diff --git a/.riahub/workflows/workflow.yaml b/.riahub/workflows/workflow.yaml
index d422ada..012ad1e 100644
--- a/.riahub/workflows/workflow.yaml
+++ b/.riahub/workflows/workflow.yaml
@@ -1,4 +1,4 @@
-name: RIA Hub Workflow Demo
+name: Modulation Recognition Demo
 
 on:
   push:
@@ -11,9 +11,6 @@ on:
 jobs:
   ria-demo:
     runs-on: ubuntu-latest-2080
-    env:
-      RIAGIT_USERNAME: ${{ secrets.USERNAME }}
-      RIAGIT_TOKEN: ${{ secrets.TOKEN }}
     steps:
       - name: Print GPU information
         run: |
@@ -24,7 +21,7 @@ jobs:
             echo "⚠️ No NVIDIA GPU found"
           fi
 
-      - name: Checkout code
+      - name: Checkout project code
         uses: actions/checkout@v4
         with:
           lfs: true
@@ -36,19 +33,24 @@ jobs:
 
       - name: Install dependencies (incl. RIA Hub utils)
         run: |
-          python -m pip install --upgrade pip
-          pip install \
-            --index-url "https://${{ secrets.RIAHUB_USER }}:${{ secrets.RIAHUB_TOKEN }}@git.riahub.ai/api/packages/qoherent/pypi/simple/" \
-            utils \
-            -r requirements.txt
+          set -e
+
+          python -m pip install --upgrade pip
+
+          echo "Trying to install utils from RIA Hub..."
+          if ! pip install \
+            --index-url "https://${{ secrets.RIAHUB_USER }}:${{ secrets.RIAHUB_TOKEN }}@git.riahub.ai/api/packages/qoherent/pypi/simple/" \
+            utils; then
+            echo "RIA Hub install failed, falling back to local wheel..."
+            pip install ./wheels/utils-*.whl
+          fi
+          pip install -r requirements.txt
 
-        
 
       - name: 1. Generate Recordings
         run: |
          mkdir -p data/recordings
-         PYTHONPATH=. python scripts/dataset_building/data_gen.py --output-dir data/recordings
-         echo "recordings produced successfully"
+         PYTHONPATH=. python scripts/dataset_manager/data_gen.py --output-dir data/recordings
 
       - name: ⬆️ Upload recordings
         uses: actions/upload-artifact@v3
@@ -59,11 +61,10 @@ jobs:
       - name: 2. Build HDF5 Dataset
         run: |
          mkdir -p data/dataset
-         PYTHONPATH=. python scripts/dataset_building/produce_dataset.py
-         echo "datasets produced successfully"
+         PYTHONPATH=. python scripts/dataset_manager/produce_dataset.py
         shell: bash
 
-      - name: 📤 Upload Dataset
+      - name: ⬆️ Upload Dataset
         uses: actions/upload-artifact@v3
         with:
           name: dataset
@@ -75,34 +76,30 @@ jobs:
             PYTORCH_NO_NNPACK: 1
         run: |
           mkdir -p checkpoint_files
-          PYTHONPATH=. python scripts/training/train.py 2>/dev/null
-          echo "training model"
+          PYTHONPATH=. python scripts/model_builder/train.py 2>/dev/null
     
       - name: 4. Plot Model
         env:
             NO_NNPACK: 1
             PYTORCH_NO_NNPACK: 1
         run: |
-          PYTHONPATH=. python scripts/training/plot_data.py 2>/dev/null
-      
+          PYTHONPATH=. python scripts/model_builder/plot_data.py 2>/dev/null
 
-      - name: Upload Checkpoints
+      - name: ⬆️ Upload Checkpoints
         uses: actions/upload-artifact@v3
         with:
           name: checkpoints
           path: checkpoint_files/*
 
-
-      - name: 5. Convert to ONNX file
+      - name: 5. Export model to ONNX graph
         env:
           NO_NNPACK: 1
           PYTORCH_NO_NNPACK: 1
         run: |
           mkdir -p onnx_files
-          MKL_DISABLE_FAST_MM=1 PYTHONPATH=. python scripts/onnx/convert_to_onnx.py 2>/dev/null
-          echo "building inference app"
+          MKL_DISABLE_FAST_MM=1 PYTHONPATH=. python scripts/application_packager/convert_to_onnx.py 2>/dev/null
           
