diff --git a/.github/workflows/resize-images.yml b/.github/workflows/resize-images.yml new file mode 100644 index 0000000000..54b38b78c6 --- /dev/null +++ b/.github/workflows/resize-images.yml @@ -0,0 +1,57 @@ +name: Resize Images +# A weekly run to resize images that changed in the last week + +on: + schedule: + - cron: "0 9 * * 1" # every Monday at 09:00 UTC + workflow_dispatch: + +jobs: + resize-images: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 # Fetch full history to compare with last week + + - name: Get changed image files from last week + id: changed-files + run: | + # Find all image files that changed since last Monday + CHANGED_IMAGES=$(git log --since="7 days ago" --name-only --pretty="" \ + -- '*.jpg' '*.jpeg' '*.png' | sort -u | tr '\n' ' ') + + echo "changed_images=$CHANGED_IMAGES" >> $GITHUB_OUTPUT + echo "Changed images: $CHANGED_IMAGES" + + # Set a flag if any images were changed + if [ -n "$CHANGED_IMAGES" ]; then + echo "has_changes=true" >> $GITHUB_OUTPUT + else + echo "has_changes=false" >> $GITHUB_OUTPUT + fi + + - name: Install ImageMagick + run: sudo apt-get update && sudo apt-get install -y imagemagick + + - name: Run tools/resize_images.sh on changed files + if: steps.changed-files.outputs.has_changes == 'true' + run: | + # Pass the changed image files to the resize script + bash tools/resize_images.sh ${{ steps.changed-files.outputs.changed_images }} + - name: Create Pull Request + uses: peter-evans/create-pull-request@v6 + if: steps.changed-files.outputs.has_changes == 'true' && success() + with: + commit-message: Resize images changed in the last week + title: Resize images changed in the last week + body: | + Resize images that were modified in the last week (Monday to Monday) + + Changed files: ${{ steps.changed-files.outputs.changed_images }} + + Triggered by workflow run ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }} + Auto-generated by create-pull-request: https://github.com/peter-evans/create-pull-request + branch: resize-images + base: main diff --git a/content/install-guides/_images/aperf.png b/content/install-guides/_images/aperf.png deleted file mode 100644 index a0f9a40761..0000000000 Binary files a/content/install-guides/_images/aperf.png and /dev/null differ diff --git a/content/install-guides/_images/aperf.webp b/content/install-guides/_images/aperf.webp new file mode 100644 index 0000000000..85f67ed286 Binary files /dev/null and b/content/install-guides/_images/aperf.webp differ diff --git a/content/install-guides/_images/aperf0.png b/content/install-guides/_images/aperf0.png deleted file mode 100644 index 8429c073f3..0000000000 Binary files a/content/install-guides/_images/aperf0.png and /dev/null differ diff --git a/content/install-guides/_images/aperf0.webp b/content/install-guides/_images/aperf0.webp new file mode 100644 index 0000000000..4db30ede31 Binary files /dev/null and b/content/install-guides/_images/aperf0.webp differ diff --git a/content/install-guides/_images/arduino_rp2040_boards.png b/content/install-guides/_images/arduino_rp2040_boards.png deleted file mode 100644 index ad41e7d49a..0000000000 Binary files a/content/install-guides/_images/arduino_rp2040_boards.png and /dev/null differ diff --git a/content/install-guides/_images/arduino_rp2040_boards.webp b/content/install-guides/_images/arduino_rp2040_boards.webp new file mode 100644 index 0000000000..53b2e831d1 Binary files /dev/null and b/content/install-guides/_images/arduino_rp2040_boards.webp differ diff --git a/content/install-guides/_images/wperf-vs-extension-sampling-preview.png b/content/install-guides/_images/wperf-vs-extension-sampling-preview.png deleted file mode 100644 index c87931ca08..0000000000 Binary files a/content/install-guides/_images/wperf-vs-extension-sampling-preview.png and /dev/null differ diff --git a/content/install-guides/_images/wperf-vs-extension-sampling-preview.webp b/content/install-guides/_images/wperf-vs-extension-sampling-preview.webp new file mode 100644 index 0000000000..b8b8cf8ac3 Binary files /dev/null and b/content/install-guides/_images/wperf-vs-extension-sampling-preview.webp differ diff --git a/content/install-guides/aperf.md b/content/install-guides/aperf.md index 0ead65808e..9ad8337985 100644 --- a/content/install-guides/aperf.md +++ b/content/install-guides/aperf.md @@ -114,7 +114,7 @@ There are a number of tabs on the left side showing the collected data. You can browse the data and see what has been collected. -![APerf #center](/install-guides/_images/aperf0.png) +![APerf #center](/install-guides/_images/aperf0.webp) {{% notice Note %}} The Kernel Config and Sysctl Data tabs are blank unless you click No. @@ -142,7 +142,7 @@ Open the `index.html` file in the `compare/` directory to see the 2 runs side by A screenshot is shown below: -![APerf #center](/install-guides/_images/aperf.png) +![APerf #center](/install-guides/_images/aperf.webp) ### How do I use an HTTP server to view reports? diff --git a/content/install-guides/arduino-pico.md b/content/install-guides/arduino-pico.md index 0d342a8942..5fe9633bde 100644 --- a/content/install-guides/arduino-pico.md +++ b/content/install-guides/arduino-pico.md @@ -40,7 +40,7 @@ From the menu select `Tools -> Board -> Boards Manager`. When the `Boards Manager` opens search for `pico` and the `Arduino Mbed OS RP2040 Boards` will be displayed. Click the `Install` button to add it to the Arduino IDE. -![Arduino Board Manager](/install-guides/_images/arduino_rp2040_boards.png) +![Arduino Board Manager](/install-guides/_images/arduino_rp2040_boards.webp) ### How do I set up the Raspberry Pi Pico W? diff --git a/content/install-guides/windows-perf-vs-extension.md b/content/install-guides/windows-perf-vs-extension.md index 2f1e9d57b2..ca8b71264d 100644 --- a/content/install-guides/windows-perf-vs-extension.md +++ b/content/install-guides/windows-perf-vs-extension.md @@ -109,7 +109,7 @@ The WindowsPerf extension is composed of several key features, each designed to The sampling interface is shown below: -![Sampling preview #center](/install-guides/_images/wperf-vs-extension-sampling-preview.png) +![Sampling preview #center](/install-guides/_images/wperf-vs-extension-sampling-preview.webp) * Counting Settings UI: Build a `wperf stat` command from scratch using the configuration interface, then view the output in VS Code or open it with Windows Performance Analyzer (WPA). The interface to configure counting is shown below: diff --git a/content/learning-paths/automotive/openadkit1_container/4_run_openadkit.md b/content/learning-paths/automotive/openadkit1_container/4_run_openadkit.md index 6ad1904a0f..8fdd2f9247 100644 --- a/content/learning-paths/automotive/openadkit1_container/4_run_openadkit.md +++ b/content/learning-paths/automotive/openadkit1_container/4_run_openadkit.md @@ -150,7 +150,7 @@ Once the script starts successfully, you will see a similar output to the image You can use your browser to monitor the simulation data in real-time. -![img1 alt-text#center](vnc_address.png "Figure 1: Execute run.sh") +![img1 alt-text#center](vnc_address.webp "Figure 1: Execute run.sh") Now you can use the browser to access visualization. In this example the URL is http://34.244.98.151:6080/vnc.html diff --git a/content/learning-paths/automotive/openadkit1_container/vnc_address.png b/content/learning-paths/automotive/openadkit1_container/vnc_address.png deleted file mode 100644 index 9e31a4b4ea..0000000000 Binary files a/content/learning-paths/automotive/openadkit1_container/vnc_address.png and /dev/null differ diff --git a/content/learning-paths/automotive/openadkit1_container/vnc_address.webp b/content/learning-paths/automotive/openadkit1_container/vnc_address.webp new file mode 100644 index 0000000000..1d77d617a3 Binary files /dev/null and b/content/learning-paths/automotive/openadkit1_container/vnc_address.webp differ diff --git a/content/learning-paths/cross-platform/floating-point-behavior/differences.png b/content/learning-paths/cross-platform/floating-point-behavior/differences.png deleted file mode 100644 index bd996f34a0..0000000000 Binary files a/content/learning-paths/cross-platform/floating-point-behavior/differences.png and /dev/null differ diff --git a/content/learning-paths/cross-platform/floating-point-behavior/differences.webp b/content/learning-paths/cross-platform/floating-point-behavior/differences.webp new file mode 100644 index 0000000000..6f53a43ff7 Binary files /dev/null and b/content/learning-paths/cross-platform/floating-point-behavior/differences.webp differ diff --git a/content/learning-paths/cross-platform/floating-point-behavior/how-to-2.md b/content/learning-paths/cross-platform/floating-point-behavior/how-to-2.md index 9b8e425bed..72fc2dd02c 100644 --- a/content/learning-paths/cross-platform/floating-point-behavior/how-to-2.md +++ b/content/learning-paths/cross-platform/floating-point-behavior/how-to-2.md @@ -91,7 +91,7 @@ Run the program on both systems: For easy comparison, the image below shows the x86 output (left) and Arm output (right). The highlighted lines show the difference in output: -![differences](./differences.png) +![differences](./differences.webp) As you can see, there are several cases where different behavior is observed in these undefined scenarios. For example, when trying to convert a signed number to an unsigned number or dealing with out-of-bounds values. diff --git a/content/learning-paths/cross-platform/gitlab/1-gitlab-runner.md b/content/learning-paths/cross-platform/gitlab/1-gitlab-runner.md index 35bd669030..39415c8045 100644 --- a/content/learning-paths/cross-platform/gitlab/1-gitlab-runner.md +++ b/content/learning-paths/cross-platform/gitlab/1-gitlab-runner.md @@ -26,7 +26,7 @@ Create a repository in your GitLab account by clicking the "+" sign on top-left After you create the repository, navigate to `Settings->CI/CD` in the left-hand pane. Expand the `Runners` section and under `Project Runners`, select `New Project Runner`. -![arm64-runner #center](_images/create-gitlab-runner.png) +![arm64-runner #center](_images/create-gitlab-runner.webp) Use `Tags` to specify the jobs that can be executed on the runner. In the `Tags` field, enter `arm64`. In `Runner description` enter `google-axion-arm64-runner` and click the `Create Runner` button @@ -69,7 +69,7 @@ Runner registered successfully. Feel free to start it, but if it's running alrea You should see the newly registered runner in the Runners section of the GitLab console as shown below. -![registered-runner #center](_images/registered-runner.png) +![registered-runner #center](_images/registered-runner.webp) To create an `amd64` GitLab runner, follow the same steps as above, except for the `Download binaries` section. Change the download url to `https://gitlab-runner-downloads.s3.amazonaws.com/latest/binaries/gitlab-runner-linux-amd64` diff --git a/content/learning-paths/cross-platform/gitlab/_images/create-gitlab-runner.png b/content/learning-paths/cross-platform/gitlab/_images/create-gitlab-runner.png deleted file mode 100644 index 9d8e3d3036..0000000000 Binary files a/content/learning-paths/cross-platform/gitlab/_images/create-gitlab-runner.png and /dev/null differ diff --git a/content/learning-paths/cross-platform/gitlab/_images/create-gitlab-runner.webp b/content/learning-paths/cross-platform/gitlab/_images/create-gitlab-runner.webp new file mode 100644 index 0000000000..c70edf335a Binary files /dev/null and b/content/learning-paths/cross-platform/gitlab/_images/create-gitlab-runner.webp differ diff --git a/content/learning-paths/cross-platform/gitlab/_images/registered-runner.png b/content/learning-paths/cross-platform/gitlab/_images/registered-runner.png deleted file mode 100644 index 35d3971929..0000000000 Binary files a/content/learning-paths/cross-platform/gitlab/_images/registered-runner.png and /dev/null differ diff --git a/content/learning-paths/cross-platform/gitlab/_images/registered-runner.webp b/content/learning-paths/cross-platform/gitlab/_images/registered-runner.webp new file mode 100644 index 0000000000..385105daec Binary files /dev/null and b/content/learning-paths/cross-platform/gitlab/_images/registered-runner.webp differ diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/neural-node-pic.jpg b/content/learning-paths/cross-platform/kleidiai-explainer/neural-node-pic.jpg deleted file mode 100644 index 23873970cb..0000000000 Binary files a/content/learning-paths/cross-platform/kleidiai-explainer/neural-node-pic.jpg and /dev/null differ diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/neural-node-pic.webp b/content/learning-paths/cross-platform/kleidiai-explainer/neural-node-pic.webp new file mode 100644 index 0000000000..69c2aeaf04 Binary files /dev/null and b/content/learning-paths/cross-platform/kleidiai-explainer/neural-node-pic.webp differ diff --git a/content/learning-paths/cross-platform/kleidiai-explainer/page1.md b/content/learning-paths/cross-platform/kleidiai-explainer/page1.md index 4d7b66de16..37d0006556 100644 --- a/content/learning-paths/cross-platform/kleidiai-explainer/page1.md +++ b/content/learning-paths/cross-platform/kleidiai-explainer/page1.md @@ -27,7 +27,7 @@ During inference, such as when trying to generate the next *token* or *word* wit For example, in the image below, *z1* is calculated as a dot product of connected *x*s and *w*s from the previous layer. A matrix multiplication operation can therefore efficiently calculate all *z* values in Layer 0. -![Neural Network example#center](neural-node-pic.jpg "Zoomed-in neural network node.") +![Neural Network example#center](neural-node-pic.webp "Zoomed-in neural network node.") In addition to *weights*, each neuron in a neural network is assigned a *bias*. These weights and biases are learned during training, and make up a model's parameters. For example, in the Llama 3 model with 8 billion parameters, the model has around 8 billion individual weights and biases that embody what the model learned during training. Generally speaking, the higher the number of parameters a model has, the more information it can retain from its training, which increases its performance capability. For more information about Llama 3 view its [Hugging Face model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B). diff --git a/content/learning-paths/cross-platform/mca-godbolt/mca_on_godbolt.md b/content/learning-paths/cross-platform/mca-godbolt/mca_on_godbolt.md index 94b6dae02d..a0b72c7c65 100644 --- a/content/learning-paths/cross-platform/mca-godbolt/mca_on_godbolt.md +++ b/content/learning-paths/cross-platform/mca-godbolt/mca_on_godbolt.md @@ -17,7 +17,7 @@ To access Compiler Explorer, open a browser and go to https://godbolt.org. This leads you to the page shown below in Figure 1. Your view might be slightly different. -![godbolt open alt-text#center](open.png "Figure 1. Compiler Explorer") +![godbolt open alt-text#center](open.webp "Figure 1. Compiler Explorer") The left side of the page contains the source code. In Figure 1, the language is set to C++, but you can click on the programming language to select a different language for the source code. diff --git a/content/learning-paths/cross-platform/mca-godbolt/open.png b/content/learning-paths/cross-platform/mca-godbolt/open.png deleted file mode 100644 index 2e4eb41dbb..0000000000 Binary files a/content/learning-paths/cross-platform/mca-godbolt/open.png and /dev/null differ diff --git a/content/learning-paths/cross-platform/mca-godbolt/open.webp b/content/learning-paths/cross-platform/mca-godbolt/open.webp new file mode 100644 index 0000000000..ba7c48c0fd Binary files /dev/null and b/content/learning-paths/cross-platform/mca-godbolt/open.webp differ diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/datasets-and-training.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/datasets-and-training.md index 472c31b5fa..a2bea92158 100644 --- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/datasets-and-training.md +++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/datasets-and-training.md @@ -72,7 +72,7 @@ Make sure to replace 'x' with the version number of Python that you have install After running the code, you will see output similar to Figure 5: -![image alt-text#center](figures/01.png "Figure 5. Output") +![image alt-text#center](figures/01.webp "Figure 5. Output") ## Train the Model @@ -134,7 +134,7 @@ for t in range(epochs): After running the code, you see the following output showing the training progress, as displayed in Figure 2. -![image alt-text#center](figures/02.png "Figure 2. Output 2") +![image alt-text#center](figures/02.webp "Figure 2. Output 2") Once the training is complete, you see output similar to: diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/figures/01.png b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/figures/01.png deleted file mode 100644 index bc0c2cbffe..0000000000 Binary files a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/figures/01.png and /dev/null differ diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/figures/01.webp b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/figures/01.webp new file mode 100644 index 0000000000..21dd818b68 Binary files /dev/null and b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/figures/01.webp differ diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/figures/02.png 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a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/inference.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/inference.md index d5589fc6f9..cf63daa725 100644 --- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/inference.md +++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/inference.md @@ -108,7 +108,7 @@ This code demonstrates how to use a saved PyTorch model for inference and visual After running the code, you should see results similar to the following figure: -![image](figures/03.png "Figure 6. Results Displayed") +![image](figures/03.webp "Figure 6. Results Displayed") ### What have you learned? diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/intro.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/intro.md index d613fed42d..164e7b605f 100644 --- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/intro.md +++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/intro.md @@ -117,7 +117,7 @@ To ensure everything is set up correctly, follow these next steps: 4. Select the Python kernel you created earlier, `pytorch-env`. To do so, click **Kernels** in the top right-hand corner. Then, click **Jupyter Kernel...**, and you will see the Python kernel as shown below: -![img1 alt-text#center](figures/1.png "Figure 1: Python kernel.") +![img1 alt-text#center](figures/1.webp "Figure 1: Python kernel.") 5. In your Jupyter notebook, run the following code to verify PyTorch is working correctly: @@ -127,6 +127,6 @@ print(torch.__version__) ``` It will look as follows: -![img2 alt-text#center](figures/2.png "Figure 2: Jupyter Notebook.") +![img2 alt-text#center](figures/2.webp "Figure 2: Jupyter Notebook.") Now you have set up your development environment, you can move on to creating a PyTorch model. diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/model.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/model.md index ca1f6b7380..667cd8f7dc 100644 --- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/model.md +++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/model.md @@ -94,7 +94,7 @@ summary(model, (1, 28, 28)) After running the notebook, you will see the output as shown in Figure 4: -![img4 alt-text#center](figures/4.png "Figure 4: Notebook Output.") +![img4 alt-text#center](figures/4.webp "Figure 4: Notebook Output.") 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If you wave your h You can further check that your code is running properly by opening the `Serial Monitor` from the `Tools` menu of the Arduino IDE. There you should see all of the output messages, including count of detected motion events, coming from your sketch. -![Debug output](_images/output.png) +![Debug output](_images/output.webp) Congratulations! You have successfully programmed your microcontroller and built a working, if simple, smart device. diff --git a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/raspberrypi_pico.md b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/raspberrypi_pico.md index 1f9fcc82b2..9ce571da90 100644 --- a/content/learning-paths/embedded-and-microcontrollers/arduino-pico/raspberrypi_pico.md +++ b/content/learning-paths/embedded-and-microcontrollers/arduino-pico/raspberrypi_pico.md @@ -39,25 +39,25 @@ If you're not familiar with a breadboard, the image above shows you how all of t ### Step 1: Seat your Raspberry Pi -![RaspberryPi Pico](_images/pico_on_breadboard.png) +![RaspberryPi Pico](_images/pico_on_breadboard.webp) Seat your Raspberry Pi Pico on the breadboard so that its rows of pins sit on either side of the center divider. Make sure that it's firmly pressed all the way down but be careful not to bend any of the pins. ### Step 2: PIR ground -![PIR ground](_images/pir_sensor_1.png) +![PIR ground](_images/pir_sensor_1.webp) Using a black jumper wire, connect the ground pin of your PIR sensor to pin #38 on your Pico. This pin is a ground voltage pin on the Pico. ### Step 3: PIR input voltage -![PIR voltage](_images/pir_sensor_2.png) +![PIR voltage](_images/pir_sensor_2.webp) Using a red wire, connect the input voltage pin of your PIR sensor to pin #36 on your Pico. This pin is a 3.3 volt pin on the Pico and will supply power to your PIR sensor. ### Step 4: PIR data -![PIR data](_images/pir_sensor_3.png) +![PIR data](_images/pir_sensor_3.webp) The last step to connecting the PIR sensor is to connect the middle data pin to pin #34 on your Pico. This is a GPIO pin that you can use to either read or write data. @@ -65,13 +65,13 @@ Note that this is GPIO #28, even though it's physical pin #34. Physical pin numb ### Step 5: Buzzer ground -![Buzzer ground](_images/piezo_1.png) +![Buzzer ground](_images/piezo_1.webp) Next, it's time to connect the buzzer. Start by connecting the buzzer's ground pin to pin #23 on your Pico. This is another ground pin that is build into your board. ### Step 6: Buzzer input -![Buzzer input](_images/piezo_2.png) +![Buzzer input](_images/piezo_2.webp) Then, connect the buzzer's input pin to pin #25 on your Pico. 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End-to-end workflow") +![End-to-end workflow#center](./Figure3.webp "Figure 3. End-to-end workflow") ## Deploy PaddleOCR text recognition model on the Corstone-300 FVP included with Arm Virtual Hardware diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/connect-and-set-up-arduino.md b/content/learning-paths/embedded-and-microcontrollers/edge/connect-and-set-up-arduino.md index f9bb54bc0e..1ccc697c5c 100644 --- a/content/learning-paths/embedded-and-microcontrollers/edge/connect-and-set-up-arduino.md +++ b/content/learning-paths/embedded-and-microcontrollers/edge/connect-and-set-up-arduino.md @@ -37,7 +37,7 @@ You will need the following components: - **Anode (long leg) of the LED** → connect to **GPIO pin D2** through a 220 Ω resistor - **Cathode (short leg)** → connect to **GND** -![Diagram showing the physical breadboard circuit connecting an LED to GPIO D2 and GND on the Arduino Nano RP2040 alt-text#center](images/led_connection.png) +![Diagram showing the physical breadboard circuit connecting an LED to GPIO D2 and GND on the Arduino Nano RP2040 alt-text#center](images/led_connection.webp) ![Schematic diagram showing the LED connected between GPIO D2 and GND with a 220 Ω resistor in series alt-text#center](images/led_connection_schematic.png) diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/images/1.png b/content/learning-paths/embedded-and-microcontrollers/edge/images/1.png deleted file mode 100644 index 395465d841..0000000000 Binary files a/content/learning-paths/embedded-and-microcontrollers/edge/images/1.png and /dev/null differ diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/images/1.webp b/content/learning-paths/embedded-and-microcontrollers/edge/images/1.webp new file mode 100644 index 0000000000..bedc8f87cc Binary files /dev/null and b/content/learning-paths/embedded-and-microcontrollers/edge/images/1.webp differ diff --git a/content/learning-paths/embedded-and-microcontrollers/edge/images/10.png 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b/content/learning-paths/embedded-and-microcontrollers/edge/software-edge-impulse.md @@ -48,7 +48,7 @@ In the following sections, you'll walk through each key page on the Edge Impulse -![Screenshot of the Edge Impulse home page showing the main navigation and project dashboard alt-text#center](images/1.png "Home page of Edge Impulse website") +![Screenshot of the Edge Impulse home page showing the main navigation and project dashboard alt-text#center](images/1.webp "Home page of Edge Impulse website") ## Create a new project @@ -59,7 +59,7 @@ For example, if you're building a keyword-spotting model, you might name it `Wak You'll also need to select the appropriate **project type** and **project settings**, as shown in the screenshot below. -![Screenshot showing the new project creation page in Edge Impulse, with fields for project name, type, and target device alt-text#center](images/3.png "New project setup") +![Screenshot showing the new project creation page in Edge Impulse, with fields for project name, type, and target device alt-text#center](images/3.webp "New project setup") ## Configure the target device @@ -69,7 +69,7 @@ You can find the full specifications for the Arduino Nano RP2040 Connect on [Ard Follow the settings shown in the screenshot to complete the configuration. -![Screenshot showing the Edge Impulse device configuration page with Arduino Nano RP2040 Connect selected alt-text#center](images/4.png "Configure Arduino Nano RP2040") +![Screenshot showing the Edge Impulse device configuration page with Arduino Nano RP2040 Connect selected alt-text#center](images/4.webp "Configure Arduino Nano RP2040") ## Add the dataset @@ -84,14 +84,14 @@ git clone https://github.com/e-dudzi/Learning-Path.git The repository contains a `Dataset.zip` file with the dataset used in this project. Extract the contents to your local machine. For convenience, the dataset is already split into **training** and **testing** sets. -![Screenshot showing the Edge Impulse interface with the Add existing data panel open, used to upload pre-recorded datasets alt-text#center](images/6.png "Adding existing data") +![Screenshot showing the Edge Impulse interface with the Add existing data panel open, used to upload pre-recorded datasets alt-text#center](images/6.webp "Adding existing data") {{% notice Note %}} Do not check the green highlighted area during upload. The dataset already includes metadata. Enabling that option may result in much slower upload times and is unnecessary for this project. {{% /notice %}} -![Screenshot showing the Data acquisition tab in Edge Impulse with uploaded samples organized by label alt-text#center](images/7.png "Dataset overview") +![Screenshot showing the Data acquisition tab in Edge Impulse with uploaded samples organized by label alt-text#center](images/7.webp "Dataset overview") ## Dataset uploaded successfully @@ -106,7 +106,7 @@ This dataset is consists of four labels: - unknown {{% /notice %}} -![Screenshot showing the Impulse design interface in Edge Impulse with input, processing, and learning blocks configured alt-text#center](images/8.png "Dataset overview") +![Screenshot showing the Impulse design interface in Edge Impulse with input, processing, and learning blocks configured alt-text#center](images/8.webp "Dataset overview") ## Create the impulse @@ -117,7 +117,7 @@ Click **Create impulse** in the menu and configure it as shown in the screenshot After configuring the impulse, make sure to **save your changes**. -![example image alt-text#center](images/9.png "Create Impulse") +![example image alt-text#center](images/9.webp "Create Impulse") ## Configure the MFCC block @@ -129,7 +129,7 @@ Set the parameters exactly as shown in the screenshot. These settings determine These defaults are chosen for this Learning Path, but you can experiment with different values once you're more familiar with Edge Impulse. -![Screenshot showing the MFCC configuration page in Edge Impulse with time and frequency parameters set for feature extraction alt-text#center](images/10.png "MFCC block configuration") +![Screenshot showing the MFCC configuration page in Edge Impulse with time and frequency parameters set for feature extraction alt-text#center](images/10.webp "MFCC block configuration") {{< notice Note >}} The green-highlighted section on the MFCC configuration page provides an estimate of how the model will perform on the target device. This includes memory usage (RAM and flash) and latency, helping ensure the model fits within hardware constraints. @@ -141,7 +141,7 @@ After saving the MFCC parameters, the next step is to generate features from you When complete, you'll see a **2D feature plot** that shows how the data is distributed across the four labels: `on`, `off`, `noise`, and `unknown`. 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100644 index 0000000000..48e4f5652a Binary files /dev/null and b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/armds_ide.webp differ diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug.md index a064b3006c..9ab6769f64 100644 --- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug.md +++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug.md @@ -89,4 +89,4 @@ Ensure your FVP instance is running and matches the model and parameters selecte After these steps, you can debug the software stack as shown in the following figure: -![FVP running #center](Select_target.png "Debug interface in GUI") +![FVP running #center](Select_target.webp "Debug interface in GUI") diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug_config.png b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug_config.png deleted file mode 100644 index a67841fbb9..0000000000 Binary files a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug_config.png and /dev/null differ diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug_config.webp b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug_config.webp new file mode 100644 index 0000000000..8e5c97a234 Binary files /dev/null and b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/debug_config.webp differ diff --git a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/run.md b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/run.md index cdbfda2db0..822835cc19 100644 --- a/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/run.md +++ b/content/learning-paths/embedded-and-microcontrollers/linux-on-fvp/run.md @@ -101,4 +101,4 @@ Always check the name of the CPU instance when switching between different FVP m You can also run the FVP using its graphical user interface: -![GUI #center](FVP.png "View of the FVP GUI") +![GUI #center](FVP.webp "View of the FVP GUI") diff --git a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/4-smart-home-assistant.md b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/4-smart-home-assistant.md index c3c4d58508..3ec24fd82c 100644 --- a/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/4-smart-home-assistant.md +++ b/content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/4-smart-home-assistant.md @@ -35,7 +35,7 @@ In the previous section, you configured a LED on GPIO pin 17. The smart home ass The code uses gpiozero with lgpio backend for Raspberry Pi 5 compatibility. You can use compatible output devices such as LEDs, relays, or small loads connected to these GPIO pins to represent actual smart home devices. All pin assignments are optimized for the Raspberry Pi 5's GPIO layout. {{% /notice %}} -![Raspberry Pi 5 connected to a breadboard with LEDs, push button, and sensor module alt-text#center](hardware.jpeg "Setup that includes a blue LED (mapped to Living Room Light on GPIO 17), a red LED, push button, and a sensor module.") +![Raspberry Pi 5 connected to a breadboard with LEDs, push button, and sensor module alt-text#center](hardware.webp "Setup that includes a blue LED (mapped to Living Room Light on GPIO 17), a red LED, push button, and a sensor module.") 