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## AI encoders demystified

When beginning the process of implementing a new deep learning / AI model, alongside deciding on the input and outputs of the model and converting your data to tfrecord files (file extension: .tfrecord.gz; you really should, the performance boost is incredible) is designing the actual architecture of the model itself. It might be tempting to shove a bunch of CNN layers at the problem to make it go away, but in this post I hope to convince you to think again.

As with any field, extensive research has already been conducted, and when it comes to AI that means that the effective encoding of various different types of data with deep learning models has already been carefully studied.

To my understanding, there are 2 broad categories of data that you're likely to encounter:

1. Images: photos, 2D maps of sensor readings, basically anything that could be plotted as an image
2. Sequenced data: text, sensor readings with only a temporal and no spatial dimension, audio, etc

Sometimes you'll encounter a combination of these two. That's beyond the scope of this blog post, but my general advice is:

• If it's 3D, consider it a 2D image with multiple channels to save VRAM
• Go look up specialised model architectures for your problem and how well they work/didn't work
• Use a Convolutional variant of a sequenced model or something
• Pick one of these encoders and modify it slightly to fix your use case

In this blog post, I'm going to give a quick overview of the various state of the art encoders for the 2 categories of data I've found on my travels so far. Hopefully, this should give you a good starting point for building your own model. By picking an encoder from this list as a starting point for your model and reframing your problem just a bit to fit, you should find that you have not only a much simpler problem/solution, but also a more effective one too.

I'll provide links to the original papers for each model, but also links to any materials I've found that I found helpful in understanding how they work. This can be useful if you find yourself in the awkward situation of needing to implement them yourself, but it's also generally good to have an understanding of the encoder you ultimately pick.

I've read a number of papers, but there's always the possibility that I've missed something. If you think this is the case, please comment below. This is especially likely if you are dealing with audio data, as I haven't looked into handling that too much yet.

Finally, also out of scope of this blog post are the various ways of framing a problem for an AI to understand it better. If this interests you, please comment below and I'll write a separate post on that.

### Images / spatial data

ConvNeXt: Image encoders are a shining example of how a simple stack of CNNs can be iteratively improved through extensive testing, and in no encoder is this more apparent than in ConvNeXt.

In 1 sentence, a ConvNeXt model can be summarised as "a ResNet but with the features of a transformer". Researchers took a bog-standard ResNet, and then iteratively tested and improved it by adding various features that you'd normally find in a transformer, which results in the model performant (*read: had the highest accuracy) encoder when compared to the other 2 on this list.

ConvNeXt is proof that CNN-based models can be significantly improved by incorporating more than just a simple stack of CNN layers.

Vision Transformer: Born from when someone asked what should happen if they tried to put an image into a normal transformer (see below), Vision Transformers are a variant of a normal transformer that handles images and other spatial data instead of sequenced data (e.g. sentences).

The current state of the art is the Swin Transformer to the best of my knowledge. Note that ConvNeXt outperforms Vision Transformers, but the specifics of course depend on your task.

ResNet: Extremely popular, you may have heard of the humble ResNet before. It was originally made a thing when someone wanted to know what would happen if they took the number of layers in a CNN-based model to the extreme. It has a 'residual connection' between blocks of layers, which avoids the 'vanishing gradient problem' which in short is when your model is too deep, and when it backpropagates the error the gradient it adjusts the weight by becomes so small that it has hardly any effect.

Since this model was invented, skip connections (or 'residual connections', as they are sometimes known) have become a regular feature in all sorts of models - especially those deeper than just a couple of layers.

Despite this significant advancement though, I recommend using a ConvNeXt encoder instead of images - it's better than and the architecture more well tuned than a bog standard ResNet.

### Sequenced Data

Transformer: The big one that is quite possibly the most famous encoder-decoder ever invented. The Transformer replaces the LSTM (see below) for handling sequenced data. It has 2 key advantages over an LSTM:

• It's more easily paralleliseable, which is great for e.g. training on GPUs
• The attention mechanism it implements revolutionises the performance of like every network ever

The impact of the Transformer on AI cannot be overstated. In short: If you think you want an LSTM, use a transformer instead.

• Paper: Attention is all you need
• Explanation: The Illustrated Transformer
• Implementation: Many around, but I have yet to find a simple enough one, so I implemented it myself from scratch. While I've tested the encoder and I know it works, I have yet to fully test the decoder. Once I have done this, I will write a separate blog post about it and add the link here.

LSTM: Short for Long Short-Term Memory, LSTMs were invented in 1997 to solve the vanishing gradient problem, in which gradients when backpropagating back through recurrent models that handle sequenced data shrink to vanishingly small values, rendering them ineffective at learning long-term relationships in sequenced data.

Superceded by the (non recurrent) Transformer, LSTMs are a recurrent model architecture, which means that their output feeds back into themselves. While this enables some unique model architectures for learning sequenced data (exhibit A), it also makes them horrible at being parallelised on a GPU (the dilated LSTM architecture attempts to remedy this, but Transformers are just better). The only thing that stands them apart from the Transformer is that the Transformer is somehow not built-in into Tensorflow as standard yet, whereas LSTMs are.

Just like Vision Transformers adapted the Transformer architecture for multidimensional data, so too are Grid LSTMs to normal LSTMs.

### Conclusion

In summary, for images encoders in priority order are: ConvNeXt, Vision Transformer (Swin Transformer), ResNet, and for text/sequenced data: Transformer, LSTM.

We've looked at a small selection of model architectures for handling a variety of different data types. This is not an exhaustive list, however. You might have another awkward type of data to handle that doesn't fit into either of these categories - e.g. specialised models exist for handling audio, but the general rule of thumb is an encoder architecture probably already exists for your use-case - even if I haven't listed it here.

Also of note are alternative use cases for data types I've covered here. For example, if I'm working with images I would use a ConvNeXt, but if model prediction latency and/or resource consumption mattered I would consider using a MobileNet](https://www.tensorflow.org/api_docs/python/tf/keras/applications/mobilenet_v3), which while a smaller model is designed for producing rapid predictions in lower resource environments - e.g. on mobile phones.

Finally, while these are encoders decoders also exist for various tasks. Often, they are tightly integrated into the encoder. For example, the U-Net is designed for image segmentation. Listing these is out of scope of this article, but if you are getting into AI to solve a specific problem (as is often the case), I strongly recommend looking to see if an existing model/decoder architecture has been designed to solve your particular problem. It is often much easier to adjust your problem to fit an existing model architecture than it is to design a completely new architecture to fit your particular problem (trust me, I've tried this already and it was a Bad Idea).

The first one you see might even not be the best / state of the art out there - e.g. Transformers are better than the more widely used LSTMs. Surveying the landscape for your particular task (and figuring out how to frame it in the first place) is critical to the success of your model.

