balvisio

#!/bin/bash

# ---------------------------------------------------------
# Customizable Settings
# ---------------------------------------------------------

MOUNT_POINT="${CASE_SAFE_MOUNT_POINT:-${HOME}/casesafe}"
VOLUME_PATH="${CASE_SAFE_VOLUME_PATH:-${HOME}/.casesafe.dmg.sparseimage}"
VOLUME_NAME="${CASE_SAFE_VOLUME_NAME:-casesafe}"
VOLUME_SIZE="${CASE_SAFE_VOLUME_SIZE:-60g}"

balvisio

FWIW: I didn't produce the content present here. I've just copy-pasted it from somewhere over the Internet, but I cannot remember exactly the original source. I was also not able to find the author's name, so I cannot give him/her the proper credit.

---

Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

balvisio

How to setup your own CA with OpenSSL

For educational reasons I've decided to create my own CA. Here is what I learned.

First things first

Lets get some context first.

balvisio

Ok, I geeked out, and this is probably more information than you need. But it completely answers the question. Sorry. ☺

Locally, I'm at this commit:

$ git show
commit d6cd1e2bd19e03a81132a23b2025920577f84e37
Author: jnthn <jnthn@jnthn.net>
Date:   Sun Apr 15 16:35:03 2012 +0200

When I added FIRST/NEXT/LAST, it was idiomatic but not quite so fast. This makes it faster. Another little bit of masak++'s program.

balvisio

gRPC is based on HTTP/2. So we have to analyze packages to solve some difficult problems related to the protocol. This wiki introduces how to do this using Wireshark because capturing packages of HTTP/2 is a little different with HTTP/TCP.

HTTP/2 via TLS is not covered in this wiki

Download Wireshark of recent versions.
Add your port to HTTP protocols:
Open Preferences
Select Protocols>HTTP. HTTP2 works too, but HTTP is enough
Add your HTTP/2 port like(50051) to TCP ports
Click OK to save the config

balvisio

import numpy as np
import keras.backend as K
import tensorflow as tf

def metrics_np(y_true, y_pred, metric_name, metric_type='standard', drop_last = True, mean_per_class=False, verbose=False):
    """ 
    Compute mean metrics of two segmentation masks, via numpy.
    
    IoU(A,B) = |A & B| / (| A U B|)
    Dice(A,B) = 2*|A & B| / (|A| + |B|)

balvisio

Types of MLflow Server Setups

There are multiple variants with respect to how the artifacts are handled: (Reference: https://mlflow.org/docs/latest/tracking.html#how-runs-and-artifacts-are-recorded)

a. In this scenario, for artifact logging, the MLflow client interacts with the remote Tracking Server and artifact storage host:

• The MLflow client uses RestStore to send a REST request to fetch the artifact store URI location from the Tracking Server
• The Tracking Server responds with an artifact store URI location (an S3 storage URI in this case)
• The MLflow client creates an instance of an S3ArtifactRepository, connects to the remote AWS host using the boto client libraries, and uploads the artifacts to the S3 bucket URI location

balvisio

#!/bin/sh

ProgName=$(basename $0)
  
sub_help(){
    echo "Usage: $ProgName <subcommand> [options]\n"
    echo "Subcommands:"
    echo "    bar   Do bar"
    echo "    baz   Run baz"
    echo ""