Merge pull request #4 from industrial-edge/fixing-inner-links

Fixing broken links

e2e-tutorials/batch_state_identifier/notebooks/40-TestPipelineLocally.ipynb

@@ -5,13 +5,13 @@
       "cell_type": "markdown",
       "metadata": {
         "SPDX-FileCopyrightText": "Copyright (C) Siemens AG 2021. All Rights Reserved.",
-		    "SPDX-License-Identifier": "MIT"
+        "SPDX-License-Identifier": "MIT"
       },
       "source": [
         "# Testing the edge configuration package\n",
         "\n",
         "In this notebook, the main goal is to test the edge configuration package created in notebook [30-CreatePipelinePackage](30-CreatePipelinePackage.ipynb)\n",
-        "driving it with the training data set which was used in notebook [20-CreateModel](20-CreateModel.ipynb).\n",
+        "driving it with the training data set which was used in notebook [10-CreateModel](10-CreateModel.ipynb).\n",
         "\n",
         "The `LocalPipelineRunner` object takes the edge configuration package and extracts its components.\n",
         "Once the components are extracted, you can run them individually by calling `run_component` with component name and structured input data.\n",
@@ -27,7 +27,7 @@
       "source": [
         "## Define a dataset to test the package\n",
         "The goal here is to create a list of input data which the `process_input(data: dict)` method will be triggered with.  \n",
-        "For this reason, we read the json files and create the list of payloads as we did in notebook [20-CreateModel](20-CreateModel.ipynb)"
+        "For this reason, we read the json files and create the list of payloads as we did in notebook [10-CreateModel](10-CreateModel.ipynb)"
       ]
     },
     {

e2e-tutorials/image_classification/notebooks/30-CreatePipelinePackage.ipynb

@@ -4,8 +4,8 @@
             "attachments": {},
             "cell_type": "markdown",
             "metadata": {
-              "SPDX-FileCopyrightText": "Copyright (C) Siemens AG 2021. All Rights Reserved.",
-                  "SPDX-License-Identifier": "MIT"
+                "SPDX-FileCopyrightText": "Copyright (C) Siemens AG 2021. All Rights Reserved.",
+                "SPDX-License-Identifier": "MIT"
             },
             "source": [
                 "# Create a TensorFlow Lite edge configuration package\n",
@@ -114,7 +114,7 @@
             "source": [
                 "### Add dependencies\n",
                 "\n",
-                "All of the required dependencies are collected in the file `runtime_requirements.txt`. See [HOWTO](../HOWTO.md) for more possibilities."
+                "All of the required dependencies are collected in the file `runtime_requirements.txt`. See [How to handle Python dependencies](../../../howto-guides/04-handle-python-dependencies.md) for more possibilities."
             ]
         },
         {

e2e-tutorials/object_detection/README.md

@@ -68,7 +68,7 @@ The notebook [01-CreateTestSet.ipynb](./notebooks/01-CreateTestSet.ipynb) explai
 
 ### 2. Analyzing and fixing the model ONNX format
 
-The notebook [10-ObjectDetectonModel.ipynb](./notebooks/10-ObjectDetectonModel.ipynb) explains the used model, the possible mistakes in the ONNX definition and how to correct them. By the end of the execution of the notebook you will have an **well prepared onnx model** to package.
+The notebook [10-ObjectDetectionModel.ipynb](./notebooks/10-ObjectDetectionModel.ipynb) explains the used model, the possible mistakes in the ONNX definition and how to correct them. By the end of the execution of the notebook you will have an **well prepared onnx model** to package.
 