## Easily write custom Tensorflow/Keras layers

At some point when working on deep learning models with Tensorflow/Keras for Python, you will inevitably encounter a need to use a layer type in your models that doesn't exist in the core Tensorflow/Keras for Python (from here on just simply Tensorflow) library.

I have encountered this need several times, and rather than e.g. subclassing tf.keras.Model, there's a much easier way - and if you just have a simple sequential model, you can even keep using tf.keras.Sequential with custom layers!

### Background

First, some brief background on how Tensorflow is put together. The most important thing to remember is Tensorflow likes very much to compile things into native code using what we can think of as an execution graph.

In this case, by execution graph I mean a directed graph that defines the flow of information through a model or some other data processing pipeline. This is best explained with a diagram:

Here, we define a simple Keras AI model for classifying images which you might define with the functional API. I haven't tested this model - it's just to illustrate an example (use something e.g. like MobileNet if you want a relatively small model for image classification).

The layer stack starts at the top and works it's way downwards.

When you call model.compile(), Tensorflow complies this graph into native code for faster execution. This is important, because when you define a custom layer, you may only use Tensorflow functions to operate on the data, not Python/Numpy/etc ones.

You may have already encountered this limitation if you have defined a Tensorflow function with tf.function(some_function).

The reason for this is the specifics of how Tensorflow compiles your model. Now consider this graph:

Basic arithmetic operations on tensors as well as more complex operators such as tf.stack, tf.linalg.matmul, etc operate on tensors as you'd expect in a REPL, but in the context of a custom layer or tf.function they operate on not a real tensor, but symbolic ones instead.

It is for this reason that when you implement a tf.function to use with tf.data.Dataset.map() for example, it only gets executed once.

### Custom layers for the win!

With this in mind, we can relatively easily put together a custom layer. It's perhaps easiest to show a trivial example and then explain it bit by bit.

I recommend declaring your custom layers each in their own file.

import tensorflow as tf

class LayerMultiplier(tf.keras.layers.Layer):
def __init__(self, multiplier=2, **kwargs):
super(LayerMultiplier, self).__init__(**kwargs)

self.param_multiplier = multiplier
self.tensor_multiplier = tf.constant(multiplier, dtype=tf.float32)

def get_config(self):
config = super(LayerMultiplier, self).get_config()

config.update({
"multiplier": self.param_multiplier
})

return config

def call(self, input_thing, training, **kwargs):
return input_thing * self.tensor_multiplier


Custom layers are subclassed from tf.keras.layers.Layer. There are a few parts to a custom layer:

The constructor (__init__) works as you'd expect. You can take in custom (hyper)parameters (which should not be tensors) here and use then to control the operation of your custom layer.

get_config() must ultimately return a dictionary of arguments to pass to instantiate a new instance of your layer. This information is saved along with the model when you save a model e.g. with tf.keras.callbacks.ModelCheckpoint in .hdf5 mode, and then used when you load a model with tf.keras.models.load_model (more on loading a model with custom layers later).

A paradigm I usually adopt here is setting self.param_ARG_NAME_HERE fields in the constructor to the value of the parameters I've taken in, and then spitting them back out again in get_config().

call() is where the magic happens. This is called when you call model.compile() with a symbolic tensor which stands in for the shape of the real tensor to build an execution graph as explained above.

The first argument is always the output of the previous layer. If your layer expects multiple inputs, then this will be an array of (potentially symbolic) tensors rather then a (potentially symbolic) tensor directly.

The second argument is whether you are in training mode or not. You might not be in training mode if:

1. You are spinning over the validation dataset
2. You are making a prediction / doing inference
3. Your layer is frozen for some reason

Sometimes you may want to do something differently if you are in training mode vs not training mode (e.g. dataset augmentation), and Tensorflow is smart enough to ensure this is handled as you'd expect.

Note also here that I use a native multiplication with the asterisk * operator. This works because Tensorflow tensors (whether symbolic or otherwise) overload this and other operators so you don't need to call tf.math.multiply, tf.math.divide, etc explicitly yourself, which makes your code neater.

That's it, that's all you need to do to define a custom layer!

### Using and saving

You can use a custom layer just like a normal one. For example, using tf.keras.Sequential:

import tensorflow as tf

from .components.LayerMultiply import LayerMultiply

def make_model(batch_size, multiplier):
model = tf.keras.Sequential([
tf.keras.layers.Dense(96),
tf.keras.layers.Dense(32),
LayerMultiply(multiplier=5)
tf.keras.layers.Dense(10, activation="softmax"),
])
model.build(input_shape=tf.TensorShape([ batch_size, 32 ]))
model.compile(
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=[
tf.keras.losses.SparseCategoricalAccuracy()
]
)
return model

The same goes here for the functional API. I like to put my custom layers in a components directory, but you can put them wherever you like. Again here, I haven't tested the model at all, it's just for illustrative purposes.

Saving works as normal, but for loading a saved model that uses a custom layer, you need to provide a dictionary of custom objects:

loaded_model = tf.keras.models.load_model(filepath_checkpoint, custom_objects={
"LayerMultiply": LayerMultiply,
})

If you have multiple custom layers, define all the ones you use here. It doesn't matter if you define extra it seems, it'll just ignore the ones that aren't used.

### Going further

This is far from all you can do. In custom layers, you can also:

• Instantiate sublayers or models (tf.keras.Model inherits from tf.keras.layers.Layer)
• Define custom trainable weights (tf.Variable)

Instantiating sublayers is very easy. Here's another example layer:

import tensorflow as tf

class LayerSimpleBlock(tf.keras.layers.Layer):
def __init__(self, units, **kwargs):
super(LayerSimpleBlock, self).__init__(**kwargs)

self.param_units = units

self.block = tf.keras.Sequential([
tf.keras.layers.Dense(self.param_units),
tf.keras.layers.Activation("gelu")
tf.keras.layers.Dense(self.param_units),
tf.keras.layers.LayerNormalization()
])

def get_config(self):
config = super(LayerSimpleBlock, self).get_config()

config.update({
"units": self.param_units
})

return config

def call(self, input_thing, training, **kwargs):
return self.block(input_thing, training=training)

This would work with a single sublayer too.

Custom trainable weights are also easy, but require a bit of extra background. If you're reading this post, you have probably heard of gradient descent. The specifics of how it works are out of scope of this blog post, but in short it's the underlying core algorithm deep learning models use to reduce error by stepping bit by bit towards lower error.

Tensorflow goes looking for all the weights in a model during the compilation process (see the explanation on execution graphs above) for you, and this includes custom weights.

