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azure links
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Shaden Smith authored and Shaden Smith committed Mar 18, 2020
commit 8fe182b3397f1f350442156a731a17d938994766
2 changes: 2 additions & 0 deletions docs/_data/navigation.yml
Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,8 @@ lnav:
- title: "Tutorials"
url: /tutorials/
children:
- title: "Getting Started on Azure"
url: /tutorials/azure/
- title: "CIFAR-10"
url: /tutorials/cifar-10/
- title: "Megatron-LM GPT2"
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10 changes: 6 additions & 4 deletions docs/_tutorials/azure.md
Original file line number Diff line number Diff line change
@@ -1,4 +1,6 @@
# DeepSpeed with Azure
---
title: "Getting Started with DeepSpeed on Azure"
---

This tutorial will help you get started running DeepSpeed on [Azure virtual
machines](https://azure.microsoft.com/en-us/services/virtual-machines/).
Expand Down Expand Up @@ -58,7 +60,7 @@ Next, we need to configure the VM environment for DeepSpeed. We provide a script
```bash
./setup_vms.sh
```
to generate a [hostfile](../README.md#resource-configuration) and SSH
to generate a [hostfile](/getting-started/#resource-configuration-multi-node) and SSH
configuration on all of the VMs. This configuration will be used by the DeepSpeed
Docker containers in the next step.

Expand All @@ -72,7 +74,7 @@ background. This will take several minutes since it needs to pull the entire Doc
image.

## Access VMs
The tool [azure_ssh.sh](azure_ssh.sh) will let you SSH into any of the VMs with this
The tool `azure_ssh.sh` will let you SSH into any of the VMs with this
syntax:
```bash
./azure_ssh.sh <node-id> [command]
Expand Down Expand Up @@ -122,7 +124,7 @@ the first DeepSpeed container:

## Megatron-LM GPT2
DeepSpeed includes an example model using Megatron-LM's GPT2. Please refer to the full
[Megatron tutorial](../docs/tutorials/MegatronGPT2Tutorial.md) for more details.
[Megatron tutorial](/tutorials/megatron/) for more details.
* In order to fully train GPT2 with DeepSpeed and ZeRO we recommend using 8 instances of
Azure's Standard_ND40rs_v2 SKU for a total of 64 NVIDIA V100 GPUs. With this setup and
a batch size of 1536 you should be able to complete 100k training steps (153.6 million
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