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Insert card and plate into a bay slot -![card plate back](./card01.jpg) +![card plate back](./card01.webp) -![card plate top](./card02.jpg) +![card plate top](./card02.webp) -![finished assembly](./assembled.jpg) +![finished assembly](./assembled.webp) diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card01.jpg b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card01.jpg deleted file mode 100644 index 7491e87b1b..0000000000 Binary files a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card01.jpg and /dev/null differ diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card01.webp b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card01.webp new file mode 100644 index 0000000000..9e44dae90a Binary files /dev/null and b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card01.webp differ diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card02.jpg b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card02.jpg deleted file mode 100644 index 3abc2ae323..0000000000 Binary files a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card02.jpg and /dev/null differ diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card02.webp b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card02.webp new file mode 100644 index 0000000000..b8c8ca1447 Binary files /dev/null and b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/card02.webp differ diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/print-parts.md b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/print-parts.md index 613862af61..c658152768 100644 --- a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/print-parts.md +++ b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/print-parts.md @@ -43,6 +43,6 @@ You can experiment with the quality settings. The parts are not complex shapes, 5. Slice and export G-code using your slicing software 6. Print -![STL files in slicing software](./slicer.png) +![STL files in slicing software](./slicer.webp) -![Parts after printing](./printed.jpg) \ No newline at end of file +![Parts after printing](./printed.webp) \ No newline at end of file diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/printed.jpg b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/printed.jpg deleted file mode 100644 index a2052e868a..0000000000 Binary files a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/printed.jpg and /dev/null differ diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/printed.webp b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/printed.webp new file mode 100644 index 0000000000..c0a3a5fa24 Binary files /dev/null and b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/printed.webp differ diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/slicer.png b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/slicer.png deleted file mode 100644 index 5d6794060f..0000000000 Binary files a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/slicer.png and /dev/null differ diff --git a/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/slicer.webp b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/slicer.webp new file mode 100644 index 0000000000..d949eb7312 Binary files /dev/null and b/content/learning-paths/embedded-and-microcontrollers/universal-sbc-chassis/slicer.webp differ diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/3-executorch-workflow.md b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/3-executorch-workflow.md index 6ae810e4b0..4862f6cc9d 100644 --- a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/3-executorch-workflow.md +++ b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/3-executorch-workflow.md @@ -44,7 +44,7 @@ The diagram below summarizes the ExecuTorch workflow from model export to deploy This three-step workflow ensures your TinyML models are performance-tuned and hardware-aware before deployment—even without access to physical silicon. -![Diagram showing the three-step ExecuTorch workflow from model export to deployment#center](./how-executorch-works-high-level.png "The three-step ExecuTorch workflow from model export to deployment") +![Diagram showing the three-step ExecuTorch workflow from model export to deployment#center](./how-executorch-works-high-level.webp "The three-step ExecuTorch workflow from model export to deployment") ## What's next? diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/Terminal and FVP Output.jpg b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/Terminal and FVP Output.jpg deleted file mode 100644 index 7c6cde8116..0000000000 Binary files a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/Terminal and FVP Output.jpg and /dev/null differ diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/Terminal and FVP Output.webp b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/Terminal and FVP Output.webp new file mode 100644 index 0000000000..2a4a276fb9 Binary files /dev/null and b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/Terminal and FVP Output.webp differ diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/how-executorch-works-high-level.png b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/how-executorch-works-high-level.png deleted file mode 100644 index 58b7369d5f..0000000000 Binary files a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/how-executorch-works-high-level.png and /dev/null differ diff --git a/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/how-executorch-works-high-level.webp b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/how-executorch-works-high-level.webp new file mode 100644 index 0000000000..f773f543ec Binary files /dev/null and b/content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/how-executorch-works-high-level.webp differ diff --git a/content/learning-paths/iot/avh_balena/2setup.md b/content/learning-paths/iot/avh_balena/2setup.md index c5d95bf9bd..56668223eb 100644 --- a/content/learning-paths/iot/avh_balena/2setup.md +++ b/content/learning-paths/iot/avh_balena/2setup.md @@ -20,7 +20,7 @@ Create a new fleet and name it `AVH-Testing`. Add a device to your fleet. Navigate to the `Devices` section of your fleet and click on the `Add Device` button. This will open a dialog to create a custom device image. -![Add Device dialog window](add_device.png) +![Add Device dialog window](add_device.webp) You can use the Raspberry Pi 4 device on Arm Virtual Hardware (AVH) as the new device. diff --git a/content/learning-paths/iot/avh_balena/3install.md b/content/learning-paths/iot/avh_balena/3install.md index 0b13a0c9fc..dafeb2af9d 100644 --- a/content/learning-paths/iot/avh_balena/3install.md +++ b/content/learning-paths/iot/avh_balena/3install.md @@ -16,7 +16,7 @@ On the next screen you will be asked to configure your device by choosing the fi Use the `Upload your own firmwware` on the right side to drag or browse for the file `balenaos_rpi4b.zip`. -![upload custom firmware screen](upload_firmware.png) +![upload custom firmware screen](upload_firmware.webp) Uploading the file takes some time. @@ -28,7 +28,7 @@ You do not need to set any advanced boot options. Wait for the device to be created. It is ready to use when you see the ready message: -![device ready](device_ready.png) +![device ready](device_ready.webp) ## Verify your device is ready diff --git a/content/learning-paths/iot/avh_balena/4deploy.md b/content/learning-paths/iot/avh_balena/4deploy.md index fd61cda207..5c2431463c 100644 --- a/content/learning-paths/iot/avh_balena/4deploy.md +++ b/content/learning-paths/iot/avh_balena/4deploy.md @@ -18,7 +18,7 @@ Open Balena Hub in a new tab by clicking on the Balena Hub button on the top rig On the Balena Hub, click `Apps` in the top navigation bar, then search for `balena-app`. This is a pre-built dashboard backed by Grafana and served by Nginx. -![balena-app page](balena_hub_app.png) +![balena-app page](balena_hub_app.webp) Click on the app to open the details page. @@ -38,7 +38,7 @@ If you have more than one device in your fleet, this process will deploy the sel Once the deployment is finished, click on your device in Balena Cloud dashboard to open up the device page. You will see that the Grafana and Nginx services have been deployed to and are running on your device. -![balena app running](balena_app_running.png) +![balena app running](balena_app_running.webp) You will also be able to see the system logs from your device, and optionally get access to the device's terminal from this Balena Cloud screen. @@ -50,10 +50,10 @@ Toggle the `Public Device URL` switch to the `On` position. A link will appear next to the switch, click on it to open the newly created public URL. -![balena-app login](balena_app_login.png) +![balena-app login](balena_app_login.webp) This will open the `balena-app` on your device in your browser. You will be presented with a log in screen, use the default username `admin` and password `admin` to log in. You will be prompted to set a new password for the `admin` user before continuing. -![balena-app dashboard](balena_app_dashboard.png) +![balena-app dashboard](balena_app_dashboard.webp) You now see the Grafana dashboard monitoring your Balena OS installation on this device, including the containers running Grafana and Nginx. diff --git a/content/learning-paths/iot/avh_balena/add_device.png b/content/learning-paths/iot/avh_balena/add_device.png deleted file mode 100644 index 1f551be03d..0000000000 Binary files a/content/learning-paths/iot/avh_balena/add_device.png and /dev/null differ diff --git a/content/learning-paths/iot/avh_balena/add_device.webp b/content/learning-paths/iot/avh_balena/add_device.webp new file mode 100644 index 0000000000..6ede194bc6 Binary files /dev/null and b/content/learning-paths/iot/avh_balena/add_device.webp differ diff --git a/content/learning-paths/iot/avh_balena/balena_app_dashboard.png b/content/learning-paths/iot/avh_balena/balena_app_dashboard.png deleted file mode 100644 index a273d123ba..0000000000 Binary files a/content/learning-paths/iot/avh_balena/balena_app_dashboard.png and /dev/null differ diff --git a/content/learning-paths/iot/avh_balena/balena_app_dashboard.webp b/content/learning-paths/iot/avh_balena/balena_app_dashboard.webp new file mode 100644 index 0000000000..76168df256 Binary files /dev/null and b/content/learning-paths/iot/avh_balena/balena_app_dashboard.webp differ diff --git a/content/learning-paths/iot/avh_balena/balena_app_login.png b/content/learning-paths/iot/avh_balena/balena_app_login.png deleted file mode 100644 index 062a0e9021..0000000000 Binary files a/content/learning-paths/iot/avh_balena/balena_app_login.png and /dev/null differ diff --git a/content/learning-paths/iot/avh_balena/balena_app_login.webp b/content/learning-paths/iot/avh_balena/balena_app_login.webp new file mode 100644 index 0000000000..ffd0bf03bf Binary files /dev/null and b/content/learning-paths/iot/avh_balena/balena_app_login.webp differ diff --git a/content/learning-paths/iot/avh_balena/balena_app_running.png b/content/learning-paths/iot/avh_balena/balena_app_running.png deleted file mode 100644 index b7676f5030..0000000000 Binary files a/content/learning-paths/iot/avh_balena/balena_app_running.png and /dev/null differ diff --git a/content/learning-paths/iot/avh_balena/balena_app_running.webp b/content/learning-paths/iot/avh_balena/balena_app_running.webp new file mode 100644 index 0000000000..fdda3d814d Binary files /dev/null and b/content/learning-paths/iot/avh_balena/balena_app_running.webp differ diff --git a/content/learning-paths/iot/avh_balena/balena_hub_app.png b/content/learning-paths/iot/avh_balena/balena_hub_app.png deleted file mode 100644 index e4c39b3e95..0000000000 Binary files a/content/learning-paths/iot/avh_balena/balena_hub_app.png and /dev/null differ diff --git a/content/learning-paths/iot/avh_balena/balena_hub_app.webp b/content/learning-paths/iot/avh_balena/balena_hub_app.webp new file mode 100644 index 0000000000..07111068b8 Binary files /dev/null and b/content/learning-paths/iot/avh_balena/balena_hub_app.webp differ diff --git a/content/learning-paths/iot/avh_balena/device_ready.png b/content/learning-paths/iot/avh_balena/device_ready.png deleted file mode 100644 index 537f426e43..0000000000 Binary files a/content/learning-paths/iot/avh_balena/device_ready.png and /dev/null differ diff --git a/content/learning-paths/iot/avh_balena/device_ready.webp b/content/learning-paths/iot/avh_balena/device_ready.webp new file mode 100644 index 0000000000..5fbdf91ec5 Binary files /dev/null and b/content/learning-paths/iot/avh_balena/device_ready.webp differ diff --git a/content/learning-paths/iot/avh_balena/upload_firmware.png b/content/learning-paths/iot/avh_balena/upload_firmware.png deleted file mode 100644 index cd2480ba67..0000000000 Binary files a/content/learning-paths/iot/avh_balena/upload_firmware.png and /dev/null differ diff --git a/content/learning-paths/iot/avh_balena/upload_firmware.webp b/content/learning-paths/iot/avh_balena/upload_firmware.webp new file mode 100644 index 0000000000..92ee57a64f Binary files /dev/null and b/content/learning-paths/iot/avh_balena/upload_firmware.webp differ diff --git a/content/learning-paths/iot/avh_greengrass/2setup.md b/content/learning-paths/iot/avh_greengrass/2setup.md index f293407968..7b5d10d05f 100644 --- a/content/learning-paths/iot/avh_greengrass/2setup.md +++ b/content/learning-paths/iot/avh_greengrass/2setup.md @@ -30,7 +30,7 @@ You will be presented with a list of devices to choose from. Select Raspberry Pi 4 from the list of devices and proceed to the configure dialog. -![create device](./create_device.png) +![create device](./create_device.webp) AWS IoT Greengrass Core runs on Ubuntu Server and Raspberry Pi OS. @@ -42,7 +42,7 @@ You do not need to set any advanced boot options. Wait for the device to be created. -![device ready](device_ready.png) +![device ready](device_ready.webp) When it is ready, log in with the default username `pi` and password `raspberry`. diff --git a/content/learning-paths/iot/avh_greengrass/3deploy.md b/content/learning-paths/iot/avh_greengrass/3deploy.md index 5b9977e83f..f265e72312 100644 --- a/content/learning-paths/iot/avh_greengrass/3deploy.md +++ b/content/learning-paths/iot/avh_greengrass/3deploy.md @@ -24,7 +24,7 @@ For the `Deployment target` select `Thing group`. For the `Target name` select the `MyGreengrassCoreGroup` that was created when you installed AWS IoT Greengrass. -![create deployment screen](gg_create_deployment.png) +![create deployment screen](gg_create_deployment.webp) The next step is to select which components should be in your deployment. @@ -32,7 +32,7 @@ As an example, you can deploy the AWS Greengrass CLI (which is different from th In the Public Components section, search for `CLI` and then select `aws.greengrass.Cli` from the list. -![select components screen](gg_select_components.png) +![select components screen](gg_select_components.webp) There is no configuration needed for the AWS Greengrass CLI component, so you can skip the next screens until you get to the `Review` step. @@ -42,11 +42,11 @@ Verify your component selection and deployment target and click the `Deploy` but After deploying you will be redirected to your `AVH-Testing` deployment page. -![deployment overview](gg_deployment_overview.png) +![deployment overview](gg_deployment_overview.webp) After a moment you should see that the deployment to your `MyGreengrassCore` device was successful. -![device components](gg_device_components.png) +![device components](gg_device_components.webp) Clicking the device will show you a list of installed and running components. @@ -81,6 +81,6 @@ You should see a similar help message printed. This is the first revision of your deployment. 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b/content/learning-paths/iot/azure-iot/aggregation.md @@ -161,7 +161,7 @@ func start ``` Once running, observe the HTTP trigger endpoint, which should appear similar to the following: -![img36 alt-text#center](figures/36.png) +![img36 alt-text#center](figures/36.webp) Next, start the simulator to stream sensor data and open the HTTP trigger endpoint URL in your web browser. You will see the calculated average temperature displayed: ![img37 alt-text#center](figures/37.png) @@ -177,7 +177,7 @@ Now that your Azure Function is fully tested and ready, it's time to deploy it t ![img39 alt-text#center](figures/39.png) 3. Wait for the deployment to complete. This process typically takes a few moments. Once deployed, your Azure Function is hosted in Azure and ready for use. 4. Open the Azure Portal, and go to your function app (in this example, "IoTTemperatureAlertFunc"). You will see the deployed functions: -![img40 alt-text#center](figures/40.png) +![img40 alt-text#center](figures/40.webp) ## Configure Function App Settings You have just deployed the functions to Azure. Previously, when testing the functions locally, you used the `local.settings.json` file to store the Cosmos DB connection string. However, this local configuration file is not deployed to Azure. Therefore, you need to update the corresponding settings directly within the Azure portal. @@ -190,7 +190,7 @@ Follow these steps to configure the Cosmos DB connection string 3. Click the **+ Add** button. 4. Enter the name you used in your code (such as "armiotcosmosdb_DOCUMENTDB"). 5. Paste the Cosmos DB connection string into the Value field: -![img41 alt-text#center](figures/41.png) +![img41 alt-text#center](figures/41.webp) 6. Click **Apply** to add the setting. 7. Click **Apply** at the bottom to apply changes. Then, confirm to save changes @@ -199,7 +199,7 @@ Once you've configured the connection string, test your deployed Azure Function 1. Return to the Overview page of your Azure Function App. 2. Click on your HTTP-triggered function (GetAverageTemperature). 3. Click Get function URL and copy the displayed URL (under default): -![img42 alt-text#center](figures/42.png) +![img42 alt-text#center](figures/42.webp) 4. Open this URL in your web browser. 5. Start your IoT simulator to begin streaming telemetry data to Cosmos DB. 6. Refresh or access the function URL again, and you should see the calculated average temperature displayed: diff --git a/content/learning-paths/iot/azure-iot/device_registration.md b/content/learning-paths/iot/azure-iot/device_registration.md index 7e3fcdaca9..b812331919 100644 --- a/content/learning-paths/iot/azure-iot/device_registration.md +++ b/content/learning-paths/iot/azure-iot/device_registration.md @@ -169,15 +169,15 @@ To connect the Python application you developed earlier to Azure IoT Hub, follow 1. Register a Device on Azure IoT Hub: * Open the Azure Portal, go to your IoT Hub, and click **Devices** under **Device management**: -![img6 alt-text#center](figures/06.png) +![img6 alt-text#center](figures/06.webp) * Click **Add Device**, enter a device ID (for example, "arm64Device01"), and leave the authentication type as **Symmetric key**: -![img7 alt-text#center](figures/07.png) +![img7 alt-text#center](figures/07.webp) * Click **Save**. 2. Next, you’ll need to retrieve the connection string to integrate your Python application with Azure IoT Hub. * From the device list, select your newly-created device, "arm64Device01". * Copy the **Primary connection string** from the device details page. 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diff --git a/content/learning-paths/iot/azure-iot/iot-hub.md b/content/learning-paths/iot/azure-iot/iot-hub.md index 30313d6570..6480e4351d 100644 --- a/content/learning-paths/iot/azure-iot/iot-hub.md +++ b/content/learning-paths/iot/azure-iot/iot-hub.md @@ -34,7 +34,7 @@ Start by creating an Azure IoT Hub by following these steps: * In the **Search resources, services, and docs** search box, type “iot hub” and press **Enter**. * From the search results, select the IoT Hub icon, as shown below: -![img2 alt-text#center](figures/02.png "Select IoT Hub icon.") +![img2 alt-text#center](figures/02.webp "Select IoT Hub icon.") 3. Click the **Create** button: ![img3 alt-text#center](figures/03.png "Select the Create button.") @@ -63,7 +63,7 @@ Start by creating an Azure IoT Hub by following these steps: * Click **Go to resource** to open the newly-created Azure IoT Hub. 12. Check IoT Hub Overview and Details: * From the IoT Hub overview page, verify important details such as the hub name, region, status, and hostname, which you’ll use to connect devices: -![img5 alt-text#center](figures/05.png "Verify IoT Hub overview") +![img5 alt-text#center](figures/05.webp "Verify IoT Hub overview") ## Next steps Now that your Azure IoT Hub is ready, you can proceed to register and configure your IoT devices. In the next step, you’ll learn how to register an Arm64-based IoT device and start streaming data using Python and Azure IoT SDK. diff --git a/content/learning-paths/iot/azure-iot/monitoring.md b/content/learning-paths/iot/azure-iot/monitoring.md index 4da27a2687..184ead66b3 100644 --- a/content/learning-paths/iot/azure-iot/monitoring.md +++ b/content/learning-paths/iot/azure-iot/monitoring.md @@ -59,9 +59,9 @@ For Python functions on Linux-based plans, local development and deployment repr You will start by creating an Azure Function App, in which you will create an Azure Function that regularly queries temperature data from Cosmos DB. In the next step, you will add the capability to send notifications, whenever the temperature reading exceeds a predefined threshold. Proceed as follows: 1. Sign in to the Azure Portal. 2. Click **Create a resource**, type “Function App”, and select it: -![img24 alt-text#center](figures/24.png) +![img24 alt-text#center](figures/24.webp) 3. Click **Create**, then select Consumption as a hosting option: -![img25 alt-text#center](figures/25.png) +![img25 alt-text#center](figures/25.webp) 4. Provide the required details: * Subscription: Your Azure subscription. * Resource Group: Select your existing IoT resource group. @@ -72,7 +72,7 @@ You will start by creating an Azure Function App, in which you will create an Az * Operating System: Select Linux as Windows is unavailable for Python. 5. Click Review + Create, and then Create. -![img26 alt-text#center](figures/26.png) +![img26 alt-text#center](figures/26.webp) ## Install Prerequisites Before writing the code make sure you have the following tools installed: @@ -98,7 +98,7 @@ Ensure you also see a v4.x.x output, indicating compatibility with Python v2 mod Follow these steps to create an Azure Function locally using Visual Studio Code: 1. In Visual Studio Code, click View->Command Palette... 2. Type "Create Function": -![img27 alt-text#center](figures/27.png) +![img27 alt-text#center](figures/27.webp) 3. Select Azure Functions: Create Function... 4. Select folder for your new function. For example create a new folder `Arm.AzureIoT.AzureFunctions` 5. Visual Studio Code will display the wizard, which enables you to configure your function: @@ -175,7 +175,7 @@ func start You should then see the following logs, depending on the generated temperature values: -![img29 alt-text#center](figures/29.png) +![img29 alt-text#center](figures/29.webp) ## Monitoring and Notifications In this section, you will extend your existing Azure Function to send email notifications using `SendGrid` whenever the temperature exceeds the defined threshold @@ -184,23 +184,23 @@ In this section, you will extend your existing Azure Function to send email noti Follow these steps to create a SendGrid account: 1. Sign in to the Azure Portal. 2. Click “Create a resource” and search for SendGrid. -![img30 alt-text#center](figures/30.png) +![img30 alt-text#center](figures/30.webp) 3. Select Twilio SendGrid, choose the Free 100 (2022) plan, and then click Subscribe. 4. Provide the following details: * Subscription: Select your Azure subscription. * Resource group: Choose your existing IoT project resource group. * Name: Enter a descriptive name (e.g., iot-alerts-sendgrid). -![img31 alt-text#center](figures/31.png) +![img31 alt-text#center](figures/31.webp) 5. Click Review + subscribe and then Subscribe. 6. On the next screen, click Configure account now: -![img32 alt-text#center](figures/32.png) +![img32 alt-text#center](figures/32.webp) 7. Accept any permissions required by SendGrid, and then enter your details to create a sender identity: -![img33 alt-text#center](figures/33.png) +![img33 alt-text#center](figures/33.webp) 8. Fill out the required details, such as your name and email address. 9. After the sender identity is verified, click API Keys in the left menu: -![img34 alt-text#center](figures/34.png) +![img34 alt-text#center](figures/34.webp) 10. Click Create API Key. In the popup window, enter a key name (e.g., iot-api-key), and select Full Access. -![img35 alt-text#center](figures/35.png) +![img35 alt-text#center](figures/35.webp) 11. Copy the generated API key securely. You will not be able to retrieve it later. ### Configure SendGrid API Key in your Azure Function diff --git a/content/learning-paths/iot/azure-iot/portal.md b/content/learning-paths/iot/azure-iot/portal.md index 6f966985e6..26dc8215d3 100644 --- a/content/learning-paths/iot/azure-iot/portal.md +++ b/content/learning-paths/iot/azure-iot/portal.md @@ -152,21 +152,21 @@ You will now deploy the web portal you have created to Azure Blob Storage, makin 2. Create a Storage Account: * Click **Create a resource**. * Search for “Storage account”. -![img45 alt-text#center](figures/45.png) +![img45 alt-text#center](figures/45.webp) * Click **Create**. ![img46 alt-text#center](figures/46.png) 3. Provide the required details: * Subscription, resource group, and storage account name (e.g. armiotstorage). * For Primary service, choose **Azure Blob Storage** or **Azure Data Lake Storage Gen 2**. * Select **Standard performance** and **Locally-redundant storage (LRS)**. -![img47 alt-text#center](figures/47.png) +![img47 alt-text#center](figures/47.webp) * Click "Review + create", then "Create". 3. Enable Static Website Hosting: * Navigate to your newly created storage account. * Under Data management, click **Static website**. * Select **Enabled**. * Set index.html as the index document name. -![img48 alt-text#center](figures/48.png) +![img48 alt-text#center](figures/48.webp) * Click **Save**. After saving, Azure provides you with a URL like: https://.z22.web.core.windows.net/. @@ -179,7 +179,7 @@ You can upload your website files directly using the Azure Portal or via Azure S 3. Open the container named ”$web” (created automatically when enabling static websites). 4. Click **Upload** and select your three website files (index.html, main.js, styles.css), and upload them. -![img49 alt-text#center](figures/49.png) +![img49 alt-text#center](figures/49.webp) ### Verify the Deployment After uploading your files, open a browser and navigate to https://.z22.web.core.windows.net/. diff --git a/content/learning-paths/iot/azure-iot/stream-analytics-dynamo-db.md b/content/learning-paths/iot/azure-iot/stream-analytics-dynamo-db.md index b51a53b308..455cf82cef 100644 --- a/content/learning-paths/iot/azure-iot/stream-analytics-dynamo-db.md +++ b/content/learning-paths/iot/azure-iot/stream-analytics-dynamo-db.md @@ -51,11 +51,11 @@ You will now configure the stream analytics job such that the telemetry data wil Start by creating the Create Cosmos DB account and database: 1. Log in to the Azure Portal. 2. Select **Create a resource**, search for “Azure Cosmos DB”, and click **Create**: -![img14 alt-text#center](figures/14.png) +![img14 alt-text#center](figures/14.webp) ![img15 alt-text#center](figures/15.png) 3. Select Azure Cosmos DB for NoSQL, then click **Create**. -![img16 alt-text#center](figures/16.png) +![img16 alt-text#center](figures/16.webp) 4. Fill in the required details: * Subscription: select your subscription. @@ -66,7 +66,7 @@ Start by creating the Create Cosmos DB account and database: * Select serverless as capacity mode. * Apply Free Tier Discount: apply * Check Limit total account throughput. -![img17 alt-text#center](figures/17.png) +![img17 alt-text#center](figures/17.webp) 5. Click **Review + create**, then click **Create**. Once the deployment completes: @@ -75,16 +75,16 @@ Once the deployment completes: * Select an appropriate partition key (recommended: /deviceId). * Enable analytical store capability to perform near real-time analytics on your operational data, without impacting the performance of transactional workloads: Off. * Click **OK**. -![img18 alt-text#center](figures/18.png) +![img18 alt-text#center](figures/18.webp) ### Modify Stream Analytics Job Now update your query in Stream Analytics to write data from IoT Hub directly into Cosmos DB: 1. Go to `IoTStreamAnalyticsJob`. 2. Under Job topology, select **Outputs**. 3. Click **Add output**, and select **Cosmos DB**: -![img19 alt-text#center](figures/19.png) +![img19 alt-text#center](figures/19.webp) 4. In the Cosmos DB pane, type "CosmosDBOutput" for the alias name, leave other fields at their default values, and click the **Save** button: -![img20 alt-text#center](figures/20.png) +![img20 alt-text#center](figures/20.webp) ### Update Your Stream Analytics Query Now that you have the output configured, lets modify the query. To do so, select Query under Job topology. Then, modify your existing query to explicitly specify your Cosmos DB output alias: @@ -102,16 +102,16 @@ FROM IoTHubInput ``` -![img21 alt-text#center](figures/21.png) +![img21 alt-text#center](figures/21.webp) Afterwards, click **Start job**, and then **Start**: -![img22 alt-text#center](figures/22.png) +![img22 alt-text#center](figures/22.webp) ## Verify data flow in Cosmos DB To verify that your data pipeline is working correctly, first start your Python IoT simulator application `iot_simulator.py`. Ensure it's actively sending telemetry data. Next, open the Azure Portal and navigate to your Azure Cosmos DB resource. Under Data Explorer, select your database and then your container (e.g., SensorReadings). Once selected, click **Items** to view your stored data. Sensor readings streamed from your IoT device will appear on the right-hand side of the Data Explorer interface, similar to the screenshot below: -![img23 alt-text#center](figures/23.png) +![img23 alt-text#center](figures/23.webp) Azure Cosmos DB stores data as JSON documents within a NoSQL (document-based) structure, making it ideal for flexible and dynamic data, such as IoT telemetry. Each record (also called a document) is stored in a container (or collection) that doesn’t enforce a rigid schema. As a result, each document can contain different fields without requiring schema changes or migrations, which is particularly valuable when collecting data from diverse IoT devices with evolving attributes. diff --git a/content/learning-paths/iot/azure-iot/stream-analytics.md b/content/learning-paths/iot/azure-iot/stream-analytics.md index fbac8d5676..a385a6e459 100644 --- a/content/learning-paths/iot/azure-iot/stream-analytics.md +++ b/content/learning-paths/iot/azure-iot/stream-analytics.md @@ -22,7 +22,7 @@ To process and analyze the telemetry data you are streaming to Azure IoT Hub, yo 1. Sign in to the Azure Portal. 