CloudTik 1.2.0 Release

CloudTik 1.2.0 release includes the following new features and enhancements:

• AI modeling for Graph SAGE neural network. Support inductive and transductive learning on homogeneous and heterogeneous graph with single or distributed training.
• AI modeling for transfer learning for common image and text classification PyTorch and TensorFlow models and datasets.
• AI modeling for XGBoost for regression and classification tasks.
• Application: Quick start application for distributed training of common AI models including BERT-large, DLRM, Mask R-CNN, ResNet-50, ResNeXt101, RNN-T, SSD-ResNet-34.
• Application: Disease prediction application using image classification model and text classification model of transfer learning.
• Application: Fraud detection application using Graph SAGE inductive model with XGBoost classification.
• Improve cluster rsync files with docker containers to be robust on avoiding dirty directories.
• Support both cluster local distributed storage and cloud storage for different data storing purposes: cloud storage for the input and final output data; cluster local storage for intermediate data shared in distributed analytics and AI training.
• Improve HDFS Fuse implementation for solving the async release issue (This is solving write after read problem). Improve HDFS NFS implementation to support set file size for setattr. (This is needed when opening and write an existing file in python)