yuhanz

#puzzle1

import numba
import numpy as np
import warnings
from lib import CudaProblem, Coord
warnings.filterwarnings(
    action="ignore", category=numba.NumbaPerformanceWarning, module="numba"
)

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import av
container = av.open('assets/example_data/videos/pose1.mp4')
frame = av.VideoFrame(640, 480, "rgb24")
frame.to_ndarray()

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from huggingface_hub import snapshot_download

# Download the entire repository
repo_path = snapshot_download(repo_id="huggingface/model-repo")

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from yt_dlp import YoutubeDL

# URL of the YouTube video
video_url = "https://www.youtube.com/watch?v=example"

# yt-dlp options
ydl_opts = {
    "format": "mp4",  # Download a progressive MP4 stream with both video and audio
    "outtmpl": "downloads/%(title)s.%(ext)s",  # Output template
}

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import clip
import torch
from PIL import Image

#image_pil = Image.open("path_to_image.jpg")
image_pil = Image.fromarray(image)
device = 'cuda'
image_input = preprocess(image_pil).unsqueeze(0).to(device)
with torch.no_grad():
     image_features = model.encode_image(image_input)

yuhanz

import numba.cuda
import torch
import numpy as np

t = torch.from_numpy(np.array([1,2,3]))
numba.cuda.as_cuda_array(t.to('cuda'))

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#define N 10000000
#include "stdio.h"

// To compile: nvcc vector_add.cu -o vector_add -ccbin "C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.41.34120\bin\Hostx64\x64"

// void vector_add(float *out, float *a, float *b, int n) {
//     for(int i = 0; i < n; i++){
//         out[i] = a[i] + b[i];
//     }
// }

yuhanz

import matplotlib.pyplot as plt
import numpy as np
import scipy.signal

# Create some data points for the curves
x = np.linspace(0, 10, 100)  # 100 points between 0 and 10
y1 = np.sin(x)  # First curve (sine wave)
y2 = np.cos(x)  # Second curve (cosine wave)
y3 = scipy.signal.correlate(y1, y2, mode = 'same')

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!pip install transformers==4.30
!pip install accelerate
!pip install trl peft
!pip install bitsandbytes
!pip install xformers==0.0.22
!pip install autoawq


from peft import LoraConfig
from peft import get_peft_model, PeftConfig, PeftModel, LoraConfig, prepare_model_for_kbit_training

yuhanz

### Download files from https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/tree/main

import time
from transformers import AutoTokenizer
import transformers
import torch
# model = "PY007/TinyLlama-1.1B-step-50K-105b"
# model = "yuhanzgithub/tinyllama"
model = "./"
tokenizer = AutoTokenizer.from_pretrained(model)