 ### 3. Creating Pre- and Postprocessing steps to support the GPURuntimeComponent
 

e2e-tutorials/object_detection/notebooks/40-TestPipelineLocally.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {
         "SPDX-FileCopyrightText": "Copyright (C) Siemens AG 2021. All Rights Reserved.",
-		    "SPDX-License-Identifier": "MIT"
+        "SPDX-License-Identifier": "MIT"
       },
       "source": [
         "# Test the edge configuration package locally"
@@ -31,7 +31,7 @@
       "source": [
         "#### Create ImageSet input from a JPEG or PNG file\n",
         "\n",
-        "As we discussed in notebook [](./20-PreAndPostProcessing.ipynb), the payload of the Pipeline is an `ImageSet` in a dictionary, which will be processed by the Preprocessing step. For this reason, we create this payload from images in folder _../src/data/processed_ with the help of method defined in notebook [10-ObjectDetectonModel.ipynb](./10-ObjectDetectonModel.ipynb) and saved into Python script [payload.py](../src/preprocessing/payload.py)."
+        "As we discussed in notebook [](./20-PreAndPostProcessing.ipynb), the payload of the Pipeline is an `ImageSet` in a dictionary, which will be processed by the Preprocessing step. For this reason, we create this payload from images in folder _../src/data/processed_ with the help of method defined in notebook [10-ObjectDetectionModel.ipynb](./10-ObjectDetectionModel.ipynb) and saved into Python script [payload.py](../src/preprocessing/payload.py)."
       ]
     },
     {

howto-guides/00-prepare-environment.md

@@ -26,4 +26,4 @@ The brief [How to setup Linux](00-setup-linux.md) guide explains how to prepare
 
 ## Prepare environments
 
-[How to prepare environment manager environments](00-prepare-environments) explains how to setup different notebook editors to work with the tutorials provided with AI SDK.
+[How to prepare environment manager environments](00-setup-environments.md) explains how to setup different notebook editors to work with the tutorials provided with AI SDK.

howto-guides/00-setup-windows.md

@@ -179,4 +179,4 @@ However, if you want to test your model locally, currently there is no official
 You can either use the full TensorFlow package, or you can build your TFLite models in a virtual Linux environment.
 
 > **Note**\
-To complete setup, follow the steps described in the [Linux setup](#linux-setup) section.
+To complete setup, follow the steps described in the [Linux setup](00-setup-linux.md) section.

