DEV Community: Dan Benitah

Git clone - that repo is too big : HELP!

Dan Benitah — Tue, 03 Dec 2024 20:54:00 +0000

Working with large repositories can be challenging, especially when you only need a specific directory. Instead of wasting time and storage, here's a guide to efficiently clone sub-directories using git-sparse-checkout.

1. Clone the Repository with Sparse Checkout

Use the --depth 1 flag to clone only the latest commit, and --filter=blob:none to avoid downloading file contents initially:

git clone --depth 1 --filter=blob:none https://github.com/danuw/azure-docs.git --sparse

Resulted in following (note the size of 307 kiB downloaded when the repo is gigabytes heavy!!)

Cloning into 'azure-docs'...
remote: Enumerating objects: 8438, done.
remote: Counting objects: 100% (8438/8438), done.
remote: Compressing objects: 100% (7673/7673), done.
remote: Total 8438 (delta 51), reused 4682 (delta 25), pack-reused 0 (from 0)
Receiving objects: 100% (8438/8438), 2.56 MiB | 16.60 MiB/s, done.
Resolving deltas: 100% (51/51), done.
remote: Enumerating objects: 85, done.
remote: Counting objects: 100% (85/85), done.
remote: Compressing objects: 100% (81/81), done.
remote: Total 85 (delta 52), reused 16 (delta 4), pack-reused 0 (from 0)
Receiving objects: 100% (85/85), 307.16 KiB | 5.04 MiB/s, done.
Resolving deltas: 100% (52/52), done.

2. Navigate to the Cloned Repository

Change your directory to the cloned repository:

cd azure-docs

3. Initialize Sparse Checkout Mode

Enable sparse checkout in cone mode, which simplifies the process of selecting specific directories:

git sparse-checkout init --cone

4. Set the Directory to be Checked Out

Specify the directory you want to check out from the repository. In this example, we are checking out the articles/iot-operations directory:

git sparse-checkout set articles/iot-operations

Note: If you encounter an error, ensure that your Git version supports sparse checkout, that the specified directory exists in the repository or that the syntax is adapted to your local system (Windows, MacOS, Linux?...)

After these steps, only the files within the articles/iot-operations directory will be checked out into your local repository, minimizing the data you download and store.

remote: Enumerating objects: 189, done.
remote: Counting objects: 100% (189/189), done.
remote: Compressing objects: 100% (182/182), done.
remote: Total 189 (delta 10), reused 106 (delta 7), pack-reused 0 (from 0)
Receiving objects: 100% (189/189), 10.96 MiB | 21.30 MiB/s, done.
Resolving deltas: 100% (10/10), done.
Updating files: 100% (190/190), done.

Wrap up

This method is particularly useful when storage space is limited or when working with a slow connection. Whether you're cloning a specific lab version or just a part of a broader documentation set (e.g., for LLMs or RAG purposes), this guide offers a simple and effective solution.

Additional Notes

Originally published as a GitHub Gist.

For those following Microsoft documentation, this tip may soon be available directly across labs and tutorials, as seen in this example and its PR.https://github.com/dotnet/AspNetCore.Docs/pull/34239)

Upgrading Python version for your Microsoft Fabric environment

Dan Benitah — Thu, 12 Sep 2024 16:40:00 +0000

I was recently stuck trying to re-run a python notebook in Fabric using the Azure Event Hub package to upload data to a KQL database. The script, which had been running smoothly for months, suddenly stopped working after an environment change or update.

UPDATE: bringing forward this note as it should be a better solution to use

Note: I later found a note about my issue in some training training material, which had been my first solution without the --force parameter. That may be a better solution for you if you needed to keep the Spark Runtime unchanged https://github.com/microsoft/FabricRTA-in-a-Day/blob/main/Lab3.md#steps

The first issue I was facing was the following when installing the package in my notebook.

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed

This error indicates that the package dependencies are conflicting, which often occurs due to Python environment version incompatibilities or misaligned dependencies.

When trying to import the package, I could see the environment was set to 3.10 (see below).
And when trying to create my own environment, I could not find azure-eventhub pypi package either (see here).

Eventually, I tried to figure out how to upgrade the python environment, and with the help of a friend, I was able to do so.

The solution was upgrade the PySpark environment.
It resolved the issue by updating the Python runtime to a compatible version with the azure-eventhub package. Switching to Spark Runtime 1.3 updated Python to version 3.11.x, resolving dependency conflicts.

