Pure torch example on MNIST dataset

Integration author(s): Matteo Bunino (CERN)

In this simple use case integration we demostrate how to use itwinai for a set of simple use cases based on the popular MNIST dataset.

Training a CNN classifier

It is possible to launch the training of a CNN classifier on the MNIST dataset using the YAML configuration file describing the whole training workflow.

```bash

Run the whole training pipeline

itwinai exec-pipeline --config-name config.yaml ```

Notice that the training "pipeline" starts by downloading the dataset if not available locally. Since on some HPC systems there is no internet connection on the compute nodes, it is advisable to run the dataloading step on the login node to download the dataset and, later, the whole pipeline on the compute nodes. To do that, you can use the pipe_steps option as below:

```bash

Download dataset and exit

itwinai exec-pipeline --config-name config.yaml +pipesteps=[dataloadingstep]

Run the whole pipeline

itwinai exec-pipeline --config-name config.yaml ```

[!NOTE] Setting HYDRA_FULL_ERROR=1 environment variable can be convenient when debugging errors that originate during the instantiation of the pipeline.

View training logs on MLFLow server (if activated from the configuration):

bash mlflow ui --backend-store-uri mllogs/mlflow/

Hyper-parameter optimization

The CNN classifier can undergo hyper-parameter optimization (HPO) to find the hyper-parameters, such as learning rate and batch size, that result in the best validation performances.

To do so, it is enough to correctly set the search_space and the tune_config in the trainer configuration in the config.yaml file. Please refer to the Ray's official documentation to know more about RunConfig, TuneConfig, ScalingConfig, and search spaces.

Inference

Now you can use the trained model to make predictions on the MNIST dataset. Notice that the inference is defined by using a different pipeline in the config.yaml file. By default, the training_pipeline is executed, but you can run other piplines by explicitly setting the +pipe_key option.

1. Create sample dataset

   python from dataloader import InferenceMNIST InferenceMNIST.generate_jpg_sample('mnist-sample-data/', 10)

2. Generate a dummy pre-trained neural network

   python import torch from model import Net dummy_nn = Net() torch.save(dummy_nn, 'mnist-pre-trained.pth')

3. Run inference command. This will generate a "mnist-predictions" folder containing a CSV file with the predictions as rows.

   bash itwinai exec-pipeline --config-name config.yaml +pipe_key=inference_pipeline

Note the same entry point as for training.

Training a GAN

In this use case you can also find an example on how to train a Generative Adversarial Network (GAN). All you need to do is specify that you wish to use the GAN by setting the +pipe_key option.

```bash

Train a GAN

itwinai exec-pipeline --config-name config.yaml +pipekey=trainingpipeline_gan ```

Docker image

Build from project root with

```bash

Local

docker buildx build -t itwinai:0.0.1-mnist-torch-0.1 -f use-cases/mnist/torch/Dockerfile .

Ghcr.io

docker buildx build -t ghcr.io/intertwin-eu/itwinai:0.0.1-mnist-torch-0.1 -f use-cases/mnist/torch/Dockerfile . docker push ghcr.io/intertwin-eu/itwinai:0.0.1-mnist-torch-0.1 ```

Training with Docker container

bash docker run -it --rm --name running-inference \ -v "$PWD":/usr/data ghcr.io/intertwin-eu/itwinai:0.01-mnist-torch-0.1 \ /bin/bash -c "itwinai exec-pipeline \ --config-path /usr/src/app \ +pipe_key=training_pipeline \ dataset_root=/usr/data/mnist-dataset "

Inference with Docker container

From wherever a sample of MNIST jpg images is available (folder called 'mnist-sample-data/'):

text ├── $PWD │ ├── mnist-sample-data | │ ├── digit_0.jpg | │ ├── digit_1.jpg | │ ├── digit_2.jpg ... | │ ├── digit_N.jpg

bash docker run -it --rm --name running-inference \ -v "$PWD":/usr/data ghcr.io/intertwin-eu/itwinai:0.01-mnist-torch-0.1 \ /bin/bash -c "itwinai exec-pipeline \ --config-path /usr/src/app \ +pipe_key=inference_pipeline \ test_data_path=/usr/data/mnist-sample-data \ inference_model_mlflow_uri=/usr/src/app/mnist-pre-trained.pth \ predictions_dir=/usr/data/mnist-predictions "

This command will store the results in a folder called "mnist-predictions":

text ├── $PWD │ ├── mnist-predictions | │ ├── predictions.csv