From 6c69f47a3c300962fc9c7b45468e15378fdf4594 Mon Sep 17 00:00:00 2001
From: Hiroshi Yoshioka <40815708+hyoshioka0128@users.noreply.github.com>
Date: Sat, 21 Dec 2024 06:18:51 +0900
Subject: [PATCH] =?UTF-8?q?Update=20README.md=20(Typo=20"a=20azure"?=
 =?UTF-8?q?=E2=86=92"an=20azure")?=
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https://github.com/microsoft/ray-on-aml/blob/master/README.md
#PingMSFTDocs
---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 09b1b7d..1827379 100644
--- a/README.md
+++ b/README.md
@@ -32,7 +32,7 @@ __Support user define docker environment to greater customize ray environment__
 
 
 ## Option 1: Run ray workload within an [azure ml job](https://learn.microsoft.com/en-us/cli/azure/ml/job?view=azure-cli-latest) (non-interactive mode)
- 1. Setup a [azure ml compute cluster](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python) 
+ 1. Setup an [azure ml compute cluster](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python) 
  2. Include ray-on-aml,azureml-defaults, azureml-mlflow and ray package(s) as job dependencies like below in conda or in your job's [environment](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?tabs=cli)
  ```
 channels:
@@ -66,7 +66,7 @@ There's no need for vnet setup.
 If you like setup an interactive ray cluster to work with from a ray client or directly on the head node, follow the following setup:
 ## Option 2: Use ray cluster interactively 
 You can setup a ray cluster and use it to develop and test interactively either from a head node or with a [ray client](https://docs.ray.io/en/latest/cluster/running-applications/job-submission/ray-client.html)
-For this, ray-on-aml relies on a [AML Compute Instance](https://learn.microsoft.com/en-us/azure/machine-learning/concept-compute-instance) (CI) as the head node or ray client machine and [AML compute cluster](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python) as a complete remote ray cluster in case the CI is used as ray client only or ray cluster worker(s) in case the CI is used as head node.
+For this, ray-on-aml relies on an [AML Compute Instance](https://learn.microsoft.com/en-us/azure/machine-learning/concept-compute-instance) (CI) as the head node or ray client machine and [AML compute cluster](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=python) as a complete remote ray cluster in case the CI is used as ray client only or ray cluster worker(s) in case the CI is used as head node.
 
 ## Architecture for Interactive Mode