You do, however, need to mark a tensor as a weight - otherwise Tensorflow will assume it's a static value. This is done through the use of tf.Variable:

tf.Variable(name="some_unique_name", initial_value=tf.random.uniform([64, 32]))

As far as I've seen so far, tf.Variable()s need to be defined in the constructor of a tf.keras.layers.Layer, for example:

import tensorflow as tf

class LayerSimpleBlock(tf.keras.layers.Layer):
def __init__(self, **kwargs):
super(LayerSimpleBlock, self).__init__(**kwargs)

self.weight = tf.Variable(name="some_unique_name", initial_value=tf.random.uniform([64, 32]))

def get_config(self):
config = super(LayerSimpleBlock, self).get_config()
return config

def call(self, input_thing, training, **kwargs):
return input_thing * weight

After you define a variable in the constructor, you can use it like a normal tensor - after all, in Tensorflow (and probably other deep learning frameworks too), tensors don't always have to hold an actual value at the time of execution as I explained above (I call tensors that don't contain an actual value like this symbolic tensors, since they are like stand-ins for the actual value that gets passed after the execution graph is compiled).

### Conclusion

We've looked at defining custom Tensorflow/Keras layers that you can use without giving tf.keras.Sequential() or the functional API. I've shown how by compiling Python function calls into native code using an execution graph, many orders of magnitude of performance gains can be obtained, fully saturating GPU usage.

We've also touched on defining custom weights in custom layers, which can be useful depending on what you're implementing. As a side note, should you need a weight in a custom loss function, you'll need to define it in the constructor of a tf.keras.layers.Layer and then pull it out and pass it to your subclass of tf.keras.losses.Loss.

By defining custom Tensorflow/Keras layers, we can implement new cutting-edge deep learning logic that are easy to use. For example, I have implemented a Transformer with a trio of custom layers, and CBAM: Convolutional Block Attention Module also looks very cool - I might implement it soon too.

I haven't posted a huge amount about AI / deep learning on here yet, but if there's any topic (machine learning or otherwise) that you'd like me to cover, I'm happy to consider it - just leave a comment below.

## NSD, Part 2: Dynamic DNS

Hey there! In the last post, I showed you how to setup nsd, the Name Server Daemon, an authoritative DNS server to serve records for a given domain. In this post, I'm going to talk through how to extend that configuration to support Dynamic DNS.

Normally, if you query, say, the A or AAAA records for a domain or subdomain like git.starbeamrainbowlabs.com, it will return the same IP address that you manually set in the DNS zone file, or if you use some online service then the value you manually set there. This is fine if your IP address does not change, but becomes problematic if your IP address may change unpredictably.

The solution, as you might have guessed, lies in dynamic DNS. Dynamic DNS is a fancy word for some kind of system where the host system that a DNS record points to (e.g. compute.bobsrockets.com) informs the DNS server about changes to its IP address.

This is done by making a network request from the host system to some kind of API that automatically updates the DNS server - usually over HTTP (though anything else could work too, but please make sure it's encrypted!).

You may already be familiar with using a HTTP API to inform your cloud-based registrar (e.g. Cloudflare, Gandi, etc) of IP address changes, but in this post we're going to set dynamic DNS up with the nsd server we configured in the previous post mentioned above.

The first order of business is to find some software to do this. You could also write a thing yourself (see also setting up a systemd service). There are several choices, but I went with dyndnsd (I may update this post if I ever write my own daemon for this).

Next, you need to determine what subdomain you'll use for dynamic dns. Since DNS is hierarchical, an entire subdomain is required - you can't just do dynamic DNS for, say, wiki.bobsrockets.com - since dyndnsd will manage it's own DNS zone file, all dynamic DNS hostnames will be under that subdomain - e.g. wiki.dyn.bobsrockets.com.

### Configuring the server

For the server, I will be assuming that the dynamic dns daemon will be running on the same server as the nsd daemon.

For this tutorial, we'll be setting it up unencrypted. This is a security risk if you are setting it up to accept requests over the Internet rather than a local trusted network! Notes on how to fix this at the end of this post.

Since this is a Ruby-based program (which I do generally recommend avoiding since Ruby is generally an inefficient language to write a program in I've observed), first we need to install gem, the Ruby package manager:

sudo apt install ruby ruby-rubygems ruby-dev

Then, we can install the gem Ruby package manager:

sudo gem install dyndnsd

Now, we need to configure it. dyndnsd is configured using a YAML (ew) configuration file. It's probably best to show an example configuration file and explain it afterwards:

# listen address and port
host: "0.0.0.0"
port: 5354
# The internal database file. We'll create this in a moment.
db: "/var/lib/dyndnsd/db.json"
# enable debug mode?
debug: false
# all hostnames are required to be cool-name.dyn.bobsrockets.com
domain: "dyn.bobsrockets.com"
# configure the updater, here we use command_with_bind_zone, params are updater-specific
updater:
name: "command_with_bind_zone"
params:
zone_file: "/etc/dyndnsd/zones/dyn.bobsrockets.com.zone"
ttl: "5m"
dns: "bobsrockets.com."
# Users with the hostnames they are allowed to create/update
users:
hosts:
- compute1.dyn.bobsrockets.com
computeuser2:
hosts:
- compute2.dyn.bobsrockets.com
- compute3.dyn.bobsrockets.com

...several things to note here that I haven't already noted in comments.

• zone_file: "/etc/nsd/zones/dyn.bobsrockets.com.zone": This is the path to the zone file dyndnsd should update.
• dns: "bobsrockets.com.": This is the fully-qualified hostname with a dot at the end of the DNS server that will be serving the DNS records (i.e. the nsd server).
• email_addr: "bob.bobsrockets.com": This sets the email address of the administrator of the system, but the @ at sign is replaced with a dot .. If your email address contains a dot . in the user (e.g. bob.rockets@example.com), then it won't work as expected here.

Also important here is that although when dealing with domains like this it is less confusing to always require a dot . at the end of fully qualified domain names, this is not always the case here.

Once you've written the config file,, create the directory /etc/dyndnsd and write it to /etc/dyndnsd/dyndnsd.yaml.

