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Map Action Model is the codebase for the continous training of Map Acion computer vision model. Developper Doc
Feature | Description | |
---|---|---|
โ๏ธ | Architecture | The project follow a modular architecture with dependencies on libraries like Brotli, Pillow, and FastAPI. It utilizes a mix of Python and related technologies for development. |
๐ฉ | Code Quality | The code quality is maintained with the use of tools such as MkDocs for documentation and potentially other linting tools based on the repository contents. |
๐ | Documentation | The project includes MKDocs for documentation generation, providing extensive and structured documentation for the codebase. |
๐ | Integrations | Key integrations include Brotli, tqdm, and FastAPI among others, suggesting a reliance on external libraries and tools for functionality. |
๐งฉ | Modularity | The project is structured in a modular way, with dependencies on various libraries like Torch, torchvision, and others, indicating potential code reusability. |
๐งช | Testing | Testing frameworks is PyTest |
๐ฆ | Dependencies | Key external libraries and dependencies include Brotli, Pillow, FastAPI, Torch, torchvision, and others, indicating a reliance on diverse libraries for functionality. |
โโโ Map-Action-Model/
โโโ .github
โ โโโ workflows
โ โโโ deploy-docs.yml
โ โโโ training-on-gpu.yml
โ โโโ unittesting.yml
โ โโโ zenml_action.yml
โโโ Dockerfile._cuda
โโโ Dockerfile.fastapi
โโโ LICENCE
โโโ _cd.yml
โโโ _ci.yml
โโโ code
โ โโโ .zen
โ โ โโโ config.yaml
โ โโโ TFLearning.ipynb
โ โโโ pipelines
โ โ โโโ zenml_pipeline.py
โ โโโ steps
โ โ โโโ dagshub_utils
โ โ โโโ data_preprocess
โ โ โโโ model
โ โ โโโ model_eval
โ โ โโโ plot_metrics
โ โ โโโ training_step
โ โโโ utilities.ipynb
โ โโโ zenml_running.py
โโโ data.dvc
โโโ data_upload.py
โโโ ma_env
โ โโโ bin
โ โ โโโ Activate.ps1
โ โ โโโ activate
โ โ โโโ activate.csh
โ โ โโโ activate.fish
โ โ โโโ pip
โ โ โโโ pip3
โ โ โโโ pip3.10
โ โ โโโ python
โ โ โโโ python3
โ โ โโโ python3.10
โ โโโ lib
โ โ โโโ python3.10
โ โโโ lib64
โ โโโ python3.10
โโโ model
โ โโโ main.py
โโโ requirements.txt
โโโ services
โ โโโ unittesting
โ โโโ Dockerfile
โโโ zenml_config
โโโ zenml_conf.yml
code
File | Summary |
---|---|
utilities.ipynb | Code snippet in code/utilities.ipynb:**Interacts with MLflow and DagsHub to manage experiment tracking within Map-Action-Model repository structure. Handles data sources and enables dataset manipulations. |
TFLearning.ipynb | Code snippet: Validates user input and updates database accordingly.Architecture: Microservices architecture with a separate service for database operations.Role: Ensures data integrity and security in the system.Critical features: Input validation, database interaction, seamless integration within the microservices ecosystem. |
zenml_running.py | zenml_running.pyin Map-Action-Modelrepo orchestrates a training pipeline using ZenML. Central to managing ML workflows, integrated with pipelines/zenml_pipeline.py`. |
code..zen
File | Summary |
---|---|
config.yaml | Code in code/.zen/config.yaml sets active stack and workspace IDs for the repository. Facilitates seamless integration with ZenML for workflow management and model pipelines. |
code.steps.model
File | Summary |
---|---|
m_a_model.py | Code snippet in m_a_model.py creates a modified VGG16 model for a specific class count. It adjusts the classifier and uses CrossEntropyLoss. This step enhances the model's adaptability and loss computation in the repository's ML pipeline architecture. |
code.steps.plot_metrics
File | Summary |
---|---|
plot_metrics.py | Code Summary:**plot_metrics.py in Map-Action-Model repo visualizes training and test loss/accuracy curves using Matplotlib. Enhances model evaluation insights for ML pipelines. |
code.steps.dagshub_utils
File | Summary |
---|---|
dagshub_data_load.py | Code snippet in dagshub_data_load.py downloads and organizes data from a CSV file and DagsHub repository for machine learning model training in the Map-Action-Model repository architecture. |
code.steps.model_eval
File | Summary |
---|---|
evaluation.py | Code Summary:**This code snippet performs testing for a PyTorch model, evaluating test data and logging metrics with MLFlow. It optimizes model performance and accuracy for the parent repository's machine learning pipeline. |
code.steps.training_step
File | Summary |
---|---|
training_step.py | Code snippet in training_step.py trains PyTorch model with provided data, logging metrics using MLFlow. Key features include model training loop, metric tracking, and PyTorch model saving. |
code.steps.data_preprocess
File | Summary |
---|---|
data_loading_pipeline.py | Code Summary**:data_loading_pipeline.py in Map-Action-Model creates PyTorch data loaders for training and testing datasets, managing dataset loading and transformation for ML pipelines. |
data_transform.py | Role:** Code snippet in data_transform.py for image preprocessing in Map-Action-Model repo architecture.Achievement: Generates image transformations for training/testing using torchvision API elegantly, ensuring data consistency. |
code.pipelines
File | Summary |
---|---|
zenml_pipeline.py | Code snippet in zenml_pipeline.py orchestrates a machine learning training pipeline. It manages data processing, model training, and evaluation, culminating in loss curves plotting. This integral component advances ML model development in the repository architecture. |
Requirements
Ensure you have the following dependencies installed on your system:
- Python:
Python 3.x
- Clone the Map-Action-Model repository:
git clone https://github.com/223MapAction/Map-Action-Model.git
- Change to the project directory:
cd Map-Action-Model
- Install the dependencies:
pip install -r requirements.txt
Use the following command to run Map-Action-Model:
python main.py
To execute tests, run:
pytest
Contributions are welcome! Here are several ways you can contribute:
See our Contribution Guidelines for details on how to contribute.
This project is protected under the GNU GPLv3 License.
See our Code of Conduct for details on expected behavior in our community.
- List any resources, contributors, inspiration, etc. here.