How to change the Spark Runtime to 1.3

Go to the Workspace settings.
Select the appropriate runtime from the dropdown list.
Save and restart your notebook.

Validation of the Environment Upgrade

Now the changes have been apply, create a new notebook ensuring the selected environment uses the Spark Runtime 1.3.

# Check if the environment upgrade was successful
import azure.eventhub
print("Environment setup successful!")

Conclusion

Upgrading the Python environment in Fabric by changing the Spark runtime resolved the dependency conflicts I faced with the azure-eventhub package. If you encounter similar issues, adjusting the runtime version can be a quick fix. Remember to validate your changes and test your script to ensure everything is working smoothly.

References:

Speeding up installation of AIO (Azure IoT Operations) deployment using a script

Dan Benitah — Sat, 01 Jun 2024 23:57:05 +0000

If you're eager to learn more about AIO (Azure IoT Operations), which is currently in private preview, it's crucial to start with a fresh install to experiment quickly and efficiently. In this post, I will guide you through the steps to reinstall your cluster using a script, ensuring a smooth learning experience. Let's get started!

For now, the assumption is that you are using fresh codespace using those steps. Updated script for physical machines with Ubuntu server coming soon...

Setup the cluster again and deploy the AIO extension

Note: if your are looking at a new version of AIO, the script may need adapting - this has been tested with AIO v0.5-preview at the time of writing

1. Set up your variables

1.a RESOURCE_GROUP

When you created your Codespace, you had to provide some variable values such as the resource group, so double check that the resource group variable is what you expect with echo $RESOURCE_GROUP, and otherwise set it using (update value as needed):

export RESOURCE_GROUP=aio-codespace-rg

1.b IOT OPS USER_EMAIL

To ensure your user can access the AIO UI, specify your IoT Ops user email with the following command, replacing with your actual email address:

export USER_EMAIL=<your-email>

Note: Remember that user needs to be a member of the tenant such as iotops@YOUTENANT.onmicrosoft.com
If you do not have one, use export USER_EMAIL=$(az ad user create --display-name 'IoT Ops User' --password <PASSOWRD> --user-principal-name iotop@<TENANTNAME>.onmicrosoft.com --force-change-password-next-sign-in false -o tsv --query userPrincipalName)

1.c Other Variables (optional)

Consider double checking other environment variables such as the $CLUSTER_NAME to suit your preferences.

2. Login

Ensure you are signed into your Azure Account by running the following command in your terminal, then follow the instructions in your browser:

az login --use-device-code

3. Run your setup script

Create a file named setup.sh in your Codespace and copy the content from this gist into it.

Once you have updated your variables and logged into Azure, you are ready to run the script with the following command:

. ./setup.sh

Note: Currently tested on Codespace, but will soon update with script for Ubuntu server including Az CLI install and other pre-requisites for re-install

4. Manual Validation

Based on experience, if your Codespace setup remains unchanged (same number of cores, etc.), you can expect this whole process to take approximately 25 minutes.

After the process completes, navigate to IoT Operations Portal and sign in with your member user account. You should see your cluster listed as connected.

Next?

Thank you for reading this far. I hope this script helps you experiment faster with AIO. In the future, I plan to:

Expand the script with new steps: As AIO evolves and more APIs become available, additional processes can be automated. You may also consider improving the script as you explore or revisit the latest features, in which case don't forget to let me know in the gist's comments.
Cover more scenarios, such as creating assets using CLI.

References:

AIO : Unable to read /etc/rancher/k3s/k3s.yaml, please start server with --write-kubeconfig-mode to modify ... permissions

Dan Benitah — Thu, 02 May 2024 16:06:52 +0000

Using Azure IoT Operations (AIO) and found that issue of permissions denied and the cluster not running with the following messages? Here is how I fixed it...

When I connect to my clusters, my first action is to check if my pods or services are running with the following command, but recently I have at times been getting the subsequent output error:

$ kubectl get service -n azure-iot-operations
WARN[0000] Unable to read /etc/rancher/k3s/k3s.yaml, please start server with --write-kubeconfig-mode to modify kube config permissions
error: error loading config file "/etc/rancher/k3s/k3s.yaml": open /etc/rancher/k3s/k3s.yaml: permission denied

I tried the following solution using this command, but that was only temporary...

Solution 1: Admin Kubeconfig Options

sudo k3s server --write-kubeconfig-mode 644

If you encounter the following...

