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1. Pull and Run the Squid Container Run the following command in your terminal. This uses the popular ubuntu/squid image.

docker run -d --name squid-local -e 'TZ=UTC' -p 3128:3128 ubuntu/squid:latest

-d: Runs in detached mode (background).

-p 3128:3128: Maps port 3128 on your machine to port 3128 in the container (Squid's default port).

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  - name: Llama 3.1 Instruct
    displayName: Llama 3.1 Instruct
    modelHubID: llama-3.1-instruct
    category: Text Generation
    type: HF
    description: The Llama 3.1 8B-Instruct, 8B instruct and 8B base NIM simplifies the deployment of the Llama 3.1 70B-Instruct, 8B instruct and 8B base tuned models which is optimized for language understanding, reasoning, and text generation use cases, and outperforms many of the available open source chat models on common industry benchmarks.
    requireLicense: True
    licenseAgreements:
    - label: Use Policy
      url: https://llama.meta.com/llama3/use-policy/

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models:
  - name: Llama-2-Chat
    displayName: Llama-2-Chat (Before 24.02)
    description: Llama-2-Chat model published by Meta
    versions:
      - latestVersionId: LLAMA-2-70B-CHAT
        sha: ff6d1c3ba37b31d4af421951c2300f2256fb3691
        latestVersionSizeInBytes: 107224858679
        createdDate: 2023-10-24T18:21:04.610Z
        updatedDate: 2024-04-23T22:57:49.590Z

smothiki

import logging
import logging as log

from pathlib import Path
from pyspark.sql import DataFrame
from pyspark.sql.types import StructType
from pyspark.sql.functions import udf
from pyspark.sql.types import StructField
from pyspark.sql.functions import lit

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train_dir = '/home/cdsw/datasettrees'
import numpy as np
import glob
import os
import matplotlib.pyplot as plt
from PIL import Image
train_list = glob.glob(train_dir+'/**/*.jpg',recursive=True)

print(train_list)

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!pip3 install torch torchvision pandas numpy onnx onnxruntime

import argparse

import torch
import torch.nn.functional as F
from torch import nn, optim
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, transforms

smothiki

all permutations of N dice roll
def combine(self, n: int, k: int) -> List[List[int]]:
      
        combs = []
        def backtrack(index, path):
            if len(path) == k:
                combs.append(list(path))
                return
            
            for i in range(index, n+1):

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class Solution {
 public:
  bool isValidSudoku(vector<vector<char>>& board) {
    unordered_set<string> seen;

    for (int i = 0; i < 9; ++i)
      for (int j = 0; j < 9; ++j) {
        if (board[i][j] == '.')
          continue;
        const string c(1, board[i][j]);

smothiki

Nearest Neighbour City

A number of cities are arranged on a graph that has been divided up like an ordinary Cartesian plane. Each city is located at an integral (x, y) coordinate intersection. City names and locations are given in the form of three arrays: c, x, and y, which are aligned by the index to provide the city name (c[i]), and its coordinates, (x[i], y[i]). Determine the name of the nearest city that shares either an x or a y coordinate with the queried city. If no other cities share an x or y coordinate, return 'NONE'. If two cities have the same distance to the queried city, q[i], consider the one with an alphabetically shorter name (i.e. 'ab' < 'aba' < 'abb') as the closest choice. The distance is the Manhattan distance, the absolute difference in x plus the absolute difference in y.

The time complexity for my solution is O(NlogK) for processing input + O(QlogK) for returning the result for all the given queries,
where N is the number of cities, K is the max number of cities with same x or y coo

smothiki

At DoorDash, menus are updated daily even hourly to keep them up-to-date. Each menu can be regarded as a tree. When the merchant sends us the latest menu, can we calculate
how many nodes have changed/added/deleted?

Assume each Node structure is as below:

class Node {
        String key;
        int value;
        List children;
}