howto-guides/03-use-variable-types.md

@@ -223,4 +223,4 @@ def process_input(data):
     }
 ```
 
-See details in [How to use `ImageSet` format for images.md](14-use-imageset-format-for-images.md)
+See details in [How to use String format for images.md](14-use-string-format-for-images.md)

howto-guides/11-process-images.md

@@ -97,7 +97,7 @@ def process_input(payload: dict):
 
 <!-- from VCA user manual, section Accessing camera data via ZeroMQ -->
 `Vision Connector` supports mainly the standardized GenICam pixel formats, as the most common `Mono8`, `RGB8` formats or `BayerXX8` formats to reduce network traffic while the color information is still recorded.
-For different pixel format it is also recommended to use the GenICam naming convention as described in section 4.35 of the [GenICam_PFNC_2_4.pdf](https://www.emva.org/wp-content/uploads/GenICam_SFNC_v2_7.pdf​,) document.
+For different pixel format it is also recommended to use the GenICam naming convention as described in section 4.35 of the [GenICam_PFNC_2_4.pdf](https://www.emva.org/wp-content/uploads/GenICam_SFNC_v2_7.pdf) document.
 
 ### `ImageSet` as Output
 

howto-guides/18-package-inference-pipelines.md

@@ -153,7 +153,7 @@ attempt to auto-wire the components passed as a list,
 - connecting the last component to the pipeline output.
 
 Some components may require special variable types, e.g. the GPU component must receive its inputs in a specific format, and it will produce the output in a specific format too.
-Please refer to [Guideline for writing runtime components](writing_components.md) for variable type handling.
+Please refer to [How to use variable types](03-use-variable-types.md) for variable type handling.
 In the general case, we recommend that you pass data from one component to the other in a single variable of type `String`, serializing and deserializing whatever data you have through a string.
 
 The low-level methods of class `Pipeline` allow you to define arbitrary wiring between components and pipeline inputs and outputs

howto-guides/README.md

@@ -16,7 +16,7 @@ SPDX-License-Identifier: MIT
   * [How to setup Linux](00-setup-linux.md)
   * [How to setup environment manager](00-setup-environment-manager.md)
   * [How to setup a notebook editor](00-setup-notebook-editor.md)
-  * [How to setup setup environment manager environments](00-setup-environments)
+  * [How to setup setup environment manager environments](00-setup-environments.md)
 * [How to define pipeline components?](01-define-components.md)
 * [How to create entrypoints for AI Inference Server to be able to feed the model with data?](02-create-entrypoint.md)
 * [How to use variables in pipelines?](03-use-variable-types.md)
@@ -30,7 +30,7 @@ SPDX-License-Identifier: MIT
 * [How to process images?](11-process-images.md)
   * [How to use Binary format for images](./12-use-binary-format-for-images.md)
   * [How to use Object format for images](./13-use-object-format-for-images.md)
-  * [How to use ImageSet format for images](./14-use-imageset-format-for-images.md)
+  * [How to use String format for images](./14-use-string-format-for-images.md)
 * [How to use Tensorflow instead of Tensorflow Light](./15-use-tensorflow-instead-of-tflight.md)
 * [How to version pacakges and use Pacakge ID](./16-version-packages.md)
 * [How to mock AI Inference Server logger locally](./17-mock-inference-server-logging.md)

howto-notebooks/model-conversion/keras-to-onnx/keras-to-onnx.ipynb

@@ -5,7 +5,7 @@
       "cell_type": "markdown",
       "metadata": {
         "SPDX-FileCopyrightText": "Copyright (C) Siemens AG 2021. All Rights Reserved.",
-		    "SPDX-License-Identifier": "MIT"
+        "SPDX-License-Identifier": "MIT"
       },
       "source": [
         "# Model Conversion for Keras models\n",
@@ -24,7 +24,7 @@
       "source": [
         "## Load the model\n",
         "\n",
-        "For this model conversion tutorial we are going to use the same model we created and trained in our [Image Classification](\"../../use-cases/image-classification/Readme.md\") example.  "
+        "For this model conversion tutorial we are going to use the same model we created and trained in our [Image Classification](../../../e2e-tutorials/image_classification/README.md) example."
       ]
     },
     {
@@ -222,7 +222,7 @@
         "\n",
         "The AI Inference Server with GPU support accepts ONNX models for execution.  \n",
         "For this purpose the model must be packaged into a `GPURuntimeComponent` step using AI Software Development Kit.  \n",
-        "For details on how to create `GPURuntimeComponent` and build pipelines that run on a GPU enabled AI Inference Server you can study the [Object Detection](\"../../use-cases/object-detection/Readme.md\") example.\n"
+        "For details on how to create `GPURuntimeComponent` and build pipelines that run on a GPU enabled AI Inference Server you can study the [Object Detection](../../../e2e-tutorials/object_detection/README.md) example.\n"
       ]
     },
     {

howto-notebooks/model-conversion/pytorch-to-onnx/pytorch-to-onnx.ipynb

@@ -5,7 +5,7 @@
       "cell_type": "markdown",
       "metadata": {
         "SPDX-FileCopyrightText": "Copyright (C) Siemens AG 2021. All Rights Reserved.",
-		    "SPDX-License-Identifier": "MIT"
+        "SPDX-License-Identifier": "MIT"
       },
       "source": [
         "# Model conversion for PyTorch models\n",
@@ -238,7 +238,7 @@
         "\n",
         "The AI Inference Server with GPU support accepts ONNX models for execution.  \n",
         "For this purpose the model must be packaged into a `GPURuntimeComponent` step using AI Software Development Kit.  \n",
-        "For details on how to create `GPURuntimeComponent` and build pipelines that run on a GPU enabled AI Inference Server you can study the [Object Detection](\"../../use-cases/object-detection/Readme.md\") example."
+        "For details on how to create `GPURuntimeComponent` and build pipelines that run on a GPU enabled AI Inference Server you can study the [Object Detection](../../../e2e-tutorials/object_detection/README.md) example."
       ]
     }
   ],