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https://github.com/vale981/ray
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[clusters][docs] Provide urls to content, fix typos (#27936)
This commit is contained in:
Dmitri Gekhtman
scv119
parent
a08ed6ba75
commit
17a8db048f
6 changed files with 35 additions and 63 deletions
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@ -55,10 +55,7 @@ Broadly speaking, it is more efficient to use a few large Ray pods than many sma
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We recommend taking a look at the [config file][ConfigLink] applied in the following command.
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```shell
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# Starting from the parent directory of cloned Ray master,
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pushd ray/doc/source/cluster/kubernetes/configs/
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kubectl apply -f xgboost-benchmark.yaml
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popd
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kubectl apply -f https://raw.githubusercontent.com/ray-project/ray/releases/2.0.0/doc/source/cluster/kubernetes/configs/xgboost-benchmark.yaml
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```
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A Ray head pod and 9 Ray worker pods will be created.
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@ -66,7 +63,7 @@ A Ray head pod and 9 Ray worker pods will be created.
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```{admonition} Optional: Deploying an autoscaling Ray cluster
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If you've set up an autoscaling node group or pool, you may wish to deploy
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an autoscaling cluster by applying the config `xgboost-benchmark-autoscaler.yaml`.
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an autoscaling cluster by applying the config [xgboost-benchmark-autoscaler.yaml][ConfigLinkAutoscaling].
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One Ray head pod will be created. Once the workload starts, the Ray autoscaler will trigger
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creation of Ray worker pods. Kubernetes autoscaling will then create nodes to place the Ray pods.
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```
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@ -100,13 +97,13 @@ We'll use the {ref}`Ray Job Python SDK <ray-job-sdk>` to submit the XGBoost work
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```
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To submit the workload, run the above Python script.
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The script is available in the Ray repository.
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The script is available [in the Ray repository][XGBSubmit].
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```shell
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# From the parent directory of cloned Ray master.
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pushd ray/doc/source/cluster/doc_code/
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# Download the above script.
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curl https://raw.githubusercontent.com/ray-project/ray/releases/2.0.0/doc/source/cluster/doc_code/xgboost_submit.py -o xgboost_submit.py
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# Run the script.
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python xgboost_submit.py
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popd
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```
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### Observe progress.
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@ -118,7 +115,7 @@ Use the following tools to observe its progress.
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To follow the job's logs, use the command printed by the above submission script.
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```shell
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# Subsitute the Ray Job's submission id.
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# Substitute the Ray Job's submission id.
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ray job logs 'raysubmit_xxxxxxxxxxxxxxxx' --follow
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```
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@ -184,6 +181,6 @@ kubectl delete raycluster raycluster-xgboost-benchmark
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If you're on a public cloud, don't forget to clean up the underlying
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node group and/or Kubernetes cluster.
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<!-- TODO: Fix this -->
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<!-- [ConfigLink]: https://raw.githubusercontent.com/ray-project/ray/291bba69fb90ee5e8401540ef55b7b74dd13f5c5/doc/source/cluster/ray-clusters-on-kubernetes/configs/xgboost-benchmark-autoscaler.yaml -->
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[ConfigLink]: https://github.com/ray-project/ray/tree/master/doc/source/cluster/
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[ConfigLink]:https://raw.githubusercontent.com/ray-project/ray/releases/2.0.0/doc/source/cluster/kubernetes/configs/xgboost-benchmark.yaml
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[ConfigLinkAutoscaling]: https://raw.githubusercontent.com/ray-project/ray/releases/2.0.0/doc/source/cluster/kubernetes/configs/xgboost-benchmark-autoscaler.yaml
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[XGBSubmit]: https://github.com/ray-project/ray/blob/releases/2.0.0/doc/source/cluster/doc_code/xgboost_submit.py
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@ -79,7 +79,7 @@
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"(kuberay-operator-deploy)=\n",
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"## Deploying the KubeRay operator\n",
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"\n",
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"Deploy the KubeRay Operator by cloning the KubeRay repo and applying the relevant configuration files from the master branch. "
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"Deploy the KubeRay Operator by applying the relevant configuration files from the KubeRay GitHub repo. "
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]
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},
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{
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@ -89,12 +89,12 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"! git clone https://github.com/ray-project/kuberay -b release-0.3\n",
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"\n",
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"# This creates the KubeRay operator and all of the resources it needs.\n",
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"! kubectl create -k kuberay/ray-operator/config/default\n",
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"! kubectl create -k \"github.com/ray-project/kuberay/ray-operator/config/default?ref=v0.3.0&timeout=90s\"\n",
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"\n",
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"# Note that we must use \"kubectl create\" in the above command. \"kubectl apply\" will not work due to https://github.com/ray-project/kuberay/issues/271"
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"# Note that we must use \"kubectl create\" in the above command. \"kubectl apply\" will not work due to https://github.com/ray-project/kuberay/issues/271\n",
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"\n",
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"# You may alternatively clone the KubeRay GitHub repo and deploy the operator's configuration from your local file system."
