Using The Azure Data Science VM With GPUs

Jennifer Marsman has some tips and tricks around using the Azure Data Science Virtual Machine on an instance running with GPU support:

To get GPU support, you need both hardware with GPUs in a datacenter, as well as the right software – namely, a virtual machine image that includes GPU drivers so you can use the GPU.

The biggest tip is to use the Deep Learning Virtual Machine!  The provisioning experience has been optimized to filter to the options that support GPU (the NC series – see below), which make it easier to set it up correctly.

Read on for the rest of the advice.

Related Posts

Data Science And Data Engineering In HDP 3.0

Saumitra Buragohain, et al, show off some of the things added to the Hortonworks Data Platform for data scientists and data engineers: We leverage the power of HDP 3.0 from efficient storage (erasure coding), GPU pooling to containerized TensorFlow and Zeppelin to enable this use case. We will the save the details for a different […]

Read More

Multi-Threaded R With Microsoft R Client

David Parr shows us how to get started with Microsoft R Client and performs some quick benchmarking: This message will pop up, and it’s worth noting as it’s got some information in it that you might need to think about: It’s worth noting that right now Microsoft r Client is lagging behind the current R version, and […]

Read More

Categories

June 2018
MTWTFSS
« May Jul »
 123
45678910
11121314151617
18192021222324
252627282930