dneprDroid

..........

$static_framework = ['<SOME_POD_NAME>']

 pre_install do |installer|
   Pod::Installer::Xcode::TargetValidator.send(:define_method, :verify_no_static_framework_transitive_dependencies) {}
   installer.pod_targets.each do |pod|
       if $static_framework.include?(pod.name)
         def pod.build_type;
           Pod::Target::BuildType.static_library

dneprDroid

git config --global credential.helper store && git fetch --all && cat ~/.git-credentials

dneprDroid

sudo lsof -i -P -n | grep LISTEN

dneprDroid

var sampleBytes = new Int8Array(4096*1000*100);

var saveByteArray = (function () {
    var a = document.createElement("a");
    document.body.appendChild(a);
    a.style = "display: none";
    return function (data, name) {
        var blob = new Blob(data, {type: "octet/stream"}),
            url = window.URL.createObjectURL(blob);
           console.log("Blob URL: ", url);

dneprDroid

;// store 3822
mov    x8, #3822

;// next: (4969 << 16) | 3822
movk    x8, #4969, lsl #16 ;// now `x8` is 325652206

;// next: (2 << 32) | ((4969 << 16) | 3822)
movk    x8, #2, lsl #32 ;// now `x8` is 8915586798

dneprDroid

import numpy as np
import matplotlib.pyplot as plt


def func1(x):
    return np.sin(x)

def func1der(x):
    return np.cos(x)

dneprDroid

import tfcoreml as tf_converter

tf_converter.convert(tf_model_path='my_model.pb',
                     mlmodel_path='my_model.mlmodel',
                     input_name_shape_dict=input_tensor_shapes,
                     output_feature_names=output_tensor_names)

dneprDroid

import coremltools 
your_model = coremltools.converters.keras.convert('your_model.h5',
                                                  input_names=['image'],
                                                  output_names=['output'],
                                                  image_input_names='image')
your_model.save('your_model_name.mlmodel')

dneprDroid

#!/usr/bin/python
 # -*- coding: utf-8 -*-

import json
import re
import time
from functools import reduce
import pyperclip 

dneprDroid

gcloud ai-platform jobs submit training ${job_name} --job-dir ${job_dir} \
  --python-version 3.5   \
  --runtime-version 1.9  \
  --package-path ./trainer            `# trainer module `  \
  --module-name trainer.train         `# file train.py from trainer module`    \
  --region ${region}     \
  --scale-tier BASIC_GPU   `# single NVIDIA Tesla K80 GPU` \
  --  `# Next, the params for train.py` \
  --weights_stage "${job_dir}/weights_stage_exported_tiny.h5"   `# exported stage pre-trained model `  \
  --weights_final "${job_dir}/weights_final_exported_tiny.h5""  `# exported final pre-trained model `  \