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Distributed Machine Learning and Deep Learning Examples

Here we provide a guide for you to run AI related examples based on CloudTik AI runtime which includes selected ML/DL frameworks and libraries.

We provide these examples in two forms:

  1. Python jobs
  2. Jupyter notebooks

Running the examples using python jobs

Running Spark Distributed Deep Learning example

This example runs Spark distributed deep learning with Hyperopt, Horovod and Tensorflow Keras API. It trains a simple ConvNet on the MNIST dataset using Keras + Horovod using Cloudtik Spark Runtime.

Download Spark Deep Learning with Horovod and Tensorflow Keras and execute:

cloudtik submit /path/to/your-cluster-config.yaml local-download-path/mnist-keras-spark-horovod-hyperopt-mlflow.py -f "your-cloud-storage-fsdir"

Replace the cloud storage fsdir with the workspace cloud storage uri or hdfs dir. For example S3, "s3a://cloudtik-workspace-bucket"

Running Spark Distributed Machine Learning example

This example runs Spark distributed training using scikit-learn. It illustrates a complete end-to-end example of loading data, training a model, distributed hyperparameter tuning, and model inference under CloudTik Spark cluster. It also illustrates how to use MLflow and model Registry.

Download Spark Machine Learning with Scikit-Learn and execute:

cloudtik submit /path/to/your-cluster-config.yaml local-download-path/iris-scikit_learn-spark-hyperopt-mlflow.py -f "your-cloud-storage-fsdir"

Replace the cloud storage fsdir with the workspace cloud storage uri or hdfs dir. For example S3, "s3a://cloudtik-workspace-bucket"

Running the examples using Jupyter notebooks

Running Spark Distributed Deep Learning example

This notebook example runs Spark distributed deep learning with Hyperopt, Horovod and Tensorflow Keras API. It trains a simple ConvNet on the MNIST dataset using Keras + Horovod using Cloudtik Spark Runtime.

  1. Upload notebook Spark Deep Learning with Horovod and Tensorflow Keras to JupyterLab. You can also download and cloudtik rsync-up the file to ~/jupyter of cluster head:
cloudtik rsync-up /path/to/your-cluster-config.yaml local-download-path/mnist-keras-spark-horovod-hyperopt-mlflow.ipynb '~/jupyter/mnist-keras-spark-horovod-hyperopt-mlflow.ipynb'

  1. Open this notebook on JupyterLab, and choose the Python 3 kernel to run the notebook.

  2. Optionally, you can check the training experiments and model registry through MLflow Web UI after the notebook finishes.

Running Spark Distributed Machine Learning example

This notebook example runs Spark distributed training using scikit-learn. It illustrates a complete end-to-end example of loading data, training a model, distributed hyperparameter tuning, and model inference under CloudTik Spark cluster. It also illustrates how to use MLflow and model Registry.

  1. Upload notebook Spark Machine Learning with Scikit-Learn to JupyterLab. You can also download and cloudtik rsync-up the file to ~/jupyter of cluster head:
cloudtik rsync-up /path/to/your-cluster-config.yaml local-download-path/iris-scikit_learn-spark-hyperopt-mlflow.ipynb '~/jupyter/iris-scikit_learn-spark-hyperopt-mlflow.ipynb'

  1. Open this notebook on JupyterLab, and choose the Python 3 kernel to run the notebook.

  2. Optionally, you can check the training experiments and model registry through MLflow Web UI after the notebook finishes.