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[autoscaler] Switch to ARM for Azure deployment (#7717)

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* switch to ARM templates for config and VMs

* switch to ARM templates for config and VMs

* auto-formatting

* addressed Scotts comment

* added missing imports

* fixed gpu templates
fixed wheel reference

* added missing reference

* cleanup wording and yamls

* Update doc/source/autoscaling.rst

Co-Authored-By: Scott Graham <5720537+gramhagen@users.noreply.github.com>

Co-authored-by: Ubuntu <marcozo@marcozodev2.zqvgrdyupqrudayw1il1agipig.jx.internal.cloudapp.net>
Co-authored-by: Scott Graham <5720537+gramhagen@users.noreply.github.com>
This commit is contained in:
Markus Cozowicz
2020-04-03 15:51:56 -07:00
committed by GitHub
co-authored by Scott Graham Ubuntu
parent 1d4823c0ec
commit b853df7a3b
9 changed files with 496 additions and 357 deletions
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doc/source/autoscaling.rst
+14 -10
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@@ -59,8 +59,12 @@ Test that it works by running the following commands from your local machine:
# Get a remote screen on the head node.
$ ray attach ray/python/ray/autoscaler/azure/example-full.yaml
$ source activate tensorflow_p36
$ # Try running a Ray program with 'ray.init(address="auto")'.
# test ray setup
# enable conda environment
$ exec bash -l
$ conda activate py37_tensorflow
$ python -c 'import ray; ray.init()'
$ exit
# Tear down the cluster.
$ ray down ray/python/ray/autoscaler/azure/example-full.yaml
@@ -69,26 +73,26 @@ Azure Portal
Alternatively, you can deploy a cluster using Azure portal directly. Please note that auto scaling is done using Azure VM Scale Sets and not through
the Ray autoscaler. This will deploy `Azure Data Science VMs (DSVM) <https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/>`_
for both the head node and an auto-scale cluster managed by `Azure Virtual Machine Scale Sets <https://azure.microsoft.com/en-us/services/virtual-machine-scale-sets/>`_.
The head node conviently exposes both SSH as well as JupyterLab.
for both the head node and the auto-scalable cluster managed by `Azure Virtual Machine Scale Sets <https://azure.microsoft.com/en-us/services/virtual-machine-scale-sets/>`_.
The head node conveniently exposes both SSH as well as JupyterLab.
.. image:: https://aka.ms/deploytoazurebutton
:target: https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2Fray-project%2Fray%2Fmaster%2Fdoc%2Fazure%2Fazure-ray-template.json
:alt: Deploy to Azure
Once the template is successfully deploy the deployment output page provides the ssh command to connect and the link to the JupyterHub on the head node (username/password as specified on the template input).
Use the following code connect to the Ray cluster.
Once the template is successfully deployed the deployment output page provides the ssh command to connect and the link to the JupyterHub on the head node (username/password as specified on the template input).
Use the following code in a Jupyter notebook to connect to the Ray cluster.
.. code-block:: python
import ray
ray.init(address='auto')
Note that on each node the `azure-init.sh <https://github.com/ray-project/ray/blob/master/doc/azure/azure-init.sh>`_ script is executed and performs
Note that on each node the `azure-init.sh <https://github.com/ray-project/ray/blob/master/doc/azure/azure-init.sh>`_ script is executed and performs the following actions:
1. activate one of the conda environments available on DSVM
2. install Ray and any other user-specified dependencies
3. setup of a systemd task (``/lib/systemd/system/ray.service``) which starting ray in head or worker mode
1. Activates one of the conda environments available on DSVM
2. Installs Ray and any other user-specified dependencies
3. Sets up a systemd task (``/lib/systemd/system/ray.service``) to start Ray in head or worker mode
GCP
~~~