Last week, we released Databricks Runtime 5.
1 Beta for Machine Learning.
As part of our commitment to provide developers the latest deep learning frameworks, this release includes the best of these libraries.
In particular, our PyTorch addition makes it simple for a developer to simply import the appropriate Python torch modules and start coding, without installing all of its myriad dependencies.
In this blog, we briefly cover these additions.
PyTorch PyTorch project is a popular deep learning Python package that provides GPU accelerated tensor computation and high-level functionalities for building deep learning networks.
PyTorch provides flexible Tensors APIs that are similar to NumPy arrays but they can be accelerated on GPUs.
Several Databricks customers asked for built-in support for PyTorch, both for single-node and distributed deep learning applications using HorovodRunner.
With this release, we are including Pytorch version 0.
4.
1 along with tesorboardX version 1.
4.
In the coming release of Databricks Runtime for ML, we plan to include PyTorch 1.
0.
To get started quickly, we have included a few examples of how to use PyTorch on Databricks for single-node and distributed deep learning in our user guide.
Updated TensorFlow To keep abreast with the fast-moving TensorFlow project and provide our customers with its latest features, we have included the latest stable version of TensorFlow 1.
12 as part of Runtime for ML.
Other Machine Learning Packages We updated the following packages XGboost 0.
8.
1 TensorFrames v0.
6.
0 Spark Deep Learning v1.
4.
0-db2 Read More Read more about Databricks Runtime (DBR) 5.
1 Beta for ML Try the Horovod notebooks for Distributed Training on DBR ML 5.
1 Beta Try Databricks for free.
Get started today Related Terms:Term: Unified AnalyticsTerm: Databricks RuntimeTerm: TensorFlow.