-      - name: Upload ONNX file
+      - name: ⬆️ Upload ONNX file
         uses: actions/upload-artifact@v3
         with:
           name: onnx-file
@@ -110,21 +107,20 @@ jobs:
 
       - name: 6. Profile ONNX model
         run: |
-          PYTHONPATH=. python scripts/onnx/profile_onnx.py
+          PYTHONPATH=. python scripts/application_packager/profile_onnx.py
       
-      - name: Upload JSON profiling data
+      - name: ⬆️ Upload JSON trace
         uses: actions/upload-artifact@v3
         with:
           name: profile-data          
           path: '**/onnxruntime_profile_*.json'
       
-      - name: 7. Convert to ORT file
+      - name: 7. Convert ONNX graph to an ORT file
         run: |
-          PYTHONPATH=. python scripts/ort/convert_to_ort.py
+          PYTHONPATH=. python scripts/application_packager/convert_to_ort.py
 
-      - name: Upload ORT file
+      - name: ⬆️ Upload ORT file
         uses: actions/upload-artifact@v3
         with:
           name: ort-file
           path: ort_files/inference_recognition_model.ort
-          
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..0ad25db
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
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+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
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+software and other kinds of works, specifically designed to ensure
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+  A patent license is "discriminatory" if it does not include within
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+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
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+
+  13. Remote Network Interaction; Use with the GNU General Public License.
+
+  Notwithstanding any other provision of this License, if you modify the
+Program, your modified version must prominently offer all users
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+
+  14. Revised Versions of this License.
+
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+  Each version is given a distinguishing version number.  If the
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+option of following the terms and conditions either of that numbered
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+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
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+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
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+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.
diff --git a/README.md b/README.md
index cd64ae1..a4bac4f 100644
--- a/README.md
+++ b/README.md
@@ -1,84 +1,201 @@
-# ModrecWorkflow Demo
-This project automates the process of generating data, training, and deploying the modulation recognition model for radio singal classification. The workflow is intended to support experimentation, reproducibility, and deployment of machine learning models for wireless signal modulation classification, such as QPSK, 16-QAM, BPSK, 
+# Modulation Recognition Demo
 
-## Getting Started
+RIA Hub Workflows is an automation platform integrated into RIA Hub. This project provides an example machine learning
+workflow for signal modulation classification, offering a practical introduction to RIA Hub Workflows
 