2. Click **Create a resource**, type “Stream Analytics job” into the search box, and press **Enter**. 3. From the search results, select **Stream Analytics job**, then click **Create**: -![img9 alt-text#center](figures/09.png) +![img9 alt-text#center](figures/09.webp) 4. Provide the necessary information: * Subscription: choose the Azure subscription you want to use for this job. * Resource group: select the resource group you previously created (e.g., your IoT project's resource group). @@ -54,7 +54,7 @@ After successfully creating the Stream Analytics job, you will need to configure * Encoding: Choose **UTF-8**. * Event compression type: Set this to None. -![img12 alt-text#center](figures/12.png) +![img12 alt-text#center](figures/12.webp) 5. After entering these details, carefully verify them for accuracy and completeness. Click **Save** to apply the changes and successfully link your Azure IoT Hub as the input source for your Stream Analytics job. @@ -80,7 +80,7 @@ This straightforward query selects all relevant fields (deviceId, temperature, p Before running this query, ensure your Python IoT simulator `iot_simulator.py` is actively sending telemetry data. After the simulator begins transmitting data, you can test and verify your Stream Analytics query within the Azure Portal using the built-in Test query feature. Doing this allows you to view live-streamed sensor data in real-time and confirm that your streaming pipeline is working as expected: -![img13 alt-text#center](figures/13.png) +![img13 alt-text#center](figures/13.webp) Constructing queries in Azure Stream Analytics involves using a straightforward SQL-like syntax specifically optimized for real-time stream processing. Typically, a query contains a `SELECT` statement to specify which fields from the incoming telemetry data to process, and a `FROM` statement to indicate the source stream. Queries can be expanded with advanced features such as filtering, aggregations, and temporal window functions to handle more complex scenarios. diff --git a/content/learning-paths/laptops-and-desktops/electron/figures/02.png b/content/learning-paths/laptops-and-desktops/electron/figures/02.png deleted file mode 100644 index 34bb9bbf94..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/electron/figures/02.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/electron/figures/02.webp b/content/learning-paths/laptops-and-desktops/electron/figures/02.webp new file mode 100644 index 0000000000..4ee241a633 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/electron/figures/02.webp differ diff --git a/content/learning-paths/laptops-and-desktops/electron/figures/03.png b/content/learning-paths/laptops-and-desktops/electron/figures/03.png deleted file mode 100644 index 76f24a268d..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/electron/figures/03.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/electron/figures/03.webp b/content/learning-paths/laptops-and-desktops/electron/figures/03.webp new file mode 100644 index 0000000000..cae9a63e2d Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/electron/figures/03.webp differ diff --git a/content/learning-paths/laptops-and-desktops/electron/how-to-1.md b/content/learning-paths/laptops-and-desktops/electron/how-to-1.md index 2d8c0a1377..9cc294f2d4 100644 --- a/content/learning-paths/laptops-and-desktops/electron/how-to-1.md +++ b/content/learning-paths/laptops-and-desktops/electron/how-to-1.md @@ -218,7 +218,7 @@ npm start The application window appears. Then click the Fetch data button and you will see the list of posts: -![fig2](figures/02.png) +![fig2](figures/02.webp) ## Styling an application The application works fine. However, it uses default styles and does not look very pretty. To change this you can use cascading style sheets (CSS) as per web applications. To style the application, proceed as follows: @@ -302,7 +302,7 @@ body { After the application launches, click the **Fetch data** button and you will see the following result: -![fig3](figures/03.png) +![fig3](figures/03.webp) ## Checkpoint You now have the application up and running. By default, it runs using the Arm64 architecture because we used Node.js for Arm64. To confirm this, open the Task Manager, click the Details tab and look for **electron.exe** processes: diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_2.png b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_2.png deleted file mode 100644 index e858ff7570..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_2.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_2.webp b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_2.webp new file mode 100644 index 0000000000..38b8964116 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_2.webp differ diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_3.png b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_3.png deleted file mode 100644 index 091bbcf465..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_3.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_3.webp b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_3.webp new file mode 100644 index 0000000000..02f0d01d7b Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_3.webp differ diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_4.png b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_4.png deleted file mode 100644 index 37f5ec96c4..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_4.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_4.webp b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_4.webp new file mode 100644 index 0000000000..cad4edcfe7 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_4.webp differ diff --git a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_llvm.md b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_llvm.md index 57caf69c84..162b1c194a 100644 --- a/content/learning-paths/laptops-and-desktops/llvm_putty/putty_llvm.md +++ b/content/learning-paths/laptops-and-desktops/llvm_putty/putty_llvm.md @@ -76,19 +76,19 @@ This opens up the `CMake Settings` dialog. You can now add a new configuration to build PuTTY for Arm. This configuration uses the LLVM toolchain installed by the Visual Studio Installer. -![putty_2](putty_2.png) +![putty_2](putty_2.webp) 6. Add a new configuration In `CMake Settings`, click on `+` sign to `Add a New Configuration` and select `arm64-Clang-Debug`. -![putty_3](putty_3.png) +![putty_3](putty_3.webp) 7. Change the toolset With the `arm64-Clang-Debug` configuration selected, change the `Toolset` from the drop-down menu to `clang_cl_arm64`. -![putty_4](putty_4.png) +![putty_4](putty_4.webp) 8. 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Neovim is a great option for the Pinebook Pro running the i3 window manager. -![i3 window manager alt-text#center](i3.jpg) +![i3 window manager alt-text#center](i3.webp) diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/create-self-hosted-runner-github.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/create-self-hosted-runner-github.md index fc85d4001f..d3585ba192 100644 --- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/create-self-hosted-runner-github.md +++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/create-self-hosted-runner-github.md @@ -11,13 +11,13 @@ layout: "learningpathall" ## Create the Self-Hosted Runner in GitHub Go to the Actions tab, and under Management, click on Runners. If you do not see the Actions tab, navigate to Settings > Actions and select Allow all actions and reusable workflows. This will display the following window: -![img8](figures/08.png) +![img8](figures/08.webp) Then, click on the 'New runner' button, followed by 'New self-hosted runner'. In the 'Add new self-hosted runner' section, proceed as follows: * Select Linux for the operating system. * Choose ARM64 for the architecture -![img9](figures/09.png) +![img9](figures/09.webp) Next, execute the following instructions on your machine: @@ -100,6 +100,6 @@ Current runner version: '2.314.1' The runner will now be visible in the GitHub actions: -![img10](figures/10.png) +![img10](figures/10.webp) You have successfully prepared all the tools needed to run the CI/CD pipeline on the self-hosted runner. 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Below the file editor, find and click the Commit changes button. This action opens the Commit changes window. 4. In the Commit changes window, you have the option to add a commit message describing your changes. After reviewing your message, click Commit changes to finalize the update: -![img12](figures/12.png) +![img12](figures/12.webp) Committing these changes to the main branch will automatically trigger the CI/CD pipeline. You can monitor the progress of this pipeline under the Actions tab of your GitHub repository: -![img13](figures/13.png) +![img13](figures/13.webp) Upon completion of the second job, which builds and pushes the Docker image, you can find the updated Docker image in your Docker Hub repository. The new image will be tagged as latest and will reflect the changes made to the index.cshtml file: -![img14](figures/14.png) +![img14](figures/14.webp) This process demonstrates how code changes in your repository can seamlessly integrate with GitHub Actions to automate the build and deployment of your application, including updating Docker images in Docker Hub. diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/prepare-github-repo.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/prepare-github-repo.md index 424d5eab49..339bd81a8c 100644 --- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/prepare-github-repo.md +++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/prepare-github-repo.md @@ -14,7 +14,7 @@ Now, you will prepare your GitHub repository as follows: 1. Go to GitHub and log in with your account credentials. 2. In the top right corner, click the + dropdown and select Import repository. -![img5](figures/05.png) +![img5](figures/05.webp) 3. In the Import your project to GitHub section: * Paste the following link https://github.com/dawidborycki/arm-lp-ci-cd-net.git into the 'Your old repository's clone URL' field. @@ -23,14 +23,14 @@ Now, you will prepare your GitHub repository as follows: After the import is complete, you will create two secrets that will store your Docker username and token. Proceed as follows 1. Go to your repository settings, and on the left, scroll down to Secrets and variables, then click Actions: -![img6](figures/06.png) +![img6](figures/06.webp) 2. Under Actions secrets and variables, click the New repository secret button. This opens the New secret window, where you should configure the secret as follows: * Name: DOCKER_USERNAME * Value: * Click the Add secret button -![img7](figures/07.png) +![img7](figures/07.webp) 3. Similarly, create another secret: * Name: DOCKER_PASSWORD diff --git a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/set-up-docker-repo.md b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/set-up-docker-repo.md index cdddbf8c5a..00a6c84c5d 100644 --- a/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/set-up-docker-repo.md +++ b/content/learning-paths/laptops-and-desktops/self_hosted_cicd_github/set-up-docker-repo.md @@ -11,21 +11,21 @@ First, create a repository on DockerHub by following these steps: 1. Log in to DockerHub, then click on *Repositories* at the top. 2. Under *Repositories*, click the *Create repository* button: -![img1](figures/01.png) +![img1](figures/01.webp) 3. In the *Create repository* window, set the repository name to sampleapp and change the repository visibility to *Private*. -![img2](figures/02.png) +![img2](figures/02.webp) 4. Click the *Create* button. This will create the repository and redirect you back to the repositories list. Note the Docker push command. It should look like: docker push /sampleapp:tagname -![img3](figures/03.png) +![img3](figures/03.webp) Now you will need to generate the access token to enable the workflow to authenticate to DockerHub. To create the access token: 1. Click on your username in the top-right corner of the Docker Hub site. 2. From the dropdown menu, select My Account, and then click the Security tab on the left: -![img4](figures/04.png) +![img4](figures/04.webp) 3. Click the New Access Token button. This opens the New Access Token window, in which: * Type ci-cd for the token name. diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/images/win11arm.png b/content/learning-paths/laptops-and-desktops/win11-vm-automation/images/win11arm.png deleted file mode 100644 index 4f31c8b4f5..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win11-vm-automation/images/win11arm.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/images/win11arm.webp b/content/learning-paths/laptops-and-desktops/win11-vm-automation/images/win11arm.webp new file mode 100644 index 0000000000..411cdd6932 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win11-vm-automation/images/win11arm.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win11-vm-automation/vm-execution-4.md b/content/learning-paths/laptops-and-desktops/win11-vm-automation/vm-execution-4.md index d0449dfd63..c053b7d3f9 100644 --- a/content/learning-paths/laptops-and-desktops/win11-vm-automation/vm-execution-4.md +++ b/content/learning-paths/laptops-and-desktops/win11-vm-automation/vm-execution-4.md @@ -22,7 +22,7 @@ The script performs three key steps. It does the following: When the virtual machine starts you will see it on your Linux desktop: -![Screenshot showing the Windows 11 desktop running in a virtual machine on an Arm-based Linux system. The Windows Start menu and taskbar are visible, confirming successful VM launch and RDP connection. alt-text#center](./images/win11arm.png "Windows 11 Arm VM desktop") +![Screenshot showing the Windows 11 desktop running in a virtual machine on an Arm-based Linux system. The Windows Start menu and taskbar are visible, confirming successful VM launch and RDP connection. alt-text#center](./images/win11arm.webp "Windows 11 Arm VM desktop") ## What does the run script do? diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/figures/01.png b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/figures/01.png deleted file mode 100644 index 499ce84633..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/figures/01.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/figures/01.webp b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/figures/01.webp new file mode 100644 index 0000000000..9ab5f80516 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/figures/01.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/how-to-1.md index 763e643ca2..9747e7cc58 100644 --- a/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/how-to-1.md +++ b/content/learning-paths/laptops-and-desktops/win_arm64ec_porting/how-to-1.md @@ -25,7 +25,7 @@ The complete project code used in this learning path is hosted [here](https://gi ## Project Setup To set up the project, start by creating the dependencies (the DLLs). In this example you will use CMake in Visual Studio 2022 to create the base project for your dependencies. You can also use MS Build/Visual C++ project templates to compile to Arm64EC by adding the architecture to your build configuration. To access CMake, open Visual Studio 2022 and click Create a new project and look for CMake Project in the window that appears. -![fig1](figures/01.png) +![fig1](figures/01.webp) Then, click **Next** and set the following configurations: diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/01.png b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/01.png deleted file mode 100644 index 9de2e19301..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/01.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/01.webp b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/01.webp new file mode 100644 index 0000000000..557f1f0a15 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/01.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/02.png b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/02.png deleted file mode 100644 index a761216690..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/02.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/02.webp b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/02.webp new file mode 100644 index 0000000000..6d7a187bd0 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/02.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/03.png b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/03.png deleted file mode 100644 index 2cae7bfc5c..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/03.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/03.webp b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/03.webp new file mode 100644 index 0000000000..ebc865055f Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/03.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/05.png b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/05.png deleted file mode 100644 index d14a5969e9..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/05.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/05.webp b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/05.webp new file mode 100644 index 0000000000..5b14026f31 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_arm_qt/figures/05.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_arm_qt/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_arm_qt/how-to-1.md index 66b2308216..b4faff2e39 100644 --- a/content/learning-paths/laptops-and-desktops/win_arm_qt/how-to-1.md +++ b/content/learning-paths/laptops-and-desktops/win_arm_qt/how-to-1.md @@ -19,19 +19,19 @@ You will need to install Qt on your Windows on Arm machine. Go to the Qt Group [ In the Qt Setup window select, as a minimum, Qt version 6.2. In this example, we use version 6.4. -![fig1](figures/01.png) +![fig1](figures/01.webp) ## Create the project After installation, open the Qt Creator, click Examples, select Qt6, and type “affine” in the search box. The list of examples should include one project: Affine Transformations. Click on it to make the source code available in Qt Creator. -![fig2](figures/02.png) +![fig2](figures/02.webp) {{% notice Note %}} If you are new to Qt Creator, consult the documentation for a quick introduction. Project files are on the left side, and double-clicking them opens them in the editor. The Run and Debug buttons are on the bottom left, or you can press F5 to run and debug your project and Ctrl + R to run it without the debugger. {{% /notice %}} Now build and run the app using default settings (F5 or Ctrl + R). After execution, the app should look like this: -![fig3](figures/03.png) +![fig3](figures/03.webp) The app continuously transforms the image of Tux (Linux's penguin character) by rotating, scaling, and shearing it. You can stop the animation by clicking Animate. It is active when the app runs inside the constructor of the XFormWidget. @@ -226,7 +226,7 @@ Now it's time to run the application using the AArch64 build. To do this, use th Start debugging by clicking on the green play icon. The application will launch: -![fig5](figures/05.png) +![fig5](figures/05.webp) Click **Run** to trigger the animation. The Tux image is transformed 10,000 times. The time needed for the transformations appears in the Application Output window at the bottom of the Qt Creator window. 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This URL takes you to the following screen: -![fig3](figures/03.png) +![fig3](figures/03.webp) Now, expand the **POST** section and click the **Try it out** button. The caption of this button will change to **Cancel**, and the **isActive** drop-down list becomes active. Set **isActive** to false, and then click **Execute**: -![fig4](figures/04.png) +![fig4](figures/04.webp) This action sends the **POST** request to **IoTController** and disables the emulator. All subsequent **GET** calls to the **IoTController** will return the last known sensor reading (which is the final reading generated before disabling the emulator). 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From the list that appears, select **IoT Core**: -![fig1](figures/01.png) +![fig1](figures/01.webp) In the AWS IoT console, click **Connect one device**: -![fig2](figures/02.png) +![fig2](figures/02.webp) This will open the **Connect one device** wizard: -![fig3](figures/03.png) +![fig3](figures/03.webp) In the first step of this wizard, **Register and secure your device**, you can see the ping command, which you can use to ensure you can connect to AWS services. @@ -44,17 +44,17 @@ Note that the **a6t44uzbanvsz-ats.iot.eu-central-1.amazonaws.com** is your endpo Click the **Next** button. This takes you to the **Register and secure your device** step, in which you type **WeatherEmulator** under the **Thing** name: -![fig4](figures/04.png) +![fig4](figures/04.webp) Then, click the **Next** button, which opens the **Choose platform and SDK** window, in which you select: * **Windows** from the **Device platform operating system**. * **Node.js** from the **AWS IoT Device SDK**. -![fig5](figures/05.png) +![fig5](figures/05.webp) Afterwards, click the **Next** button, which takes you to the **Download connection kit** step, in which you click the **Download connection kit** button to get the zip with certificates and the sample code, which sends data to the AWS cloud: -![fig6](figures/06.png) +![fig6](figures/06.webp) After downloading the kit, click the **Next** button. This takes you to the **Run connection kit**, in which you click the **Continue** button. This takes you back to the AWS IoT screen. @@ -83,11 +83,11 @@ Before you can connect the device to the AWS IoT Core, you need to configure the To update the policy, under the AWS IoT console, click **Security** -> **Policies**: -![fig7](figures/07.png) +![fig7](figures/07.webp) There should be one policy, **WeatherEmulator-Policy**. Click that policy. This opens another view, in which you can see the policy details. In that view, click **Edit active version** button, which opens the Edit policy view: -![fig7](figures/08.png) +![fig7](figures/08.webp) Modify the first entry ('iot:Publish,iot:Receive,iot:PublishRetain') such that you replace the 'topic/sdk/test/js' with 'topic/*'. This will enable the application to publish and receive messages from any topic. Similarly, modify the second entry, and replace 'topicfilter/sdk/test/js' with 'topicfilter/*'. diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot/how-to-3.md b/content/learning-paths/laptops-and-desktops/win_aws_iot/how-to-3.md index 53ef18ebb9..708eaa4cd1 100644 --- a/content/learning-paths/laptops-and-desktops/win_aws_iot/how-to-3.md +++ b/content/learning-paths/laptops-and-desktops/win_aws_iot/how-to-3.md @@ -16,5 +16,5 @@ Under AWS IoT Core, click on **MQTT test client** under the **Test** menu. In the 'Topic Filter' field, type 'Emulators/Weather/SensorReadings', and click the **Subscribe** button. 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Open AWS IoT Core, and look for *Rules* under *Message routing*: -![fig1](figures/01.png) +![fig1](figures/01.webp) 2. Click the *Create rule* button. This will opens *Create rule* view, in which you configure the rule: -![fig2](figures/02.png) +![fig2](figures/02.webp) 3. Use the *Create rule* view to configure the rule as follows. Firstly, under the rule name, type: send_message_to_dynamodb. Then, click the *Next* button. This will take you to the Configure SQL statement, where you use the following SQL query: @@ -25,25 +25,25 @@ Start by creating the rule: SELECT * FROM 'Emulators/Weather/SensorReadings' ``` -![fig3](figures/03.png) +![fig3](figures/03.webp) 4. Click the *Next* button. -![fig4](figures/04.png) +![fig4](figures/04.webp) 5. This opens the *Attach rule actions*, where under *Action 1*, select *DynamoDBv2*. This activates additional controls (see figure below): -![fig5](figures/05.png) +![fig5](figures/05.webp) 6. Click the *Create DynamoDB* table. This will open another tab, where you type *SensorReadings* for the Table name and *timestamp* for the partition key. Ensure to check *Default settings*, scroll down, and click the *Create table* button. Wait a few moments for the table to be created. Then, return to the *Create rule* view. -![fig6](figures/06.png) +![fig6](figures/06.webp) 7. In the *Create rule* view, click the *Refresh* button next to the *Choose DynamoDB* table name drop-down. Then, select the *SensorReadings* table from that drop-down. Afterward, click the *Create new role* button (located below the IAM role). This activates the *Create role* pop-up. 8. In the *Create role* pop-up window, type 'weather_station_dynamo_db', and click the *Create* button. By now, the *Attach rule* actions screen looks like this: -![fig7](figures/07.png) +![fig7](figures/07.webp) 9. Scroll down and click the *Next* button. This displays the summary screen, where you click the *Create* button. @@ -66,7 +66,7 @@ This starts the emulator, which streams data to the AWS IoT Core. The output of To ensure this data is written to the DynamoDB table, go to the AWS console, and in the search box type 'DynamoDB'. Select *DynamoDB* from the list. This takes you to the DynamoDB dashboard, where you click *Explore items* on the left. Then, check the *SensorReadings* radio button, and you see the items in the table: -![fig8](figures/08.png) +![fig8](figures/08.webp) Note that the values in the table are the same as those generated by the emulator. This ensures that you have successfully integrated AWS IoT Core with DynamoDB. diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/create-rule.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/create-rule.md index 0bb4a84cc7..082479d20c 100644 --- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/create-rule.md +++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/create-rule.md @@ -14,11 +14,11 @@ Start by creating the rule: 1. Open AWS IoT Core, and look for *Rules* under *Message routing*: -![fig1](figures/01.png) +![fig1](figures/01.webp) 2. Click the *Create rule* button. This opens the *Create rule* view, in which you configure the rule: -![fig2](figures/02.png) +![fig2](figures/02.webp) 3. Use the *Create rule* view to configure the rule. Firstly, under the rule name, type: **check_temperature**. Then, click the *Next* button. This takes you to the Configure SQL statement, where you use the following SQL query: @@ -36,15 +36,15 @@ This query ensures that only temperature readings are sent to the AWS Lambda fun 6. Click the *Create a Lambda function* button. This opens another tab. Select *Author from scratch*, type **SendNotification** for the function name, select *Node.js 20.x* for the runtime, and *arm64* for the architecture: -![fig4](figures/04.png) +![fig4](figures/04.webp) 7. Scroll down, and under the *Execution role*, select *Create a new role from AWS policy templates*. This activates additional controls, where you type **sns-email-role** for the *Role name*. Then, click the drop-down list *Policy templates*, and select *Amazon SNS publish policy*: -![fig5](figures/05.png) +![fig5](figures/05.webp) 8. Click the *Create function* button. This takes you to the AWS Lambda Console, which looks as shown below: -![fig6](figures/06.png) +![fig6](figures/06.webp) 9. Keep the AWS Lambda Console open in a separate tab and go back to the *Create rule* wizard. Select *SendNotification* from the Lambda function dropdown, and ensure that the Lambda function version points to $LATEST: @@ -54,7 +54,7 @@ This query ensures that only temperature readings are sent to the AWS Lambda fun 11. Go back to AWS Lambda Console and refresh the page. You will now see that your function accepts the AWS IoT trigger: -![fig8](figures/08.png) +![fig8](figures/08.webp) This means that the AWS Lambda function you have just created has an input trigger set to AWS IoT. 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files a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/figures/24.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/figures/24.webp b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/figures/24.webp new file mode 100644 index 0000000000..796a3ec7fa Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/figures/24.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/implement-lambda.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/implement-lambda.md index aee054e155..c36dcd150c 100644 --- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/implement-lambda.md +++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda/implement-lambda.md @@ -171,7 +171,7 @@ In summary, this Lambda function creates an SNS topic and subscribes an email ad After making these changes, the Lambda code editor looks as shown below: -![fig9](figures/09.png) +![fig9](figures/09.webp) Now, click the Deploy button to apply code changes. @@ -180,7 +180,7 @@ Now, click the Deploy button to apply code changes. ### Test Events You will now create two test events to manually invoke the Lambda function. Click the Test button. This opens the Configure test event window: -![fig10](figures/10.png) +![fig10](figures/10.webp) In this window, type **temperature-normal-level** for the Event name, and then under Event JSON paste the following payload: @@ -194,7 +194,7 @@ Finally, click the *Save* button. This takes you back to the Lambda function con Click the triangle icon, in the Test button. This expands the menu, from which you select *Configure test event*: -![fig11](figures/11.png) +![fig11](figures/11.webp) This opens the *Configure test event* window, where you select *Create new event*, change the event name to **temperature-high-level**, and paste the following payload for the Event JSON: @@ -206,18 +206,18 @@ This opens the *Configure test event* window, where you select *Create new event The *Configure test event* window should looks as shown: -![fig12](figures/12.png) +![fig12](figures/12.webp) Click the *Save* button to add a new test event. This takes you back to the Lambda function console. ### Testing the function In the Lambda function console, click the triangle icon next to the Test button, and select *temperature-normal-level* event: -![fig13](figures/13.png) +![fig13](figures/13.webp) Then, click the *Test* button. The Lambda function is invoked, and you see the following execution result: -![fig14](figures/14.png) +![fig14](figures/14.webp) This means that our Lambda function does not have the necessary permissions to create the SNS topic. @@ -231,40 +231,40 @@ Specifically, you will attach the policy giving the Lambda a full access to the To modify the role, proceed as shown: 1. Go the AWS console, and type **IAM** in the search box. Then, select IAM from the list: -![fig15](figures/15.png) +![fig15](figures/15.webp) 2. This takes you to the IAM Dashboard, where you click *Roles under Access management*: -![fig16](figures/16.png) +![fig16](figures/16.webp) 3. Click the *sns-email-role*. This opens another screen, which should look like shown below: -![fig17](figures/17.png) +![fig17](figures/17.webp) 4. Click the *Add permissions* button. This activates the drop-down list, from which you select *Attach policies*. 