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######  
	Triton Inference Server provides a cloud and edge inferencing solution optimized for both CPUs and GPUs. 
	Triton supports an HTTP/REST and GRPC protocol that allows remote clients to request inferencing for any 
	model being managed by the server.
######



################### INSTALL DOCKER ########################
# SET UP THE REPOSITORY

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#!/bin/bash
## This gist contains instructions about cuda v10.2 and cudnn 8.1.1 installation in Ubuntu 18.04 

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

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# Requirements: 
#    OS: Ubuntu18.04 LTS
#    Python >= 3.8
#    Cuda: 10.2, 
#    CudaNN 8.1.1

# Download TensorRT 7.2.3 for Linux and CUDA 10.2 from https://developer.nvidia.com/nvidia-tensorrt-7x-download for ubuntu 18.04
# or from this link https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/7.2.3/local_repos/nv-tensorrt-repo-ubuntu1804-cuda10.2-trt7.2.3.4-ga-20210226_1-1_amd64.deb

# OPEN terminal

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def Network(iteration):
    
   
    X, Y, W1, W2, b1, b2 = initialization()
    
    for i in range(iteration):
        
        Z1,A1,Z2,A2,error = forward_prop(X,Y,W1,W2,b1,b2)
    
        da1,da2,dz1,dz2,dw1,dw2,db1,db2 = backward_prop(X, Y, W1, W2, b1, b2, Z1,A1,Z2,A2)

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def update_parameters(w1,w2,bias1,bias2, dw1,dw2,dz1,dz2,db1,db2,learning_rate=0.5):
    
    w1 = w1 - (learning_rate * dw1)
    bias1 = bias1 - (learning_rate * dz1)
    
    w2 = w2 - (learning_rate * dw2)
    bias2 = bias2 - (learning_rate * dz2)
    
    return w1,w2,bias1,bias2

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def backward_prop(x, y, w1, w2, bias1, bias2, z1,a1,z2,a2):
    
    
    da2 = a2 * (1-a2)
    dz2 = (a2-y)
    dw2 = np.multiply(a1.T, dz2)
    db2 = dz2
    
  
    da1 = a1 * (1-a1)

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def forward_prop(x,y,w1,w2,bias1,bias2):
    
    Z1 = np.dot(w1,x) + bias1
    A1 = sigmoid(Z1)
    
    
    Z2 = np.dot(w2,A1) + bias2
    A2 = sigmoid(Z2)
    
    error = compute_error(A2,y)  

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def compute_error(a3, Y):
    
    m = Y.shape[0]
    
    logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)
    cost = 1./m * np.sum(logprobs)
    
    return cost

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import numpy as np

def initialization():
    
    #  ইউনপুট ভেক্টর 
    X = np.array([.10,.30,.50]).reshape(3,1)
    
    #  ওয়েট ম্যাট্রিক্স 
    W1 = np.array([
            [0.15,0.20,0.25],

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def softmax(x):
    """Compute softmax values for each sets of scores in x."""
    e_x = np.exp(x - np.max(x))
    s = e_x / e_x.sum()
    return s