abgoswam

abgoswam

# -*- coding: utf-8 -*-
"""
Created on Wed Jul 12 19:06:38 2017

@author: agoswami
"""

#socket_echo_client_uds.py
import socket
import sys

abgoswam

using System;
using System.IO;
using System.IO.Pipes;
using System.Text;

class PipeServerBinary
{
    static void Main()
    {
        using (NamedPipeServerStream pipe = new NamedPipeServerStream("testpipe"))

abgoswam

# -*- coding: utf-8 -*-
"""
Created on Mon Jul 17 13:13:26 2017

@author: agoswami
"""

class Car(object):
    @staticmethod
    def factory(type):

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# Our application logic will be added here
def cnn_model_fn(features, labels, mode):
  """Model function for CNN."""
  # Input Layer
  input_layer = tf.reshape(features["x"], [-1, 32, 32, 3], name="input_tensor")

  # Convolutional Layer #1
  conv1 = tf.layers.conv2d(
      inputs=input_layer,
      filters=32,

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def get_batch(x, y, batch_size):
#    print("x shape : {0}".format(x.shape))
#    print("y shape : {0}".format(y.shape))
    n_samples = x.shape[0]
    indices = np.random.choice(n_samples, batch_size)
    return x[indices], y[indices], indices

# clear old variables
tf.reset_default_graph()
X = tf.placeholder(tf.float32, [None, 32, 32, 3], name="Input")

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import * as ort from 'onnxruntime-node';

// use an async context to call onnxruntime functions.
async function main() {

    ort.InferenceSession
    const session = await ort.InferenceSession.create('./model4.onnx');

    // prepare feeds. use model input names as keys.
    const feeds = {

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import * as ort from 'onnxruntime-web';

// use an async context to call onnxruntime functions.
async function main() {

    ort.InferenceSession
    const session = await ort.InferenceSession.create('./model4.onnx',   {
        executionProviders: ["wasm"],
      });

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# Different kinds of HF quantization summarized here
# https://github.com/huggingface/transformers/blob/main/docs/source/en/kv_cache.md
# replace lines 68-76 accordingly

from numpy import random
import argparse
import re
import json
import os
# import deepspeed

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import os
from azure.ai.ml import MLClient
from azure.ai.ml.entities import WorkspaceConnection
from azure.identity import DefaultAzureCredential
from azure.ai.ml.entities import UsernamePasswordConfiguration

# Enter details of your AML workspace
subscription_id = ""
resource_group = ""
workspace_name = ""