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]
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},
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{
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@ -156,8 +156,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Deploy the Ray Cluster CR:\n",
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"! kubectl apply -f kuberay/ray-operator/config/samples/ray-cluster.autoscaler.yaml\n",
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"# Deploy a sample Ray Cluster CR from the KubeRay repo:\n",
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"! kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/release-0.3/ray-operator/config/samples/ray-cluster.autoscaler.yaml\n",
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"\n",
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"# This Ray cluster is named `raycluster-autoscaler` because it has optional Ray Autoscaler support enabled."
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]
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@ -306,8 +306,7 @@
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"execution_count": 2,
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"id": "d3dae5fd",
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"metadata": {},
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"outputs": [
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],
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"outputs": [],
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"source": [
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"! kubectl get service raycluster-autoscaler-head-svc\n",
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"\n",
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@ -379,7 +378,7 @@
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"\n",
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"### Deleting a Ray Cluster\n",
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"\n",
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"To delete the Ray Cluster we deployed in this example, you can run either of the following commands."
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"To delete the Ray Cluster we deployed in this example, run the following command."
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]
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},
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{
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@ -393,25 +392,6 @@
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"! kubectl delete raycluster raycluster-autoscaler"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d7aa0221",
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"metadata": {},
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"source": [
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"**OR**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "112e6d2e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Delete by reference to the yaml file we used to define the RayCluster CR \n",
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"! kubectl delete -f kuberay/ray-operator/config/samples/ray-cluster.autoscaler.yaml"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0de87d9d",
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@ -450,7 +430,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"! kubectl delete -k kuberay/ray-operator/config/default"
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"! kubectl delete -k \"github.com/ray-project/kuberay/ray-operator/config/default?ref=v0.3.0&timeout=90s\""
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]
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},
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{
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@ -490,7 +470,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.11"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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@ -44,13 +44,12 @@ First, follow the [quickstart guide](kuberay-quickstart) to create an autoscalin
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```bash
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# Optionally use kind to run the examples locally.
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# kind create cluster
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# $ kind create cluster
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$ git clone https://github.com/ray-project/kuberay -b release-0.3
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# Create the KubeRay operator.
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$ kubectl create -k kuberay/ray-operator/config/default
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$ kubectl create -k "github.com/ray-project/kuberay/ray-operator/config/default?ref=v0.3.0&timeout=90s"
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# Create an autoscaling Ray cluster.
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$ kubectl apply -f kuberay/ray-operator/config/samples/ray-cluster.autoscaler.yaml
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$ kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/release-0.3/ray-operator/config/samples/ray-cluster.autoscaler.yaml
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```
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Now, we can run a Ray program on the head pod that uses [``request_resources``](ref-autoscaler-sdk) to scale the cluster to a total of 3 CPUs. The head and worker pods in our [example cluster config](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-cluster.autoscaler.yaml) each have a capacity of 1 CPU, and we specified a minimum of 1 worker pod. Thus, the request should trigger upscaling of one additional worker pod.