$ sudo k3s server --write-kubeconfig-mode 644
[sudo] password for dan:
INFO[0000] Starting k3s v1.28.7+k3s1 (051b14b2)
INFO[0000] Configuring sqlite3 database connection pooling: maxIdleConns=2, maxOpenConns=0, connMaxLifetime=0s
INFO[0000] Configuring database table schema and indexes, this may take a moment...
INFO[0000] Database tables and indexes are up to date
INFO[0000] Kine available at unix://kine.sock
INFO[0000] Reconciling bootstrap data between datastore and disk
FATA[0000] starting kubernetes: preparing server: init cluster datastore and https: listen tcp :6443: bind: address already in use

...you can just stop the service with this command first:

sudo systemctl stop k3s

Solution 2: Directly provide access to the file for all users

sudo chmod 644 /etc/rancher/k3s/k3s.yaml

Your k3s cluster should now start and you will be able to get the list of pods/services confirming everything is running as expected.

kubectl get pods -n azure-iot-operations

This issue occurred every time I reset my ubuntu server's kubernetes cluster, but I will just apply that going forward to continue testing with my user and run kubectl commands without sudo ...

References:

Live Average Carbon Intensity rating with Octopus Home Mini in Home Assistant

Dan Benitah — Wed, 20 Mar 2024 00:09:20 +0000

As mentioned to Adam Jackson on Decarbonize Software 2023 the other day, here is a quick note on how to create a live consumption carbon intensity rating widget (sorry a bit of a mouthful) on your Home Assistant dashboard.

Pre-requisites:

You have an instance of Home Assistant already configured
HACS is already set up on your Home Assistant instance. Otherwise instructions can be found here: https://hacs.xyz/docs/setup/download
You are signed up to Octopus Energy provider and have requested a Octopus Home Mini - if you are not yet an Octopus Energy customer, feel free to use my referral code iron-sun-306 for £50 off your first bill

I was recently really excited to get an Octopus Home Mini. Compared to a smart meter that only reports on aggregated readings over 30 minutes blocks, the Home Mini allows for 10 seconds to 1 minute old readings which can significantly help improve smart home automation in the house. Let's face it, there is nearly no automation relevant over 30 minutes old data when it comes to energy. I have solar panels, and in a country like the UK, the sun really comes and goes and automations would need to be based on 1 to 5 minutes averages to stay relevant.

Anyway, getting up to 1 minute old consumption carbon rating can be achieve with these simple steps:

1. Install CO2 Signal Home Assistant Integration

This is available via the integration tab in Home Assistant.
CO2 Signal Home Assistant Integration

2. Install Octopus Integration and enable the Home Mini

This requires the installation of HACS first.
Octopus Home Assistant Integration

3. Set up a template that combines the values from both

Configuration

In your configuration.yaml, add the following line (to create your templates in a dedicated file - that is neater)

template: !include templates.yaml

Create that template.yaml file (alongside your configuration.yaml) and add the following content:

template:
  - sensor:
      - name: "Current hourly Electricity Carbon Emission Rating"
        unit_of_measurement: "gCO2eq/h"
        state: >
            {% set currentco2rating = states('sensor.co2_signal_co2_intensity') | float %}
            {% set liveusage = states('sensor.octopus_energy_electricity_22j0484346_1013001886662_current_demand') | float %}

            {% set totalRating = currentco2rating * liveusage/1000 %}
            {% if totalRating > 0 %}
            {{ totalRating | round(1, default=0) }}
            {%- else -%}
              0
            {%- endif %}

Note to create your own template, you can use the Template Editor under the template tab of the Developer Tools to test it.

Dashboard

You can then set up a dashboard with your current consumption or rating using the sensor card, to help you compare your consumption. The sensor card shows both the current value as well as the history over the past configured period (for me it is set to last 24h).
You could also add gauges to create some red/amber/green zones to at a glance understand how you are currently doing.

Here I am starting the battery charging when the power is at its lowest carbon intensity for the day:

This shows that when the consumption is negative, we are keeping our rating positive to a minimum of zero.

Finally this show charging the car when the power is at its lowest of the day (similar to charging the battery):

Wrap up

Adding your carbon intensity to your dashboard and combining it to your consumption makes it easy to see whether our consumption is carbon aware, i.e. whether it uses more energy cleaner, in other words, when the electricity production comes primarily from renewable energy.