With the config file written, we now need to create and assign permissions to the data directory it will be using. Do that like so:

sudo useradd --no-create-home --system --home /var/lib/dyndnsd dyndnsd
sudo mkdir /var/lib/dyndnsd
sudo chown dyndnsd:dyndnsd /var/lib/dyndnsd

Also, we need to create the zone file and assign the correct permissions so that it can write to it:

sudo mkdir /etc/dyndnsd/zones
sudo chown dyndnsd:dyndnsd /etc/dyndnsd/zones
# symlink the zone file into the nsd zones directory. This way dyndns isn't allowed to write to all of /etc/nsd/zones - just the 1 zone file it is supposed to update.
sudo ln -s /etc/dyndnsd/zones/dyn.bobsrockets.com.zone /etc/nsd/zones/dyn.bobsrockets.com.zone

Now, we can write a systemd service file to run dyndnsd for us:

[Unit]
Description=dyndnsd: Dynamic DNS record updater
Documentation=https://github.com/cmur2/dyndnsd

[Service]
User=dyndnsd
Group=dyndnsd
ExecStart=/usr/local/bin/dyndnsd /etc/dyndnsd/dyndnsd.yaml
StandardOutput=syslog
StandardError=syslog
SyslogIdentifier=dyndnsd

[Install]
WantedBy=multi-user.target

Save this to /etc/systemd/system/dyndnsd.service. Then, start the daemon like so:

sudo systemctl daemon-reload
sudo systemctl enable --now dyndnsd.service

Finally, don't forget to update your firewall to allow requests through to dyndnsd. For UFW, do this:

sudo ufw allow 5354/tcp comment dyndnsd

That completes the configuration of dyndnsd on the server. Now we just need to update the nsd config file to tell it about the new zone.

nsd's config file should be at /etc/nsd/nsd.conf. Open it for editing, and add the following to the bottom:

zone:
name: dyn.bobsrockets.com
zonefile: dyn.bobsrockets.com.zone

...and you're done on the server!

### Configuring the client(s)

For the clients, all that needs doing is configuring them to make regular requests to the dyndnsd server to keep it appraised of their IP addresses. This is done by making a HTTP request, so we can test it with curl like this:

curl http://computeuser:alongandrandomstring@bobsrockets.com:5354/nic/update?hostname=compute1.dyn.bobsrockets.com

...where computeuser is the username, alongandrandomstring is the password, and compute1.dyn.bobsrockets.com is the hostname it should update.

The server will be able to tell what the IP address is it should set for the subdomain compute1.dyn.bobsrockets.com by the IP address of the client making the request.

The simplest way of automating this is using cron. Add the following cronjob (sudo crontab -e to edit the crontab):

*/5 * * * *     curl -sS http://computeuser:alongandrandomstring@bobsrockets.com:5354/nic/update?hostname=compute1.dyn.bobsrockets.com

....and that's it! It really is that simple. Windows users will need to setup a scheduled task instead and install curl, but that's outside the scope of this post.

### Conclusion

In this post, I've given a whistle-stop tour of setting up a simple dynamic dns server. This can be useful if a host as a dynamic IP address on a local network but it still needs a (sub)domain for some reason.

Note that this is not suitable for untrusted networks! For example, setting dyndnsd to accept requests over the Internet is a Bad Idea, as this simple setup is not encrypted.

If you do want to set this up over an untrusted network, you must encrypt the connection to avoid nasty DNS poisoning attacks. Assuming you already have a working reverse proxy setup on the same machine (e.g. Nginx), you'll need to add a new virtual host (a server { } block in Nginx) that reverse-proxies to your dyndnsd daemon and sets the X-Real-IP HTTP header, and then ensure port 5354 is closed on your firewall to prevent direct access.

This is beyond this scope of this post and slightly different depending on your setup, but if there's the demand I can blog about how to do this.

## The NSD Authoritative DNS Server: What, why, and how

In a previous blog post, I explained how to setup unbound, a recursive resolving DNS server. I demonstrated how to setup a simple split-horizon DNS setup, and forward DNS requests to an upstream DNS server - potentially over DNS-over-TLS.

Recently, for reasons that are rather complicated, I found myself in an awkward situation which required an authoritative DNS server - and given my love of explaining complicated and rather niche concepts here on my blog, I thought this would be a fabulous opportunity to write a 2-part series :P

In this post, I'm going to outline the difference between a recursive resolver and an authoritative DNS server, and explain why you'd want one and how to set one up. I'll explain how it fits as a part of a wider system.

Go grab your snacks - you'll be learning more about DNS than you ever wanted to know....

### DNS in a (small) nutshell

As I'm sure you know if you're reading this, DNS stands for the Domain Name System. It translates domain names (e.g. starbeamrainbowlabs.com.) into IP addresses (e.g. 5.196.73.75, or 2001:41d0:e:74b::1). Every network-connected system will make use of a DNS server at one point or another.

DNS functions on records. These define how a given domain name should be resolved to it's corresponding IP address (or vice verse, but that's out-of-scope of this post). While there are many different types of DNS record, here's a quick reference for the most common one's you'll encounter when reading this post.

• A: As simple as it gets. An A record defines the corresponding IPv4 address for a domain name.
• AAAA: Like an A record, but for IPv6.
• CNAME: An alias, like a symlink in a filesystem [Linux] or a directory junction [Windows]
• NS: Specifies the domain name of the authoritative DNS server that holds DNS records for this domain. See more on this below.

### A tale of 2 (DNS) servers

Consider your laptop, desktop, phone, or other device you're reading this on right now. Normally (if you are using DHCP, which is a story for another time), your router (which usually acts as the DHCP server on most home networks) will tell you what DNS server(s) to use.

These servers that your device talks to is what's known as a recursive resolving DNS server. These DNS servers do not have any DNS records themselves: their entire purpose is to ask other DNS servers to resolve queries for them.

At first this seems rather counterintuitive. Why bother when you can have a server that actually hosts the DNS records themselves and just ask that every time instead?

Given the size of the Internet today, this is unfortunately not possible. If we all used the same DNS server that hosted all DNS records, it would be drowned in DNS queries that even the best Internet connection would not be abel to handle. It would also be a single point of failure - bringing the entire Internet crashing down every time maintenance was required.

To this end, a more scaleable system was developed. By having multiple DNS servers between users and the authoritative DNS servers that actually hold the real DNS records, we can ensure the system scales virtually infinitely.

The next question that probably comes to mind is where the name recursive resolvers DNS server comes from. This name comes from the way that these recursive DNS servers ask other DNS servers for the answer to a query, instead of answering based on records they hold locally (though most recursive resolving DNS servers also have a cache for performance, but this is also a tale for another time).

Some recursive resolving DNS servers - such as the one built into your home router - simply asks 1 or 2 upstream DNS servers - usually either provided by your ISP or manually set by you (I recommend 1.1.1.1/1.0.0.1), but others are truly recursive.

Take peppermint.mooncarrot.space. for example. If we had absolutely no idea where to start resolving this domain, we would first ask a DNS root server for help. Domain names are hierarchical in nature - sub.example.com. is a subdomain of example.com.. The same goes then that mooncarrot.space. is a subdomain of space., which is itself a subdomain of ., the DNS root zone. It is no accident that all the domain names in this blog post have a dot at the end of them (try entering starbeamrainbowlabs.com. into your browser, and watch as your browser auto-hides the trailing dot .).

In this way, if we know the IP address of a DNS root server (e.g. 193.0.14.129, or 2001:7fd::1), we can recurse through this hierarchical tree to discover the IP address associated with a domain name we want to resolve

First, we'd ask a root server to tell us the authoritative DNS server for the space. domain name. We do this by asking it for the NS record for the space. domain.

