kalradivyanshu

# install flashinfer by running:
# pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/

import torch
import flashinfer
from math import sqrt, ceil

torch.manual_seed(0)

num_layers = 32

kalradivyanshu

import numpy as np

def count_parameters_tflite(model):
    details = model.get_tensor_details()
    # Calculate the total number of trainable parameters
    total_params = 0
    for detail in details:
        shape = detail['shape']
        if 'weight' in detail['name'] or 'bias' in detail['name']:
            total_params += np.prod(shape)

kalradivyanshu

// CC: cc -O3 -Wall -Wextra $(pkg-config --cflags --static SvtAv1Enc) enc.c $(pkg-config --libs --static SvtAv1Enc)

#include <pthread.h> // for pthread_exit, pthread_create, pthread_join
#include <stdbool.h> // for bool, false
#include <stdint.h> // for uint8_t, uint64_t, uint16_t, uint32_t
#include <stdio.h> // for size_t, NULL, fprintf, fputs, fwrite
#include <stdlib.h> // for calloc, free, strtoul
#include "EbSvtAv1.h" // for EbSvtIOFormat, EbBufferHeaderType, EB_E...
#include "EbSvtAv1Enc.h" // for svt_av1_enc_parse_parameter, svt_av1_en...
#include "EbSvtAv1Formats.h" // for EB_EIGHT_BIT, EB_YUV420

kalradivyanshu

package main

import (
	"fmt"
	"os"
	"time"

	"github.com/pion/rtcp"
	"github.com/pion/webrtc/v2"
	"github.com/pion/webrtc/v2/pkg/media"

kalradivyanshu

Homebrew build logs for mysql on macOS 10.13.5
Build date: 2018-07-17 11:59:12

kalradivyanshu

import tensorflow as tf
import time
m1 = []
m2 = []
result = []
i = 10
while i <= 10000:
    m1.append(tf.random_uniform(shape = [i, i]))
    m2.append(tf.random_uniform(shape = [i, i]))
    i *= 10

kalradivyanshu

import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
from tensorflow.examples.tutorials.mnist import input_data
from time import time
import sys
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

image_size = 28

x = tf.placeholder(tf.float32, [None, 784])

kalradivyanshu

from tensorflow.examples.tutorials.mnist import input_data
import sys
layers = int(sys.argv[1])

import tensorflow as tf
from time import time
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])
hiddenLayer = tf.layers.dense(x, 1000, activation = tf.nn.tanh)