EDIT 26/03/24: Thank you Ronan Glemain (@ronanglemain) for the correction tips, especially around fixing the unit of measure (from gCO2eq/kWh to gCO2eq/h) - Template has been updated though images still currently the original with the mistaken unit

Making customised chocolates using 3D printing

Dan Benitah — Tue, 19 Mar 2024 14:06:24 +0000

I was recently in search for an unusual Valentine's Day idea, came across silicon molds again and decided to give a go in the context of creating custom Valentine's Day chocolates.

First, I will say that that silicon moulds are a very impressive thing. They reproduce in details shapes, and are easy to use to recreate any object you may already have.
Second, the silicon here does not apply to resin. For I have only focused on FDM printers

Pre-requisites

Food safe silicon. I used this one
Mould frame. I recommend using this one as it offers great flexibility
Silicon mat. I used this one
Optionally you could use a heated mat like this one to accelerate your moulds solidifying. For now, I have placed mine near the radiators overnight (but might reconsider after the winter :) )

1. Create the model

3D print the desired shape: I 3D printed some custom shapes.
I also used a coin my son gave me to see how it dealt with intricate details (and it did like a chef! but more on that below)

If you are new to 3D modelling, you could start by using tinkercad.com. You could start with a basic shape like below (i.e. a shape and a message)

In this article, how about we use some code? Using OpenSCAD, you can use this gist code to generate the STL to 3D print.

2. Cast the mould

Prepare the mould frame

Start here so you can get a better idea of quantities needed to avoid wasting the silicon

Place the pieces you would like to print and assemble the mould frame around them. Make sure to arrange pieces in the space to optimise the size of the frame and overall layout (such as leaving sufficient space between pieces).
As a base at first, I used a plastic bag which leaves slight bumpy marks.

Now make sure to use the provided double sided sellotape to stick the frame, as well as each piece to the base. This will ensure the silicon does not escape outside the frame before it dries, and that the pieces will not move during the solidification process of the silicon. (The first time, some of the 3D printed pieces did move/ float, but generally did not stay stuck to the bottom and ended up with a layer of silicon in between).

Prepare the silicon

The silicon linked above is a 1:1 by volume, so you need the same amount of each liquid (part A and part B).

Make sure to mix it properly using the provided wooden piece. Stirring for some extra time, and every so often scraping the sides to ensure no liquid has been left unmixed may help get the most homogeneous substance possible.

Create the mould

Pour the silicon mixture slowly to ensure no bubbles get trapped, or the risk to move some of the pieces (although taped should be good enough)

Let it set for a few hours. When finished, gently remove the models from the silicon and let it rest for 24h (I saw the latter in the silicon's instructions and I assume this is part of keeping it food-safe, so I would encourage to do so to be extra safe).

3. Recreate your model in chocolate

Prepare the chocolate

For optimal texture results, it is advised to "temper" the chocolate. See instructions I followed here.

Pour the chocolate and let it set

By now, your chocolate is creamy.
As for the silicon, go slowly when pouring to reduce the amount of bubbles. You may even want to push a tiny bit at first into the nuts and crannies of your mould so that the details definitely come out fine (bubbles where the key features are, could really impact the look of the final result).

4. Final result

Once the chocolate has set, gently remove from the silicon, show it to get the effect it deserves and enjoy...

Here are some examples of the final result

Next steps?

So first, some lessons learnt from my first attempts include:

use sellotape to stick the models to the base
if 3D printing multiple models, it may be worth grouping as one piece (like a chocolate tablet) so everything keeps in place
sand the 3D prints for a smoother finish
use a spatula (or more generally proper utensils!)

Going forward, it would be interesting to:

find food safe silicon compatible with resin and experiment with new shapes to allow for finer details (e.g. it is hard to print a logo on a small piece that would retain the correct level of details)
try deeper chocolates shapes (the ones we are used to get from chocolate shop with more volume) as well as experiment with fillings such as praline, or crushed nuts/almonds etc...

In short, fun has only just began and please do not hesitate to share your creations below ...

Thanks to @fergusk for some of the tips mentioned above

Your organization has enabled or enforced SAML SSO ... you must re-authorize the OAuth Application `GitHub for VS Code`

Dan Benitah — Sun, 10 Mar 2024 00:53:40 +0000

I just came across the following while trying to clone a repo:

The "contoso" organization has enabled or enforced SAML SSO. To access this repository, you must re-authorize the OAuth Application GitHub for VS Code

I searched how to enable per folder authentication for git.

The easiest solution was to simply run

gh auth login

Happy cloning...