Once we know the address of the authoritative DNS server for space., we can ask it to give us the NS record for mooncarrot.space. for us. We may repeat this process a number of times - I'll omit the specific details of this for brevity (if anyone's interested, I can write a full deep dive post into this, how it works, and how it's kept secure - comment below) - and then we can finally ask the authoritative DNS server we've tracked down to resolve the domain name peppermint.mooncarrot.space. to an IP address for us (e.g. by asking for the associated A or AAAA record).

### Authoritative DNS servers

With this in mind, we can now move on to the main purpose of this post: setting up an authoritative DNS server. As you might have guessed by now, the purpose of an authoritative DNS server is to hold records about 1 or more domain names.

While most of the time the authoritative DNS server for your domain name will be either your registrar or someone like Cloudflare, there are a number of circumstances in which it can be useful to run your own authoritative DNS server(s) and not rely on your registrar:

• If you need more control over the DNS records served for your domain than your registrar provides
• Serving complex DNS records for a domain name on an internal network (split-horizon DNS)
• Setting up your own dynamic DNS system (i.e. where you dynamically update the IP address(es) that a domain name resolves to via an API call)

Other situations certainly exist, but these are 2 that come to mind at the moment (comment below if you have any other uses for authoritative DNS servers).

The specific situation I found myself was a combination of the latter 2 points here, so that's the context in which I'll be talking.

To set one up, we first need some software to do this. There are a number of DNS servers out there:

• Bind9 [recursive; authoritative]
• Unbound [recursive; not really authoritative; my favourite]
• Dnsmasq [recursive]
• systemd-resolved [recursive; it always breaks for me so I don't use it]

As mentioned Unbound is my favourite, so for this post I'll be showing you how to use it's equally cool sibling, nsd (Name Server Daemon).

### The Name Server Daemon

Now that I've explained what an authoritative DNS server is and why it's important, I'll show you how to install and configure one, and then convince another recursive resolving DNS server that's under your control to ask your new authoritative DNS server instead of it's default upstream to resolve DNS queries for a given domain name.

It goes without saying that I'll be using Linux here. If you haven't already, I strongly recommend using Linux for hosting a DNS server (or any other kind of server). You'll have a bad day if you don't.

I will also be assuming that you have a level of familiarity with the Linux terminal. If you don't learn your terminal and then come back here.

nsd is available in all major distributions of Linux in the default repositories. Adjust as appropriate for your distribution:

sudo apt install nsd

nsd has 2 configuration files that are important. First is /etc/nsd/nsd.conf, which configures the nsd daemon itself. Let's do this one first. If there's an existing config file here, move it aside and then paste in something like this:

server:
port: 5353

server-count: 1

logfile: "/var/log/nsd.log"
pidfile: "/run/nsd.pid"

# The zonefile directive(s) below is prefixed by this path
zonesdir: /etc/nsd/zones

zone:
name: example.com
zonefile: example.com.zone

...replace example.com with the domain name that you want the authoritative DNS server to serve DNS records for. You can also have multiple zone: blocks for different (sub)domains - even if those domain names are subdomains of others.

For example, I could have a zone: block for both example.com and dyn.example.com. This can be useful if you want to run your own dynamic DNS server, which will write out a full DNS zone file (a file that contains DNS records) without regard to any other DNS records that might have been in that DNS zone.

Replace also 5353 with the port you want nsd to listen on. In my case I have my authoritative DNS server running on the same box as the regular recursive resolver, so I've had to move the authoritative DNS server aside to a different port as dnsmasq (the recursive DNS server I have running on this particular box) has already taken port 53.

Next up, create the directory /etc/nsd/zones, and then open up example.com.zone for editing inside that new directory. In here, we will put the actual DNS records we want nsd to serve.

The format of this file is governed by RFC1035 section 5 and RFC1034 section 3.6.1, but the nsd docs provide a simpler example. See also the wikipedia page on DNS zone files.

Here's an example:

; example.com.
$TTL 300 example.com. IN SOA a.root-servers.net. admin.example.com. ( 2022090501 ; Serial 3H ; refresh after 3 hours 1H ; retry after 1 hour 1W ; expire after 1 week 1D) ; minimum TTL of 1 day ; Name Server IN NS dns.example.com. @ IN A 5.196.73.75 example.com. IN AAAA 2001:41d0:e:74b::1 www IN CNAME @ ci IN CNAME @ Some notes about the format to help you understand it: • Make sure ALL your fully-qualified domain names have the trailing dot at the end otherwise you'll have a bad day. • $TTL 300 specifies the default TTL (Time To Live, or the time DNS records can be cached for) in seconds for all subsequent DNS records.
• Replace example.com. with your domain name.
• admin.example.com. should be the email address of the person responsible for the DNS zone file, with the @ replaced with a dot instead.
• dns.example.com. in the NS record must be set to the domain name of the authoritative DNS server serving the zone file.
• @ IN A 5.196.73.75 is the format for defining an A record (see the introduction to this blog post) for example.com. - @ is automatically replaced with the domain name in question - in this case example.com.
• When declaring a record, if you don't add the trailing dot then it is assumed you're referring to a subdomain of the domain this DNS zone file is for - e.g. if you put www it assumes you mean www.example.com.

Once you're done, all that's left for configuring nsd is to start it up for the first time (and on boot). Do that like so:

sudo systemctl restart nsd
sudo systemctl enable nsd

Now, you should be able to query it to test it. I like to use dig for this:

dig -p 5353 +short @dns.example.com example.com

...this should return a result based on the DNS zone file you defined above. Replace 5353 with the port number your authoritative DNS server is running on, or omit -p 5353 altogether if it's running on port 53.

Try it out by updating your DNS zone file and reloading nsd: sudo systemctl reload nsd

Congratulations! You now have an authoritative DNS server under your control! This does not mean that it will be queried by any other DNS servers on your network though - read on.....

### Integration with the rest of your network

The final part of this post will cover integrating an authoritative DNS server with another DNS server on your network - usually a recursive one. How you do this will vary depending on the target DNS server you want to convince to talk to your authoritative DNS server.

#### For Unbound:

I've actually covered this in a previous blog post. Simply update /etc/unbound/unbound.conf with a new block like this:

forward-zone:
name: "example.com."
forward-addr: 127.0.0.1@5353

...where example.com. is the domain name to forward for (WITH THE TRAILING DOT; and all subdomains thereof), 127.0.0.1 is the IP address of the authoritative DNS server, and 5353 is the port number of the authoritative DNS server.