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@ -66,7 +65,7 @@ $ kubectl get pods --selector=ray.io/cluster=raycluster-autoscaler --selector=ra
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Then, we can run the Ray program using ``kubectl exec``:
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```bash
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$ kubectl exec raycluster-autoscaler-head-xxxxx -it -c ray-head -- python -c \"import ray; ray.init(); ray.autoscaler.sdk.request_resources(num_cpus=3)
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$ kubectl exec raycluster-autoscaler-head-xxxxx -it -c ray-head -- python -c "import ray; ray.init(); ray.autoscaler.sdk.request_resources(num_cpus=3)"
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```
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The last command should have triggered Ray pod upscaling. To confirm the new worker pod is up, let's query the RayCluster's pods again:
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@ -122,10 +122,7 @@ for instructions on this step.
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Now, run the following commands to deploy the Fluent Bit ConfigMap and a single-pod RayCluster with
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a Fluent Bit sidecar.
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```shell
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# Starting from the parent of cloned Ray master.
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pushd ray/doc/source/cluster/kubernetes/configs/
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kubectl apply -f ray-cluster.log.yaml
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popd
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kubectl apply -f https://raw.githubusercontent.com/ray-project/ray/releases/2.0.0/doc/source/cluster/kubernetes/configs/ray-cluster.log.yaml
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```
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Determine the Ray pod's name with
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@ -145,6 +142,4 @@ kubectl logs raycluster-complete-logs-head-xxxxx -c fluentbit
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[Fluentd]: https://docs.fluentd.org/
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[Promtail]: https://grafana.com/docs/loki/latest/clients/promtail/
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[KubDoc]: https://kubernetes.io/docs/concepts/cluster-administration/logging/
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<!-- TODO: fix this -->
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[ConfigLink]: https://github.com/ray-project/ray/tree/master/doc/source/cluster/
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<!-- [ConfigLink]: https://raw.githubusercontent.com/ray-project/ray/779e9f7c5733ef9a471ad2bb61723158ff942e92/doc/source/cluster/ray-clusters-on-kubernetes/configs/ray-cluster.log.yaml -->
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[ConfigLink]: https://raw.githubusercontent.com/ray-project/ray/releases/2.0.0/doc/source/cluster/kubernetes/configs/ray-cluster.log.yaml
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@ -70,13 +70,13 @@ We'll use the {ref}`Ray Job Python SDK <ray-job-sdk>` to submit the XGBoost work
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```
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To submit the workload, run the above Python script.
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The script is also available in the Ray repository.
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The script is available [in the Ray repository][XGBSubmit].
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```shell
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# From the parent directory of cloned Ray master.
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pushd ray/doc/source/cluster/doc_code/
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# Download the above script.
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curl https://raw.githubusercontent.com/ray-project/ray/releases/2.0.0/doc/source/cluster/doc_code/xgboost_submit.py -o xgboost_submit.py
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# Run the script.
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python xgboost_submit.py
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popd
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```
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### Observe progress
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@ -130,3 +130,5 @@ Delete your Ray cluster with the following command:
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```shell
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ray down -y cluster.yaml
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```
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[XGBSubmit]: https://github.com/ray-project/ray/blob/releases/2.0.0/doc/source/cluster/doc_code/xgboost_submit.py
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@ -151,9 +151,8 @@ class StandardAutoscaler:
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`ray start --head --autoscaling-config=/path/to/config.yaml` on a instance
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that has permission to launch other instances, or you can also use `ray up
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/path/to/config.yaml` from your laptop, which will configure the right
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AWS/Cloud roles automatically. See the documentation for a full definition
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of autoscaling behavior:
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https://docs.ray.io/en/master/cluster/autoscaling.html
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AWS/Cloud roles automatically. See the Ray documentation
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(https://docs.ray.io/en/latest/) for a full definition of autoscaling behavior.
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StandardAutoscaler's `update` method is periodically called in
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`monitor.py`'s monitoring loop.
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