Add basic tutorial for usage of KubeRay without CodeFlare

Signed-off-by: Anish Asthana <[email protected]>
opendatahub-io · Sep 14, 2023 · 5469046 · 5469046
1 parent b3d5af1
commit 5469046
Show file tree

Hide file tree

Showing 3 changed files with 388 additions and 0 deletions.
diff --git a/ray/quick-start.md b/ray/quick-start.md
@@ -0,0 +1,13 @@
+# KubeRay quickstart
+
+There is a demo notebook in the tutorial directory that you can run to get started with KubeRay. It will walk you through the process of setting up a cluster and running a simple example.
+
+## Setup
+
+You can follow the instructions in the [base readme](../README.md) to install the Distributed Workolads stack.
+
+## Notebook
+
+At this point you should be able to go to your notebook spawner page and select your notebook image of choice.
+
+You can access the spawner page through the Open Data Hub dashboard. The default route should be `https://odh-dashboard-<your ODH namespace>.apps.<your cluster's uri>`. Once you are on your dashboard, you can select "Launch application" on the Jupyter application. This will take you to your notebook spawner page. After that, simply upload the notebook and ray cluster template from the tutorial directory and you should be good to go.
diff --git a/ray/tutorial/kuberay.ipynb b/ray/tutorial/kuberay.ipynb
@@ -0,0 +1,251 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66fe6c71-2cb2-4425-a12a-c5b531a28155",
+   "metadata": {},
+   "source": [
+    "# Using KubeRay to run Distributed Workloads without CodeFlare\n",
+    "\n",
+    "This notebook demonstrates a quick workflow using Ray from a notebook without the codeflare-sdk.\n",
+    "The current usage patterns for KubeRay require manual oc commands to be run from your notebook, so you will need to authenticate manually. We recommend usage of the codeflare-sdk alongside CodeFlare for an easier experience. An example notebook showing an almost identical usecase can be found at https://github.com/project-codeflare/codeflare-sdk/blob/main/demo-notebooks/guided-demos/3_basic_interactive.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "163b9d63-709e-435e-9933-988328831eba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --upgrade ray==\"2.5.0\"\n",
+    "!pip install pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b436207-9d87-4ce1-8909-6116e729a753",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## You need to get your token to authenticate to the OpenShift Cluster.\n",
+    "\n",
+    "1. Go to the OpenShift Console\n",
+    "2. Click on the arrow next to your username\n",
+    "3. Click on \"Copy login command\"\n",
+    "4. Once authenticated, copy the entire section under \"Log in with this token. It will look similar to the following\n",
+    "oc login --token=<token> --server=<url>\n",
+    "5. Run the following cell, making sure to use your token and server. The \"!\" at the beginning of the command is required."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ea2c953-a9b7-4f19-941b-517a832ff379",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!oc login --token=<token> --server=<url>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "88eb9d0a-846d-43ce-8730-020ac05cd4e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raycluster.ray.io \"imdb-ray-test\" deleted\n",
+      "raycluster.ray.io/imdb-ray-test created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!oc delete -f test.yaml\n",
+    "!oc apply -f test.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb671ebf-7317-4ae2-bb65-81b16b1f78e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!oc get pods -o wide | grep imdb-ray-test |  awk '{print $1, $6, $7 }'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e8952d2-1633-4094-903c-a422b96ffbf5",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "As you can see from the above output, we have 2 worker nodes and a head node for the ray cluster. Each node has a separate IP address and different physical node it has been scheduled on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d53c20b0-19b4-437d-9694-174a6d443426",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!oc get svc | grep imdb-ray-test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11768c18-20c8-407c-9b4a-320264a0b8c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ray\n",
+    "from ray.air.config import ScalingConfig\n",
+    "\n",
+    "# Copy the service name from above. If you are using the default service and namespace,\n",
+    "# the ray_cluster_uri is ray://imdb-ray-test-head-svc.opendatahub.svc:10001\n",
+    "\n",
+    "ray_cluster_uri = \"ray://imdb-ray-test-head-svc.opendatahub.svc:10001\"\n",
+    "\n",
+    "#install additional libraries that will be required for model training\n",
+    "runtime_env = {\"pip\": [\"transformers\", \"datasets\", \"evaluate\", \"pyarrow<7.0.0\", \"accelerate\"]}\n",
+    "\n",
+    "# NOTE: This will work for in-cluster notebook servers (RHODS/ODH), but not for local machines\n",
+    "# To see how to connect from your laptop, go to demo-notebooks/additional-demos/local_interactive.ipynb\n",
+    "ray.init(address=ray_cluster_uri, runtime_env=runtime_env)\n",
+    "\n",
+    "print(\"Ray cluster is up and running: \", ray.is_initialized())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "566eba0c-6be2-4cb4-9aa4-9e147433642e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@ray.remote\n",
+    "def train_fn():\n",
+    "    from datasets import load_dataset\n",
+    "    import transformers\n",
+    "    from transformers import AutoTokenizer, TrainingArguments\n",
+    "    from transformers import AutoModelForSequenceClassification\n",
+    "    import numpy as np\n",
