Recent developments in artificial intelligence have ushered in an era of so-called “deep learning”. This is a set of algorithms that allow computers to learn complex patterns and relationships by feeding them large amounts of data.
Deep neural networks are particularly powerful, able to perform tasks such as image recognition and natural language processing. They also work well when there is little or no context for what you want the computer to do – think about it like using Google to find answers to questions!
With all this power comes new challenges, however. When developing AI applications, how do you get the software to run effectively? And how do you ensure that it works properly once it does?
These days, with the availability of cheap GPU (graphics processor unit) cards designed to run deep learning software, it is possible to develop your own AI solutions. That is why it is important to be familiar with the basics of running deep learning software on a range of platforms.
In this article, we will look at how to deploy deep learning models onto a Raspberry Pi. If you already use a Raspberry Pi then you can begin preparing to train and test your model here!
We will assume that you are comfortable working with Python and Linux commands. If not, don’t worry – these concepts are easily learned and many online resources exist to help.
Back up your model
Even though you can now run most of these models directly off of Amazon or Google’s Cloud, it is still important to back up your trained neural network.
This way, you are not stuck with their servers if they go down or something happens to their cloud service.
Luckily, there are many ways to backup deep learning models. Some free, some paid, but all very effective!
Here we will discuss five best practices for backing up your trained networks. More advanced users may want to try one of these methods out before diving into more complex ones.
1) Use an online storage provider
You have probably used them before – Dropbox, Google Drive, OneDrive, and iCloud. These are great tools that allow you to easily access your files from any device.
They also offer solid security features such as 2-factor authentication which secures your account even more.
2) Pick a format that works well
The most common file types are.zip,.otb (Open Text Format), and.hdf (.Hierarchical Data Format). Each has its benefits and drawbacks, so choose one that makes sense for you.
3) Use the same tool every time
Using the same software source every time can help prevent mistakes or mishaps.
Buy a SD card
When you start training your model, the first thing that will take up significant computer resources are the files for your neural network. These files need to be organized properly so they can be accessed efficiently, which is where having an adequate amount of storage space becomes important.
Most people use flash memory cards (SD cards) for this purpose, but there is one major drawback: most computers cannot read and write directly to these cards using their own software. This makes it difficult to update your models because you have to either buy additional equipment or use external programs like Google Cloud Storage or Amazon Web Services’s cloud service.
Create a fresh Raspbian image
The first step is creating a new installation of Linux for your raspberry pi. You can do this by using either of these two methods, depending on what computer software you have access to.
If you have Windows or macOS software, you can use rasbpa-tool which comes preinstalled with all raspberry pis. This tool will create an Ubuntu or Debian install for you, depending on which one you select during setup.
You will then need to download both Python 3 and TensorFlow 2 installed, before installing the deep learning model onto your raspberry pi.
For those who use Google Chrome as their browser, you can also easily add the web interface to your device by adding it as a account under google developer program.
Install the GPU-optimized version of Caffe
There are two main reasons why you would want to use GPUs for deep learning. The first is computational power, and the second is speed. Computational power refers to how much computing power your model has access to. More powerful models can achieve better results more quickly, which means faster training times!
GPUs have many parallel processing units that work together to perform complex calculations very fast. Since most neural networks require lots of computations, this makes them perfect candidates for using a GPU.
The downside to having a GPU is that it usually costs more than your normal CPU. A GPU will typically cost around $100 – $300, while a regular laptop or desktop computer cpu will run about $50–$150. This could be difficult if you already have an expensive computer, but don’t need all of the extra features that a GPU has.
There are several free and low cost ways to get a GPU. You can build your own with different types of cards, or you can use a pre-built unit. In this article, we will talk about some easy ways to deploy your trained model on a Raspberry PI!.
Transfer your model to the SD card
Now that you have trained your model, it is time to deploy it! You will need to transfer your neural network onto an external storage device such as a computer or hard drive.
This process is called exporting the model. Once completed, you can now run this model anywhere!
You may also want to consider using GPU instead of CPU for faster training and deployment. There are many ways to do this with raspberry pi. We will not go into detail about these settings here but just know that they exist!
There are several free and paid softwares available which can help you export your model more efficiently.
Run the Pi with the SD card
The next step is to run your deep learning model on your Raspberry Pi! To do this, you will need to insert the microSD card that has your neural network onto your computer. You can then access all of the files and settings within it using an app or software program.
Most laptops and computers have an internal hard drive and operating system pre-installed. This makes it easy to get started quickly by just installing the apps and programs you want directly onto those.
For our purposes here, we will be using Windows as your operating system and Python for both the deployment application and the neural networking software.
Connect to the Pi with SSH
Now that you have all of your files organized, it is time to connect to your raspberry pi remotely using an application called ssh!
SSH stands for secure shell, which allows for two-way communication between yourself and the raspberry pi. This is done through a program called putty or terminal software.
By doing this, you can easily upload your model via FTP (File Transfer Protocol) or use SFTP (Secure File Transmission Protocol). You would then need to make sure that you have enough free space on your raspberry pi before uploading as well as having an internet connection!
After everything has been uploaded successfully, you can close out the ssh window and sync up your computer with the raspberry pi device wirelessly or through a cable modem/router.
Change the Pi’s ownership
The second step is changing the pi’s user account so that it can access all of the files, folders, and programs you want it to use. This is important to note as some software cannot run under root users!
To do this, open up your raspberry pi configuration tool by going into settings -> system -> rasbperry config -> tools. Then choose user accounts from the drop down menu.
Once there, click add new user and set up a username and password for the new admin user. Now that the user has been created, go back into terminal window and out put the following command followed by entering your newly made admin credentials.