-1. Clone the Repository
 
+## 📡 The machine learning development workflow
+
+The development of intelligent radio solutions involves multiple steps:
+
+1. First, we need to prepare a machine learning-ready dataset. This involves signal synthesis or capture, followed by 
+dataset curation to extract and qualify examples. Finally, we need to perform any required data preprocessing—such as
+augmentation—and split the dataset into training and test sets.
+
+
+2. Secondly, we need to design and train a model. This is typically an iterative process, often accelerated using
+techniques such as Neural Architecture Search (NAS) and hyperparameter optimization (HPO), which help automate the
+discovery of an effective model structure and optimal hyperparameter settings.
+
+
+3. Once a machine learning model has been trained and validated, the next step is to build an inference application. 
+This step transforms the model from a research artifact into a practical tool capable of making predictions in 
+real-world conditions. Building an inference application typically involves several steps including model 
+optimization, packaging and integration, and monitoring and logging.
+
+This is a lot of work, and much of it involves tedious software development and repetitive tasks, like setting up and
+configuring infrastructure. What's more? There is a shortage of domain expertize in ML and MLOps for radio. That's
+where we come in. RIA Hub offers a no-code and low-code solution for automating the end-to-end development of
+intelligent radio systems.
+
+
+## ▶️ RIA Hub Workflows
+
+One of the core principles of RIA Hub is Workflows, which allow users to run jobs in isolated Docker containers.
+
+You can create workflows in one of two ways: 
+- Writing YAML and placing it in the special `.riahub/workflows/` directory in your repository.
+
+
+- Using RIA Hub's built-in tools for Dataset Management, Model Building, and Application Development, which will
+automatically generate the YAML workflow file(s) for you.
+
+Workflows can be configured to run automatically on push and pull request events. You can monitor and manage running 
+workflows in the 'Workflows' tab in your repository.
+
+Workflows require a _runner_, which retrieves job definitions from RIA Hub, executes them in isolated containers, and
+reports the results back to RIA Hub. The next section outlines the convenience and advantage of using Qoherent-hosted
+runners. The workflow configuration defines the specifications and settings of the available job containers.
+
+The best part? RIA Hub Workflows are built on [Gitea Actions](https://docs.gitea.com/usage/actions/overview) (similar to [GitHub Actions](https://github.com/features/actions)), providing a
+familiar syntax and allowing you to leverage a wide range of third-party Actions.
+
+
+## ⚙️ Qoherent-hosted runners
+
+Qoherent-hosted runners are workflow runners that Qoherent provides and manages to run your workflows and jobs in
+RIA Hub Workflows.
+
+Why use Qoherent-hosted runners?
+- Start running workflows right away, without the need to set up your own infrastructure.
+- Qoherent maintains runners equipped with access to hardware and tools common for radio ML development, including
+SDR testbeds and common embedded targets.
+
+If you want to learn more about the runners we have available, [contact us](https://www.qoherent.ai/contact/) directly. We can also provide
+custom runners equipped with specific radio hardware and RAN software upon request.
+
+Want to register your own runner? No problem! Please refer to the RIA Hub documentation for more details.
+
+
+## 🔍 Modulation Recognition
+
+In radio, the modulation scheme refers to the method used to encode information onto a carrier signal. Common schemes
+such as BPSK, QPSK, and QAM vary the amplitude, phase, or frequency of the signal in structured ways to represent
+digital data. These schemes are fundamental to nearly all wireless communication systems, enabling efficient and
+reliable transmission over different channels and under various noise conditions.
+
+Machine learning-based modulation classification helps identify which modulation scheme is being used, especially
+in scenarios where prior knowledge of the signal format is unavailable or unreliable. Traditional methods often rely
+on expert-designed features and rule-based algorithms, which can struggle in real-world environments with multipath,
+interference, or hardware impairments. In contrast, ML-based approaches can learn complex patterns directly from 
+raw signal data, offering higher robustness and adaptability. This is particularly valuable in applications like
+cognitive radio, spectrum monitoring, electronic warfare, and autonomous communication systems, where accurate and
+fast modulation recognition is critical.
+
+
+## 🚀 Getting started
+
+1. Fork the project repo, using the button in the upper right hand corner.
+
+
+2. Enable Workflows (*Settings → Advanced Settings → Enable Repository Actions*).
+_TODO: Remove this point once default units have been updated to include actions in forks_
+
+
+3. Check for available runners. The runner management tab can found at the top of the 'Workflows' tab in your
+repository. If no runners are available, you'll need to register one before proceeding.
+
+
+4. Configure Git API credentials, if not suitable credentials are already set. This is required for accessing Utils
+in the job container. This requires three steps:
+
+   - Create a personal access token with the following permissions: `read:packages` (*User Settings → Applications → Manage Access Tokens*).
+
+   - Create a Workflow Variable `RIAHUB_USER` with your RIA Hub username (*Repo Settings → Actions → Variables Management*)
+
+   - Create a Workflow Secret `RIAHUB_TOKEN` with the token created above (*Repo Settings → Actions → Secrets Management*)
+
+   _TODO: Remove this point once the Utils wheel file has been added to this project._
+
+
+5. Clone down the project. For example:
 ```commandline
-git clone https://github.com/yourorg/modrec-workflow.git
+git clone https://git.riahub.ai/user/modrec-workflow.git
 cd modrec-workflow
 ```
 