5. In the *Attach policy to sns-email-role* view, type *SNSFull* in the search box. This filters the list of policies to display one item: *AmazonSNSFullAccess*. Check the check-box on the left of the policy name, and then click the *Add permissions* button: -![fig18](figures/18.png) +![fig18](figures/18.webp) The role has been updated. You can now go back to AWS Lambda. ### Testing the function After updating the role, the SendNotification Lambda function can create SNS topics and send emails. To test this, in the Lambda function console, select the *temperature-high-level* test event, and click the Test button. You will see that the test event was processed without an error: -![fig19](figures/19.png) +![fig19](figures/19.webp) Go to your mailbox and look for an email from AWS Notification. The first email asks you to confirm the subscription: -![fig20](figures/20.png) +![fig20](figures/20.webp) Click the *Confirm subscription* link. Then, invoke the *temperature-high-level* event one more time to see that you received an alert email. It will appear like this: -![fig21](figures/21.png) +![fig21](figures/21.webp) Now, you can start the weather station emulator you created in this [Learning Path](/learning-paths/laptops-and-desktops/win_aws_iot/). Observe the values generated by the emulator. When the temperature exceeds the threshold of 35, you will receive an email notification: -![fig22](figures/22.png) +![fig22](figures/22.webp) ## Environment Variables In the above code, you hardcoded an email, SNS topic, and temperature threshold. This means that every time those values change, you need to modify the function code and redeploy it. In practice, it is better to use environment variables. @@ -274,11 +274,11 @@ You can modify the function code to use environment variables. First, you need t Proceed as shown: 1. In the Lambda function dashboard, click the *Configuration* tab, and select *Environment variables*: -![fig23](figures/23.png) +![fig23](figures/23.webp) 2. Click the *Edit* button. This opens the *Edit environment variables* view, where you click *Add environment variable*. This activates additional controls, where you type **EMAIL** for *Key* and your email for *Value*. Click the *Add environment variable* button one more time, and add another variable with **TEMPERATURE_THRESHOLD** as *Key*, and **35** as *Value*: -![fig24](figures/24.png) +![fig24](figures/24.webp) 3. Click the *Save* button. diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/create-lambda.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/create-lambda.md index db4218db18..0a611f7b91 100644 --- a/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/create-lambda.md +++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_lambda_dynamodb/create-lambda.md @@ -23,19 +23,19 @@ Go the AWS Lambda console. Then, create the function as shown below: At this point your wizard should look as per the image below: -![fig2](figures/02.png) +![fig2](figures/02.webp) Next, in the *Create function* wizard expand *Change default execution role*, and proceed as follows: 1. Select **Create a new role from AWS policy templates**. 2. Type **role-lambda-to-dynamodb** under the Role name. 3. Under *Policy templates - optional* select **Simple microservice permissions**. -![fig3](figures/03.png) +![fig3](figures/03.webp) This ensures that your AWS Lambda function has all the necessary permissions to access the items in the DynamoDB table. Finally, scroll down and click the **Create function** button. 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This will open the *Configure test event* window, in which you type **Test** in the Event name and click the **Save** button. This will take you back to the `GetAverageTemperature` function dashboard, where you click the **Test** button again. You will see the execution status, including the average temperature value as shown below: -![fig6](figures/06.png) +![fig6](figures/06.webp) # Summary In this learning path you have implemented an AWS Lambda function that processes data stored in a DynamoDB table. You utilized AWS SDK for JavaScript v3 to interact with DynamoDB. Specifically, to create a DynamoDB Query, you constructed a DynamoDB Scan Command with a FilterExpression to retrieve records from the last N minutes. diff --git a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/add-lambda.md b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/add-lambda.md index 06a7e14c2e..8b94e6df9b 100644 --- a/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/add-lambda.md +++ b/content/learning-paths/laptops-and-desktops/win_aws_iot_s3/add-lambda.md @@ -13,7 +13,7 @@ You will now use the AWS Lambda console to retrieve the AWS Lambda endpoint for 1. Go to the AWS Lambda console, and click the `GetAverageTemperature` Lambda function. 2. In the Lambda function dashboard, click the **Configuration** tab and then the **Function URL** as shown below: -![fig2](figures/02.png) +![fig2](figures/02.webp) 3. Under the Function URL, click the **Create Function URL** button. 4. 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The binaries can be found under the `buil By default, the application renders the Google homepage: -![fig2](figures/02.png) +![fig2](figures/02.webp) ## Modify the application You will now modify the `cefsimple` application to render a custom page. This page will fetch data from `JSONPlaceholder` and display it in a table. @@ -221,7 +221,7 @@ You will now create a custom index.html file to fetch the data from the JSONPlac Re-run the application and you will see the following: -![fig3](figures/03.png) +![fig3](figures/03.webp) This example demonstrates the versatility and power of CEF applications. One of their key strengths is the ability to modify the rendered content dynamically. This means you can change what's displayed in the application, like updating the HTML or CSS, without having to rebuild the entire application each time. This feature significantly streamlines the development process, making it quicker and more efficient. @@ -296,7 +296,7 @@ button:hover { After re-running the application you will see the following: -![fig4](figures/04.png) +![fig4](figures/04.webp) ## Summary CEF is a powerful tool for integrating web browser functionalities into desktop applications, utilizing modern web technologies. You have learned how to setup a project, create a basic application, and use CMake for CEF projects. Additionally, you have explored more advanced features like fetching and displaying data from JSONPlaceholder, adding and styling HTML elements like tables and buttons through separate CSS files all on your Windows on Arm machine. diff --git a/content/learning-paths/laptops-and-desktops/win_forms/figures/01.png b/content/learning-paths/laptops-and-desktops/win_forms/figures/01.png deleted file mode 100644 index eb3d7362bd..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_forms/figures/01.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_forms/figures/01.webp b/content/learning-paths/laptops-and-desktops/win_forms/figures/01.webp new file mode 100644 index 0000000000..21eaf6bfa8 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_forms/figures/01.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_forms/figures/02.png b/content/learning-paths/laptops-and-desktops/win_forms/figures/02.png deleted file mode 100644 index 994f52ee1e..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_forms/figures/02.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_forms/figures/02.webp b/content/learning-paths/laptops-and-desktops/win_forms/figures/02.webp new file mode 100644 index 0000000000..ac0c335058 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_forms/figures/02.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_forms/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_forms/how-to-1.md index 1363a4d065..22cf649a92 100644 --- a/content/learning-paths/laptops-and-desktops/win_forms/how-to-1.md +++ b/content/learning-paths/laptops-and-desktops/win_forms/how-to-1.md @@ -21,11 +21,11 @@ Before you begin, install Visual Studio 2022 with .NET Desktop Development workl ## Create the project Start by creating the project. Open Visual Studio and then click 'Create a new project'. -![fig1](figures/01.png) +![fig1](figures/01.webp) In the 'Create a new project' window, select the 'Windows Forms App' template. -![fig2](figures/02.png) +![fig2](figures/02.webp) This will open the 'Configure your new project' view, in which you should configure the project as follows (refer to the figure below): diff --git a/content/learning-paths/laptops-and-desktops/win_net/net2.png b/content/learning-paths/laptops-and-desktops/win_net/net2.png deleted file mode 100644 index 6dd195cde9..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_net/net2.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_net/net2.webp b/content/learning-paths/laptops-and-desktops/win_net/net2.webp new file mode 100644 index 0000000000..bdf9bcbf3e Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_net/net2.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_net/win_net.md b/content/learning-paths/laptops-and-desktops/win_net/win_net.md index 4562424798..4895b5db69 100644 --- a/content/learning-paths/laptops-and-desktops/win_net/win_net.md +++ b/content/learning-paths/laptops-and-desktops/win_net/win_net.md @@ -27,7 +27,7 @@ To modify your existing installation of Visual Studio: 3. On the Workloads tab, click on the `.NET desktop development` checkbox and then select `Modify`. -![img2](net2.png) +![img2](net2.webp) After the installation completes, close the installer. diff --git a/content/learning-paths/laptops-and-desktops/win_net8/figures/01.png b/content/learning-paths/laptops-and-desktops/win_net8/figures/01.png deleted file mode 100644 index e3086395cb..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_net8/figures/01.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_net8/figures/01.webp b/content/learning-paths/laptops-and-desktops/win_net8/figures/01.webp new file mode 100644 index 0000000000..593d4ed834 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_net8/figures/01.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_net8/figures/02.png b/content/learning-paths/laptops-and-desktops/win_net8/figures/02.png deleted file mode 100644 index db64244e55..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_net8/figures/02.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_net8/figures/02.webp b/content/learning-paths/laptops-and-desktops/win_net8/figures/02.webp new file mode 100644 index 0000000000..82ed193e81 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_net8/figures/02.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_net8/figures/03.png b/content/learning-paths/laptops-and-desktops/win_net8/figures/03.png deleted file mode 100644 index 2f2685758e..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_net8/figures/03.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_net8/figures/03.webp b/content/learning-paths/laptops-and-desktops/win_net8/figures/03.webp new file mode 100644 index 0000000000..f9672c63d6 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_net8/figures/03.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_net8/figures/04.png b/content/learning-paths/laptops-and-desktops/win_net8/figures/04.png deleted file mode 100644 index e8b319df5f..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_net8/figures/04.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_net8/figures/04.webp b/content/learning-paths/laptops-and-desktops/win_net8/figures/04.webp new file mode 100644 index 0000000000..5611f6fa43 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_net8/figures/04.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_net8/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_net8/how-to-1.md index 5c2ac50369..d7311ecc25 100644 --- a/content/learning-paths/laptops-and-desktops/win_net8/how-to-1.md +++ b/content/learning-paths/laptops-and-desktops/win_net8/how-to-1.md @@ -95,10 +95,10 @@ dotnet run -c Release -f net8.0 The command will build and run the performance benchmarks. The output will look as follows: -![fig1](figures/01.png) +![fig1](figures/01.webp) The output lists all available benchmarks. You can type * to run all benchmarks or type a number to select individual performance tests. In this example, let's type **483** to execute the Perf_Doubles benchmark. After a while you will see a summary of the performance test. A portion of this output is shown in the figure below: -![fig2](figures/02.png) +![fig2](figures/02.webp) By default, the table summarizes the performance test results. You can see each performance test's execution time and statistics (mean, median, minimum, maximum, and standard deviation) which gives you a comprehensive overview of code performance. diff --git a/content/learning-paths/laptops-and-desktops/win_net8/how-to-2.md b/content/learning-paths/laptops-and-desktops/win_net8/how-to-2.md index 455ecea184..aaa97d4534 100644 --- a/content/learning-paths/laptops-and-desktops/win_net8/how-to-2.md +++ b/content/learning-paths/laptops-and-desktops/win_net8/how-to-2.md @@ -32,7 +32,7 @@ dotnet run -c Release Wait a few moments for the application to execute all the benchmarks and you will then see the results of the following form: -![fig3](figures/03.png) +![fig3](figures/03.webp) You will now compare execution times to x64 architecture (emulator on arm64) by typing: @@ -42,7 +42,7 @@ dotnet run -c Release -a x64 The above command will lead to significantly longer execution times than before: -![fig4](figures/04.png) +![fig4](figures/04.webp) The operations all take more time on the emulated x64 than when you execute them natively on Arm64. 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Next, open the Solution Explorer (View -> Solution Explorer) to view the created projects: -![fig6](figures/06.png) +![fig6](figures/06.webp) ## Understanding the project structure The .NET MAUI project template is structured to enable the development of cross-platform applications using a single codebase, targeting Android, iOS, macOS, and Windows. Therefore, the project we have just created contains the following elements: @@ -66,4 +66,4 @@ The .NET MAUI project template is structured to enable the development of cross- You can now run the created application. To do so, click _Debug/Start Debugging_. The running application will look like the figure below: -![fig7](figures/07.png) +![fig7](figures/07.webp) diff --git a/content/learning-paths/laptops-and-desktops/win_net_maui/how-to-2.md b/content/learning-paths/laptops-and-desktops/win_net_maui/how-to-2.md index 3befe54f38..c6f77f37e6 100644 --- a/content/learning-paths/laptops-and-desktops/win_net_maui/how-to-2.md +++ b/content/learning-paths/laptops-and-desktops/win_net_maui/how-to-2.md @@ -230,7 +230,7 @@ You will now build and run the application for x64 and Arm64 configurations. Pro 1. Start by creating the Arm64 configuration. To do this, click the Debug drop-down, and select Configuration Manager... as shown in the figure below: -![fig8](figures/08.png) +![fig8](figures/08.webp) 2. In the Configuration Manager, select from the Active solution platform drop-down: @@ -244,7 +244,7 @@ You will now build and run the application for x64 and Arm64 configurations. Pro ``` 6. Next, select ARM64 from the solution platform drop-down, and then click the Windows Machine button: -![fig10](figures/10.png) +![fig10](figures/10.webp) 7. The application will start after a while. Run vector calculations for various execution counts: 10, 100, 1000, and 10000 8. Repeat the same for x64. Your results from the two runs should look similar to the following figures: diff --git a/content/learning-paths/laptops-and-desktops/win_python_onnx/figures/01.png b/content/learning-paths/laptops-and-desktops/win_python_onnx/figures/01.png deleted file mode 100644 index 414f1a4fb0..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_python_onnx/figures/01.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_python_onnx/figures/01.webp b/content/learning-paths/laptops-and-desktops/win_python_onnx/figures/01.webp new file mode 100644 index 0000000000..60796acb86 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_python_onnx/figures/01.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_python_onnx/inference.md b/content/learning-paths/laptops-and-desktops/win_python_onnx/inference.md index ff7b72dcf0..66538d1699 100644 --- a/content/learning-paths/laptops-and-desktops/win_python_onnx/inference.md +++ b/content/learning-paths/laptops-and-desktops/win_python_onnx/inference.md @@ -30,7 +30,7 @@ py -V:3.13 .\main.py ``` The code will display a sample inference result similar to the image below: -![fig1](figures/01.png) +![fig1](figures/01.webp) Upon closing the displayed image, the script will output the computation time: ```output PS C:\Users\db\onnx> py -V:3.13 .\main.py diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/net2.png b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/net2.png deleted file mode 100644 index 6dd195cde9..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/net2.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/net2.webp b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/net2.webp new file mode 100644 index 0000000000..bdf9bcbf3e Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_sandbox_dot_net_cicd/net2.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/03.png b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/03.png deleted file mode 100644 index c936aa1992..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/03.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/03.webp b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/03.webp new file mode 100644 index 0000000000..c17840bbad Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/03.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/09.png b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/09.png deleted file mode 100644 index d7f50c2a83..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/09.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/09.webp b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/09.webp new file mode 100644 index 0000000000..3efd7878be Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/figures/09.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/how-to-1.md index 538731b5db..92d3a4d881 100644 --- a/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/how-to-1.md +++ b/content/learning-paths/laptops-and-desktops/win_win32_dll_porting/how-to-1.md @@ -129,7 +129,7 @@ You will now create the main application which will use the above DLL. To do so, 2. Select Add -> New Project... This will open the **Add a new project** window. 3. In the **Add a new project** window, select the **Console App project** template and click the **Next** button: -![fig3](figures/03.png) +![fig3](figures/03.webp) 4. In the **Configure your new project window**, set the project name to MainApplication and click the **Create** button @@ -221,7 +221,7 @@ The project is now ready and we can launch the application for various platforms ## Running the application To launch the application, select **x64** from the platform dropdown, and then click **Local Windows Debugger**: -![fig9](figures/09.png) +![fig9](figures/09.webp) The application will launch and you will observe the execution time for the x86_64 platform: diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/figures/01.png b/content/learning-paths/laptops-and-desktops/win_winui3/figures/01.png deleted file mode 100644 index 5136f7d791..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_winui3/figures/01.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/figures/01.webp b/content/learning-paths/laptops-and-desktops/win_winui3/figures/01.webp new file mode 100644 index 0000000000..46c0d519e3 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_winui3/figures/01.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/figures/03.png b/content/learning-paths/laptops-and-desktops/win_winui3/figures/03.png deleted file mode 100644 index 5d536b606e..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_winui3/figures/03.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/figures/03.webp b/content/learning-paths/laptops-and-desktops/win_winui3/figures/03.webp new file mode 100644 index 0000000000..4d127944d0 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_winui3/figures/03.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/figures/04.png b/content/learning-paths/laptops-and-desktops/win_winui3/figures/04.png deleted file mode 100644 index 952467fb06..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_winui3/figures/04.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/figures/04.webp b/content/learning-paths/laptops-and-desktops/win_winui3/figures/04.webp new file mode 100644 index 0000000000..e07b7e0711 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_winui3/figures/04.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_winui3/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_winui3/how-to-1.md index 5d8d88f8e2..3687f01b8f 100644 --- a/content/learning-paths/laptops-and-desktops/win_winui3/how-to-1.md +++ b/content/learning-paths/laptops-and-desktops/win_winui3/how-to-1.md @@ -25,7 +25,7 @@ Before you begin the implementation, install Visual Studio 2022 with the followi 2. Universal Windows Platform development 3. After selecting these workloads, expand the .NET desktop development group under the Installation details and ensure that the 'Windows App SDK C# Templates' option is selected. -![fig1](figures/01.png) +![fig1](figures/01.webp) Then, click the 'Individual components' tab and check '.NET 6.0 Runtime (Long Term Support)'. @@ -34,11 +34,11 @@ Then, click the 'Individual components' tab and check '.NET 6.0 Runtime (Long Te ## Create the project Open Visual Studio and click 'Create a new project'. -![fig3](figures/03.png) +![fig3](figures/03.webp) In the next window, search for the 'Blank App, Packaged (WinUI 3 in Desktop)' template. Select this template and click the 'Next' button. -![fig4](figures/04.png) +![fig4](figures/04.webp) This action opens the 'Configure your new project' window. Here, you should: diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/figures/06.png b/content/learning-paths/laptops-and-desktops/win_wpf/figures/06.png deleted file mode 100644 index 03321acdf2..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_wpf/figures/06.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/figures/06.webp b/content/learning-paths/laptops-and-desktops/win_wpf/figures/06.webp new file mode 100644 index 0000000000..ee3c38c2f3 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_wpf/figures/06.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/figures/08.png b/content/learning-paths/laptops-and-desktops/win_wpf/figures/08.png deleted file mode 100644 index 327279d7b6..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_wpf/figures/08.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/figures/08.webp b/content/learning-paths/laptops-and-desktops/win_wpf/figures/08.webp new file mode 100644 index 0000000000..273d3ce463 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_wpf/figures/08.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/figures/11.png b/content/learning-paths/laptops-and-desktops/win_wpf/figures/11.png deleted file mode 100644 index 7908e6627f..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_wpf/figures/11.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/figures/11.webp b/content/learning-paths/laptops-and-desktops/win_wpf/figures/11.webp new file mode 100644 index 0000000000..b12c220b08 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_wpf/figures/11.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_wpf/how-to-1.md index 015106fd51..3b37680a46 100644 --- a/content/learning-paths/laptops-and-desktops/win_wpf/how-to-1.md +++ b/content/learning-paths/laptops-and-desktops/win_wpf/how-to-1.md @@ -143,7 +143,7 @@ Before starting the UI design, install the Syncfusion.SfChart.WPF NuGet package 2. In Solution Explorer, right-click on Dependencies under the Arm64.DesktopApp.WPF project. From the context menu, select Manage NuGet Packages... to open the NuGet Package Manager. 3. In the NuGet Package Manager, search for Syncfusion Chart WPF. Then select Syncfusion.SfChart.WPF from the results and click the Install button. Refer to the figure below for guidance: -![fig6](figures/06.png) +![fig6](figures/06.webp) 4. A Preview Changes window will appear. Click the Apply button to confirm and proceed with the changes to the solution. The installation of the NuGet package will complete. diff --git a/content/learning-paths/laptops-and-desktops/win_wpf/how-to-2.md b/content/learning-paths/laptops-and-desktops/win_wpf/how-to-2.md index af83d4c117..50229b6f32 100644 --- a/content/learning-paths/laptops-and-desktops/win_wpf/how-to-2.md +++ b/content/learning-paths/laptops-and-desktops/win_wpf/how-to-2.md @@ -14,7 +14,7 @@ To prepare an application for ARM64 and x86_64 builds, proceed as follows: 1. Click the **Any CPU** drop-down menu and select Configuration Manager...: -![fig8](figures/08.png) +![fig8](figures/08.webp) 2. In the Configuration Manager, click New... from the **Active Solution Platform** drop-down menu: @@ -28,7 +28,7 @@ To prepare an application for ARM64 and x86_64 builds, proceed as follows: To build and run the application, select the **solution platform** (for example, x86_64) and then click the **Arm64.Desktop.WPF button**: -![fig11](figures/11.png) +![fig11](figures/11.webp) When the application launches, type the execution count value, e.g., 2000 and click the "Run calculations" button. The application will display the column with the label denoting the computation time. Repeat this for other execution counts: 4000, 6000, 8000 and 10000. Your results should look like the following figure: diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/01.png b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/01.png deleted file mode 100644 index eb3d7362bd..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/01.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/01.webp b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/01.webp new file mode 100644 index 0000000000..21eaf6bfa8 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/01.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/02.png b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/02.png deleted file mode 100644 index 9f54dff746..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/02.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/02.webp b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/02.webp new file mode 100644 index 0000000000..7cedd22c58 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/02.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/07.png b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/07.png deleted file mode 100644 index 5839ec9fb6..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/07.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/07.webp b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/07.webp new file mode 100644 index 0000000000..b8085c743c Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/figures/07.webp differ diff --git a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/how-to-1.md b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/how-to-1.md index 8dcfc92c55..4b30d9cb51 100644 --- a/content/learning-paths/laptops-and-desktops/win_xamarin_forms/how-to-1.md +++ b/content/learning-paths/laptops-and-desktops/win_xamarin_forms/how-to-1.md @@ -21,11 +21,11 @@ Before you begin the implementation, install Visual Studio 2022 with the followi ## Create the project Open Visual Studio and click 'Create a new project'. -![fig1](figures/01.png) +![fig1](figures/01.webp) In the next window, search for the 'Mobile App (Xamarin.Forms)' template. -![fig2](figures/02.png) +![fig2](figures/02.webp) This will open the 'Configure your new project' view, in which you should configure the project as follows (refer to the figure below): @@ -69,7 +69,7 @@ Before starting the UI design, install the `Syncfusion.SfChart.WPF` NuGet packag 2. In Solution Explorer, right-click on 'Dependencies' under the Arm64.MobileApp.XamarinForms project. From the context menu, select 'Manage NuGet Packages...' to open the NuGet Package Manager. 3. In the NuGet Package Manager, search for 'Sf chart.' Then select 'Syncfusion.SfChart.WPF' from the results and click the 'Install' button. Refer to the figure below for guidance: -![fig7](figures/07.png) +![fig7](figures/07.webp) 4. A 'Preview Changes' window will appear. Click the 'Apply' button to confirm and proceed with the changes to the solution. The installation of the NuGet package will complete. 5. Finally, in the 'License Acceptance' window that appears, click the 'I Accept' button to agree to the terms and complete the installation process. diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/SPE-settings.png b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/SPE-settings.png deleted file mode 100644 index be7e91b010..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/SPE-settings.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/SPE-settings.webp b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/SPE-settings.webp new file mode 100644 index 0000000000..d6a898e870 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/SPE-settings.webp differ diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/counting-feature.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/counting-feature.md index 10a21a5e31..020a418e0a 100644 --- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/counting-feature.md +++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/counting-feature.md @@ -50,4 +50,4 @@ You can click the `Open in WPA` button to open the collected counts in the Windo A screenshot of WPA is below: -![Open in WPA #center](./open-in-wpa.png) +![Open in WPA #center](./open-in-wpa.webp) diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/open-in-wpa.png b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/open-in-wpa.png deleted file mode 100644 index 1066c5cfef..