Then, restart Unbound like so:

sudo systemctl restart unbound

#### For dnsmasq:

Dnsmasq's main config file is located at /etc/dnsmasq.conf, but there may be other config files located in /etc/dnsmasq.d/ that might interfere. Either way, update dnsmasq's config file with this directive:

server=/example.com./127.0.0.1#5353

...where example.com. is the domain name to forward for (WITH THE TRAILING DOT; and all subdomains thereof), 127.0.0.1 is the IP address of the authoritative DNS server, and 5353 is the port number of the authoritative DNS server.

If there's another server=/example.com./... directive elsewhere in your dnsmasq config, it may override your new definition.

Then, restart dnsmasq like so:

sudo systemctl restart dnsmasq

If there's another DNS server that I haven't included here that you use, please leave a comment on how to reconfigure it to forward a specific domain name to a different DNS server.

### Conclusion

In this post, I've talked about the difference between an authoritative DNS server and a recursive resolving DNS server. I've shown why authoritative DNS servers are useful, and alluded to reasons why running your own authoritative DNS server can be beneficial.

In the second post in this 2-part miniseries, I'm going to go into detail on dynamic DNS, why it's useful, and how to set up a dynamic dns server.

As always, this blog post is a starting point - not an ending point. DNS is a surprisingly deep subject: from DNS root hint files to mDNS (multicast DNS) to the various different DNS record types, there are many interesting and useful things to learn about it.

After all, it's always DNS..... especially when you don't think it is.

## Mounting LVM partitions from the terminal on Linux

Hello there! Recently I found myself with the interesting task of mounting an LVM partition by hand. It wasn't completely straightforward and there was a bunch of guesswork involved, so I thought I'd document the process here.

For those who aren't aware, LVM stands for the Logical Volume Manager, and it's present on Linux system to make managing partitions easier. It can:

• Move and resize partitions while they are still mounted
• Span multiple disks

....but to my knowledge it doesn't have any redundancy (use Btrfs) or encryption (use LUKS) built in. It is commonly used to manage the partitions on your Linux desktop, as then you don't need to reboot it into a live Linux environment to fiddle with your partitions as much.

LVM works on a layered system. There are 3 layers to it:

1. Physical Volumes: Normal physical partitions on the disk.
2. Volume Groups: Groups of logical (LVM) partitions.
3. Logical Volumes: LVM-managed partitions.

In summary, logical volumes are part of a volume group, which spans 1 or more physical disks.

With this in mind, first list the available physical volumes and their associated volume groups, and identify which is the one you want to mount:

sudo vgdisplay

Notice the VG Size in the output. Comparing it with the output of lsblk -o NAME,RO,SIZE,RM,TYPE,MOUNTPOINT,LABEL,VENDOR,MODEL can be helpful to identify which one is which.

I encountered a situation where I had 2 with the same name - one from my host system I was working on, and another from the target disk I was trying to mount. In my situation each disk had it's own volume group assigned to it, so I needed to rename one of the volumes.

To do this, take the value of the VG UUID field of the volume group you want to rename from the output of sudo vgdisplay above, and then rename it like this:

sudo vgrename SOME_ID NEW_NAME

...for example, I did this:

sudo vgrename 5o1LoG-jFdv-v1Xm-m0Ca-vYmt-D5Wf-9AAFLm examplename

With that done, we can now locate the logical volume we want to mount. Do this by listing the logical volumes in the volume group you're interested in:

sudo lvdisplay vg_name

Note down the name of the logical volume you want to mount. Now we just need to figure out where it is actually located in /dev so that we can mount it. Despite the LV Path field appearing to show us this, it's not actually correct - at least on my system.

Instead, list the contents of /dev/mapper:

ls /dev/mapper

You should see the name of the logical volume that you want to mount in the form volumegroup-logicalvolumename. Once found, you should be able to mount it like so:

sudo mount /dev/mapper/volumegroup-logicalvolumename path/to/directory

...replacing path/to/directory with the path to the (empty) directory you want to mount it to.

If you can't find it, then it is probably because you plugged the drive in question in after you booted up. In this case, it's probable that the volume group is not active. You can check this is the case or not like so:

sudo lvscan

If it isn't active, then you can activate it like this:

sudo lvchange -a y vg_name

...replacing vg_name with the name of the volume group you want to activate. Once done, you can then mount the logical volume as I mentioned above.

Once you are done, unmounting it is a case of reversing these steps. First, unmount the partition:

sudo umount path/to/mount_point

Then, disable the volume group again:

sudo lvchange -a n vg_name

Finally, flush any cached writes to disk, just in case:

sync

Now, you can unplug the device from your machine.

That wraps up this quick tutorial. If you spot any mistakes in this, please do leave a comment below and I'll correct it.

## Excluding domains from Encrypted DNS

Heya! I've got a quick tip for you that was annoying to look up. When using Encrypted DNS (either by DNS-over-TLS or DNS-over-HTTPS), your DNS requests will often go directly to Cloudflare or Google.

This is all well and good if you have a setup like my home network where DNS for my entire network goes through an Unbound instance which forwards to Cloudflare via Encrypted DNS (associated blog post; it's great for ensuring devices that don't support encrypted DNS are also secure), but things get more complicated if you're another network with Firefox on your laptop. In such a scenario, you most likely want Firefox configured with private/encrypted DNS enabled - but if you have domains on that network (e.g. if it's a network with split-horizon DNS with local Intranet sites), then it's awkward because you have to keep turning encrypted DNS on and off again.

A pretty specific situation that can be annoying and difficult to diagnose, to be sure. The easiest way to spot the issue is to see if the site you are accessing is local to (or hosted on) the network you're connected to, and check that while it doesn't work on your local device, but it does work on other devices on that network.

But no longer! I have discovered a setting in Firefox that allows you do set specific domains that resolved via your system's DNS resolver (for Linux users, that's what is specified in /etc/resolv.conf).

To edit it, first navigate to about:config and dismiss the warning. Then, find the network.trr.builtin-excluded-domains setting. By default for me it's localhost,local.

Once you've located it, you can add the domains you want to exclude from resolving via encrypted DNS to the comma-separated list. It supports wildcards too, so you can do something like this:

localhost,local,mooncarrot.space,*.mooncarrot.space

I'm sure that Chrome has a setting for this too, but I don't use it (for reasons that I could fill an entirely separate blog post with).

I'm mainly posting this for my own reference, but hopefully it helps others too :-)

## lnav basics tutorial

Last year, I blogged about lnav. lnav is a fantastic tool for analysing log files, and after getting a question from CrimsonTome I thought I'd write up a longer-form tutorial on the basics of using it, as I personally find it exceedingly useful.

I'll be using an Ubuntu Server 20.04 instance for this tutorial, but anything Linuxy will work just fine. As mentioned in my previous post, it's available in the default repositories for your distribution. For apt-based systems, install like so:

sudo apt install lnav

Adjust for your own package manager. For example, pacman-based distributions should do this:

sudo pacman -S lnav

lnav operates on 1 or more input files. It's common to use logrotate to rotate log files, so this is what I'd recommend to analyse all your logs of a particular type in 1 go (here I analyse generic syslog logs):

lnav /var/log/syslog*

On your system you may need to sudo that. Once you've got lnav started, you may need to wait a moment for it to parse all the log files - especially if you have multi-million line logfiles.