-2. Configure the Workflow
-
-All workflow parameters (data paths, model architecture, training settings) are set in 'conf/app.yaml'
-
-Example:
-```commandline
-dataset:
-  input_dir: data/recordings
-  num_slices: 8
-  train_split: 0.8
-  val_split : 0.2
+6. Set the workflow runner in `.riahub/workflows/workflow.yaml`. The runner is set on line 13: 
+```yaml
+runs-on: ubuntu-latest-2080
 ```
-
-### Configure GitHub Secrets
-
-Before running the pipeline, add the following repository secrets in GitHub (Settings → Secrets and variables → Actions):
-
-- **RIAHUB_USER**: Your RIA Hub username.  
-- **RIAHUB_TOKEN**: RIA Hub access token with `read:packages` scope (from your RIA Hub account **Settings → Access Tokens**).  
-- **CLONER_TOKEN**: Personal access token for `stark_cloner_bot` with `read_repository` scope (from your on-prem Git server user settings).  
-
-Once secrets are configured, you can run the pipeline:
+**Note:** We recommend running this demo on a GPU-enabled runner. If a GPU runner is not available, you can still run 
+the workflow, but we suggest reducing the number of training epochs to keep runtime reasonable.
 
 
-3. Run the Pipeline
-Once you update the changes to app.yaml, you can make any push or pull to your repo to start running the workflow
-
-## Artifacts Created
-After Successful execution, the workflow produces serveral artifacts in the output
-- dataset
-  - This is a folder containing to .h5 datasets called train and val
-- Checkpoints
-  - Contains saved model checkpoints, each checkpoint includes the models learned weights at various stages of training
-- ONNX File
-  - The ONNX file contains the trained model in a standardized format that allows it to be run efficiently across different platforms and deployment environments.
-- JSON Trace File (*json)
-  - Captures a full trace of model training and inference perfomance for profiling and debugging
-  - Useful for identifying performance bottlenecks, optimizing resource usage, and tracking metadata
-- ORT File (*ort)
-  - This is an ONNX Runtime (ORT) model file, optimized for fast inference on various platforms
-  - Why is it Useful?
-    - You can deploy this file on edge devices, servers or integrate it into the production systems for real-time signal classification
-    - ORT files are class-platform and allow easy inference acceleration using ONNX Runtime
+7. (Optional) Configure the workflow. All parameters—including file paths, model architecture, and training 
+settings—are set in `conf/app.yaml`. Want to jump right in? No problem, the default configuration is suitable.
 
 
-## How to View the JSON Trace File
-
-Access this [link](https://ui.perfetto.dev/) 
-Click on Open Trace File -> Select your specific JSON trace file
-Explore detailed visualizations of performance metrics, timelines, and resource usage to diagnose bottlenecks and optimize your workflow.
+8. Push changes. This will automatically trigger the workflow. You can monitor workflow progress under the 'Workflows'
+tab in the repository.
 
 
-
-## Submiting Issues
-Found a bug or have a feature request?
-Please submit an issue via the GitHub Issues page.
-When reporting bugs, include:
-Steps to reproduce
-  - Error logs and screenshots (if applicable)
-  - Your app.yaml configuration (if relevant)
+9. Inspect the workflow output. You can expand and collapse individual steps to view terminal output. A check
+mark indicates that the step completed successfully.
 
 
+10. Inspect the workflow artifacts. Additional information on workflow artifacts can be found in the next section.
 
-## Developer Details
-Coding Guidelines:
-  Follow PEP 8 for Python code style.
-  Include type annotations for all public functions and methods.
-  Write clear docstrings for modules, classes, and functions.
-  Use descriptive commit messages and reference issue numbers when relevant.
-Contributing
-  All contributions must be reviewed via pull requests.
-  Run all tests and ensure code passes lint checks before submission.
\ No newline at end of file
+
+## Workflow artifacts
+
+This workflow generates several artifacts, including:
+
+- `recordings`: Folder of synthetic signal recordings.
+
+
+- `dataset`: The training and validation datasets: `train.h5` and `val.h5`, respectively.
+
+
+- `checkpoints`: Saved model checkpoints. Each checkpoint contains the model’s learned weights at various
+stages of training.
+
+
+- `onnx-file`: The trained model as an [ONNX](https://onnx.ai/) graph.
+
+
+- `ort-file`: Model in `.ORT` format, recommended for edge deployments. (`.ORT` files are optimized and serialized
+by [ONNX Runtime](https://onnxruntime.ai/) for more efficient loading and execution.)
+
+
+- `profile-data`: Model execution traces, in JSON format. See the section below for instructions on how to inspect the
+trace using Perfetto.
+
+
+## 📊 Inspecting the model trace using Perfetto
+
+[Perfetto](https://ui.perfetto.dev/) is an open-source trace visualization tool developed by Google. It provides a powerful web-based
+interface for inspecting model execution traces. Perfetto is especially useful for identifying bottlenecks.
+
+To inspect model trace, navigate to Perfetto. Select *Navigation → Open trace file*, and choose the JSON trace file 
+includes in the `profile-data` artifact.
+
+
+## 🤝 Contribution
+
+We welcome contributions from the community! Whether it's an enhancement, bug fix, or new tutorial, your 
+input is valuable. To get started, please [contact us](https://www.qoherent.ai/contact/) directly, we're looking forward to collaborating with
+you. 🚀
+
+If you encounter any issues or to report a security vulnerability, please submit a [bug report](https://git.riahub.ai/qoherent/modrec-workflow/issues).
+
+Qoherent is dedicated to fostering a friendly, safe, and inclusive environment for everyone. For more information on
+our commitment to diversity, please refer to our [Diversity Statement](https://github.com/qoherent/.github/blob/main/docs/DIVERSITY_STATEMENT.md). 
+
+We kindly insist that all contributors review and adhere to our [Code of Conduct](https://github.com/qoherent/.github/blob/main/docs/CODE_OF_CONDUCT.md) and that all code contributors 
+review our [Coding Guidelines](https://github.com/qoherent/.github/blob/main/docs/CODING.md).
+
+
+## 🖊️ Authorship
+
+This demonstration was developed by [Liyu Xiao](https://www.linkedin.com/in/liyu-xiao-593176206/) during his summer co-op term at Qoherent.
+
+If you like this project, don’t forget to give it a star! ⭐
+
+
+## 📄 License
+
+This example is **free and open-source**, released under [AGPLv3](https://www.gnu.org/licenses/agpl-3.0.en.html).
+
+Alternative licensing options are available. Alternative licensing options are available. Please [contact us](https://www.qoherent.ai/contact/)
+for further details.
diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 0000000..7973e67
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,3 @@
+To report a security vulnerability, please submit a bug report to this project's issue tracker. Be sure to explicitly
+state that the issue concerns a security vulnerability. This helps us classify and manage security reports separately
+from general issues.
diff --git a/conf/app.yaml b/conf/app.yaml
index 80a52af..c1f1c4c 100644
--- a/conf/app.yaml
+++ b/conf/app.yaml
@@ -1,20 +1,16 @@
-general:
-  # Run mode. Options are 'prod' or 'dev'.
-  run_mode: prod
-
 dataset:
-  #number of slices you want to split each recording into
-  num_slices: 8
+  # Seed for the random number generator, used for signal generation
+  seed: 42
 
-  #training/val split between the 2 data sets
-  train_split: 0.8
-  val_split : 0.2
+  # Number of samples per recording
+  recording_length: 1024
 
-  #used to initialize a random number generator.
-  seed: 25
-
-  #multiple modulations to contain in the dataset
-  modulation_types: [bpsk, qpsk, qam16, qam64]
+  # List of signal modulation schemes to include in the dataset
+  modulation_types:
+    - bpsk
+    - qpsk
+    - qam16
+    - qam64
 
   # Rolloff factor for pulse shaping filter (0 < beta <= 1)
   beta: 0.3
@@ -23,20 +19,18 @@ dataset:
   sps: 4
 
   # SNR sweep range: start, stop (exclusive), and step (in dB)
-  snr_start: -6     # Start value of SNR sweep (in dB)
-  snr_stop: 13      # Stop value (exclusive) of SNR sweep (in dB)
-  snr_step: 3       # Step size for SNR sweep (in dB)
+  snr_start: -6
+  snr_stop: 13
+  snr_step: 3
 
-  # Number of iterations (samples) per modulation and SNR combination