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/open-in-wpa.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/open-in-wpa.webp b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/open-in-wpa.webp new file mode 100644 index 0000000000..ddbdbb8a8b Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/open-in-wpa.webp differ diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-feature.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-feature.md index d6c8b87469..f624462a9e 100644 --- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-feature.md +++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-feature.md @@ -29,7 +29,7 @@ As you configure the settings, the dialog provides a real-time preview of the Wi When you are satisfied with your configurations, click `Save`. -![Sampling overview #center](./sampling-overview.png) +![Sampling overview #center](./sampling-overview.webp) ## How do I initiate sampling? @@ -51,7 +51,7 @@ Any accessible source file appears as a clickable hyperlink. Selecting it will d Based on the insights gathered from the sampling results, proceed to make any required optimizations or edits to your source code. -![Sampling results #center](./sampling-results.png) +![Sampling results #center](./sampling-results.webp) ## How do I evaluate the result after making changes? diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-overview.png b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-overview.png deleted file mode 100644 index 3e7299c269..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-overview.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-overview.webp b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-overview.webp new file mode 100644 index 0000000000..8be7d7b6aa Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-overview.webp differ diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-results.png b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-results.png deleted file mode 100644 index 08047d4ded..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-results.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-results.webp b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-results.webp new file mode 100644 index 0000000000..efc6389046 Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/sampling-results.webp differ diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-feature.md b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-feature.md index f7a50845d0..20bb716f37 100644 --- a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-feature.md +++ b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-feature.md @@ -86,7 +86,7 @@ To do so you need to go to go to `Tools -> WindowsPerf Host Data` scroll to the 6. **Saving Your Settings**: - Once you are satisfied with your configurations, click `save`. -![SPE settings #center](./SPE-settings.png) +![SPE settings #center](./SPE-settings.webp) ## Initiating the Sampling Process @@ -106,4 +106,4 @@ To do so you need to go to go to `Tools -> WindowsPerf Host Data` scroll to the All the SPE results will be prefixed by your SPE device version name (e.g. `FEAT_SPEv1p1`) {{% /notice %}} -![SPE results #center](./spe-results.png) +![SPE results #center](./spe-results.webp) diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-results.png b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-results.png deleted file mode 100644 index 89a4818255..0000000000 Binary files a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-results.png and /dev/null differ diff --git a/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-results.webp b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-results.webp new file mode 100644 index 0000000000..b88dcf029f Binary files /dev/null and b/content/learning-paths/laptops-and-desktops/windowsperf-vs-extension/spe-results.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_afrc.png b/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_afrc.png deleted file mode 100644 index f5205cff03..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_afrc.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_afrc.webp b/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_afrc.webp new file mode 100644 index 0000000000..0254b013ee Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_afrc.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_default.png b/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_default.png deleted file mode 100644 index d5b6412839..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_default.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_default.webp b/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_default.webp new file mode 100644 index 0000000000..d59ec67c07 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/afrc/images/sponza_default.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/afrc/p1-intro.md b/content/learning-paths/mobile-graphics-and-gaming/afrc/p1-intro.md index 54a28bd1a7..c334b91d9b 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/afrc/p1-intro.md +++ b/content/learning-paths/mobile-graphics-and-gaming/afrc/p1-intro.md @@ -28,7 +28,7 @@ So much so that, to the naked eye, the compressed and uncompressed images look t   | Uncompressed | AFRC 2BPC (bits per component) | | ----------- | ----------- | -| ![Uncompressed](images/sponza_default.png) | ![AFRC 2BPC](images/sponza_afrc.png) | +| ![Uncompressed](images/sponza_default.webp) | ![AFRC 2BPC](images/sponza_afrc.webp) | More information can be found in [this blog on Arm® Immortalis™](https://community.arm.com/arm-community-blogs/b/graphics-gaming-and-vr-blog/posts/arm-immortalis-g715-developer-overview) and in [this Vulkan sample](https://github.com/KhronosGroup/Vulkan-Samples/blob/main/samples/performance/image_compression_control/README.adoc). diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/4-run.md b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/4-run.md index 277a8a2f26..6dfd397888 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/4-run.md +++ b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/4-run.md @@ -28,8 +28,8 @@ cd $HOME/ai-camera-pipelines bin/cinematic_mode resources/test_input.png test_output.png resources/depth_and_saliency_v3_2_assortedv2_w_augment_mobilenetv2_int8_only_ptq.tflite ``` -![example image alt-text#center](test_input2.png "Input image") -![example image alt-text#center](test_output2.png "Image with blur applied") +![example image alt-text#center](test_input2.webp "Input image") +![example image alt-text#center](test_output2.webp "Image with blur applied") ## Low-Light Enhancement @@ -40,8 +40,8 @@ cd $HOME/ai-camera-pipelines bin/low_light_image_enhancement resources/test_input.png test_output2_lime.png resources/HDRNetLIME_lr_coeffs_v1_1_0_mixed_low_light_perceptual_l1_loss_float32.tflite ``` -![example image alt-text#center](test_input2.png "Input image") -![example image alt-text#center](test_output2_lime.png "Image with low-light enhancement applied") +![example image alt-text#center](test_input2.webp "Input image") +![example image alt-text#center](test_output2_lime.webp "Image with low-light enhancement applied") ### Neural denoising diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_input2.png b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_input2.png deleted file mode 100644 index 42d323adc4..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_input2.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_input2.webp b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_input2.webp new file mode 100644 index 0000000000..a95ff62e0c Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_input2.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2.png b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2.png deleted file mode 100644 index d834ce721d..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2.webp b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2.webp new file mode 100644 index 0000000000..9035f8a595 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2_lime.png b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2_lime.png deleted file mode 100644 index 18042cafea..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2_lime.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2_lime.webp b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2_lime.webp new file mode 100644 index 0000000000..3ea5a52430 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/ai-camera-pipelines/test_output2_lime.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/images/fa_analysis.png b/content/learning-paths/mobile-graphics-and-gaming/ams/images/fa_analysis.png deleted file mode 100644 index f0552e53a5..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/ams/images/fa_analysis.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/images/fa_analysis.webp b/content/learning-paths/mobile-graphics-and-gaming/ams/images/fa_analysis.webp new file mode 100644 index 0000000000..d707713423 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/ams/images/fa_analysis.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/images/stop_capture.png b/content/learning-paths/mobile-graphics-and-gaming/ams/images/stop_capture.png deleted file mode 100644 index c493f22fe5..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/ams/images/stop_capture.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/images/stop_capture.webp b/content/learning-paths/mobile-graphics-and-gaming/ams/images/stop_capture.webp new file mode 100644 index 0000000000..712061521a Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/ams/images/stop_capture.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/ams/streamline.md b/content/learning-paths/mobile-graphics-and-gaming/ams/streamline.md index 790d97a0fe..e6de786905 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/ams/streamline.md +++ b/content/learning-paths/mobile-graphics-and-gaming/ams/streamline.md @@ -36,7 +36,7 @@ Optionally, you can set a preferred location to store your captures using `Windo 1. The application starts automatically on the device. Interact with the application as desired for the profiling run you wish to do. 1. When you have collected enough data, click `Stop capture`. -![Stop capture #center](images/stop_capture.png "Stop Capture") +![Stop capture #center](images/stop_capture.webp "Stop Capture") Streamline will stop capturing data, remove the daemon, and process the captured data. diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt.png b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt.png deleted file mode 100644 index d95c51389f..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt.webp b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt.webp new file mode 100644 index 0000000000..c92c9aa72e Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt_redundant_rps.png b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt_redundant_rps.png deleted file mode 100644 index f642472ef1..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt_redundant_rps.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt_redundant_rps.webp b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt_redundant_rps.webp new file mode 100644 index 0000000000..edc40b4d4f Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/Render_graph_egypt_redundant_rps.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/analyze.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/analyze.md index e7f383535a..100ca052ff 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/analyze.md +++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/analyze.md @@ -7,7 +7,7 @@ layout: learningpathall --- When the analysis completes, you will see Frame Advisor's `Analysis` screen. -![Frame Advisor's Analysis screen alt-text#center](fa_analysis.png "Figure 1. Frame Advisor's Analysis screen") +![Frame Advisor's Analysis screen alt-text#center](fa_analysis.webp "Figure 1. Frame Advisor's Analysis screen") 1. Captured frames are listed in the Frame hierarchy view. Here, we can see that this frame draws over 657,000 primitives using 426 draw calls within 16 render passes. diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/analyze_render_graph.md b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/analyze_render_graph.md index 2a277009d1..f722a31fb7 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/analyze_render_graph.md +++ b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/analyze_render_graph.md @@ -19,6 +19,6 @@ Render passes flow from left to right. The render pass that outputs to the swapc 1. In this example, we can see that some render passes have no consumers at all and that they do not contribute to the final rendered output. - ![Redundant render passes in Frame Advisor's Render Graph alt-text#center](Render_graph_egypt_redundant_rps.png "Figure 4. Redundant render passes") + ![Redundant render passes in Frame Advisor's Render Graph alt-text#center](Render_graph_egypt_redundant_rps.webp "Figure 4. Redundant render passes") These render passes could therefore be removed, without affecting the output, saving processing power and bandwidth. diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/fa_analysis.png b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/fa_analysis.png deleted file mode 100644 index f0552e53a5..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/fa_analysis.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/fa_analysis.webp b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/fa_analysis.webp new file mode 100644 index 0000000000..d707713423 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/analyze_a_frame_with_frame_advisor/fa_analysis.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/03.png b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/03.png deleted file mode 100644 index 80e41973f2..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/03.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/03.webp b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/03.webp new file mode 100644 index 0000000000..7cdd71981a Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/03.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/04.png b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/04.png deleted file mode 100644 index d098da4e1a..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/04.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/04.webp b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/04.webp new file mode 100644 index 0000000000..5dff926a31 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/04.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/05.png b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/05.png deleted file mode 100644 index 8fa7609f69..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/05.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/05.webp b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/05.webp new file mode 100644 index 0000000000..020b398ad0 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/05.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/07.png b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/07.png deleted file mode 100644 index 5993f29b22..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/07.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/07.webp b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/07.webp new file mode 100644 index 0000000000..25dbf409ea Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/07.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/08.png b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/08.png deleted file mode 100644 index a01e883efc..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/08.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/08.webp b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/08.webp new file mode 100644 index 0000000000..4e9e32a790 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/android_halide/Figures/08.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/android.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/android.md index 58ba9f918b..ba6eb63972 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/android.md +++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/android.md @@ -41,14 +41,14 @@ Before integrating Halide into your Android application, ensure you have the nec ### Creating the project 1. Open Android Studio. 2. Select New Project > Native C++. -![img4](Figures/04.png) +![img4](Figures/04.webp) ### Configure the project 1. Set the project Name to Arm.Halide.AndroidDemo. 2. Choose Kotlin as the language. 3. Set Minimum SDK to API 24. 4. Click Next. -![img5](Figures/05.png) +![img5](Figures/05.webp) 5. Select C++17 from the C++ Standard dropdown list. ![img6](Figures/06.png) 6. Click Finish. @@ -202,7 +202,7 @@ Now, you'll define the application's User Interface, consisting of two buttons a Now you can run the app to view the UI: -![img7](Figures/07.png) +![img7](Figures/07.webp) ## Processing You will now implement the image processing code. First, pick up an image you want to process. Here we use the camera man. Then, under the Arm.Halide.AndroidDemo/src/main create assets folder, and save the image under that folder as img.png. @@ -351,7 +351,7 @@ Note that performing the grayscale conversion in Halide allows us to exploit ope The JNI integration occurs through an external method declaration, blurThresholdImage, loaded via the companion object at app startup. The native library (armhalideandroiddemo) containing this function is compiled separately and integrated into the application (native-lib.cpp). You will now need to create blurThresholdImage function. To do so, in Android Studio put the cursor above blurThresholdImage function, and then click Create JNI function for blurThresholdImage: -![img8](Figures/08.png) +![img8](Figures/08.webp) This will generate a new function in the native-lib.cpp: ```cpp diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_halide/processing-workflow.md b/content/learning-paths/mobile-graphics-and-gaming/android_halide/processing-workflow.md index 9536a75bfc..13d5546327 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/android_halide/processing-workflow.md +++ b/content/learning-paths/mobile-graphics-and-gaming/android_halide/processing-workflow.md @@ -207,7 +207,7 @@ Run the executable: ``` The output should look as in the figure below: -![img3](Figures/03.png) +![img3](Figures/03.webp) ## Parallelization and Tiling In this section, you will explore two complementary scheduling optimizations provided by Halide: Parallelization and Tiling. Both techniques help enhance performance but achieve it through different mechanisms—parallelization leverages multiple CPU cores, whereas tiling improves cache efficiency by optimizing data locality. diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/dot_product_neon.md b/content/learning-paths/mobile-graphics-and-gaming/android_neon/dot_product_neon.md index 0fe58bfab5..0bfa6a0d5e 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/android_neon/dot_product_neon.md +++ b/content/learning-paths/mobile-graphics-and-gaming/android_neon/dot_product_neon.md @@ -234,7 +234,7 @@ You have now added all the code to this file and can proceed to building the app By default, the Build Variant is `debug` for this project. Select `Build-> Select Build Variant...` and change the Active Build Variant to `release` as shown in the image below: -![img7](neon7.png) +![img7](neon7.webp) Now build the code. Select `Build-> Make Project`. Once the build completes, you should see output similar to what is shown below: diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/hello_neon.md b/content/learning-paths/mobile-graphics-and-gaming/android_neon/hello_neon.md index 44f35a7a46..f9e0e72a3e 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/android_neon/hello_neon.md +++ b/content/learning-paths/mobile-graphics-and-gaming/android_neon/hello_neon.md @@ -149,7 +149,7 @@ Build Analyzer results available 10. Run this application on an 64-bit Arm powered smartphone running Android. We ran the app on a Google Pixel 7 phone using a USB cable connected to the development machine running Android Studio. You can also pair your phone over Wi-Fi. Connect your device and select `Tools->Device Manager`. Select the `Physical` tab and your connected phone should show up on the list of devices as shown in the image below: -![img6](neon6.png) +![img6](neon6.webp) Then select `Run->Run app` or press `Shift+F10` to run the app on the connected phone. diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon6.png b/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon6.png deleted file mode 100644 index 3970129006..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon6.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon6.webp b/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon6.webp new file mode 100644 index 0000000000..178e7f7320 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon6.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon7.png b/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon7.png deleted file mode 100644 index cdfc380dae..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon7.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon7.webp b/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon7.webp new file mode 100644 index 0000000000..1a66dc0cfe Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/android_neon/neon7.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/create-project.md b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/create-project.md index 53f37a1b4e..5e2909bbd2 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/create-project.md +++ b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_camera/create-project.md @@ -17,7 +17,7 @@ Follow these steps to create a project and add OpenCV: 2. In the New Project window, select **Empty Views Activity**: -![img2](figures/02.png) +![img2](figures/02.webp) 3. Configure the project as follows (see figure below): - Name: **Arm64.OpenCV.Camera**. @@ -27,13 +27,13 @@ Follow these steps to create a project and add OpenCV: - Minimum SDK: **API 24**. - Build configuration language: **Kotlin DSL**. -![img3](figures/03.png) +![img3](figures/03.webp) 4. Click the **Finish** button. The project will be ready in a few moments, and Android Studio should appear as shown below: -![img4](figures/04.png) +![img4](figures/04.webp) ## Add OpenCV support To add OpenCV for Arm64, open the *build.gradle.ts (Module: app)*, and add the following line under the dependencies: @@ -44,7 +44,7 @@ implementation("org.opencv:opencv:4.10.0") Then, click the **Sync Now** link in the top pane that appears: -![img5](figures/05.png) +![img5](figures/05.webp) From here on, you can use OpenCV in your application. @@ -56,7 +56,7 @@ To initialize OpenCV and check the initialization status, follow these steps: 2. Click the highlighted icon in the top-right corner to switch to the XML view. -![img6](figures/06.png) +![img6](figures/06.webp) 3. Modify the `activity_main.xml` file as shown below: @@ -129,6 +129,6 @@ Finally, you call updateControls(), which updates the UI based on whether OpenCV ## Launch the application You can now launch the application in the simulator. To do so, use the top menu, where you click Run -> Run ‘app’. The application should start, and you should see the “OpenCV initialized” text as shown below: -![img7](figures/07.png) +![img7](figures/07.webp) You have successfully added OpenCV to the Android application. 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b/content/learning-paths/mobile-graphics-and-gaming/android_opencv_facedetection/create-project.md @@ -20,7 +20,7 @@ Follow these steps to create a project and add OpenCV: 2. In the New Project window, select **Empty Views Activity**: -![img2](figures/02.png) +![img2](figures/02.webp) 3. Configure the project as follows (see figure below): - Name: **Arm64.OpenCV.FaceDetection**. @@ -30,7 +30,7 @@ Follow these steps to create a project and add OpenCV: - Minimum SDK: **API 24**. - Build configuration language: **Kotlin DSL**. -![img3](figures/03.png) +![img3](figures/03.webp) 4. Click the **Finish** button. @@ -51,7 +51,7 @@ You will now initialize OpenCV and prepare the application to stream frames from 1. Under the Project in the left window, double-click *app/res/layout/activity_main.xml*. This opens the view designer. 2. Click the highlighted icon in the top right corner to switch to the XML view. -![img4](figures/04.png) +![img4](figures/04.webp) 3. Modify the *activity_main.xml* as shown below: @@ -218,7 +218,7 @@ The above code also declares CameraBridgeViewBase.CvCameraViewListener2 methods: ## Launch the application You have just prepared the application to stream camera frames. You can now launch the application in the emulator. To do so, click the green 'run' button as shown here: -![click run](figures/05.png) +![click run](figures/05.webp) The application should start, and you should see the app running in an emulator. 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Open Android Studio on your development machine, and click the **+ New Project** icon. 2. In the **New Project** window, select **Empty Views Activity**: -![img1 alt-text#center](figures/01.png "Figure 1: Creating a new project.") +![img1 alt-text#center](figures/01.webp "Figure 1: Creating a new project.") 3. Figure 2 shows you how to configure the project: - Name: **Arm64.KleidiCV.Demo**. @@ -26,7 +26,7 @@ Start by creating a new project, and configuring it: - Minimum SDK: **API 24**. - Build configuration language: **Kotlin DSL**. -![img2 alt-text#center](figures/02.png "Figure 2: Configuring your new project.") +![img2 alt-text#center](figures/02.webp "Figure 2: Configuring your new project.") 4. 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Create a project using the **Native C++ Project** option in the Phone and Tablet section and click Next, as shown below: -![img2](figures/02.png) +![img2](figures/02.webp) 3. Set the application name to **ARM SVE2**, select `Kotlin` as the language, leave the Minimum SDK selection as `API 24: Android 7.0 (Nougat)`, ensure that the build configuration is set to use Kotlin DSL, and click Next, as shown below: -![img3](figures/03.png) +![img3](figures/03.webp) 4. Leave the `Toolchain Default` selection for C++ Standard and then click Finish, as shown below: @@ -44,7 +44,7 @@ The process of creating and configuring this project is similar to that used for You have just created the Android application project, which comprises a `MainActivity`. The `MainActivity` contains a single TextView control, which displays the Hello from C++ string as shown in the figure below. This string is retrieved by invoking a `stringFromJNI` function from the C++ library, implemented under the `app/cpp/native-lib.cpp` file and built using the NDK and `CMakeLists.txt`. -![img5](figures/05.png) +![img5](figures/05.webp) SVE2 support is not enabled by default. To enable it, open the `CMakeLists.txt` (under app/cpp), and modify it by adding the following target_compile_options: diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/part2.md b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/part2.md index c2918b3313..fe414abd82 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/android_sve2/part2.md +++ b/content/learning-paths/mobile-graphics-and-gaming/android_sve2/part2.md @@ -201,7 +201,7 @@ This method generates three pseudo-random vectors of floats and a fourth vector ## Build and Run the code To test the code described above, you will need an Armv8-powered device (for this example, we used a Samsung Galaxy S22). Follow [these steps](https://developer.android.com/studio/run/device) to prepare your device for development. Once your phone is configured, it will appear in the drop-down list at the top of Android Studio. -![img7](figures/07.png) +![img7](figures/07.webp) Select your phone and then click the play icon. This action will build and deploy the application to your device. Next, enter the vector length, for example, 10,000, and click the **Run calculations** button. Repeat the process after selecting the "Use SVE2?" checkbox. You should observe the following results: diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/1-webgpu-fundamentals.md b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/1-webgpu-fundamentals.md index 4ffcff0a43..df79425dfe 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/1-webgpu-fundamentals.md +++ b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/1-webgpu-fundamentals.md @@ -24,7 +24,7 @@ WebGPU is available through web browsers using the webgpu.h header file. The high-level view of WebGPU is shown below in Figure 1: -![Figure 1: WebGPU high level view #center](images/webgpu_highlevel.png "Figure 1: WebGPU High-Level View") +![Figure 1: WebGPU high level view #center](images/webgpu_highlevel.webp "Figure 1: WebGPU High-Level View") ## What are the benefits of WebGPU? diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/images/webgpu_highlevel.png b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/images/webgpu_highlevel.png deleted file mode 100644 index f3248888a3..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/images/webgpu_highlevel.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/images/webgpu_highlevel.webp b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/images/webgpu_highlevel.webp new file mode 100644 index 0000000000..1e5cccb0d5 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/android_webgpu_dawn/images/webgpu_highlevel.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/1-ray-tracing.md b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/1-ray-tracing.md index d9fd770f06..3d84ba160c 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/1-ray-tracing.md +++ b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/1-ray-tracing.md @@ -15,9 +15,9 @@ The demo is named **Steel Arms**. Created with Unreal Engine 5.3, Steel Arms bri The following screenshots are from scenes in **Steel Arms** which is powered by Unreal Lumen. Several optimization tips and techniques were used in the development of **Steel Arms** for achieving the best performance with Lumen. This learning path will start with an introduction to ray tracing and then cover the best practices for hardware ray tracing in Lumen. -![](images/Garage.png) +![](images/Garage.webp) -![](images/Garage2.png) +![](images/Garage2.webp) ## What is Ray Tracing? diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage.png b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage.png deleted file mode 100644 index ac645476e1..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage.webp b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage.webp new file mode 100644 index 0000000000..eecbb61cd7 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage2.png b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage2.png deleted file mode 100644 index a9aa31fc13..