hidden_states = False
transformer_model = TFDistilBertForSequenceClassification.from_pretrained(distil_bert, config = config)[0]

input_ids = tf.keras.layers.Input(shape=(128,), name='input_token', dtype='int32')

Akashdesarda

from transformers import DistilBertTokenizer, RobertaTokenizer, 
distil_bert = 'distilbert-base-uncased' # Pick any desired pre-trained model
roberta = 'roberta-base-uncase'

# Defining DistilBERT tokonizer
tokenizer = DistilBertTokenizer.from_pretrained(distil_bert, do_lower_case=True, add_special_tokens=True,
                                                max_length=128, pad_to_max_length=True)
# Defining RoBERTa tokinizer
tokenizer = RobertaTokenizer.from_pretrained(roberta, do_lower_case=True, add_special_tokens=True,
                                                max_length=128, pad_to_max_length=True)

Akashdesarda

# depth should be 9n+2 (eg 56 or 110)

# Model definition
num_filters_in = 32
num_res_block = int((depth - 2) / 9)

inputs = Input(shape=input_shape)

# ResNet V2 performs Conv2D on X before spiting into two path
X = residual_block(X=inputs, num_filters=num_filters_in, conv_first=True)