+  # Number of iterations (signal recordings) per modulation and SNR combination
   num_iterations: 3
 
-  # Number of samples per generated recording
-  recording_length: 1024
-
-  # Settings for each modulation scheme
-  # Keys must match entries in `modulation_types`
-  #   - `num_bits_per_symbol`: how many bits each symbol encodes (e.g., 1 for BPSK, 4 for 16-QAM)
-  #   - `constellation_type`: type of modulation (e.g., "psk", "qam", "fsk", "ofdm")
+  # Modulation scheme settings; keys must match the `modulation_types` list above
+  # Each entry includes:
+  #   - num_bits_per_symbol: bits encoded per symbol (e.g., 1 for BPSK, 4 for 16-QAM)
+  #   - constellation_type: modulation category (e.g., "psk", "qam", "fsk", "ofdm")
+  # TODO: Combine entries for 'modulation_types' and 'modulation_settings'
   modulation_settings:
     bpsk:
       num_bits_per_symbol: 1
@@ -51,20 +45,25 @@ dataset:
       num_bits_per_symbol: 6
       constellation_type: qam
 
+  # Number of slices to cut from each recording
+  num_slices: 8
 
+  # Training and validation split ratios; must sum to 1
+  train_split: 0.8
+  val_split : 0.2
 
 training:
-  # Number of training samples processed together before the model updates its weights
+  # Number of training examples processed together before the model updates its weights
   batch_size: 256
 
   # Number of complete passes through the training dataset during training
   epochs: 5
 
-  # Learning rate: how much weights are updated after every batch
-  # Suggested range for fine-tuning: 1e-6 to 1e-4
+  # Learning rate: step size for weight updates after each batch
+  # Recommended range for fine-tuning: 1e-6 to 1e-4
   learning_rate: 1e-4
 
-  # Whether to use GPU acceleration for training (if available)
+  # Enable GPU acceleration for training if available
   use_gpu: true
 
   # Dropout rate for individual neurons/layers (probability of dropping out a unit)
@@ -73,13 +72,12 @@ training:
   # Drop path rate: probability of dropping entire residual paths (stochastic depth)
   drop_path_rate: 0.2
 
-  # Weight decay (L2 regularization) to help prevent overfitting
+  # Weight decay (L2 regularization) coefficient to help prevent overfitting
   wd: 0.01
 
-
 app:
-  # Optimization style for ORT conversion. Options: 'Fixed', 'None'
-  optimization_style: Fixed
+  # Optimization style for ORT conversion; options: 'Fixed', 'None'
+  optimization_style: "Fixed"
 
-  # Target platform architecture. Common options: 'amd64', 'arm64'
-  target_platform: amd64
\ No newline at end of file
+  # Target platform architecture; common options: 'amd64', 'arm64'
+  target_platform: "amd64"
diff --git a/helpers/app_settings.py b/helpers/app_settings.py
index b019f92..8d3fd57 100644
--- a/helpers/app_settings.py
+++ b/helpers/app_settings.py
@@ -6,11 +6,6 @@ from typing import Dict
 import yaml
 
 
-@dataclass
-class GeneralConfig:
-    run_mode: str
-
-
 @dataclass
 class DataSetConfig:
     num_slices: int
@@ -54,7 +49,6 @@ class AppSettings:
             config_data = yaml.safe_load(f)
 
         # Parse the loaded YAML into dataclass objects
-        self.general = GeneralConfig(**config_data["general"])
         self.dataset = DataSetConfig(**config_data["dataset"])
         self.training = TrainingConfig(**config_data["training"])
         self.app = AppConfig(**config_data["app"])
diff --git a/scripts/onnx/convert_to_onnx.py b/scripts/application_packager/convert_to_onnx.py
similarity index 90%
rename from scripts/onnx/convert_to_onnx.py
rename to scripts/application_packager/convert_to_onnx.py
index 7a3d209..f022c39 100644
--- a/scripts/onnx/convert_to_onnx.py
+++ b/scripts/application_packager/convert_to_onnx.py
@@ -2,8 +2,8 @@ import os
 
 import numpy as np
 import torch
+from scripts.model_builder.mobilenetv3 import RFClassifier, mobilenetv3
 
-from scripts.training.mobilenetv3 import mobilenetv3, RFClassifier
 from helpers.app_settings import get_app_settings
 
 
@@ -12,8 +12,8 @@ def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
     Convert a PyTorch model to ONNX format.
 
     Parameters:
-        output_path (str): The path to save the converted ONNX model.
-        fp16 (bool): 16 float point percision
+        ckpt_path (str): The path to save the converted ONNX model.