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage2.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage2.webp b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage2.webp new file mode 100644 index 0000000000..68b0201518 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/best-practices-for-hwrt-lumen-performance/images/Garage2.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/images/2/view binding.png b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/images/2/view binding.png deleted file mode 100644 index 92cb171353..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/images/2/view binding.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/images/2/view binding.webp b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/images/2/view binding.webp new file mode 100644 index 0000000000..5778e629a7 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/build-android-selfie-app-using-mediapipe-multimodality/images/2/view binding.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/6-build-android-chat-app.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/6-build-android-chat-app.md index f62301100b..93e4c467bf 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/6-build-android-chat-app.md +++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/6-build-android-chat-app.md @@ -64,13 +64,13 @@ adb push /data/local/tmp/llama/ 1. Use Android Studio's device explorer to look for the model files. -![Device Explorer](device-explorer.png "Figure 1. Android Studio Device Explorer") +![Device Explorer](device-explorer.webp "Figure 1. Android Studio Device Explorer") 2. Upload the files. If the files are not on the device, use the device explorer to copy them. -![Files Upload](device-explorer-upload.png "Figure 2. Android Studio upload files using Device Explorer") +![Files Upload](device-explorer-upload.webp "Figure 2. Android Studio upload files using Device Explorer") ## Build the Android Package Kit diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer-upload.png b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer-upload.png deleted file mode 100644 index 6302af6466..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer-upload.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer-upload.webp b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer-upload.webp new file mode 100644 index 0000000000..7db94cdf2c Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer-upload.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer.png b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer.png deleted file mode 100644 index dccf23221e..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer.webp b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer.webp new file mode 100644 index 0000000000..af01dea112 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/device-explorer.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/02-ue.md b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/02-ue.md index 3801d49c43..7bb5d299cf 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/02-ue.md +++ b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/02-ue.md @@ -64,7 +64,7 @@ After reopening the Unreal Engine project, ensure that the Arm ASR plugin is ena 1. Go to **Edit > Plugins**, search for **Arm ASR**, and make sure that it is checked. - ![Verify the plugin](images/verify_plugin.png "Verify the plugin") + ![Verify the plugin](images/verify_plugin.webp "Verify the plugin") ![Plugin screen](images/plugin_screen.png "Plugin screen") diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/arm_asr_view.png b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/arm_asr_view.png deleted file mode 100644 index ffe4238a09..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/arm_asr_view.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/arm_asr_view.webp b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/arm_asr_view.webp new file mode 100644 index 0000000000..8eeb583ea6 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/arm_asr_view.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/third_person_pack_opening_screen.png b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/third_person_pack_opening_screen.png deleted file mode 100644 index ab4564614d..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/third_person_pack_opening_screen.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/third_person_pack_opening_screen.webp b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/third_person_pack_opening_screen.webp new file mode 100644 index 0000000000..67e500192d Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/third_person_pack_opening_screen.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/verify_plugin.png b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/verify_plugin.png deleted file mode 100644 index abcca0cab6..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/verify_plugin.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/verify_plugin.webp b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/verify_plugin.webp new file mode 100644 index 0000000000..25e2207814 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/get-started-with-arm-asr/images/verify_plugin.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/1-what-is-lumen.md b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/1-what-is-lumen.md index 93a8006218..6ebb075850 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/1-what-is-lumen.md +++ b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/1-what-is-lumen.md @@ -12,8 +12,8 @@ Lumen introduces a new ray-tracing based solution that allows developers to rend You can see the improvements in the rendering quality by comparing the following two images of the same scene. The first image uses only direct lighting where details in the areas beyond the direct lighting range (such as the background) are not visible. In contrast, the second image utilizes Lumen lighting, incorporating both direct and indirect lighting. Now, you can discern many more details in the background that were previously hidden, as Lumen takes light bounces into account. -![](images/no_lumen.png "Figure 1. The scene without Lumen has only dirct lighting.") +![](images/no_lumen.webp "Figure 1. The scene without Lumen has only dirct lighting.") -![](images/lumen.png "Figure 2. The scene with Lumen has both direct and indirect lighting.") +![](images/lumen.webp "Figure 2. The scene with Lumen has both direct and indirect lighting.") Lumen supports both software and hardware ray tracing. Software ray tracing uses a simplified Lumen scene to replace actual geometries in the scene. In contrast, hardware ray tracing uses actual geometries to trace rays, resulting in better lighting quality. diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/lumen.png b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/lumen.png deleted file mode 100644 index fc2112b3e9..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/lumen.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/lumen.webp b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/lumen.webp new file mode 100644 index 0000000000..6b7f3c500a Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/lumen.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/no_lumen.png b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/no_lumen.png deleted file mode 100644 index 3953066c8d..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/no_lumen.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/no_lumen.webp b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/no_lumen.webp new file mode 100644 index 0000000000..14f3cefff0 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/how-to-enable-hwrt-on-lumen-for-android-devices/images/no_lumen.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/2-emulation-layer.md b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/2-emulation-layer.md index 3ea024ac64..731c7886af 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/2-emulation-layer.md +++ b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/2-emulation-layer.md @@ -47,7 +47,7 @@ Keep the Vulkan Configurator running to enable the emulation layers during engin 1. Open Unreal Engine and create a new **Third Person** template project using the **C++** option. -![Unreal Engine project selection screen showing C++ Third Person template#center](./images/unreal_startup.png "Figure 3: Create a new C++ project in Unreal Engine.") +![Unreal Engine project selection screen showing C++ Third Person template#center](./images/unreal_startup.webp "Figure 3: Create a new C++ project in Unreal Engine.") 2. Open the project in **Visual Studio**. Build it from source through **Build** > **Build Solution** or with `Ctrl+Shift+B`. diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/images/unreal_startup.png b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/images/unreal_startup.png deleted file mode 100644 index 6d2acfdce9..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/images/unreal_startup.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/images/unreal_startup.webp b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/images/unreal_startup.webp new file mode 100644 index 0000000000..6e21c0ab81 Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/nss-unreal/images/unreal_startup.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/2-preparation.md b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/2-preparation.md index 1bf6bb834a..1e7d2bf7a0 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/2-preparation.md +++ b/content/learning-paths/mobile-graphics-and-gaming/profiling-unity-apps-on-android/2-preparation.md @@ -134,4 +134,4 @@ You will now deploy the sample to your Android device. 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d78f6e987c..71fb9f037a 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/rt06_reflections.md +++ b/content/learning-paths/mobile-graphics-and-gaming/ray_tracing/rt06_reflections.md @@ -28,14 +28,14 @@ As you can see, reflections are ideal for ray tracing. Other traditional techniq It is common to find corner cases and bugs on Screen Space Reflection at the same time. This technique is more difficult to implement, requiring more magic numbers. The main limitation of Screen Space Reflections is that they depend on the G-buffer information, so occluded objects and objects outside the view frustum cannot be reflected, causing visible artifacts that are common in current games. {{< tabpane >}} - {{< tab header="Example 1: SSR" title="Example 1: Screen space reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_1_ssr.png">}}{{< /tab >}} - {{< tab header="Example 1: RT" title="Example 1: Ray tracing reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_1_rt.png">}}{{< /tab >}} - {{< tab header="Example 2: SSR" title="Example 2: Screen space reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_2_ssr.png">}}{{< /tab >}} - {{< tab header="Example 2: RT" title="Example 2: Ray tracing reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_2_rt.png">}}{{< /tab >}} - {{< tab header="Example 3: SSR" title="Example 3: Screen space reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_3_ssr.png">}}{{< /tab >}} - {{< tab header="Example 3: RT" title="Example 3: Ray tracing reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_3_rt.png">}}{{< /tab >}} - {{< tab header="Example 4: SSR" title="Example 4: Screen space reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_4_ssr.png">}}{{< /tab >}} - {{< tab header="Example 4: RT" title="Example 4: Ray tracing reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_4_rt.png">}}{{< /tab >}} + {{< tab header="Example 1: SSR" title="Example 1: Screen space reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_1_ssr.webp">}}{{< /tab >}} + {{< tab header="Example 1: RT" title="Example 1: Ray tracing reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_1_rt.webp">}}{{< /tab >}} + {{< tab header="Example 2: SSR" title="Example 2: Screen space reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_2_ssr.webp">}}{{< /tab >}} + {{< tab header="Example 2: RT" title="Example 2: Ray tracing reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_2_rt.webp">}}{{< /tab >}} + {{< tab header="Example 3: SSR" title="Example 3: Screen space reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_3_ssr.webp">}}{{< /tab >}} + {{< tab header="Example 3: RT" title="Example 3: Ray tracing reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_3_rt.webp">}}{{< /tab >}} + {{< tab header="Example 4: SSR" title="Example 4: Screen space reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_4_ssr.webp">}}{{< /tab >}} + {{< tab header="Example 4: RT" title="Example 4: Ray tracing reflections" img_src="/learning-paths/mobile-graphics-and-gaming/ray_tracing/images/reflections_artifacts_4_rt.webp">}}{{< /tab >}} {{< /tabpane >}} ### Generating Reflection rays from the G-buffer diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/3-converting-model.md b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/3-converting-model.md index e1af045a54..db62d0bb1e 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/3-converting-model.md +++ b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/3-converting-model.md @@ -18,7 +18,7 @@ The open-source model consists of three main submodules. They are described in t |AutoEncoder| Compresses audio waveforms into a latent representation for processing by the DiT model, and decompresses the output back into audio. | -![Model structure#center](./model.png) +![Model structure#center](./model.webp) In this section, you will explore two different conversion routes, to convert the submodules to [LiteRT](https://ai.google.dev/edge/litert) format. Both methods will be run using Python wrapper scripts from the examples repository. diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/model.png b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/model.png deleted file mode 100644 index c4545fdd91..0000000000 Binary files a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/model.png and /dev/null differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/model.webp b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/model.webp new file mode 100644 index 0000000000..2423c04d6e Binary files /dev/null and b/content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/model.webp differ diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/06-the-unity-project.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/06-the-unity-project.md index 6b3683c4cb..fe818d5ac1 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/06-the-unity-project.md +++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/06-the-unity-project.md @@ -32,7 +32,7 @@ From here onwards, you can ignore the "Ready to Play" version of the scene. Inst 1. To get a better look at the scene from the player's perspective, right click on _ML-Player_ and select _Align View to Selected_. This will change the scene view to the viewpoint of your player character at ground level. You will see the inside of the arena and the enemy character in the distance. - ![Align View to ML-Player](images/ml-player-aligned-view.png "Figure 2. Align View to ML-Player") + ![Align View to ML-Player](images/ml-player-aligned-view.webp "Figure 2. Align View to ML-Player") 1. Check that the Package Manager is updated and everything is correctly installed by navigating to _Window->Package Manager_. @@ -174,6 +174,6 @@ ML-Core contains various managers and controllers common to many apps and games. The entire scene is lit from a single light called "Directional Light". The light is marked as "Realtime" so it doesn't include any baked lighting. -![Environment](images/environment-mesh-light.png "Figure 12. Our world consists of a single light and a single mesh for the whole battle arena.") +![Environment](images/environment-mesh-light.webp "Figure 12. Our world consists of a single light and a single mesh for the whole battle arena.") (You could add baked lighting or more realtime lights to make the lighting more interesting, but this is outside the scope of this learning path. If you do experiment with the lighting, remember that adding extra lights and changing some options can affect performance.) diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/08-training-our-ml-agents.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/08-training-our-ml-agents.md index 484f87a0a9..7589fdc271 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/08-training-our-ml-agents.md +++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/08-training-our-ml-agents.md @@ -172,7 +172,7 @@ Go to the _Project_ tab (normally bottom left) and navigate to _Assets->#DevSumm Once the training scene is loaded, it should look similar to this: -![Training Scene](images/training-scene.png "Figure 2. Training Scene") +![Training Scene](images/training-scene.webp "Figure 2. Training Scene") ### Start the training @@ -366,7 +366,7 @@ This section describes how TensorBoard can be used to monitor the training progr 1. Now open up your browser to http://localhost:6006 (the port that was passed to the launch command), which should look similar to this: - ![TensorBoard UI](images/tensorboard-ui.png "Figure 7. TensorBoard UI") + ![TensorBoard UI](images/tensorboard-ui.webp "Figure 7. TensorBoard UI") 1. Some cards to take a note of: diff --git a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/09-optimizations.md b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/09-optimizations.md index 6b372d93c6..e40e674623 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/09-optimizations.md +++ b/content/learning-paths/mobile-graphics-and-gaming/using_unity_machine_learning_agents/09-optimizations.md @@ -9,7 +9,7 @@ layout: learningpathall A few things were done to reduce the time it takes to train the ML agents to a reasonable level. -![Training Scene](images/training-scene.png "Figure 1. The Training Scene (again)") +![Training Scene](images/training-scene.webp "Figure 1. 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a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/azure-cobalt.md b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/azure-cobalt.md index fec4d190bb..6d382b66ca 100644 --- a/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/azure-cobalt.md +++ b/content/learning-paths/servers-and-cloud-computing/azure-cobalt-cicd-aks/azure-cobalt.md @@ -21,7 +21,7 @@ Select `Create Azure Virtual Machine`, and fill in the details such as `Name`, a In the `Size` field, click on `See all sizes` and select the `D-Series v6` family of VMs. Select `D2psv6` from the list and create the VM. -![azure-cobalt-vm #center](_images/azure-cobalt-vm.png) +![azure-cobalt-vm #center](_images/azure-cobalt-vm.webp) {{% notice Note %}} To learn more about Arm-based VMs in Azure, refer to "Getting Started with Microsoft Azure" in [*Get started with Arm-based cloud instances*](/learning-paths/servers-and-cloud-computing/csp/azure). @@ -251,11 +251,11 @@ It has the following main steps: The next step is to trigger the pipeline manually by navigating to `Actions` tab in the GitHub repository. Select `Deploy .NET app`, and click on `Run Workflow`. You can also execute the pipeline by making a commit to the repository. Once the pipeline executes successfully, you will see the Actions output in a format similar to what is shown below: -![github-run #center](_images/github-run.png) +![github-run #center](_images/github-run.webp) You can check your kubernetes cluster and see new application pods deployed on the cluster as shown below: -![kubernetes-deployment #center](_images/kubernetes-deployment.png) +![kubernetes-deployment #center](_images/kubernetes-deployment.webp) diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/images/log.png b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/images/log.png deleted file mode 100644 index cc8757c740..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/images/log.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/images/log.webp b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/images/log.webp new file mode 100644 index 0000000000..310d3fbb2e Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/images/log.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/validation.md b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/validation.md index 05041e9809..7cc648e455 100644 --- a/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/validation.md +++ b/content/learning-paths/servers-and-cloud-computing/buildkite-gcp/validation.md @@ -37,7 +37,7 @@ The main steps you'll see are: - Creating the Buildx builder - Building and pushing the multi-architecture Docker image -![Screenshot of the Buildkite dashboard displaying real-time build logs, showing each pipeline step and its status for monitoring progress alt-text#center](images/log.png "Monitor the build") +![Screenshot of the Buildkite dashboard displaying real-time build logs, showing each pipeline step and its status for monitoring progress alt-text#center](images/log.webp "Monitor the build") ## Verify multi-arch image diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/cca-essentials.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/cca-essentials.md index 4a4b232e2b..ea33d05e96 100644 --- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/cca-essentials.md +++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/cca-essentials.md @@ -32,6 +32,6 @@ The attestation verification service is hosted by Linaro, so it is not necessary Figure 1 demonstrates the software architecture that you will construct to run the attestation example. -![cca-essentials](cca-essentials.png "Figure 1: Software architecture for running attestation.") +![cca-essentials](cca-essentials.webp "Figure 1: Software architecture for running attestation.") You can now proceed to the next section to run the end-to-end attestation example with the software components and architecture as described here. diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/cca-essentials.png b/content/learning-paths/servers-and-cloud-computing/cca-essentials/cca-essentials.png deleted file mode 100644 index 61d0ababea..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/cca-essentials/cca-essentials.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/cca-essentials.webp b/content/learning-paths/servers-and-cloud-computing/cca-essentials/cca-essentials.webp new file mode 100644 index 0000000000..27c1f729a5 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/cca-essentials/cca-essentials.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/cca-trustee.md b/content/learning-paths/servers-and-cloud-computing/cca-trustee/cca-trustee.md index d217544637..182b83c8eb 100644 --- a/content/learning-paths/servers-and-cloud-computing/cca-trustee/cca-trustee.md +++ b/content/learning-paths/servers-and-cloud-computing/cca-trustee/cca-trustee.md @@ -99,6 +99,6 @@ When a resource is requested, the KBS uses the attestation result to decide whet This diagram shows the software architecture you will construct to run the attestation example: -![Software architecture for running attestation alt-text#center](trustee.png "Software architecture for running attestation") +![Software architecture for running attestation alt-text#center](trustee.webp "Software architecture for running attestation") Proceed to the next section to run the end-to-end attestation example using the components and architecture described here. diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/trustee.png b/content/learning-paths/servers-and-cloud-computing/cca-trustee/trustee.png deleted file mode 100644 index 3f8d9dae13..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/cca-trustee/trustee.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/cca-trustee/trustee.webp b/content/learning-paths/servers-and-cloud-computing/cca-trustee/trustee.webp new file mode 100644 index 0000000000..2837ede447 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/cca-trustee/trustee.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/cert-validation-and-algorithm.png b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/cert-validation-and-algorithm.png deleted file mode 100644 index c3c6b54f3f..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/cert-validation-and-algorithm.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/cert-validation-and-algorithm.webp b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/cert-validation-and-algorithm.webp new file mode 100644 index 0000000000..a707eda1e2 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/cert-validation-and-algorithm.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/domain-and-cert.md b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/domain-and-cert.md index fa6515caf8..1b69d6a989 100644 --- a/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/domain-and-cert.md +++ b/content/learning-paths/servers-and-cloud-computing/cca-veraison-aws/domain-and-cert.md @@ -63,7 +63,7 @@ Use the **Add another name to this certificate** button to create the secondary For the validation method, you should use **DNS validation**, which is the recommended default. You can also use the default **RSA 2048** for the certificate key algorithm as shown below. -![Certificate Validation and Key Algorithm](./cert-validation-and-algorithm.png) +![Certificate Validation and Key Algorithm](./cert-validation-and-algorithm.webp) Now click **Request** to request the certificate from the Certificate Manager. diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/codebuild.md b/content/learning-paths/servers-and-cloud-computing/codebuild/codebuild.md index 8ece25d5f7..cdb7c01548 100644 --- a/content/learning-paths/servers-and-cloud-computing/codebuild/codebuild.md +++ b/content/learning-paths/servers-and-cloud-computing/codebuild/codebuild.md @@ -72,7 +72,7 @@ The Source section is where the GitHub project information is entered. To connec After the GitHub connection is made enter the GitHub URL for the project. Enter the link to the `hello-world` project in GitHub. -![alt-text #center](images/codebuild_source.png) +![alt-text #center](images/codebuild_source.webp) The Environment section contains the information about the Docker image to use for the build. This is NOT the Docker image you are building, it is the image used to build the image. CodeBuild runs a container to build the image and you need to make sure all of the tools needed are contained in the Docker image used to run the build. This is sometimes referred to as Docker-in-Docker. diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/images/codebuild_source.png b/content/learning-paths/servers-and-cloud-computing/codebuild/images/codebuild_source.png deleted file mode 100644 index ef58f356b4..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/codebuild/images/codebuild_source.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/codebuild/images/codebuild_source.webp b/content/learning-paths/servers-and-cloud-computing/codebuild/images/codebuild_source.webp new file mode 100644 index 0000000000..6f75af199b Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/codebuild/images/codebuild_source.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/csp/alibaba.md b/content/learning-paths/servers-and-cloud-computing/csp/alibaba.md index 80828d44f0..8d16b7f940 100644 --- a/content/learning-paths/servers-and-cloud-computing/csp/alibaba.md +++ b/content/learning-paths/servers-and-cloud-computing/csp/alibaba.md @@ -34,25 +34,25 @@ The easiest way to launch your instance is via the [ECS Console](https://ecs.con Navigate to `Elastic Compute Service` by search or the menu. -![alibaba #center](images/588897d0-6c77-ee64-c7aa-235cbf460426.png "Navigate to the ECS Dashboard") +![alibaba #center](images/588897d0-6c77-ee64-c7aa-235cbf460426.webp "Navigate to the ECS Dashboard") Use the `Create ECS Instance` button to get started. Select `Custom Launch` configuration. -![alibaba #center](images/bf7f23d0-2afb-6a84-60aa-78dc1c27be39.png "Create Instance") +![alibaba #center](images/bf7f23d0-2afb-6a84-60aa-78dc1c27be39.webp "Create Instance") -![alibaba #center](images/2bd139ad-a28b-8e82-8dae-c0d7e54a4ebc.png "Custom Launch") +![alibaba #center](images/2bd139ad-a28b-8e82-8dae-c0d7e54a4ebc.webp "Custom Launch") ### Select a Billing Method `Subscription`, `Pay-as-you-go`, or `Preemptible Instance` options are available. If you are experimenting initially, select `Preemptible Instance` for the lowest cost. You will be prompted for pricing options later. -![alibaba #center](images/99554b4b-5f15-2dce-4de4-c59e26a3a90e.png "Select a Billing Method") +![alibaba #center](images/99554b4b-5f15-2dce-4de4-c59e26a3a90e.webp "Select a Billing Method") ### Select Instance Type Using the `Type-based Selection` tab, set `Architecture` as `ARM`, and `Category` as `General Purpose`, to see the available instance types. If you already know the desired instance type, you can enter this in the filter. -![alibaba #center](images/6ccdd195-7020-b944-4eed-846edf808b2a.png "Select Instance Type") +![alibaba #center](images/6ccdd195-7020-b944-4eed-846edf808b2a.webp "Select Instance Type") ### Select OS image @@ -60,29 +60,29 @@ There are many images available on the [Alibaba Cloud Marketplace](https://marke For now, select `Ubuntu` version (e.g. `20.04 64-bit for ARM`) from the pull-down menu. -![alibaba #center](images/c0329674-b751-5aa5-09b8-f2786a338d8e.png "Select OS Image") +![alibaba #center](images/c0329674-b751-5aa5-09b8-f2786a338d8e.webp "Select OS Image") ### Other settings Other settings, such as storage size are selectable. For now, use the default selection. Click `Next` to move to `Networking`. Proceed with the default selection for `Networking`. Click `Next` to move to `System Configurations`. -![alibaba #center](images/08b7ccdd-003f-b23d-de99-471aa11eb285.png "Configure Storage Options") +![alibaba #center](images/08b7ccdd-003f-b23d-de99-471aa11eb285.webp "Configure Storage Options") -![alibaba #center](images/ee8331cb-caec-a3a1-5ded-f7eb56f3e558.png "Configure Network Options") +![alibaba #center](images/ee8331cb-caec-a3a1-5ded-f7eb56f3e558.webp "Configure Network Options") ### Set a Key Pair and other security settings To be able to access the instance (see later), you must use a [key pair](https://www.alibabacloud.com/help/en/elastic-compute-service/latest/key-pairs). If this is your first time logging in, use the `Create Key Pair` dialog to create your key. The `public-key` will be downloaded to your local machine. When created, select from the pull-down. -![alibaba #center](images/bf231337-39ba-5d61-6bbb-4c9d2c3f8d6c.png "Select or create a key pair") +![alibaba #center](images/bf231337-39ba-5d61-6bbb-4c9d2c3f8d6c.webp "Select or create a key pair") Select `Logon Credentials` and set `Logon Username` to `ecs-user` and an appropriate `password` if desired. -![alibaba #center](images/8be6f704-0b04-1a19-e156-9f4b5e2f6d10.png "Set Username and Password for logon") +![alibaba #center](images/8be6f704-0b04-1a19-e156-9f4b5e2f6d10.webp "Set Username and Password for logon") Other settings such as `Instance Name` and `Description` are free-form for appropriate personal input. Other settings can be left as default. -![alibaba #center](images/fd90820b-5ac5-6fae-831c-1505028e4393.png "Advanced Settings(Optional)") +![alibaba #center](images/fd90820b-5ac5-6fae-831c-1505028e4393.webp "Advanced Settings(Optional)") In the `Preview` stage, click `Create Instance`. 