Fixing Power BI report's credentials details error in Microsoft Fabric

Dan Benitah — Mon, 05 Feb 2024 23:20:41 +0000

After publishing the Power BI report from the QueenOfTheSky demo, I encountered an issue in Fabric. Despite being directed to my Power BI workspace homepage, it wasn't clear how to proceed next.

Issue Summary

Following the steps in the build Power BI report section, I encountered an error post-publishing. The Power BI desktop app was used to open and fill in the report (.pibt), which was then published. However, upon accessing the report, an error banner indicated a connection issue and provided a link to edit settings. This link redirected me to the Power BI homepage without clear instructions on updating the connection settings.

The solution involves three seperate methods to update the connection settings for the report, identified as QueensOfTheSky_rpt:

via the setting menu
through workspace's lineage view
semantic model using the workspace list view
accessing the semantic model settings directly from the report

All methods are detailed below, with steps to guide you through updating your credentials.

How to Update Your Credentials

Update your credentials using the settings menu

From anywhere, select the Settings Menu > Power BI Settings > Semantic model tab > Select your report (QueensOfTheSky_rpt in our case) > Datasource credentials

Update your credentials using the workspace's lineage view

On your workspace page, select the ellipsis on your report's semantic model card and go to settings. You can now update your datasource credentials.

Using the list view

In your workspace, look for your semantic model (I filtered by "rpt"). Click the ellipsis and select settings.

Semantic model settings from the report

While on your report:

(1) Select the 3 dots > view Semantic model

(2) File > Settings

That's it for now

Additional guidance might be provided as Microsoft continues to refine and address these navigational challenges.

This guide aims to help you navigate and resolve connectivity issues in Power BI, ensuring a smoother experience with the QueenOfTheSky demo.

And a big THANK YOU to the contributors who created that cool demo!

EDIT late Feb : added 4th method

Running out of space on a developer's machine

Dan Benitah — Wed, 20 Dec 2023 15:21:00 +0000

As developers, we often find our hard drives mysteriously filling up, slowing down our workflow.
First reflex is often to remove the waste and for developers, that includes downloaded packages that linger until we return our unused projects.
Let's explore a simple, yet powerful command-line I came across to reclaim hat precious disk space, making room for more coding and less clutter.

So I started with a 250 GB SD full

The commands we will need

Disk space

Command to get the disk space (I used an SD card smaller than my project folder until full).

 diskutil info -all

You can also use

Remove a child folder from a directory

Looking for a command to recursively remove child node_module folder, I found the following command at https://stackoverflow.com/a/70549487/4819888 (it seems to have equivalents for Windows and Linux too)

find . -name '<folder to remove>' -type d -prune -print -exec rm -rf '{}' \;

Cleaning up node_modules folders

find . -name 'node_modules' -type d -prune -print -exec rm -rf '{}' \;

After running this command and clearing 22 node_module folders, it went down to 76GB

Cleaning up bin folders

find . -name 'node_modules' -type d -prune -print -exec rm -rf '{}' \;

After running this command and clearing down 490 bin folders, I had a further 20 GB of additional free space.

I was thinking next we could tackle python packages but I could not find consistent naming convention though "pip" was what brought back most results. Also more on dangling docker images here. Anyway...

Conclusion

So here we are with a reduction of 78.43% of the space my projects took in no time.
What other folders shall we look to remove? Have you noticed similar savings?
What other tips might you have to share?

Troubleshooting Environment Errors while following Edge Impulse's walkthrough of End-To-End Keyword Spotting with Synthetic Data

Dan Benitah — Fri, 17 Mar 2023 15:17:28 +0000

Are you struggling to create a model that can detect the keyword "R2D2"? I faced a similar challenge until I came across a helpful video on Training Models with Synthetic Data: End-to-End Keyword Spotting Walkthrough with Edge Impulse.

However, I encountered some issues while following the video tutorial and had to troubleshoot them.

In this article, I will share the steps I took to overcome these obstacles, in case you run into the same problems.

Here's a quick summary of the issues I faced:

hidden files not accessible warning: jupyter lab Refusing to serve hidden file, via 404 Error, use flag 'ContentsManager.allow_hidden' to enable
ffmpeg and ffprobe not recognised
DefaultCredentialsError

Some pre-requisites

First, ensure that you have node installed. If not, you can follow this tutorial to install it.
If npm is not recognised, run source ~/.bash_profile to load it into the current shell.