+        fp16 (bool): 16 float point precision
     """
     settings = get_app_settings()
 
@@ -68,8 +68,6 @@ def convert_to_onnx(ckpt_path: str, fp16: bool = False) -> None:
 
 if __name__ == "__main__":
 
-    settings = get_app_settings()
-
     model_checkpoint = "inference_recognition_model.ckpt"
 
     print("Converting to ONNX...")
diff --git a/scripts/ort/convert_to_ort.py b/scripts/application_packager/convert_to_ort.py
similarity index 99%
rename from scripts/ort/convert_to_ort.py
rename to scripts/application_packager/convert_to_ort.py
index 593268d..55782d7 100644
--- a/scripts/ort/convert_to_ort.py
+++ b/scripts/application_packager/convert_to_ort.py
@@ -1,4 +1,5 @@
 import subprocess
+
 from helpers.app_settings import get_app_settings
 
 settings = get_app_settings()
diff --git a/scripts/onnx/profile_onnx.py b/scripts/application_packager/profile_onnx.py
similarity index 97%
rename from scripts/onnx/profile_onnx.py
rename to scripts/application_packager/profile_onnx.py
index 7f6ff04..8ef12ed 100644
--- a/scripts/onnx/profile_onnx.py
+++ b/scripts/application_packager/profile_onnx.py
@@ -1,9 +1,9 @@
-import onnxruntime as ort
-import numpy as np
-from helpers.app_settings import get_app_settings
+import json
 import os
 import time
-import json
+
+import numpy as np
+import onnxruntime as ort
 
 
 def profile_onnx_model(
@@ -84,6 +84,5 @@ def profile_onnx_model(
 
 
 if __name__ == "__main__":
-    settings = get_app_settings()
     output_path = os.path.join("onnx_files", "inference_recognition_model.onnx")
     profile_onnx_model(output_path)
diff --git a/scripts/dataset_building/data_gen.py b/scripts/dataset_manager/data_gen.py
similarity index 99%
rename from scripts/dataset_building/data_gen.py
rename to scripts/dataset_manager/data_gen.py
index 0fac831..6cbbf73 100644
--- a/scripts/dataset_building/data_gen.py
+++ b/scripts/dataset_manager/data_gen.py
@@ -1,7 +1,9 @@
-from utils.data import Recording
-import numpy as np
-from utils.signal import block_generator
 import argparse
+
+import numpy as np
+from utils.data import Recording
+from utils.signal import block_generator
+
 from helpers.app_settings import get_app_settings
 
 settings = get_app_settings().dataset
diff --git a/scripts/dataset_building/produce_dataset.py b/scripts/dataset_manager/produce_dataset.py
similarity index 98%
rename from scripts/dataset_building/produce_dataset.py
rename to scripts/dataset_manager/produce_dataset.py
index b2e65c2..55207d9 100644
--- a/scripts/dataset_building/produce_dataset.py
+++ b/scripts/dataset_manager/produce_dataset.py
@@ -1,7 +1,11 @@
-import os, h5py, numpy as np
+import os
 from typing import List
-from utils.io import from_npy
+
+import h5py
+import numpy as np
 from split_dataset import split, split_recording
+from utils.io import from_npy
+
 from helpers.app_settings import DataSetConfig, get_app_settings
 
 meta_dtype = np.dtype(
@@ -46,8 +50,6 @@ def write_hdf5_file(records: List, output_path: str, dataset_name: str = "data")
         )
 
     first_rec, _ = records[0]  # records[0] is a tuple of (data, md)
-    sample = first_rec
-    shape, dtype = sample.shape, sample.dtype
 
     with h5py.File(output_path, "w") as hf:
         data_arr = np.stack([rec[0] for rec in records])
diff --git a/scripts/dataset_building/split_dataset.py b/scripts/dataset_manager/split_dataset.py
similarity index 98%
rename from scripts/dataset_building/split_dataset.py
rename to scripts/dataset_manager/split_dataset.py
index 732c84b..0f820b1 100644
--- a/scripts/dataset_building/split_dataset.py
+++ b/scripts/dataset_manager/split_dataset.py
@@ -1,6 +1,7 @@
 import random
 from collections import defaultdict
-from typing import List, Tuple, Dict
+from typing import Dict, List, Tuple
+
 import numpy as np
 
 
diff --git a/scripts/training/cm_plotter.py b/scripts/model_builder/cm_plotter.py
similarity index 99%
rename from scripts/training/cm_plotter.py
rename to scripts/model_builder/cm_plotter.py
index 429293c..13770e3 100644
--- a/scripts/training/cm_plotter.py
+++ b/scripts/model_builder/cm_plotter.py
@@ -1,5 +1,6 @@
-import numpy as np
 from typing import Optional
+
+import numpy as np
 from matplotlib import pyplot as plt
 from sklearn.metrics import confusion_matrix
 
diff --git a/scripts/training/mobilenetv3.py b/scripts/model_builder/mobilenetv3.py
similarity index 94%
rename from scripts/training/mobilenetv3.py
rename to scripts/model_builder/mobilenetv3.py
index 83556bd..9de7f27 100644
--- a/scripts/training/mobilenetv3.py
+++ b/scripts/model_builder/mobilenetv3.py
@@ -1,8 +1,8 @@
-import numpy as np
-import torch
-import timm
-from torch import nn
 import lightning as L
+import numpy as np
+import timm
+import torch
+from torch import nn
 
 sizes = [
     "mobilenetv3_large_075",
@@ -24,11 +24,9 @@ class SqueezeExcite(nn.Module):
     def __init__(
         self,
         in_chs,
-        se_ratio=0.25,
         reduced_base_chs=None,
         act_layer=nn.SiLU,
         gate_fn=torch.sigmoid,
-        divisor=1,
         **_,
     ):
         super(SqueezeExcite, self).__init__()
@@ -77,13 +75,6 @@ class GBN(torch.nn.Module):
         self.act = act
 
     def forward(self, x):
-        # chunks = x.chunk(int(np.ceil(x.shape[0] / self.virtual_batch_size)), 0)
-        # res = [self.bn(x_) for x_ in chunks]
-        # return self.drop(self.act(torch.cat(res, dim=0)))
-        # x = self.bn(x)
-        # x = self.act(x)
-        # x = self.drop(x)
-        # return x
         return self.drop(self.act(self.bn(x)))
 
 
diff --git a/scripts/training/modulation_dataset.py b/scripts/model_builder/modulation_dataset.py
similarity index 98%
rename from scripts/training/modulation_dataset.py
rename to scripts/model_builder/modulation_dataset.py
index 5f65dc5..5826739 100644
--- a/scripts/training/modulation_dataset.py
+++ b/scripts/model_builder/modulation_dataset.py
@@ -1,10 +1,12 @@
-import sys, os
+import os
+import sys
 
 sys.path.insert(0, os.path.abspath("../.."))  # or ".." if needed
+import h5py
 import numpy as np
 import torch
 from torch.utils.data import Dataset
-import h5py
+
 from helpers.app_settings import get_app_settings
 
 settings = get_app_settings()
diff --git a/scripts/training/plot_data.py b/scripts/model_builder/plot_data.py
similarity index 93%
rename from scripts/training/plot_data.py
rename to scripts/model_builder/plot_data.py
index 531c684..8acc1ff 100644
--- a/scripts/training/plot_data.py
+++ b/scripts/model_builder/plot_data.py
@@ -1,15 +1,15 @@
 import os
-import torch
+
 import numpy as np
+import torch
 from sklearn.metrics import classification_report
 
 os.environ["NNPACK"] = "0"
 from matplotlib import pyplot as plt
+from mobilenetv3 import RFClassifier, mobilenetv3
+from modulation_dataset import ModulationH5Dataset
 
-from scripts.training.mobilenetv3 import mobilenetv3, RFClassifier
 from helpers.app_settings import get_app_settings
-from cm_plotter import plot_confusion_matrix
-from scripts.training.modulation_dataset import ModulationH5Dataset
 
 
 def load_validation_data():
@@ -141,5 +141,4 @@ def plot_confusion_matrix_with_counts(
 
 if __name__ == "__main__":
     settings = get_app_settings()
-    ckpt_path = os.path.join("checkpoint_files", "inference_recognition_model.ckpt")
-    evaluate_checkpoint(ckpt_path)
+    evaluate_checkpoint(os.path.join("checkpoint_files", "inference_recognition_model.ckpt"))
diff --git a/scripts/training/train.py b/scripts/model_builder/train.py
similarity index 99%
rename from scripts/training/train.py
rename to scripts/model_builder/train.py
index 5d334ec..560a9d5 100644
--- a/scripts/training/train.py
+++ b/scripts/model_builder/train.py
@@ -1,14 +1,16 @@
-import sys, os
+import os
+import sys
 
 os.environ["NNPACK"] = "0"
 import lightning as L
-from lightning.pytorch.callbacks import ModelCheckpoint
+import mobilenetv3
 import torch
 import torch.nn.functional as F
 import torchmetrics
-from helpers.app_settings import get_app_settings
+from lightning.pytorch.callbacks import ModelCheckpoint
 from modulation_dataset import ModulationH5Dataset
-import mobilenetv3
+
+from helpers.app_settings import get_app_settings
 
 script_dir = os.path.dirname(os.path.abspath(__file__))
 data_dir = os.path.abspath(os.path.join(script_dir, ".."))
diff --git a/wheels/utils-0.1.2.dev0-py3-none-any (3).whl b/wheels/utils-0.1.2.dev0-py3-none-any (3).whl
new file mode 100644
index 0000000..d065a8b
Binary files /dev/null and b/wheels/utils-0.1.2.dev0-py3-none-any (3).whl differ