Getting Started

note

This is a sample with databricks 10.4.x-gpu-ml-scala2.12 runtime

1. Reinstall horovod using our prepared script

We build on top of torchvision, horovod and pytorch_lightning, so we need to reinstall horovod by building on specific versions of those packages. Download our horovod installation script and upload it to databricks dbfs.

Add the path of this script to Init Scripts section when configuring the spark cluster. Restarting the cluster automatically installs horovod v0.25.0 with pytorch_lightning v1.5.0 and torchvision v0.12.0.

2. Install SynapseML Deep Learning Component

You could install the single synapseml-deep-learning wheel package to get the full functionality of deep vision classification. Run the following command:

pip install synapseml==1.0.11

An alternative is installing the SynapseML jar package in library management section, by adding:

Coordinate: com.microsoft.azure:synapseml_2.12:1.0.11
Repository: https://mmlspark.azureedge.net/maven

note

If you install the jar package, follow the first two cells of this sample to ensure horovod recognizes SynapseML.

3. Try our sample notebook

You could follow the rest of this [sample](../Quickstart%20-%20Fine-Tune a Vision Classifier) and have a try on your own dataset.

Supported models (backbone parameter for DeepVisionClassifer) should be string format of Torchvision-supported models; You could also check by running backbone in torchvision.models.__dict__.