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https://github.com/dermatologist/kedro-tf-utils

Kedro pipelines for multimodal ML in TensorFlow.
https://github.com/dermatologist/kedro-tf-utils

hacktoberfest healthcare kedro multi-modal tensorflow

Last synced: 15 days ago
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Kedro pipelines for multimodal ML in TensorFlow.

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README 

        
# Kedro Tf Utils :toolbox:



Provides Kedro pipeline components for multi-modal fusion and training for [text](https://github.com/dermatologist/kedro-tf-text) and [image](https://github.com/dermatologist/kedro-tf-image) models for healthcare as in [X-rays](https://github.com/dermatologist/kedro-dicom) and radiology reports. Use this [template](https://github.com/dermatologist/kedro-multimodal) to import these pipelines and perform multi-modal ML.



[![kedro-tf-utils](https://github.com/dermatologist/kedro-tf-utils/blob/develop/notes/utils.drawio.svg)](https://github.com/dermatologist/kedro-tf-utils/blob/develop/notes/utils.drawio.svg)



## How to install



- pip install git+https://github.com/dermatologist/kedro-tf-utils.git



## How to build fusion model



```

from from kedro_tf_utils.pipelines.fusion.pipelines import create_fusion_pipeline



# Add any number of models after parameters

fusion_inputs = {

    "parameters": "params:fusion",

    "tabular_model": "tabular_model",

    "bert_model": "bert_model",

    "text_model": "text_model"

    "image_model": "chexnet_model",

}

fusion_pipeline = create_fusion_pipeline(**fusion_inputs)



# In pipeline_registry

"__default__": other_pipelines + fusion_pipeline

```



## How to build training pipeline



```

from from kedro_tf_utils.pipelines.train.pipelines import create_train_pipeline



# model and outputs are required

args = {"parameters": "params:train", "model": "fusion_model",

                  "bert_data": "text_data", "image_data": "image_data", "outputs": "trained_model"}

train_pipeline = create_train_pipeline(**args)

# In pipeline_registry

"__default__": train_pipeline

```



## How to build embedding pipeline

(For use with [kedro-graph](https://github.com/dermatologist/kedro-graph))



```

from from kedro_tf_utils.pipelines.embedding.pipelines import create_embedding_pipeline



# model and outputs are required

# model and data should be of same type.

# output is a PickleDataset

args = {"parameters": "params:embedding", "model": "tabular_model",

                  "tabular_data": "tabular_data", "outputs": "embeddings"}

embedding_pipeline = create_embedding_pipeline(**args)

# In pipeline_registry

"__default__": create_embedding_pipeline

```



## [Catalogue](conf/base/catalog.yml)

## [params:fusion](conf/base/parameters/fusion.yml)

* VISUALIZE: /path/to/save/model/structure.png

## [params:train](conf/base/parameters/train.yml)

* CALLBACKS: /path/to/save/checkpoints

* SERVING: /path/to/save/new/model/with/tf/serving/b64/image/layer

## TODO

* Checkpoints and model wrapped for TF serving b64 image string support only supports local folders currently.



## Troubleshoot



### BERT model has some issues related to tensorflow_text

* Downloaded BERT models will not copy vocab.txt in assets folder to the newly created fusion model. This has to be manually copied.

* The class_num in TfModelWeights must be equal to to NCLASSES during training. Otherwise it throws an error:  Tensorflow estimator ValueError: logits and labels must have the same shape ((?, 1) vs (?,))



## Contributors



* [Bell Eapen](https://nuchange.ca) | [![Twitter Follow](https://img.shields.io/twitter/follow/beapen?style=social)](https://twitter.com/beapen)

## Kedro Overview



This is your new Kedro project, which was generated using `Kedro 0.18.1`.



Take a look at the [Kedro documentation](https://kedro.readthedocs.io) to get started.



## Rules and guidelines



In order to get the best out of the template:



* Don't remove any lines from the `.gitignore` file we provide

* Make sure your results can be reproduced by following a [data engineering convention](https://kedro.readthedocs.io/en/stable/faq/faq.html#what-is-data-engineering-convention)

* Don't commit data to your repository

* Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in `conf/local/`



## How to install dependencies



Declare any dependencies in `src/requirements.txt` for `pip` installation and `src/environment.yml` for `conda` installation.



To install them, run:



```

pip install -r src/requirements.txt

```



## How to run your Kedro pipeline



You can run your Kedro project with:



```

kedro run

```



## How to test your Kedro project



Have a look at the file `src/tests/test_run.py` for instructions on how to write your tests. You can run your tests as follows:



```

kedro test

```



To configure the coverage threshold, go to the `.coveragerc` file.



## Project dependencies



To generate or update the dependency requirements for your project:



```

kedro build-reqs

```



This will `pip-compile` the contents of `src/requirements.txt` into a new file `src/requirements.lock`. You can see the output of the resolution by opening `src/requirements.lock`.



After this, if you'd like to update your project requirements, please update `src/requirements.txt` and re-run `kedro build-reqs`.



[Further information about project dependencies](https://kedro.readthedocs.io/en/stable/kedro_project_setup/dependencies.html#project-specific-dependencies)



## How to work with Kedro and notebooks



> Note: Using `kedro jupyter` or `kedro ipython` to run your notebook provides these variables in scope: `context`, `catalog`, and `startup_error`.

>

> Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run `pip install -r src/requirements.txt` you will not need to take any extra steps before you use them.



### Jupyter

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:



```

pip install jupyter

```



After installing Jupyter, you can start a local notebook server:



```

kedro jupyter notebook

```



### JupyterLab

To use JupyterLab, you need to install it:



```

pip install jupyterlab

```



You can also start JupyterLab:



```

kedro jupyter lab

```



### IPython

And if you want to run an IPython session:



```

kedro ipython

```



### How to convert notebook cells to nodes in a Kedro project

You can move notebook code over into a Kedro project structure using a mixture of [cell tagging](https://jupyter-notebook.readthedocs.io/en/stable/changelog.html#release-5-0-0) and Kedro CLI commands.



By adding the `node` tag to a cell and running the command below, the cell's source code will be copied over to a Python file within `src//nodes/`:



```

kedro jupyter convert 

```

> *Note:* The name of the Python file matches the name of the original notebook.



Alternatively, you may want to transform all your notebooks in one go. Run the following command to convert all notebook files found in the project root directory and under any of its sub-folders:



```

kedro jupyter convert --all

```



### How to ignore notebook output cells in `git`

To automatically strip out all output cell contents before committing to `git`, you can run `kedro activate-nbstripout`. This will add a hook in `.git/config` which will run `nbstripout` before anything is committed to `git`.



> *Note:* Your output cells will be retained locally.



## Package your Kedro project



[Further information about building project documentation and packaging your project](https://kedro.readthedocs.io/en/stable/tutorial/package_a_project.html)