After it's finished loading, we can get to analysing the logs at hand. The most recent logs appear at the bottom, and you'll notice that lnav will have coloured various parts of each log message - the reason for this will become apparently later on. lnav should also livestream log lines from disk too.

Use the arrow keys or scroll up / down to navigate log messages.

lnav operates via a command pallette system, which if you use GitHub's [Atom IDE] (https://atom.io/) or Sublime Text (which is apparently where the feature originated) may already be familiar to you. In lnav's case, it's also crossed with a simple shell. Let's start with the most important command: :filter-out.

To execute a command, simply start typing. Commands in lnav are prefixed with a colon :. :filter-out takes a regular expression as it's only argument and filters all log lines which match the given regular expression out and hides them. Sticking with our earlier syslog theme, here's an example:

:filter-out kernel:

You'll notice that once you've finished typing :filter-out, lnav will show you some help in a pane at the bottom of the screen showing you how to use that command.

:filter-out has a twin that's also useful to remember: :filter-in. Unlike :filter-out, :filter-in does the opposite - anything that doesn't match the specified pattern is hidden from view. Very useful if you know what kind of log messages you're looking for, and they are a (potentially very small) subset of a much larger and more unstructured log file.

:filter-in dovecot:

To delete all existing filters and reset the view, hit Ctrl + R.

lnav has many other built-in commands. Check out the full reference here: https://docs.lnav.org/en/latest/commands.html.

The other feature that lnav comes with is also the most powerful: SQLite3 support. By parsing common log file formats (advanced users can extend lnav by defining their own custom formats, but the specifics of how to do this are best left to the lnav documentation), it can enable you to query your log files by writing arbitrary SQLite queries!

To understand how to query a file, first hit the p key. This will show you how lnav has parsed the log line at the top of the screen (scroll as normal to look at different lines, and hit p again to hide). Here's an example:

Using this information, we can then make an SQL query against the data. Press semicolon ; to open the SQL query prompt, and then enter something like this:

SELECT * FROM syslog_log WHERE log_procname == "gitea";

....hit the enter key when you're done composing your query, and the results should then appear! You can scroll through them just like you do with the regular log viewer - you just can't use :filter-in and :filter-out until you leave the query results window with the q key (this would be a really useful feature though!).

If you're running lnav on your Nginx logs (located in /var/log/nginx/ by default), then I find this query to be of particular use:

SELECT COUNT(cs_referer) AS count, cs_referer FROM access_log GROUP BY cs_referer ORDER BY COUNT(cs_referer) DESC

That concludes this basic tutorial on lnav. There are many more features that lnav offers:

• :filter-expr for filtering the main view by SQL query
• Analysing files on remote hosts over SSH
• Search logs for a given string (press / and start typing)
• Too many others to list here

Check out the full documentation here: https://docs.lnav.org/

## A much easier way to install custom versions of Python

Recently, I wrote a rather extensive blog post about compiling Python from source: Installing Python, Keras, and Tensorflow from source.

Since then, I've learnt of multiple other different ways to do that which are much easier as it turns out to achieve that goal.

For context, the purpose of running a specific version of Python in the first place was because on my University's High-Performance Computer (HPC) Viper, it doesn't have a version of Python new enough to run the latest version of Tensorflow.

### Using miniconda

After contacting the Viper team at the suggestion of my supervisor, I discovered that they already had a mechanism in place for specifying which version of Python to use. It seems obvious in hindsight - since they are sure to have been asked about this before, they already had a solution in the form of miniconda.

module load python/anaconda/4.6/miniconda/3.7

If you don't have access to Viper, then worry not. I've got other methods in store which might be better suited to your environment in later sections.

Once loaded, you can specify a version of Python like so:

conda create -n py python=3.8

The -n py specifies the name of the environment you'd like to create, and can be anything you like. Perhaps you could use the name of the project you're working on would be a good idea. The python=3.8 is the version of Python you want to use. You can list the versions of Python available like so:

conda search -f python

Then, to activate the new environment, do this:

conda init bash
conda activate py
exec bash

Replace py with the name of the environment you created above.

Now, you should have the specific version of Python you wanted installed and ready to use.

Edit 2022-03-30: Added conda install pip step, as some systems don't natively have pip by default which causes issues.

The last thing we need to do here is to install pip inside the virtual conda environment. Do that like so:

conda install pip

You can also install packages with pip, and it should all come out in the wash.

For Viper users, further information about miniconda can be found here: Applications/Miniconda Last

## Gentoo Project Prefix

Another option I've been made aware of is Gentoo's Project Prefix. Essentially, it installs Gentoo (a distribution of Linux) inside a directory without root privileges. It doesn't work very well on Ubuntu, however due to this bug, but it should work on other systems.

They provide a bootstrap script that you can run that helps you bootstrap the system. It asks you a few questions, and then gets to work compiling everything required (since Gentoo is a distribution that compiles everything from source).

If you have multiple versions of gcc available, try telling it about a slightly older version of GCC if it fails to install.

If you can get it to install, a Gentoo Prefix install allows the installation whatever software you like!

## pyenv

The last solution to the problem I'm aware of is pyenv. It automates the process of downloading and compiling specified versions of Python, and also updates you shell automatically. It does require some additional dependencies to be installed though, which could be somewhat awkward if you don't have sudo access to your system. I haven't actually tried it myself, but it may be worth looking into if the other 2 options don't work for you.

## Conclusion

There's always more than 1 way to do something, and it's always worth asking if there's a better way if the way you're currently using seems hugely complicated.

## Installing Python, Keras, and Tensorflow from source

I found myself in the interesting position recently of needing to compile Python from source. The reasoning behind this is complicated, but it boils down to a need to use Python with Tensorflow / Keras for some natural language processing AI, as Tensorflow.js isn't going to cut it for the next stage of my PhD.

The target upon which I'm aiming to be running things currently is Viper, my University's high-performance computer (HPC). Unfortunately, the version of Python on said HPC is rather old, which necessitated obtaining a later version. Since I obviously don't have sudo permissions on Viper, I couldn't use the default system package manager. Incredibly, pre-compiled Python binaries are not distributed for Linux either, which meant that I ended up compiling from source.

