XGBoost vs Tensorpack: What are the differences?

Developers describe XGBoost as "Scalable and Flexible Gradient Boosting". Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow. On the other hand, Tensorpack is detailed as "A neural network training interface based on TensorFlow". It is a Neural Net Training Interface on TensorFlow, with focus on speed + flexibility. It is a training interface based on TensorFlow, which means: you’ll use mostly tensorpack high-level APIs to do training, rather than TensorFlow low-level APIs.

XGBoost belongs to "Python Build Tools" category of the tech stack, while Tensorpack can be primarily classified under "Machine Learning Tools".

Some of the features offered by XGBoost are:

• Flexible
• Portable
• Multiple Languages

On the other hand, Tensorpack provides the following key features:

• Training interface based on TensorFlow
• Focus on training speed
• Focus on large datasets

Tensorpack is an open source tool with 5.36K GitHub stars and 1.64K GitHub forks. Here's a link to Tensorpack's open source repository on GitHub.