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b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/figures/23.webp new file mode 100644 index 0000000000..f15ea00d44 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/figures/23.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-2.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-2.md index 5a4eac0aa1..6b32749448 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-2.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-2.md @@ -10,16 +10,16 @@ layout: learningpathall ### Virtual Machine Azure Resource Let's start by creating the Virtual Machine. Go to https://portal.azure.com and sign in. You will see this screen which enables you to create Azure resources: -![Azure portal#left](figures/01.png "Figure 1. A fragment of the Azure Portal") +![Azure portal#left](figures/01.webp "Figure 1. A fragment of the Azure Portal") In the search box, type **VM**, and pick the first item on the list (Virtual machines): -![Selecting Virtual Machine resource#left](figures/02.png "Figure 2. Selecting the virtual machine Azure resource") +![Selecting Virtual Machine resource#left](figures/02.webp "Figure 2. Selecting the virtual machine Azure resource") In the Virtual machines, click **+ Create** (in the top left corner), and then select **Azure virtual machine**: -![Creating Virtual Machine#left](figures/03.png "Figure 3. Creating the virtual machine") +![Creating Virtual Machine#left](figures/03.webp "Figure 3. Creating the virtual machine") This takes you to the **Create a Virtual Machine** wizard as shown below: -![Creating Virtual Machine#left](figures/04.png "Figure 4. Virtual machine wizard") +![Creating Virtual Machine#left](figures/04.webp "Figure 4. Virtual machine wizard") ### Virtual machine configuration Configure your VM with the following settings: @@ -40,7 +40,7 @@ Configure your VM with the following settings: {{% notice Note %}} This size might not be available due to quota limits. If this happens, click **Request quota link** (which is located next to the **VM size** name). This will activate a New Quota Request. Type **2** under New limit and click Submit. Wait a few minutes for the quota limits to be updated.{{% /notice %}} At this point, your configuration should look like this example: -![Creating Virtual Machine#left](figures/05.png "Figure 5. Virtual machine wizard (configured)") +![Creating Virtual Machine#left](figures/05.webp "Figure 5. Virtual machine wizard (configured)") Let's now configure other aspects of the Virtual Machine: 1. Administrator account. **Select Password**, and then populate with: @@ -49,11 +49,11 @@ Let's now configure other aspects of the Virtual Machine: * Confirm password: re-type your password 2. Inbound port rules. Keep them default: **SSH(22)** -![Creating Virtual Machine#left](figures/06.png "Figure 6. Administrator account and inbound rules of the virtual machine") +![Creating Virtual Machine#left](figures/06.webp "Figure 6. Administrator account and inbound rules of the virtual machine") Click the **Review + create** button. The Azure Portal will validate your configuration and, if all is correct, you will see a green box saying: **Validation passed**: -![Creating Virtual Machine#left](figures/07.png "Figure 7. A summary of the create virtual machine wizard") +![Creating Virtual Machine#left](figures/07.webp "Figure 7. A summary of the create virtual machine wizard") Click the Create button. The portal will now start the process of creating your VM. You will notice that, apart from the VM itself, the portal creates additional resources for you: * Public IP address (you'll need to use it to connect to the VM and then to see your application running) @@ -63,9 +63,9 @@ Click the Create button. The portal will now start the process of creating your * Virtual disk Wait a few moments for the deployment to be completed: -![Creating Virtual Machine#left](figures/08.png "Figure 8. A confirmation screen") +![Creating Virtual Machine#left](figures/08.webp "Figure 8. A confirmation screen") Finally, click the **Go to resource** button. You'll be redirected to the overview of your newly created virtual machine in Microsoft Azure: -![Creating Virtual Machine#left](figures/09.png "Figure 9. An overview of the virtual machine") +![Creating Virtual Machine#left](figures/09.webp "Figure 9. An overview of the virtual machine") Note your Public IP address (top right part of the overview). In this tutorial, this is **52.149.156.228**. diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-3.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-3.md index 5c193b7a2a..1861508630 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-3.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-3.md @@ -17,10 +17,10 @@ To open the Cloud Shell, click on the Cloud Shell icon located in the top right ![Azure Cloud Shell#left](figures/10.png "Figure 10. A top right corner of the Azure Portal") This will activate a new popup window which appears at the bottom of the Azure Portal: -![Azure Cloud Shell#left](figures/11.png "Figure 11. Mounting a storage") +![Azure Cloud Shell#left](figures/11.webp "Figure 11. Mounting a storage") Click **Create storage**, and wait until the storage account is created. A Cloud Shell terminal should be ready. If it asks you for the interpreter, select **Bash**. The Cloud Shell terminal will look as follows: -![Azure Cloud Shell#left](figures/12.png "Figure 12. Cloud shell") +![Azure Cloud Shell#left](figures/12.webp "Figure 12. Cloud shell") ### Connecting to the Virtual Machine using Azure Cloud Shell You can now use the Cloud Shell to connect to the VM via SSH. To do so, type the Public IP address (remember to use the Public IP address of your virtual machine): @@ -30,6 +30,6 @@ ssh arm@52.149.156.228 ``` This will initiate the connection with the VM. The SSH will display the following message: Are you sure you want to continue connecting (yes/no/[fingerprint])? Type **yes**. Then provide your password to authenticate: -![Azure Cloud Shell#left](figures/13.png "Figure 13. Authentication") +![Azure Cloud Shell#left](figures/13.webp "Figure 13. Authentication") You now have terminal access to the VM. You can use it in the next step to install application dependencies (.NET SDK). diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-4.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-4.md index 003e3d6a8c..53d6c4905b 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-4.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-4.md @@ -61,7 +61,7 @@ dotnet run --urls "http://0.0.0.0:8080" ``` After completing this step, you will see the following output: -![Application#left](figures/14.png "Figure 14. Cloning and running the application") +![Application#left](figures/14.webp "Figure 14. Cloning and running the application") The application is ready and listening for the requests on port 8080. However, the network traffic is blocked on all ports except 22. You will need to configure the Network Security Group to enable the traffic. @@ -69,7 +69,7 @@ The application is ready and listening for the requests on port 8080. However, t To allow traffic on port 8080 for the Virtual Machine **vm-arm64**, proceed as follows: 1. In the search box of the Azure Portal, type **vm-arm64** and select this resource 2. In the **vm-arm64** screen, click the **Networking** tab on the left (it's under **Settings**). You will see the following screen: -![Application#left](figures/15.png "Figure 15. Networking tab of the virtual machine") +![Application#left](figures/15.webp "Figure 15. Networking tab of the virtual machine") In the **Networking** tab of the Virtual Machine, click the **Add inbound port rule** button (it's on the right). This will open a new popup window **Add inbound security rule**: ![Application#left](figures/16.png "Figure 16. Adding inbound port rule") @@ -88,7 +88,7 @@ Ensure the rule is configured as follows: Then, click **Add** and wait for the security rule to be applied. Once this is done, open your web browser and type the public IP address of your VM followed by 8080 port: **52.149.156.228:8080**. You'll see that the application is up and running: -![Application#left](figures/17.png "Figure 17. An application deployed to Azure virtual machine") +![Application#left](figures/17.webp "Figure 17. An application deployed to Azure virtual machine") ## Summary This part of the tutorial has shown you how to create an arm64-powered Virtual Machine in Microsoft Azure, how to connect to that VM using SSH, and how to install the tools required to build and run the .NET web application. Finally, you have learned how to configure a network security group to enable inbound traffic on port 8080. diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-6.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-6.md index 2dfd90e1a0..d2119ad987 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-6.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-6.md @@ -34,7 +34,7 @@ sudo docker build -t people.webapp:v1 . ``` Then, type your password, and you will see the Docker build progress: -![command prompt#left](figures/19.png "Figure 19. Building a Docker image") +![command prompt#left](figures/19.webp "Figure 19. Building a Docker image") {{% notice Note %}} In the above example, we used the -t flag to tag the image (people.webapp:v1). Also, we used . to set the build context to the working directory. diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-7.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-7.md index b450d9f57f..aa044ad6f1 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-7.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-7.md @@ -32,7 +32,7 @@ az acr create -n people -g rg-arm64 --sku Basic ``` The output of the above commands will look as shown below: -![command prompt#left](figures/20.png "Figure 20. Creating the Azure Container Registry") +![command prompt#left](figures/20.webp "Figure 20. Creating the Azure Container Registry") In the next step, we must configure the role assignment so that the current Azure user can push Docker images to the Azure Container Registry. To do so, we use the WSL terminal, in which we type: ```console @@ -46,4 +46,4 @@ az role assignment create --assignee $USER_ID --role AcrPush --scope $ACR_ID {{% notice Note %}} You’ll need to replace with the username you used. {{% /notice %}} The last command's output will look as follows: -![command prompt#left](figures/21.png "Figure 21. Creating the role assignment") +![command prompt#left](figures/21.webp "Figure 21. Creating the role assignment") diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-8.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-8.md index 82db22ed79..fb8605a381 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-8.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part1/how-to-8.md @@ -33,7 +33,7 @@ The last command does not provide any output. To ensure the local image was corr sudo docker images ``` -![command prompt#left](figures/22.png "Figure 22. Tagging a local Docker image") +![command prompt#left](figures/22.webp "Figure 22. Tagging a local Docker image") Finally, we push the image to the remote registry. We proceed as follows: @@ -58,7 +58,7 @@ az acr repository show -n people --repository people.webapp -o table ``` The output from the commands above is shown here: -![command prompt#left](figures/23.png "Figure 23. Pushing a Docker image to the Azure Container Registry") +![command prompt#left](figures/23.webp "Figure 23. Pushing a Docker image to the Azure Container Registry") ## Summary This part of the tutorial taught you how to create the container registry in Microsoft Azure and push the local Docker image to the remote repository. 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procedure will open the Container instances, in which you click the ** At this point, the Create container instance wizard will look as shown below: -![Azure#left](figures/02.png) +![Azure#left](figures/02.webp) Then, continue with the other settings: 1. SKU: **Standard** @@ -41,11 +41,11 @@ Then, continue with the other settings: You should end up with the following configuration: -![Azure#left](figures/03.png) +![Azure#left](figures/03.webp) Now you will need to configure the port mapping. Let's click the **Next: Networking >** button, and under the Networking tab scroll down to Ports. Then, add an 8080 port for the TCP protocol: -![Azure#left](figures/04.png) +![Azure#left](figures/04.webp) This setting enables us to create the port mapping, which will map port 8080 of the container instance to the corresponding point inside the running container. @@ -53,8 +53,8 @@ This setting enables us to create the port mapping, which will map port 8080 of Finally, click the **Review + create** button. This will run the final validation of your configuration, and you will see the following screen: -![Azure#left](figures/05.png) +![Azure#left](figures/05.webp) Click the **Create** button, and wait a few moments for the resource to be created. You will then see the confirmation screen, where you click the **Go to resource** button. -![Azure#left](figures/06.png) +![Azure#left](figures/06.webp) diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/how-to-2.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/how-to-2.md index 984013e096..10659b4065 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/how-to-2.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/how-to-2.md @@ -12,10 +12,10 @@ To see that the containerized application is up and running. To check this, use ### Public IP To get the public IP address, use an Overview tab of the aspnet-sample container instance (see below). The IP is displayed under the IP address (Public). In my case, that is **20.75.152.37**. -![Azure#left](figures/07.png) +![Azure#left](figures/07.webp) Copy the IP address to the clipboard, and then open the web browser and paste this IP address followed by the 8080 port to the web browser address bar, e.g., **20.75.152.37:8080**: -![Azure#left](figures/08.png) +![Azure#left](figures/08.webp) This result shows that the Azure Container Instance uses x64 processor architecture. diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/how-to-3.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/how-to-3.md index 80734edc23..4bf9f32f26 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/how-to-3.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part2/how-to-3.md @@ -47,7 +47,7 @@ You can now create another Azure Container Instance. To do this, go to the Azure 12. OS type: **Linux** 13. 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This will open the Node pools tab. Under the Node pools tab, click the hyperlink **Standard DS2_v2 (change)** under the Node size column of the agent pool: -![AKS#left](figures/04.png) +![AKS#left](figures/04.webp) You will see the Update node pool wizard, in which you do the following: 1. Under the Scale method, select **Manual** @@ -45,21 +45,21 @@ You will see the Update node pool wizard, in which you do the following: 4. This will open Select a VM size screen, where you look for **D2pds_v5 VM** (the arm64-based VM you used in the first part of this learning series). 5. Click the **Select** button -![AKS#left](figures/05.png) +![AKS#left](figures/05.webp) Your Update node pool wizard should look as follows: -![AKS#left](figures/06.png) +![AKS#left](figures/06.webp) Click the **Update** button. This will take you back to the Create Kubernetes cluster wizard, where you click the **Next: Networking >** button. Under the **Networking** tab, scroll down to Network policy and select **None**. Then click the **Next: Integrations >** button. Under integrations, look for the Container registry and select **people** (or your Azure Container Registry, if you used a different name): -![AKS#left](figures/07.png) +![AKS#left](figures/07.webp) Finally, click the **Review + create** button and wait for the validation to complete. Then, click the **Create** button: -![AKS#left](figures/08.png) +![AKS#left](figures/08.webp) Wait a few moments for the cluster to be deployed, and in the confirmation screen, click the **Go to resource** button. diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/how-to-3.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/how-to-3.md index 272c885615..d7e8fed76b 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/how-to-3.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/how-to-3.md @@ -34,6 +34,6 @@ kubectl get nodes The output of this command will look as shown below: -![AKS#left](figures/10.png) +![AKS#left](figures/10.webp) We have one node, which we will now use to deploy the application. diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/how-to-4.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/how-to-4.md index 357e88d1be..79531ddd7c 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/how-to-4.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part3/how-to-4.md @@ -33,7 +33,7 @@ code people_aks.yaml 4. This will open the code/text editor in Azure: -![AKS#left](figures/11.png) +![AKS#left](figures/11.webp) 5. Check line number 17. In our case, it points to the fully qualified container image name, **image: people.azurecr.io/people.webapp:v1**. This includes the container registry login server, image name, and image tag. The login server is the same one we created in part 2 of this learning path series. If you use a different name for either the container registry or the container name, update line 17 correspondingly. Then save the changes to the same file, people_aks.yml (CTRL+S), and close the editor (CTRL+Q). @@ -55,7 +55,7 @@ kubectl get svc This will display the list of services as shown below: -![AKS#left](figures/12.png) +![AKS#left](figures/12.webp) Note the IP address under the **EXTERNAL-IP** column of **people-web-appsvc**. That is the Public IP where you can find your application. In my case, that is **4.156.88.80**. 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b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/how-to-2.md @@ -38,7 +38,7 @@ pulumi new azure-typescript The last command starts the wizard, but you first need to log in to Pulumi (see below). -![Pulumi#left](figures/01.png) +![Pulumi#left](figures/01.webp) You can use the access token or press ENTER to log in using a web browser. @@ -50,4 +50,4 @@ After you log in to Pulumi, configure the project as follows: The wizard will save this configuration and proceed to install the npm packages required by Pulumi: -![Pulumi#left](figures/02.png) +![Pulumi#left](figures/02.webp) diff --git a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/how-to-4.md b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/how-to-4.md index be157a27bd..c6bba4d6b1 100644 --- a/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/how-to-4.md +++ b/content/learning-paths/servers-and-cloud-computing/from-iot-to-the-cloud-part4/how-to-4.md @@ -63,15 +63,15 @@ pulumi up The Pulumi will analyze your index.ts file to get the list of resources to be deployed. Then, it will display the list of resources to be deployed: -![Pulumi#left](figures/03.png) +![Pulumi#left](figures/03.webp) Confirm the deployment by selecting yes and pressing enter. Then, wait a few moments for the resources to be deployed. Once the deployment is done, you will see the public IP address of your Azure Container Instance. Here, that is **4.236.196.236**. -![Pulumi#left](figures/04.png) +![Pulumi#left](figures/04.webp) Copy the IP address and supplement it by 8080. Then, type it in the address bar of your web browser. You will see the application up and running. Compare this result to what we get in part 2 of this series. -![Pulumi#left](figures/05.png) +![Pulumi#left](figures/05.webp) ## Clean up We have just deployed Azure resources using the Infrastructure as Code. The advantage of this approach is that we can now update the cloud infrastructure by modifying the index.ts and re-running the pulumi up command. It will compare the new declaration with the current state of the cloud deployment and update if needed. Also, you can use a single Pulumi command to de-provision all resources declared in the index.ts. To do so, you type: @@ -82,11 +82,11 @@ pulumi down Pulumi will ask you to confirm your choice (select yes and press enter): -![Pulumi#left](figures/06.png) +![Pulumi#left](figures/06.webp) After a few moments, you will see the delete confirmation message: -![Pulumi#left](figures/07.png) +![Pulumi#left](figures/07.webp) ## Summary You learned how to use infrastructure as code using Pulumi in this learning path. This approach is particularly beneficial as you can code cloud infrastructure as an application. Therefore, you can keep the declarations of the cloud infrastructure in the git repository and apply typical workflows you use for the application code development and deployment, like pull requests. \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/gh-copilot-simple/copilot-test.md b/content/learning-paths/servers-and-cloud-computing/gh-copilot-simple/copilot-test.md index 8b8598490e..1492731b22 100644 --- a/content/learning-paths/servers-and-cloud-computing/gh-copilot-simple/copilot-test.md +++ b/content/learning-paths/servers-and-cloud-computing/gh-copilot-simple/copilot-test.md @@ -45,7 +45,7 @@ Lastly, the chat output from your extension is printed. Here it is in VS Code: -![#Copilot output](_images/output.png) +![#Copilot output](_images/output.webp) Your GitHub Copilot Extension is now responding to chat prompts. diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/baseline-testing.md b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/baseline-testing.md index 45623d22e6..0d8d747aef 100644 --- a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/baseline-testing.md +++ b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/baseline-testing.md @@ -156,7 +156,7 @@ echo "http://$(curl -s ifconfig.me)/" Open this URL in your browser. You should see the styled HTML landing page served by your Go application. -![Go web server running on Azure Cobalt 100 Arm64 alt-text#center](images/go-web.png "Go web server running on Azure Cobalt 100 Arm64") +![Go web server running on Azure Cobalt 100 Arm64 alt-text#center](images/go-web.webp "Go web server running on Azure Cobalt 100 Arm64") ## Baseline Testing Complete diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/images/go-web.png b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/images/go-web.png deleted file mode 100644 index 66618480f2..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/images/go-web.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/golang-on-azure/images/go-web.webp b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/images/go-web.webp new file mode 100644 index 0000000000..fe16a3fcac Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/golang-on-azure/images/go-web.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md index 5bdd5fa0ca..3207d88cb6 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md @@ -77,9 +77,9 @@ In your browser, open: `http://${tomcat_ip}:8080/examples`. You should see the Tomcat welcome page and examples, as shown below: -![Screenshot of the Tomcat homepage showing version and welcome panel alt-text#center](./_images/lp-tomcat-homepage.png "Apache Tomcat homepage") +![Screenshot of the Tomcat homepage showing version and welcome panel alt-text#center](./_images/lp-tomcat-homepage.webp "Apache Tomcat homepage") -![Screenshot of the Tomcat examples page showing servlet and JSP demo links alt-text#center](./_images/lp-tomcat-examples.png "Apache Tomcat examples") +![Screenshot of the Tomcat examples page showing servlet and JSP demo links alt-text#center](./_images/lp-tomcat-examples.webp "Apache Tomcat examples") {{% notice Note %}}Make sure port 8080 is open in the security group of the IP address for your Arm-based Linux machine.{{% /notice%}} diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md index cd1f236620..1d69ee7126 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md @@ -51,4 +51,4 @@ Alternatively, if you already know the process ID (PID): Open the generated `profile.html` file in a browser to view your Java flame graph: -![Flame graph visualization showing Java method stack traces captured using async-profile alt-text#center](_images/lp-flamegraph-async.png "Java flame graph built using async-profiler") +![Flame graph visualization showing Java method stack traces captured using async-profile alt-text#center](_images/lp-flamegraph-async.webp "Java flame graph built using async-profiler") diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md 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a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-tomcat-homepage.webp b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-tomcat-homepage.webp new file mode 100644 index 0000000000..463a244aeb Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-tomcat-homepage.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/http-scaling.md b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/http-scaling.md index ddda708890..2df8a0375b 100644 --- a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/http-scaling.md +++ b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/http-scaling.md @@ -274,7 +274,7 @@ Expected behavior: You can also monitor traffic and scaling in the Kedify dashboard: -![Kedify dashboard showing request load and scaling over time alt-text#center](images/load.png "Kedify dashboard: request load and scaling over time") +![Kedify dashboard showing request load and scaling over time alt-text#center](images/load.webp "Kedify dashboard: request load and scaling over time") ## Clean up diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/images/load.png b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/images/load.png deleted file mode 100644 index c51d0b92dd..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/images/load.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/images/load.webp b/content/learning-paths/servers-and-cloud-computing/kedify-http-autoscaling/images/load.webp new file mode 100644 index 0000000000..079c45229a Binary files /dev/null and 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a80c5d46b5..ed7f2cdbfc 100644 --- a/content/learning-paths/servers-and-cloud-computing/llama-vision/conclusion.md +++ b/content/learning-paths/servers-and-cloud-computing/llama-vision/conclusion.md @@ -35,7 +35,7 @@ For this to work, you must ensure that the `allow-my-ip` tag is present on your Upload an image and enter your prompt in the UI to generate a response. You should see the LLM generating a response based on the prompt, using the image as context as shown below: -![browser_output](browser_output.png) +![browser_output](browser_output.webp) ## Further Interaction and Custom Applications diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/2_llama.cpp_intro.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/2_llama.cpp_intro.md index 75bf788463..40a8e87fd0 100644 --- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/2_llama.cpp_intro.md +++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/2_llama.cpp_intro.md @@ -70,7 +70,7 @@ Most autoregressive LLMs are decoder-only models. This refers to the transformer Decoder-only models like LLaMA have become dominant for text generation because they are simpler to train at scale, can handle both understanding and generation tasks, and are more efficient for text generation. This diagram introduces the idea of Prefill and Decode stages of autoregressive LLMs: -![Diagram illustrating the two stages of autoregressive LLM inference: Prefill stage processing input tokens and Decode stage generating output tokens sequentially alt-text#center](images/llm_prefill_decode.jpg "Prefill and Decode stages") +![Diagram illustrating the two stages of autoregressive LLM inference: Prefill stage processing input tokens and Decode stage generating output tokens sequentially alt-text#center](images/llm_prefill_decode.webp "Prefill and Decode stages") The Prefill stage is shown below, and as you can see, multiple input tokens of the prompt are processed simultaneously. @@ -78,11 +78,11 @@ In the context of Large Language Models (LLMs), a *matrix* is a two-dimensional This stage mainly performs GEMM operations (General Matrix Multiply; where one matrix is multiplied by another matrix) to generate the first output token. -![Diagram showing the Prefill stage processing multiple input tokens in parallel through transformer blocks using GEMM operations alt-text#center](images/transformer_prefill.jpg "Prefill stage") +![Diagram showing the Prefill stage processing multiple input tokens in parallel through transformer blocks using GEMM operations alt-text#center](images/transformer_prefill.webp "Prefill stage") At the Decode stage, the model utilizes the [KV cache](https://huggingface.co/blog/not-lain/kv-caching) (Key-Value cache; which is stored attention information from previous tokens). This stage mainly performs GEMV operations (General Matrix-Vector multiply - where a vector is multiplied by a matrix) to generate subsequent output tokens one by one. -![Diagram showing the Decode stage generating tokens one by one using KV cache and GEMV operations alt-text#center](images/transformer_decode.jpg "Decode stage") +![Diagram showing the Decode stage generating tokens one by one using KV cache and GEMV operations alt-text#center](images/transformer_decode.webp "Decode stage") ## Summary diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/5_operator_deepdive.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/5_operator_deepdive.md index 4ac130aafa..dc893706f2 100644 --- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/5_operator_deepdive.md +++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/5_operator_deepdive.md @@ -151,7 +151,7 @@ When moving the cursor over an annotation channel, Streamline shows: - The operator type - The shape and size of the source tensors -![Close-up screenshot of annotation channel tooltip showing tensor node details including operator type and tensor dimensions alt-text#center](images/prefill_annotation_channel_3.png "Annotation channel zoom in") +![Close-up screenshot of annotation channel tooltip showing tensor node details including operator type and tensor dimensions alt-text#center](images/prefill_annotation_channel_3.webp "Annotation channel zoom in") The example above shows a `GGML_OP_MUL_MAT` operator for the `FFN_UP` node. The source tensors have shapes [1024, 2816] and [1024, 68]. diff --git a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/6_multithread_analyze.md b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/6_multithread_analyze.md index 0d3afbc47e..dbd7390c8f 100644 --- a/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/6_multithread_analyze.md +++ b/content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/6_multithread_analyze.md @@ -46,7 +46,7 @@ In the screenshot above, you can observe that two threads are created and they a You can also use the Annotation Channel view to analyze operator execution on a per-thread basis. Each thread generates its own annotation channel independently, allowing you to see how work is distributed across parallel execution units. -![Screenshot showing Streamline annotation channels with multiple threads executing the same tensor node simultaneously alt-text#center](images/multi_thread_annotation_channel.png "Multi-thread annotation channels") +![Screenshot showing Streamline annotation channels with multiple threads executing the same tensor node simultaneously alt-text#center](images/multi_thread_annotation_channel.webp "Multi-thread annotation channels") In the screenshot above, at the highlighted time, both threads are executing the same node. In this particular case, the node is the result_output linear layer. You can see how the workload is distributed across threads, with each thread processing a different portion of the matrix computation. 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b/content/learning-paths/servers-and-cloud-computing/milvus-rag/create_cluster.webp new file mode 100644 index 0000000000..2c9774f955 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/milvus-rag/create_cluster.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md index 433ba8ea44..c67dbb683d 100644 --- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md @@ -15,7 +15,7 @@ After you register, [create a cluster](https://docs.zilliz.com/docs/create-clust Now create a **Dedicated** cluster deployed in AWS using Arm-based machines to store and retrieve the vector data as shown: -![cluster](create_cluster.png) +![cluster](create_cluster.webp) When you select the **Create Cluster** Button, you should see the cluster running in your **Default Project**. diff --git a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/5_rdv3_modify.md b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/5_rdv3_modify.md index cce9ab4d05..a42fcc66f6 100644 --- a/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/5_rdv3_modify.md +++ b/content/learning-paths/servers-and-cloud-computing/neoverse-rdv3-swstack/5_rdv3_modify.md @@ -78,7 +78,7 @@ export MODEL="$HOME/FVP_RD_V3_R1/models/Linux64_armv8l_GCC-9.3/FVP_RD_V3_R1" # This starts the dual-chip simulation. 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In the EC2 Dashboard, click **Launch Instance** and add the following information in these corresponding data fields to configure your setup: * Name: enter **arm-server**. @@ -28,7 +28,7 @@ To set up an Arm-powered EC2 instance, follow these steps: The configuration should look like the configuration fields that Figure 6 shows: -![Figure 6 alt-text#center](figures/06.png "Figure 6: Configuration Fields.") +![Figure 6 alt-text#center](figures/06.webp "Figure 6: Configuration Fields.") 4. Scroll down to **Key pair** (login), and click **Create new key pair**. This displays the **Create key pair** window. @@ -49,7 +49,7 @@ The configuration should look like the configuration fields that Figure 6 shows: * Description: **arm-security-group**. * Inbound security groups. -![fig8](figures/08.png) +![fig8](figures/08.webp) 6. Configure **Inbound Security Group Rules** by selecting **Add Rule** and then setting the following details: * Type: **Custom TCP**. @@ -61,13 +61,13 @@ Repeat this step for all three ports that the application is using. This example The configuration should look like: -![fig9](figures/09.png) +![fig9](figures/09.webp) 7. Launch an instance by clicking the **Launch instance** button. You should see the green box with the **Success** label. This box also contains a link to the EC2 instance. Click on it, and it takes you to the instance dashboard, as Figure 10 shows: -![fig10](figures/10.png) +![fig10](figures/10.webp) ### Deploy the application Once the EC2 instance is ready, you can connect to it, and deploy the application. Follow these steps to connect: @@ -129,7 +129,7 @@ The application runs the same way as it does locally. You should see the followi Finally, open the application in the web browser, using the EC2's public IP: -![fig13](figures/13.png) +![fig13](figures/13.webp) ### Summary You have successfully deployed the Aspire app on to an Arm-powered AWS EC2 instance. 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Configure the Remaining Settings: @@ -37,7 +37,7 @@ The configuration setup should resemble the following: * Identity and API Access: keep the default settings. * Firewall Settings: Check **Allow HTTP traffic** and **Allow HTTPS traffic**. -![fig15](figures/15.png) +![fig15](figures/15.webp) 7. Click the **Create** Button and wait for the VM to be created. @@ -45,7 +45,7 @@ The configuration setup should resemble the following: After creating the VM, connect to it as follows: 1. In **Compute Engine**, click the SSH drop-down menu next to your VM, and select **Open in browser window**: -![fig16](figures/16.png) +![fig16](figures/16.webp) 2. This opens a browser window. First, click the **Authorize** button: @@ -53,7 +53,7 @@ After creating the VM, connect to it as follows: 3. You will then see the terminal of your VM: -![fig18](figures/18.png) +![fig18](figures/18.webp) ### Installing dependencies and deploying an app Once the connection is established, you can install the required dependencies (.NET SDK, Aspire workload, and Git), fetch the application code, and deploy it. @@ -100,7 +100,7 @@ dotnet run --project NetAspire.Arm.AppHost ``` You will see output similar to this: -![fig19](figures/19.png) +![fig19](figures/19.webp) ### Making your application public diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/modify_project.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/modify_project.md index 6e56a43a4d..aa550c00d0 100644 --- a/content/learning-paths/servers-and-cloud-computing/net-aspire/modify_project.md +++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/modify_project.md @@ -82,6 +82,6 @@ dotnet run --project NetAspire.Arm.AppHost Next, navigate to the web frontend, select **Weather**, and then return to the dashboard. Click **Traces** and note that the operation now takes significantly longer to complete — approximately four seconds in the example below: -![fig4 alt-text#center](figures/04.png "Figure 4: Traces Example.") +![fig4 alt-text#center](figures/04.webp "Figure 4: Traces Example.") You are now ready to deploy the application to the cloud. diff --git a/content/learning-paths/servers-and-cloud-computing/net-aspire/run_app.md b/content/learning-paths/servers-and-cloud-computing/net-aspire/run_app.md index 2bc850baaa..a9a006292c 100644 --- a/content/learning-paths/servers-and-cloud-computing/net-aspire/run_app.md +++ b/content/learning-paths/servers-and-cloud-computing/net-aspire/run_app.md @@ -39,16 +39,16 @@ In this case, it is: [https://localhost:17222/login?t=81f99566c9ec462e66f5eab5aa This directs you to the application dashboard, as Figure 1 shows: -![fig1 alt-text#center](figures/01.png "Figure 1: Application Dashboard.") +![fig1 alt-text#center](figures/01.webp "Figure 1: Application Dashboard.") On the dashboard, locate and click the endpoint link for `NetAspire.Arm.Web`. This takes you to the Blazor-based web application. In the Blazor app, navigate to the Weather section to access and display data retrieved from the WeatherForecast API: -![fig2 alt-text#center](figures/02.png "Figure 2: Data Displayed from WeatherForecast API.") +![fig2 alt-text#center](figures/02.webp "Figure 2: Data Displayed from WeatherForecast API.") Now return to the dashboard, and select the **Traces** option. This section provides detailed telemetry tracing, allowing you to view the flow of requests, track service dependencies, and analyze performance metrics for your application: -![fig3 alt-text#center](figures/03.png "Figure 3: Traces.") +![fig3 alt-text#center](figures/03.webp "Figure 3: Traces.") By following these steps, you can explore the key components of the .NET Aspire application, including its dashboard, data interaction through APIs, and telemetry tracing capabilities. diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/baseline.md b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/baseline.md index 5144c44095..6b86202f76 100644 --- a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/baseline.md +++ b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/baseline.md @@ -129,6 +129,6 @@ http:/// ## Verify the page renders You should see your custom page instead of the default welcome page: -![Custom static website served by NGINX on Azure VM alt-text#center](images/nginx-web.png "Custom static website served by NGINX on an Azure Arm64 VM") +![Custom static website served by NGINX on Azure VM alt-text#center](images/nginx-web.webp "Custom static website served by NGINX on an Azure Arm64 VM") This verifies the basic functionality of the NGINX installation. You can now proceed to benchmarking NGINX performance on your Arm-based Azure VM. diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/images/nginx-web.png b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/images/nginx-web.png deleted file mode 100644 index 152bf727a6..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/images/nginx-web.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/images/nginx-web.webp b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/images/nginx-web.webp new file mode 100644 index 0000000000..e3fc37aba7 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/nginx-on-azure/images/nginx-web.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/analysis.md b/content/learning-paths/servers-and-cloud-computing/onnx/analysis.md index 8939fad493..4703630e42 100644 --- a/content/learning-paths/servers-and-cloud-computing/onnx/analysis.md +++ b/content/learning-paths/servers-and-cloud-computing/onnx/analysis.md @@ -9,7 +9,7 @@ layout: learningpathall To begin, input the text prompt as shown in the example below: -![output](output.png) +![output](output.webp) ## Observe performance metrics diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/chatbot.md b/content/learning-paths/servers-and-cloud-computing/onnx/chatbot.md index 005c213d7f..d5ef226205 100644 --- a/content/learning-paths/servers-and-cloud-computing/onnx/chatbot.md +++ b/content/learning-paths/servers-and-cloud-computing/onnx/chatbot.md @@ -126,4 +126,4 @@ python3 phi4.py -m cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4 -e cpu -g ``` You should see output similar to the image below when the server starts successfully: -![server](server.png) +![server](server.webp) diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/output.png b/content/learning-paths/servers-and-cloud-computing/onnx/output.png deleted file mode 100644 index f3d16c82ee..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/onnx/output.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/output.webp b/content/learning-paths/servers-and-cloud-computing/onnx/output.webp new file mode 100644 index 0000000000..aace1caaf2 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/onnx/output.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/server.png b/content/learning-paths/servers-and-cloud-computing/onnx/server.png deleted file mode 100644 index 64d9848288..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/onnx/server.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/onnx/server.webp b/content/learning-paths/servers-and-cloud-computing/onnx/server.webp new file mode 100644 index 0000000000..df92445f3d Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/onnx/server.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/3_openbmc_simulate.md b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/3_openbmc_simulate.md index e38eb595d2..08a126e457 100644 --- a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/3_openbmc_simulate.md +++ b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/3_openbmc_simulate.md @@ -94,7 +94,7 @@ The simulation proceeds to the **CSSv3 r1** GRUB menu. Press **Enter** to boot. A successful run shows login prompts on both BMC and host consoles. You can also confirm final state in the Web UI or using UART output. -![img2 BMC and host consoles each showing a login prompt after a successful OpenBMC + host UEFI simulation on RD-V3 FVP#center](openbmc_cssv3_sim.jpg "Simulation success with BMC and host consoles") +![img2 BMC and host consoles each showing a login prompt after a successful OpenBMC + host UEFI simulation on RD-V3 FVP#center](openbmc_cssv3_sim.webp "Simulation success with BMC and host consoles") Shown here is a short recording that illustrates OpenBMC and UEFI interaction during pre-silicon execution. diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/openbmc_cssv3_sim.jpg b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/openbmc_cssv3_sim.jpg deleted file mode 100644 index 36d70252da..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/openbmc_cssv3_sim.jpg and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/openbmc_cssv3_sim.webp b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/openbmc_cssv3_sim.webp new file mode 100644 index 0000000000..8cc911e9d4 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/openbmc-rdv3/openbmc_cssv3_sim.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/bundles.png b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/bundles.png deleted file mode 100644 index c91688d8c7..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/bundles.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/bundles.webp b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/bundles.webp new file mode 100644 index 0000000000..11a7dc4557 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/bundles.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/intro.md b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/intro.md index ad4e4392cc..9301535ecc 100644 --- a/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/intro.md +++ b/content/learning-paths/servers-and-cloud-computing/pmuv3_plugin_learning_path/intro.md @@ -28,7 +28,7 @@ The PMUv3 plugin groups performance events together into categories called bundl The events in each bundle and the derived performance metrics are shown in the table below: -![example image alt-text#center](bundles.png "Table 1. Bundled Events") +![example image alt-text#center](bundles.webp "Table 1. Bundled Events") Next, learn how to get the PMUv3 plugin and use it in an application. diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/images/v1-spe-report-full.png b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/images/v1-spe-report-full.png deleted file mode 100644 index 27a63d120b..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/images/v1-spe-report-full.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/images/v1-spe-report-full.webp b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/images/v1-spe-report-full.webp new file mode 100644 index 0000000000..0a997c6884 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/images/v1-spe-report-full.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/streamline-cli.md b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/streamline-cli.md index ff6bc4bc99..efdf251735 100644 --- a/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/streamline-cli.md +++ b/content/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/streamline-cli.md @@ -94,7 +94,7 @@ Arm recommends that you profile an optimized release build of your application, 1. View your report in Excel or other compatible application. In functions reports, problem areas are indicated in red, to help you focus on the main problems. - ![An example functions report](images/v1-spe-report-full.png) + ![An example functions report](images/v1-spe-report-full.webp) See our [example report](/learning-paths/servers-and-cloud-computing/profiling-for-neoverse/example) to learn more about how to interpret the results. diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/images/chatbot.png b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/images/chatbot.png deleted file mode 100644 index e1fd04e6b7..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/images/chatbot.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/images/chatbot.webp b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/images/chatbot.webp new file mode 100644 index 0000000000..f09fa32fea Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/images/chatbot.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/pytorch-llama-frontend.md b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/pytorch-llama-frontend.md index 8f2442978b..1e174888b7 100644 --- a/content/learning-paths/servers-and-cloud-computing/pytorch-llama/pytorch-llama-frontend.md +++ b/content/learning-paths/servers-and-cloud-computing/pytorch-llama/pytorch-llama-frontend.md @@ -73,7 +73,7 @@ Collecting usage statistics. To deactivate, set browser.gatherUsageStats to fals Open the local URL from the link above in a browser and you should see the chatbot running: -![Chatbot](images/chatbot.png) +![Chatbot](images/chatbot.webp) {{% notice Note %}} If you are running a server in the cloud, the local URL may not connect when starting the frontend server. If this happens, stop the frontend server and reconnect to your instance using port forwarding (see code below). After reconnecting, activate the `venv` and start the Streamlit frontend server. diff --git a/content/learning-paths/servers-and-cloud-computing/rag/chatbot.md b/content/learning-paths/servers-and-cloud-computing/rag/chatbot.md index 1cd6eb3488..e7389d9e5e 100644 --- a/content/learning-paths/servers-and-cloud-computing/rag/chatbot.md +++ b/content/learning-paths/servers-and-cloud-computing/rag/chatbot.md @@ -71,7 +71,7 @@ Follow these steps: 1. Enter your query in the prompt field of the web application. 2. Submit the query to receive a response from the LLM. -![RAG_IMG3](rag_img3.png) +![RAG_IMG3](rag_img3.webp) While the response is streamed to the frontend for immediate viewing, you can monitor the performance metrics on the backend server terminal. This provides insights into the processing speed and efficiency of the LLM. diff --git a/content/learning-paths/servers-and-cloud-computing/rag/rag_img3.png b/content/learning-paths/servers-and-cloud-computing/rag/rag_img3.png deleted file mode 100644 index 424c0af431..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/rag/rag_img3.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/rag/rag_img3.webp b/content/learning-paths/servers-and-cloud-computing/rag/rag_img3.webp new file mode 100644 index 0000000000..223dedb3f5 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/rag/rag_img3.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/debugging-bl1-3.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/debugging-bl1-3.md index 6d108acfd1..8d973a31d3 100644 --- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/debugging-bl1-3.md +++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/debugging-bl1-3.md @@ -49,7 +49,7 @@ add-symbol-file "//rd-infra/tf-a/build/rdn2/debug/bl31/bl31.elf" EL3: If you would like to add platform-specific debug files, the memory locations are in the corresponding ``platform_h.def`` file. {{% /notice %}} -![tfa symbols alt-text#center](images/tfa-symbols.png "Figure 2. Load TF-A symbols") +![tfa symbols alt-text#center](images/tfa-symbols.webp "Figure 2. Load TF-A symbols") These commands load the symbol files and specify the memory address location, updating **workspace** to include the path to your own workspace directory. @@ -63,7 +63,7 @@ Set a breakpoint in the function you would like to debug. In this example, you c Simply interrupt the CPU and enter debug command `set $pc += 4'; you can now step through and debug the TF-A boot flow. -![bl1 breakpoint alt-text#center](images/bl1_breakpoint.png "Figure 5. BL1 breakpoint") +![bl1 breakpoint alt-text#center](images/bl1_breakpoint.webp "Figure 5. BL1 breakpoint") ## Alternate break method Another method of setting a breakpoint without modifying TF-A is by launching the model with ``--break``. diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/debugging-scp-lcp-rse-2.md b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/debugging-scp-lcp-rse-2.md index 5ef2564bbb..3ef8d86be2 100644 --- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/debugging-scp-lcp-rse-2.md +++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/debugging-scp-lcp-rse-2.md @@ -40,7 +40,7 @@ Click **Browse for model running on local host**. Select the correct model, and click **Finish**. -![connect model alt-text#center](images/connect_model.png "Figure 4. Connect model") +![connect model alt-text#center](images/connect_model.webp "Figure 4. Connect model") In the **Edit configuration and launch** panel, in the **Connection** tab, select the correct target. @@ -86,11 +86,11 @@ break cmn700_discovery continue ``` -![scp breakpoint 1 alt-text#center](images/scp_breakpoint1.png "Figure 7. cmn700_discovery() breakpoint") +![scp breakpoint 1 alt-text#center](images/scp_breakpoint1.webp "Figure 7. cmn700_discovery() breakpoint") Set another breakpoint at a debug print statement. -![scp breakpoint 2 alt-text#center](images/scp_breakpoint2.png "Figure 8. SCP breakpoint 2") +![scp breakpoint 2 alt-text#center](images/scp_breakpoint2.webp "Figure 8. SCP breakpoint 2") Observe the output in the SCP UART window. diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/images/bl1_breakpoint.png b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/images/bl1_breakpoint.png deleted file mode 100644 index 16ae2d700b..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/images/bl1_breakpoint.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/images/bl1_breakpoint.webp b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/images/bl1_breakpoint.webp new file mode 100644 index 0000000000..cb42d66a24 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/images/bl1_breakpoint.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/images/connect_model.png 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b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/setup-1.md index 4a5773539f..85df2dc400 100644 --- a/content/learning-paths/servers-and-cloud-computing/refinfra-debug/setup-1.md +++ b/content/learning-paths/servers-and-cloud-computing/refinfra-debug/setup-1.md @@ -22,7 +22,7 @@ To prepare for debugging, remove the `–R` parameter from the `PARAMS=` section When `-R` is used, the FVP continues execution and does not wait for the debug connection. -![modify parameters alt-text#center](images/modify_params.png "Figure 1. Modify run_model.sh") +![modify parameters alt-text#center](images/modify_params.webp "Figure 1. Modify run_model.sh") {{% notice Debug server %}} To start a `CADI` debug server, use the `-S` option. For an `Iris` debug server, change this to `-I` instead. diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_images/grafana.png b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_images/grafana.png deleted file mode 100644 index e905486b9c..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_images/grafana.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_images/grafana.webp b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_images/grafana.webp new file mode 100644 index 0000000000..d4f533e758 Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_images/grafana.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/cluster-monitoring.md b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/cluster-monitoring.md index 579867460f..2a112635e9 100644 --- a/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/cluster-monitoring.md +++ b/content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/cluster-monitoring.md @@ -113,4 +113,4 @@ Locate a `Kubernetes/Compute Resources/Node (Pods)` dashboard and click on it. You should see a dashboard like below for your Kubernetes cluster: -![grafana #center](_images/grafana.png "Figure 8: Dashboard for Kubernetes Cluster.") +![grafana #center](_images/grafana.webp "Figure 8: Dashboard for Kubernetes Cluster.") diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/figures/01.png b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/figures/01.png deleted file mode 100644 index 7f9c441115..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/figures/01.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/figures/01.webp b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/figures/01.webp new file mode 100644 index 0000000000..730cf2f7da Binary files /dev/null and b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/figures/01.webp differ diff --git a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/lambda.md b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/lambda.md index 96d874183c..e130a4568d 100644 --- a/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/lambda.md +++ b/content/learning-paths/servers-and-cloud-computing/serverless-framework-aws-intro/lambda.md @@ -176,7 +176,7 @@ You will see the following output: Finally, log into the AWS console. Change the region to us-east-1 (N. Virginia). Then, go to the Lambda dashboard to see the deployed Lambda function. It will be named *AwsServerlessLambda-dev-hello*: -![fig1](figures/01.png) +![fig1](figures/01.webp) ## Summary In this Learning Path, you learned about the Serverless Framework, its benefits, and its integration with Infrastructure as Code (IaC) for managing cloud resources. You set up the Serverless Framework for AWS and created a project. We explained its structure, specifically focusing on the serverless.yml file, including the structure and configuration options for setting up a basic Serverless service with AWS Lambda. Finally, we explored the Lambda handler function and deployed the resources to AWS. diff --git a/content/learning-paths/servers-and-cloud-computing/top-down-n1/analysis-1.md b/content/learning-paths/servers-and-cloud-computing/top-down-n1/analysis-1.md index 98bfd8b1e5..4f15c1258f 100644 --- a/content/learning-paths/servers-and-cloud-computing/top-down-n1/analysis-1.md +++ b/content/learning-paths/servers-and-cloud-computing/top-down-n1/analysis-1.md @@ -287,7 +287,7 @@ perf report When the report opens, use the down arrow key to highlight the `main` function: -![stride #center](report-1.png) +![stride #center](report-1.webp) Press return to annotate the main function. @@ -295,7 +295,7 @@ Press return to annotate the main function. You will see the source code of the `main` function with the percent of samples for each line of source code. -![main #center](report-3.png) +![main #center](report-3.webp) All the samples are on `subs` instruction which is right after the load instruction which reads the array and causes the high cache miss rate. 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if true, do not perform any file modifications +export quality=85 # quality for webp conversion (1-100) +export max_width=1600 # maximum width before resizing +export target_width=1280 # target width for resizing +export max_size_kb=200 # maximum file size in KB before resizing + +# +# Function: confirm_args +# Purpose: confirm that at least one image argument is provided +# +confirm_args() { + if [ $# -eq 0 ]; then + echo "No images provided as arguments" + echo "Usage: $0 ..." + exit 1 + fi +} + +# +# Function: process_env_tunables +# Purpose: pull/configure tunables from environment +# +process_env_tunables() { + # Dry Run + if [ -z "${DRY_RUN}" ]; then + export dry_run=${DRY_RUN} + fi + + # Quality + if [ -n "${QUALITY}" ]; then + export quality=${QUALITY} + fi + # Max Width + if [ -n "${MAX_WIDTH}" ]; then + export max_width=${MAX_WIDTH} + fi + # Target Width + if [ -n "${TARGET_WIDTH}" ]; then + export target_width=${TARGET_WIDTH} + fi + # Max Size KB + if [ -n "${MAX_SIZE_KB}" ]; then + export max_size_kb=${MAX_SIZE_KB} + fi +} + +# +# Function: process_images +# Purpose: process all images passed as arguments +# +process_images() { + # loop through all image arguments + for img in "$@"; do + # Skip if file doesn't exist + [ -f "$img" ] || { + echo "⚠️ File not found: $img" + continue + } + + # Skip if not an image file + if [[ ! "$img" =~ \.(png|jpg|jpeg)$ ]]; then + if [[ "$img" == *.webp ]]; then + echo "Skipping $img (already .webp)" + continue + fi + echo "Skipping non-image file: $img" + continue + fi + + # Get width using ImageMagick + echo "Checking $img..." + width=$(identify -format "%w" "$img" 2>/dev/null || echo 0) + + # Get file size in KB (macOS stat is different) + filesize=$(stat -f%z "$img" 2>/dev/null || echo 0) + kbsize=$((filesize / 1024)) + + # Define new filename + base="${img%.*}" + ext="${img##*.}" + webp_img="${base}.webp" + + # image stats + echo "Image: $img | Size: ${kbsize}KB | Width: ${width}px" + + # Resize + convert if needed + if [ "$kbsize" -gt ${max_size_kb} ] && [ "$width" -gt ${max_width} ]; then + if [ "$dry_run" = "true" ]; then + echo "DRY RUN: Would resize $img → $webp_img" + echo "DRY RUN: Would remove $img" + echo "DRY RUN: Would update markdown references" + else + # Set quality dynamically based on file size + if [ "$kbsize" -ge 15000 ]; then # ≥ 15MB + quality=50 + elif [ "$kbsize" -ge 5000 ]; then # 5–15MB + quality=70 + else # < 5MB + quality=85 + fi + echo "Optimizing $img (${kbsize}KB, ${width}px), quality=$quality" + # Resize and convert to WebP. If error occurs, capture it and exit. + error=$(magick "$img" -resize 1280x\> -quality $quality "$webp_img" 2>&1) + convert_status=$? + if [ $convert_status -ne 0 ]; then + echo "⚠️ Error converting $img to WebP format." + if [ -z "${error}" ]; then + error="Unknown error" + fi + echo "Error details: $error" + echo "⚠️ Exiting script." + exit 1 + else + echo "Created $webp_img.... removing $img..." + rm "$img" + fi + + img_name=$(basename "$img") # create_device.png + webp_name=$(basename "$webp_img") # create_device.webp + img_dir=$(dirname "$img") # The directory of the image + + # Only scan markdown files in the same directory + find "$img_dir" "$(dirname "$img_dir")" -name "*.md" 2>/dev/null | while read -r md_file; do + if grep -q "$img_name" "$md_file"; then + echo "Replacing $img_name → $webp_name in $md_file" + sed -i '' "s|($img_name|(${webp_name}|g" "$md_file" + sed -i '' "s|/$img_name|/${webp_name}|g" "$md_file" + fi + done + fi + else + if [ "$dry_run" = "true" ]; then + echo "DRY RUN: Would skip $img (size: ${kbsize}KB, width: ${width}px)" + else + echo "Skipping $img since it's small enough: (${kbsize}KB, ${width}px)" + fi + fi + done +} + +# +# main() function +# Purpose: entry point for the script +# +main() { + # confirm arguments + confirm_args $* + + # pull/configure tunables from environment + process_env_tunables $* + + # process all images passed as arguments + process_images $* + + # we are done! + echo "🎉 Optimization complete." +} + +# Invoke main() with all script arguments +main $*