I am going to be assuming that you have a directory at $HOME/software in which we will be working. In there, there should be a number of subdirectories: • bin: For binaries, already added to your PATH • lib: For library files - we'll be configuring this correctly in this guide • repos: For git repositories we clone Make sure you have your snacks - this was a long ride to figure out and write - and it's an equally long ride to follow. I recommend reading this all the way through before actually executing anything to get an overall idea as to the process you'll be following and the assumptions I've made to keep this post a reasonable length. ### Setting up Before we begin, we need some dependencies: • gcc - The compiler • git - For checking out the cpython git repository • readline - An optional dependency of cpython (presumably for the REPL) On Viper, we can load these like so: module load utilities/multi module load gcc/10.2.0 module load readline/7.0 ### Compiling openssl We also need to clone the openssl git repo and build it from source: cd ~/software/repos git clone git://git.openssl.org/openssl.git; # Clone the git repo cd openssl; # cd into it git checkout OpenSSL_1_1_1-stable; # Checkout the latest stable branch (do git branch -a to list all branches; Python will complain at you during build if you choose the wrong one and tell you what versions it supports) ./config; # Configure openssl ready for compilation make -j "$(nproc)"                              # Build openssl

With openssl compiled, we need to copy the resulting binaries to our ~/software/lib directory:

cp lib*.so* ~/software/lib;
# We're done, cd back to the parent directory
cd ..;

To finish up openssl, we need to update some environment variables to let the C++ compiler and linker know about it, but we'll talk about those after dealing with another dependency that Python requires.

### Compiling libffi

libffi is another dependency of Python that's needed if you want to use Tensorflow. To start, go to the libgffi GitHub releases page in your web browser, and copy the URL for the latest release file. It should look something like this:

cd ~/software/lib
curl -OL URL_HERE

Note that we do it this way, because otherwise we'd have to run the autogen.sh script which requires yet more dependencies that you're unlikely to have installed.

Then extract it and delete the tar.gz file:

tar -xzf libffi-3.3.tar.gz
rm libffi-3.3.tar.gz

Now, we can configure and compile it:

./configure --prefix=$HOME/software make -j "$(nproc)"

Before we install it, we need to create a quick alias:

cd ~/software;
ln -s lib lib64;
cd -;

libffi for some reason likes to install to the lib64 directory, rather than our pre-existing lib directory, so creating an alias makes it so that it installs to the right place.

### Updating the environment

Now that we've dealt with the dependencies, we now need to update our environment so that the compiler knows where to find them. Do that like so:

export LD_LIBRARY_PATH="$HOME/software/lib:${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"; export LDFLAGS="-L$HOME/software/lib -L$HOME/software/include$LDFLAGS";
export CPPFLAGS="-I$HOME/software/include -I$HOME/software/repos/openssl/include -I$HOME/software/repos/openssl/include/openssl$CPPFLAGS"

It is also advisable to update your ~/.bashrc with these settings, as you may need to come back and recompile a different version of Python in the future.

Personally, I have a file at ~/software/setup.sh which I run with source $HOME/software/setuop.sh in my ~/.bashrc file to keep things neat and tidy. ### Compiling Python Now that we have openssl and libffi compiled, we can turn our attention to Python. First, clone the cpython git repo: git clone https://github.com/python/cpython.git cd cpython; Then, checkout the latest tag. This essentially checks out the latest stable release: git checkout "$(git tag | grep -ivP '[ab]|rc' | tail -n1)"

Important: If you're intention is to use tensorflow, check the Tensorflow Install page for supported Python versions. It's probable that it doesn't yet support the latest version of Python, so you might need to checkout a different tag here. For some reason, Python is really bad at propagating new versions out to the community quickly.

Before we can start the compilation process, we need to configure it. We're going for performance, so execute the configure script like so:

./configure --with-lto --enable-optimizations --with-openssl=/absolute/path/to/openssl_repo_dir

Replace /absolute/path/to/openssl_repo with the absolute path to the above openssl repo.

Now, we're ready to compile Python. Do that like so:

make -j "$(nproc)" This will take a while, but once it's done it should have built Python successfully. For a sanity check, we can also test it like so: make -j "$(nproc)" test

The Python binary compiled should be called simply python, and be located in the root of the git repository. Now that we've compiled it, we need to make a few tweaks to ensure that our shell uses our newly compiled version by default and not the older version from the host system. Personally, I keep my ~/bin folder under version control, so I install host-specific to ~/software, and put ~/software/bin in my PATH like so:

export PATH=$HOME/software/bin With this in mind, we need to create some symbolic links in ~/software/bin that point to our new Python installation: cd$HOME/software/bin;
ln -s relative/path/to/python_binary python
ln -s relative/path/to/python_binary python3
ln -s relative/path/to/python_binary python3.9

Replace relative/path/to/python_binary with the relative path tot he Python binary we compiled above.

To finish up the Python installation, we need to get pip up and running, the Python package manager. We can do this using the inbuilt ensurepip module, which can bootstrap a pip installation for us:

python -m ensurepip --user

This bootstraps pip into our local user directory. This is probably what you want, since if you try and install directly the shebang incorrectly points to the system's version of Python, which doesn't exist.

Then, update your ~/.bash_aliases and add the following:

export LD_LIBRARY_PATH=/absolute/path/to/openssl_repo_dir/lib:$LD_LIBRARY_PATH; alias pip='python -m pip' alias pip3='python -m pip' ...replacing /absolute/path/to/openssl_repo_dir with the path to the openssl git repo we cloned earlier. The next stage is to use virtualenv to locally install our Python packages that we want to use for our project. This is good practice, because it keeps our dependencies locally installed to a single project, so they don't clash with different versions in other projects. Before we can use virtualenv though, we have to install it: pip install virtualenv Unfortunately, Python / pip is not very clever at detecting the actual Python installation location, so in order to actually use virtualenv, we have to use a wrapper script - because the [shebang]() in the main ~/.local/bin/virtualenv entrypoint does not use /usr/bin/env to auto-detect the python binary location. Save the following to ~/software/bin (or any other location that's in your PATH ahead of ~/.local/bin): #!/usr/bin/env bash exec python ~/.local/bin/virtualenv "$@"

For example:

# Write the script to disk
nano ~/software/bin/virtualenv;
# chmod it to make it executable
chmod +x ~/software/bin/virtualenv

### Installing Keras and tensorflow-gpu

With all that out of the way, we can finally use virtualenv to install Keras and tensorflow-gpu. Let's create a new directory and create a virtual environment to install our packages in:

mkdir tensorflow-test
cd tensorflow-test;

### Conclusion

In this tutorially-kinda post, I've talked through how to manage user accounts for the Mosquitto MQTT. I've also talked about how to enable and manage access control lists too.

This should make your MQTT server more secure. The other thing you can do to make your MQTT server more secure is enable TLS encryption. I'm going to hold off on showing that in this file because I'm still unsure about the best way of doing it (getting Mosquitto to do it vs using Nginx as a reverse proxy - I'm currently testing the former), but if there's the demand I'll post about it in the future.

Art by Mythdael