Next, install Python using pyenv by following that tutorial. Once installed, you can install version 3.10.8 by running the following command:

pyenv install -v 3.10.8

Setting up the Environment

Clone the Edge Impulse notebooks repository by running:

git clone https://github.com/edgeimpulse/notebooks.git

Navigate to the notebooks folder inside it:

cd notebooks/notebooks/

Create a virtual environment for Python 3.10.8:

pyenv virtualenv 3.10.8 edgeimpulsenotebooks

Activate the new virtual environment:

pyenv local edgeimpulsenotebooks

Install the required dependencies:

pip install requests
pip install pydub
pip install google-cloud-texttospeech
pip install jupyterlab

When I ran the notebook, I encountered issues with ffmpeg and ffprobe not being found. To resolve this, I followed the instructions outlined here.

pip install ffmpeg-downloader
ffdl install --add-path

After the installation completes, you will be prompted to ensure that ffmpeg is in your path. Copy and paste the provided line into your ~/.zsh_profile (or ~/.bash_profile if you're using that instead) and save the file.

export PATH="/Users/dan.benitah/Library/Application Support/ffmpeg-downloader/ffmpeg:${PATH}"

To ensure that ffmpeg is loaded, run:

source ~/.zsh_profile

... and test the command:

ffmpeg

NOTE: I found it useful to test my commands directly in the jupiter-labs' terminal window to confirm what was not recognised or erroring more clearly.

One last thing before we can launch the lab. I kept on getting a warning that hidden files were not accessible and using --ContentsManager.allow_hidden=True seems to have solved it.

You should now be able to launch the Jupyter Lab environment from the shell where all these commands are available (npm, ffmpeg, ffprobe...) :

jupyter-lab --ContentsManager.allow_hidden=True

Finally, to follow along with the Google instructions from the video, enable the API in the Google Console at https://console.cloud.google.com/speech/text-to-speech. It's worth noting that enabling the Text-to-Speech API alone might not be sufficient. In my case, I also needed to enable this API for it to work (may have been a combination of things).

To check if your APIs are enabled, look at the notification bar for notifications like this:

Once you've completed these steps, you should be able to resume watching the video and follow along with the instructions. If you encounter any issues or have any questions, feel free to leave a comment below.

Now the model detects when I say "R2D2" (yes that was my word :) ), I will need to look at these next steps:

embed the library into a new project to act on detected keywords
iterate on the model to improve its detection

DEV Community: Dan Benitah

Git clone - that repo is too big : HELP!

1. Clone the Repository with Sparse Checkout

2. Navigate to the Cloned Repository

3. Initialize Sparse Checkout Mode

4. Set the Directory to be Checked Out

Wrap up

Upgrading Python version for your Microsoft Fabric environment

How to change the Spark Runtime to 1.3

Validation of the Environment Upgrade

Conclusion

Speeding up installation of AIO (Azure IoT Operations) deployment using a script

Setup the cluster again and deploy the AIO extension

1. Set up your variables

1.a RESOURCE_GROUP

1.b IOT OPS USER_EMAIL

1.c Other Variables (optional)

2. Login

3. Run your setup script

4. Manual Validation

Next?

AIO : Unable to read /etc/rancher/k3s/k3s.yaml, please start server with --write-kubeconfig-mode to modify ... permissions

Solution 1: Admin Kubeconfig Options

Solution 2: Directly provide access to the file for all users

Live Average Carbon Intensity rating with Octopus Home Mini in Home Assistant

1. Install CO2 Signal Home Assistant Integration

2. Install Octopus Integration and enable the Home Mini

3. Set up a template that combines the values from both

Configuration

Dashboard

Wrap up

Making customised chocolates using 3D printing

Pre-requisites

1. Create the model

2. Cast the mould

Prepare the mould frame

Prepare the silicon

Create the mould

3. Recreate your model in chocolate

Prepare the chocolate

Pour the chocolate and let it set

4. Final result

Next steps?

Your organization has enabled or enforced SAML SSO ... you must re-authorize the OAuth Application `GitHub for VS Code`

Fixing Power BI report's credentials details error in Microsoft Fabric

Issue Summary

How to Update Your Credentials

Update your credentials using the settings menu

Update your credentials using the workspace's lineage view

Using the list view

Semantic model settings from the report

That's it for now

Running out of space on a developer's machine

The commands we will need

Disk space

Remove a child folder from a directory

Cleaning up node_modules folders

Cleaning up bin folders

Conclusion

Troubleshooting Environment Errors while following Edge Impulse's walkthrough of End-To-End Keyword Spotting with Synthetic Data

Some pre-requisites

Setting up the Environment

Next: