EU AI Act has been passed by EU parliament
It feels like we’ve been hearing about and talking about the EU AI Act for a very long time now. But on Wednesday 13th March 2024, the EU Parliament finally voted to approve the Act. While this is a major milestone, we haven’t crossed the finish line. There are a few steps to complete, although these are minor steps and are part of the process.
The remaining timeline is:
- The EU AI Act will undergo final linguistic approval by lawyer-linguists in April. This is considered a formality step.
- It will then be published in the Official EU Journal
- 21 days after being published it will come into effect (probably in May)
- The Prohibited Systems provisions will come into force six months later (probably by end of 2024)
- All other provisions in the Act will come into force over the next 2-3 years
If you haven’t already started looking at and evaluating the various elements of AI deployed in your organisation, now is the time to start. It’s time to prepare and explore what changes, if any, you need to make. If you don’t the penalties for non-compliance are hefty, with fines of up to €35 million or 7% of global turnover.
The first thing you need to address is the Prohibited AI Systems and the EU AI Act outlines the following and will need to be addressed before the end of 2024:
- Manipulative and Deceptive Practices: systems that use subliminal techniques to materially distort a person’s decision-making capacity, leading to significant harm. This includes systems that manipulate behaviour or decisions in a way that the individual would not have otherwise made.
- Exploitation of Vulnerabilities: systems that target individuals or groups based on age, disability, or socio-economic status to distort behaviour in harmful ways.
- Biometric Categorisation: systems that categorise individuals based on biometric data to infer sensitive information like race, political opinions, or sexual orientation. This prohibition does not cover any labelling or filtering of lawfully acquired biometric datasets, such as images. There are also exceptions for law enforcement.
- Social Scoring: systems designed to evaluate individuals or groups over time based on their social behaviour or predicted personal characteristics, leading to detrimental treatment.
- Real-time Biometric Identification: The use of real-time remote biometric identification systems in publicly accessible spaces for law enforcement is heavily restricted, with allowances only under narrowly defined circumstances that require judicial or independent administrative approval.
- Risk Assessment in Criminal Offences: systems that assess the risk of individuals committing criminal offences based solely on profiling, except when supporting human assessment already based on factual evidence.
- Facial Recognition Databases: systems that create or expand facial recognition databases through untargeted scraping of images are prohibited.
- Emotion Inference in Workplaces and Educational Institutions: The use of AI to infer emotions in sensitive environments like workplaces and schools is banned, barring exceptions for medical or safety reasons.
In addition to the timeline given above we also have:
- 12 months after entry into force: Obligations on providers of general purpose AI models go into effect. Appointment of member state competent authorities. Annual Commission review of and possible amendments to the list of prohibited AI.
- after 18 months: Commission implementing act on post-market monitoring
- after 24 months: Obligations on high-risk AI systems specifically listed in Annex III, which includes AI systems in biometrics, critical infrastructure, education, employment, access to essential public services, law enforcement, immigration and administration of justice. Member states to have implemented rules on penalties, including administrative fines. Member state authorities to have established at least one operational AI regulatory sandbox. Commission review and possible amendment of the last of high-risk AI systems.
- after 36 months: Obligations for high-rish AI systems that are not prescribed in Annex III but are intended to be used as a safety component of a product, or the AI is itself a product, and the product is required to undergo a third-party conformity assessment under existing specific laws, for example, toys, radio equipment, in vitro diagnostic medical devices, civil aviation security and agricultural vehicles.
The EU has provided an official compliance check that helps identify which parts of the EU AI Act apply in a given use case.
Cohere and OpenAI API Keys
To access and use the Generative AI features in the Oracle Database you’ll need access to the API of a LLM. In this post, I’ll step through what you need to do to get API keys from Cohere and OpenAI. These are the two main LLMs for use with the database and others will be accessible over time.
Cohere API
First, go to the Cohere API Dashboard. You can sign-up using your Google or GitHub accounts to sign in. Or create an account by clicking on Sign-up? (Bottom right-hand corner of page). Fill in your email address and a suitable password. Then confirm your sign-up using the email they just sent to you.
When you sign-up to Cohere, you are initially creating a Trial (Free) account. For now, this will be enough for playing with Select AI. There are some restrictions (shown below) but these might change over time so make sure to check this out.
From your Cohere Dashboard, you can access your API key, and use this to set-up your access to the LLM from your remote (app, database, etc) environment. This is a Trial API Key, that is rate-limited, so is kind of ok for testing and evaluation. If you need more, you’ll need to upgrade your account.
Open API
For Open API, you’ll need to create an account – Sign-up here.
Initially, you’ll be set-up with a Trial Account but you may need to upgrade this by depositing some money into your account. The minimum is $10 (plus taxes) and this should be enough to allow you to have a good play with using the API, and only top-up as needed after that, particularly if you go into production use. When you get logged into OpenAI, go to your Dashboard and click on the ‘Create new secret key’ button.
When you’ve obtained the API keys check out my other posts on how to allow access to these from the database and start using the Oracle Select AI features (and other products and code libraries) .
You only need an API key from one of these, but I’ve shown both. This allows you to decide which one you’d like to use.
Check out the other posts about Select AI.
Oracle SQL Dev – VS Code – Recovering Deleted Connections
With the current early release, there is no way to organise your Database connections like you can in the full Oracle SQL Developer. We are told this will/might be possible in a future release but it might be later this year (or longer) before that feature will be available.
In a previous post, I showed how to import your connections from the full SQL Developer into SQL Dev VS Code. While this is a bit of a fudge, yet relatively straight forward to do, you may or may not want all those connections in your SQL Dev VS Code environment. Typically, you will use different tools, such as SQLcl, SQL*Plus, SQL Developer, etc to perform different tasks, and will only want those connections set up in one of those tools.
As shown in my previous post, SQL Dev VS Code and SQLcl share the same set of connections. These connections and their associated files are stored in the same folder on your computer. On my computer/laptop (which is currently a Mac) this connections folder can be found in the $HOME/.dbtools.
I kind of forgot this important little detail and started to clean up my connections in SQL Dev VS Code, but removing some of my old and less frequently used Connections. Only to discover, that these were no longer listed or available to use in SQLcl, using the connmgr command.
The question I had was, How can I recover these connections?
One option was to reimport the connections into SQLcl following the steps given in a previous blog post. When I do that, the connections are refreshed/overwritten in SQLcl, and because of the shared folder will automatically reappear in SQL Dev VS Code the next time I open it or by clicking on the refresh icon in the Connections pane in SQL Dev VS Code.
But was there a simpler solution? Yes there is, so let’s walk through a simple scenario to illustrate what you need to do.
In SQL Dev VS Code, you can delete a connection by right clicking the connection and selecting Delete from the popup menu. You’ll be asked to confirm the deletion of the connection.
Open a Terminal window and go to your $HOME/.dbtools/connections directory.
In this folder, you will see the deleted connections lists with ‘.removed’. These are your deleted connections. Some might have their original connection name with ‘.removed’ and others will have some weird name, for example, ‘zZqtNdeinniqNhofxqNI9Q.removed‘.
To make the deleted connection usable again just rename the directory removing the ‘.removed’ part. For example,
mv B01-Student2-Brendan.removed B01-Student2-BrendanIf you go back to SQL Dev VS Code, the connection will reappear in the list of Connections after about 5 seconds (on my laptop) but if it doesn’t then click on the refresh icon.
Moving Out of the Cloud – or something hybrid – The Key is Managing your Costs Carefully
Over the past decade, we have been hearing the call of the Cloud, from various vendors. The Cloud has provided a wonderful technology shift in the wider IT industry and accelerated the introduction of new technologies and more efficient ways of doing things. There were a lot of promises made or implied in migrating to the Cloud, and these are still being made.
In February 2023, Andy Jassy (CEO of AWS) highlighted: “I think it’s also useful to remember that 90% to 95% of the global IT spend remains on-premises.”. Despite all the talk, pushing customers and customers deciding for themselves to go to the Cloud, there is still a very significant percentage of IT spending remaining on-premises. If you look at the revenue growth of these Cloud providers (Microsoft, Google, AWS and yes even Oracle), if all that revenue equates to just 5-10% of IT spend, just think of the potential revenues if they can convert 1-2% of that spend. It would be Huge!
We have seen lots of competition between the Cloud vendors with multiple price reductions over the last few years. Get the customers to sign up for their Cloud services. They will sign up when things look cheap, but will it remain that way.
In more recent years, say from 2022 onwards, we have seen some questioning the value of going to the Cloud. It seems to get more expensive as time progresses and in some cases receiving surprisingly large Bills. What at first appears to be cheap and quick to spin up new services, turns out to hit the credit card hard at a later time. We have seen many articles by some well-known companies that were early adopters of the Cloud, and have migrated back to being fully on-premises.
The following are some related articles and documentary about the Cloud and exit from the Cloud.
Why companies are leaving the cloud
90-95% of Global IT spend remains on-premises – says Amazon CEO
And check out this documentary from clouded.tv.
Oracle SQL Dev – VS Code – Import connections via SQLcl
I mentioned in my previous post about importing your SQL Developer connections into the VS Code Extension. The following are the steps you need to complete. A more direct method to import directly from SQL Developer will be available in an upcoming extension update.
To complete this migration of your database connects, you’ll need to have SQLcl (SQL Command Line) version 23.3 installed, as it comes with a new Connection Manager feature.
The first step involves exporting your SQL Developer connections. To do this right-click on ‘Oracle Connections’ in SQL Developer, and then select ‘Export Connections’ from the pop-up menu. Select all your connections and then go through the remaining steps of giving a file name, giving a password, and then Finish.
When the file has been created and the connection exported, go open SQLcl (needs to be a minimum of version 23.3), in /nolog mode.
Set the password used for the SQL Developers connections file. In this example ‘MyPassword’ is the password I entered when creating the file.
SQL> secret set mySecret MyPassword
Secret mySecret storedNow we can import the connections file.
SQL> connmgr import -key mySecret /Users/brendan.tierney/SQLDev-connections.json
Importing connection 23c-VM-STUDENT: Success
Importing connection B01-Student-Admin: Success
Importing connection B01-Student-Brendan: Success
Importing connection Oracle 23c - Docker - SYS: Success
Importing connection Oracle 23c - Docker - HR: Success
Importing connection 23-Free-VM-SYS: Success
Importing connection B01-Student2-Admin: Success
Importing connection 23-Free-VM-STUDENT: Success
Importing connection 23c-VM-SYSTEM: Success
Importing connection Oracle 23c - Docker - System: Success
Importing connection Oracle 23c - Docker - Benchmark: Success
Importing connection 23-Free-VM-SYSTEM: Success
Importing connection 23c-VM-SYS: Success
Importing connection Oracle 23c - Docker - SH: Success
Importing connection 23c-VM-STUDENT2: Success
Importing connection Oracle 23c - Docker - Brendan: Success
Importing connection B01-Student2-Brendan: Success
17 connection(s) processedIf you need to reimport the connections file, you’ll need to use the replace option. Here’s an example.
connmgr import -duplicates replace -key mySecret /Users/brendan.tierney/SQLDev-connections.jsonYou can see above the connections were imported. You can list these by running
SQL> connmgr list
23-Free-VM-STUDENT
23-Free-VM-SYS
23-Free-VM-SYSTEM
...To connect to one of these connections using its label by running the following, where the label for the connection is in quotes
SQL> connect -name "Oracle 23c - Docker - Brendan"
Connected.
SQL> show connection
COMMAND_PROPERTIES:
type: STORE
name: Oracle 23c - Docker - Brendan
user: brendan
CONNECTION:
BRENDAN@jdbc:oracle:thin:@//localhost:1521/FREEPDB1
CONNECTION_IDENTIFIER:
jdbc:oracle:thin:@//localhost:1521/FREEPDB1
CONNECTION_DB_VERSION:
Oracle Database 23c Free, Release 23.0.0.0.0 - Developer-Release
Version 23.2.0.0.0
NOLOG:
false
PRELIMAUTH:
falseWe’ve just imported and verified the import of our SQL Developer connections into SQLcl. The next step is to import them into VS Code. To do this is very easy. If you don’t have VS Code open, open it and the connections will be automatically imported from SQLcl. How easy is that! or if you have VS Code already open just click on the refresh button in the Connections section.
Note: The connections listed in VS code are shared with SQLcl. If you delete any of these connections in VS Code they will be deleted/removed from SQLcl. It’s the one set of connection details being shared by both applications. So some care is needed. I’m sure future releases of the VS Code extension will provide better options for managing your connections.
SQL Developer for VSCode
We now have a new/different tool for developers to access their Oracle Databases. Traditionally, developers have been using Oracle SQL Developer for maybe 20+ years (if you started using Project Raptor). SQL Developer has developed into a bit of a big beast of a tool, with it trying to be everything to everyone including developers, DBAs, and others. But it does seem like SQL Developer might be coming to an end of life, although that could be for some years to come as it is so wildly used. There have been many challenges with SQL Developer over the years and one of the main challenges is getting new developers to use it. From my experience, developers tell me they just didn’t like it, didn’t like the look and feel of it, it was difficult to use, etc., etc. The list would go on and on and most of those developers would prefer to use other tools (for example DBeaver). For those that are terminal/command line only person, you have SQL*Plus and the modern version called SQLCl (SQL Command Line).
But there is a new kid on the block called Oracle SQL Developer Extension for VS Code. Some reports suggest this will be the future, as all future development will be for this new product, and no new development for the original SQL Developer, with only bug fixing being done.
Although Oracle SQL Developer for VS Code might be the future, it has only recently been released and with all new releases, it can be a little limited in functionality. We are told new functionality will be constantly added, maybe on a monthly or quarterly basis, but let’s see. Look at it, as of now (spring 2024) as being a teenager who will mature over time. And with that, Oracle is trying to appeal to a wider set of developers and with the popularity of VS Code it is not surprising they have made this move. VS Code will automatically update the SQL Developer extension as new updates come available.
To install this Oracle SQL Developer for VS Code, just open VS Code, go to the VS Code Marketplace, search for the extension and install. That’s all there is to it.
Oh, and it’s FREE to use. No licence is needed as it comes under the Oracle Free Use Terms Conditions (FUTC) License.
Installing Oracle Developer for VS Code will keep the extension updated automatically for you, so you don’t have to worry about what version you’re on.
Creating a Connection
If you’ve been using the traditional Oracle SQL Developer, you’ll already have some database connections. At the time of writing this post, there is no easy way to import those. Oracle does say, it will be coming soon (hopefully by the time you are reading this post the import feature will be available), but there is a workaround for this but it does involve a slightly complicated process. I’ll give an example in another post.
To manually create a connection, click the Oracle Explore (the Database icon on left-hand side of the screen). This will open a connections section. The top section is labelled SQL Developer and Connections just under it. You can create connections to your Databases here. There is also an Oracle Cloud Infrastructure Connections. I’ll come back to that another time.
Click on the + icon. A screen like the following will open, and you can enter your database connection details there. In this example, I’m creating a connection to a schema on a 23c Oracle Database running on Docker. These are the same connection details you have in SQL Developer.
After testing the connection, to make sure it works (a message will display at the bottom of VS Code), you can click on the Save connection. It will now appear in the Databases connections drop-down list and just click on this connection to open it.
Viewing Schema Objects
To see what objects you have in the schema, just click on the connection. It will connect to the schema, and will then display the object list under the schema. You might need to adjust the size of the sections under this Database Connections section to be able to see the full list. Or you might need to close some of them.
SQL Worksheet (writing SQL)
To open a SQL Worksheet, right-click on your Database Connection and click on ‘Open SQL Worksheet’. To run a SQL statement you can click on the Arrow icon in the menu. When you run a SQL statement the results will appear at the bottom of the screen in the Query Results section.
You can run the same query in a couple of other ways. The first is to run it as a script, and the output will be displayed in the ‘Script Output’ tab (beside the ‘Query Results’) and you can also run it in SQLcl (SQL Command Line). SQLcl comes as part of the VS Code extension, although you might have it already installed on your computer, so there might be some slight differences in the version. When you run your SQL query using the SQLcl, it will start SQLcl in the ‘Terminal’ tab, log you into your schema (automatically), run the query and display the results.
There’s a lot to get used to with SQL Developer Extension for VS Code, and there is a lot of functionality yet to come. For a time, you’ll probably end up using both tools. For some, this will not work and they’ll just keep using the traditional SQL Developer. For those people, at some point, you’ll have to make the swap and the sooner you start that transition the easier it will be.
EU AI Regulations: Common Questions and Answers
As the EU AI Regulations move to the next phase, after reaching a political agreement last Nov/Dec 2023, the European Commission has published answers to 28 of the most common questions about the act. These questions are:
- Why do we need to regulate the use of Artificial Intelligence?
- Which risks will the new AI rules address?
- To whom does the AI Act apply?
- What are the risk categories?
- How do I know whether an AI system is high-risk?
- What are the obligations for providers of high-risk AI systems?
- What are examples for high-risk use cases as defined in Annex III?
- How are general-purpose AI models being regulated?
- Why is 10^25 FLOPs an appropriate threshold for GPAI with systemic risks?
- Is the AI Act future-proof?
- How does the AI Act regulate biometric identification?
- Why are particular rules needed for remote biometric identification?
- How do the rules protect fundamental rights?
- What is a fundamental rights impact assessment? Who has to conduct such an assessment, and when?
- How does this regulation address racial and gender bias in AI?
- When will the AI Act be fully applicable?
- How will the AI Act be enforced?
- Why is a European Artificial Intelligence Board needed and what will it do?
- What are the tasks of the European AI Office?
- What is the difference between the AI Board, AI Office, Advisory Forum and Scientific Panel of independent experts?
- What are the penalties for infringement?
- What can individuals do that are affected by a rule violation?
- How do the voluntary codes of conduct for high-risk AI systems work?
- How do the codes of practice for general purpose AI models work?
- Does the AI Act contain provisions regarding environmental protection and sustainability?
- How can the new rules support innovation?
- Besides the AI Act, how will the EU facilitate and support innovation in AI?
- What is the international dimension of the EU’s approach?
This list of Questions and Answers are a beneficial read and clearly addresses common questions you might have seen being addressed in other media outlets. With these being provided and answered by the commission gives us a clear explanation of what is involved.
In addition to the webpage containing these questions and answers, they provide a PDF with them too.
Annual Look at Database Trends (Jan 2024)
Each January I take a little time to look back on the Database market over the previous calendar year. This year I’ll have a look at 2023 (obviously!) and how things have changed and evolved.
In my post from last year (click here) I mentioned the behaviour of some vendors and how they like to be-little other vendors. That kind of behaviour is not really acceptable, but they kept on doing it during 2023 up to a point. That point occurred during the Autumn of 2023. It was during this period there was some degree of consolidation in the IT industry with staff reductions through redundancies, contracts not being renewed, and so on. These changes seemed to have an impact on the messages these companies were putting out and everything seemed to calm down. These staff reductions have continued into 2024.
The first half of the year was generally quiet until we reached the Summer. We then experienced a flurry of activity. The first was the release of the new SQL standard (SQL:2023). There were some discussions about the changes included (for example Property Graph Queries), but the news quickly fizzled out as SQL:2023 was primarily a maintenance release, where the standard was catching up on what many of the database vendors had already implemented over the preceding years. Two new topics seemed to take over the marketing space over the summer months and early autumn. These included LLMs and Vector Databases. Over the Autumn we have seen some releases across vendors incorporating various elements of these and we’ll see more during 2024. Although there have been a lot of marketing on these topics, it still remains to be seen what the real impact of these will be on your average, everyday type of enterprise application. In a similar manner to previous “new killer features” specialised database vendors, we are seeing all the mainstream database vendors incorporating these new features. Just like what has happened over the last 30 years, these specialised vendors will slowly or quickly disappear, as the multi-model database vendors incorporate the features and allow organisations to work with their database rather than having to maintain several different vendors. Another database topic that seemed to attract a lot of attention over the past few years was Distributed SQL (or previously called NewSQL). Again some of the activity around this topic and suppliers seemed to drop off in the second half of 2023. Time will tell what is happening here, maybe it is going through a similar time the NewSQL era had (the previous incarnation). The survivors of that era now call themselves Distributed SQL (Databases), which I think is a better name as it describes what they are doing more clearly. The size of this market is still relatively small. Again time will tell.
There was been some consolidation in the open source vendor market, with some mergers, buyouts, financial difficulties and some shutting down. There have been some high-profile cases not just from the software/support supplier side of things but also from the cloud hosting side of things. Not everyone and not every application can be hosted in the cloud, as Microsoft CEO reported in early 2023 that 90+% of IT spending is still for on-premises. We have also seen several reports and articles of companies reporting their exit from the Cloud (due to costs) and how much they have saved moving back to on-premises data centres.
Two popular sites that constantly monitor the wider internet and judge how popular Databases are globally. These sites are DB-Engines and TOPDB Top Database index. These are well-known and are frequently cited. The image below, based on DB-Engines, shows the position of each Database in the top 20 and compares their position changes to 12 months previously. Here we have a comparison for 2023 and 2022 and the changes in positions. You’ll see there have been no changes in the positions of the top six Databases and minor positional changes for the next five Databases.
Although there has been some positive change for Postgres, given the numbers are based on log scale, this small change is small. The one notable mover in this table is Snowflake, which isn’t surprising really given what they offer and how they’ve been increasing their market share gradually over the years.
The TOPDB Top Database Index is another popular website and measures the popularity of Databases. It does this in a different way to DB-Engines. It can be an interesting exercise to cross-compare the results between the two websites. The image below compares the results from the past three years from TOPDB Top Database Index. We can see there is very little difference in the positions of most Databases. The point of interest here is the percentage Share of the top ten Databases. Have a look at the Databases that changed by more than one percentage point, and for those Databases (which had a lot of Marketing dollars) which moved very little, despite what some of their associated vendors try to get you to believe.
Changing/Increasing Cell Width in Juypter Notebook
When working with Jupyter Notebook you might notice the cell width can vary from time to time, and mostly when you use different screens, with different resolutions.
This can make your code appear slightly odd on the screen with only a certain amount being used. You can of into the default settings to change the sizing, but this might not suit in most cases.
It would be good to be able to adjust this dynamically. In such a situation, you can use one of the following options.
The first option is to use the IPython option to change the display settings. This first example adjusts everything (menu, toolbar and cells) to 50% of the screen width.
from IPython.display import display, HTML
display(HTML("<style>.container { width:50% !important; }</style>"))This might not give you the result you want, but it helps to illustrate how to use this command. By changing the percentage, you can get a better outcome. For example, by changing the percentage to 100%.
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))Keep a careful eye on making these changes, as I’ve found Jupyter stops responding to these changes. A quick refresh of the page will reset everything back to the default settings. Then just run the command you want.
An alternative is to make the changes to the CSS.
from IPython.display import display, HTML
display(HTML(data="""
<style>
div#notebook-container { width: 95%; }
div#menubar-container { width: 65%; }
div#maintoolbar-container { width: 99%; }
</style>
"""))You might want to change those percentages to 100%.
If we need to make the changes permanent we can locate the CSS file: custom.css. Depending on your system it will be located in different places.
For Linux and virtual environments have a look at the following directories.
~/.jupyter/custom/custom.css
Using Python for OCI Vision – Part 1
I’ve written a few blog posts over the past few weeks/months on how to use OCI Vision, which is part of the Oracle OCI AI Services. My blog posts have shown how to get started with OCI Vision, right up to creating your own customer models using this service.
In this post, the first in a series of blog posts, I’ll give you some examples of how to use these custom AI Vision models using Python. Being able to do this, opens the models you create to a larger audience of developers, who can now easily integrate these custom models into their applications.
In a previous post, I covered how to setup and configure your OCI connection. Have a look at that post as you will need to have completed the steps in it before you can follow the steps below.
To inspect the config file we can spool the contents of that file
!cat ~/.oci/config
I’ve hidden some of the details as these are related to my Oracle Cloud accountThis allows us to quickly inspect that we have everything setup correctly.
The next step is to load this config file, and its settings, into our Python environment.
config = oci.config.from_file()
config
We can now list all the projects I’ve created in my compartment for OCI Vision services.
#My Compartment ID
COMPARTMENT_ID = "<compartment-id>"
#List all the AI Vision Projects available in My compartment
response = ai_service_vision_client.list_projects(compartment_id=COMPARTMENT_ID)
#response.data
for item in response.data.items:
print('- ', item.display_name)
print(' ProjectId= ', item.id)
print('')Which lists the following OCI Vision projects.
We can also list out all the Models in my various projects. When listing these I print out the OCID of each, as this is needed when we want to use one of these models to process an image. I’ve redacted these as there is a minor cost associated with each time these are called.
#List all the AI Vision Models available in My compartment
list_models = ai_service_vision_client.list_models(
# this is the compartment containing the model
compartment_id=COMPARTMENT_ID
)
print("Compartment Id=", COMPARTMENT_ID)
print("")
for item in list_models.data.items:
print(' ', item.display_name, '--', item.model_type)
print(' OCID= ',item.id)
print(' ProjectId= ', item.project_id)
print('')
I’ll have other posts in this series on using the pre-built and custom model to label different image files on my desktop.
OCI Vision – Image model based on Object Detection
If you look back over recent blog posts you’ll see I’ve posted a few on using OCI Vision for image processing, image classification and object detection. This is another post to the series and looks to build an object detection model for images. In a previous post, I showed how to prepare an image dataset using OCI Data Labeling and using the bounding box method to outline particular objects in the image. In my examples, this involved drawing a bounding box around a Cat or a Dog in an image. After doing that the next step is to create an object detection model in OCI Vision and to test to see how well it works.
Let’s start with the OCI Vision project I created previously (see previous post).
We can add a new model to this existing project. Click on the Create Model button
In the Create and Train model setup, select the newly defined dataset setup for object detection using bounding boxes. Then Create the model.
For my model, I selected the option to run for a maximum of 24 hours. It didn’t take that long and was finished in a little over an hour. The dataset is small as it only consists of 100 images.
After the model was created, click into the model to view the details. This screen also allows you to upload images. The model will be applied to the images and any objects will be identified on the image and the label displayed to the right of the image. Here are a few of the images used to evaluate and these are the same images used to evaluate the previously created image classification model.
If you look closely at these images we can see the object box drawn on the images. For the image on the right, we can see there are two boxes drawn, one for the front part of the cat, then for the tail of the cat. I
Python for OCI installation and configuration
Having set up your Oracle Cloud account and perhaps initiated some services, such as an Autonomous Database or an AI Service, you have some options with how you’ll interact with those services. The first is to use the online interfaces and tools for them. Another is to access these OCI services from your local PC or laptop. This latter method gives you greater flexibility for accessing these services, automating various tasks and building out some applications. This post will step through how to install the necessary Python library, how to configure for connecting to OCI and will demo some of the simpler APIs for accessing the OCI services. This will help you verify your connection is working correctly.
The simple part of this is installing the OCI library!
pip3 install oci
Like I said that’s the simple part.
The next step involves the configuration of a connection to your OCI tenancy. But before you can do this you need to have an account on OCI. If you don’t have one already, the easiest and FREE way to do this is to set up an account on the Oracle Free Tier. There is some Free stuff you can use, plus getting a few hundred dollars to spend on some of the other non-Free cloud services. When you’ve completed this you can move on to the next step.
We need to create the config file that will contain the required credentials and information for working with OCI. By default, this file is stored in : ~/.oci/config
mkdir ~/oci
cd ociNow create the config file, using vi or something similar.
vi config
Edit the file to contain the following, but look out for the parts that need to be changed/updated to match your OCI account details.
[ADMIN_USER]
user=ocid1.user.oc1..<unique_ID>
fingerprint=<your_fingerprint>
tenancy = ocid1.tenancy.oc1..<unique_ID>
region = us-phoenix-1
key_file=<path to key .pem file>
The above details can be generated by creating an API key for your OCI user. Copy and Pate the default details to the config file.
- [ADMIN_USER] > you can name this anything you want, but it will referenced in Python.
- user > enter the user ocid. OCID is the unique resource identifier that OCI provides for each resource.
- fingerprint > refers to the fingerprint of the public key you configured for the user.
- tenancy > your tenancy OCID.
- region > the region that you are subscribed to.
- key_file > the path to the .pem file you generated.
Just download the .epm file and the config file details. Add them to the config file, and give the full path to the .epm file, including it’s name.
You are now ready to use the OCI Python library to access and use your OCI cloud environment. Let’s run some tests to see if everything works and connects ok.
config = oci.config.from_file(
"~/.oci/config")
identity = oci.identity.IdentityClient(config)
user = identity.get_user(config["user"]).data
print(user)The above connects to OCI using the config file we’ve just created and will print the following details about the connect.
The following lists all the available shapes available to me in the us-ashburn-1 region.
database_client = oci.database.DatabaseClient(config)
# Send the request to service, some parameters are not required, see API
# doc for more info
list_db_system_shapes_response = database_client.list_db_system_shapes(
compartment_id=compart_id,
availability_domain="us-ashburn-1")
# Get the data from response
print(list_db_system_shapes_response.data) The listing for the above is very long, so I’ll leave that for you to explore.
For more details of the APIs available check out the Documentation and the GitHub Respository for more examples.
Ora-lytics 
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