+    "    from datasets import load_metric\n",
+    "    import ray\n",
+    "    from ray import tune\n",
+    "    from ray.train.huggingface import HuggingFaceTrainer\n",
+    "\n",
+    "    dataset = load_dataset(\"imdb\")\n",
+    "    tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
+    "\n",
+    "    def tokenize_function(examples):\n",
+    "        return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n",
+    "\n",
+    "    tokenized_datasets = dataset.map(tokenize_function, batched=True)\n",
+    "\n",
+    "    #using a fraction of dataset but you can run with the full dataset\n",
+    "    small_train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42).select(range(100))\n",
+    "    small_eval_dataset = tokenized_datasets[\"test\"].shuffle(seed=42).select(range(100))\n",
+    "\n",
+    "    print(f\"len of train {small_train_dataset} and test {small_eval_dataset}\")\n",
+    "\n",
+    "    ray_train_ds = ray.data.from_huggingface(small_train_dataset)\n",
+    "    ray_evaluation_ds = ray.data.from_huggingface(small_eval_dataset)\n",
+    "\n",
+    "    def compute_metrics(eval_pred):\n",
+    "        metric = load_metric(\"accuracy\")\n",
+    "        logits, labels = eval_pred\n",
+    "        predictions = np.argmax(logits, axis=-1)\n",
+    "        return metric.compute(predictions=predictions, references=labels)\n",
+    "\n",
+    "    def trainer_init_per_worker(train_dataset, eval_dataset, **config):\n",
+    "        model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2)\n",
+    "\n",
+    "        training_args = TrainingArguments(\"/tmp/hf_imdb/test\", eval_steps=1, disable_tqdm=True, \n",
+    "                                          num_train_epochs=1, skip_memory_metrics=True,\n",
+    "                                          learning_rate=2e-5,\n",
+    "                                          per_device_train_batch_size=16,\n",
+    "                                          per_device_eval_batch_size=16,                                \n",
+    "                                          weight_decay=0.01,)\n",
+    "        return transformers.Trainer(\n",
+    "            model=model,\n",
+    "            args=training_args,\n",
+    "            train_dataset=train_dataset,\n",
+    "            eval_dataset=eval_dataset,\n",
+    "            compute_metrics=compute_metrics\n",
+    "        )\n",
+    "\n",
+    "    scaling_config = ScalingConfig(num_workers=2, use_gpu=False) #num workers is the number of gpus\n",
+    "\n",
+    "    # we are using the ray native HuggingFaceTrainer, but you can swap out to use non ray Huggingface Trainer. Both have the same method signature. \n",
+    "    # the ray native HFTrainer has built in support for scaling to multiple GPUs\n",
+    "    trainer = HuggingFaceTrainer(\n",
+    "        trainer_init_per_worker=trainer_init_per_worker,\n",
+    "        scaling_config=scaling_config,\n",
+    "        datasets={\"train\": ray_train_ds, \"evaluation\": ray_evaluation_ds},\n",
+    "    )\n",
+    "    result = trainer.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4458b86-8699-44b7-9785-d8ae8d0e29d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ray.get(train_fn.remote())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a525ec28-c485-43f5-afc1-155de4ed4149",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ray.cancel(ref)\n",
+    "ray.shutdown()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00fe92e0-abb8-47d5-8a6d-b49c78bd230c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!oc delete -f test.yaml"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ray/tutorial/test.yaml b/ray/tutorial/test.yaml
@@ -0,0 +1,124 @@
+apiVersion: ray.io/v1alpha1
+kind: RayCluster
+metadata:
+  labels:
+    controller-tools.k8s.io: '1.0'
+  name: imdb-ray-test
+  namespace: opendatahub
+spec:
+  autoscalerOptions:
+    idleTimeoutSeconds: 60
+    imagePullPolicy: Always
+    resources:
+      limits:
+        cpu: 500m
+        memory: 512Mi
+      requests:
+        cpu: 500m
+        memory: 512Mi
+    upscalingMode: Default
+  enableInTreeAutoscaling: false
+  headGroupSpec:
+    rayStartParams:
+      block: 'true'
+      dashboard-host: 0.0.0.0
+      num-gpus: '0'
+    serviceType: ClusterIP
+    template:
+      spec:
+        containers:
+        - env:
+          - name: MY_POD_IP
+            valueFrom:
+              fieldRef:
+                fieldPath: status.podIP
+          - name: RAY_USE_TLS
+            value: '0'
+          - name: RAY_TLS_SERVER_CERT
+            value: /home/ray/workspace/tls/server.crt
+          - name: RAY_TLS_SERVER_KEY
+            value: /home/ray/workspace/tls/server.key
+          - name: RAY_TLS_CA_CERT
+            value: /home/ray/workspace/tls/ca.crt
+          image: quay.io/project-codeflare/ray:2.5.0-py38-cu116
+          imagePullPolicy: Always
+          lifecycle:
+            preStop:
+              exec:
+                command:
+                - /bin/sh
+                - -c
+                - ray stop
+          name: ray-head
+          ports:
+          - containerPort: 6379
+            name: gcs
+          - containerPort: 8265
+            name: dashboard
+          - containerPort: 10001
+            name: client
+          resources:
+            limits:
+              cpu: 2
+              memory: 16G
+              nvidia.com/gpu: 0
+            requests:
+              cpu: 2
+              memory: 16G
+              nvidia.com/gpu: 0
+        imagePullSecrets: []
+  rayVersion: 2.5.0
+  workerGroupSpecs:
+  - groupName: small-group-jobtest
+    maxReplicas: 2
+    minReplicas: 2
+    rayStartParams:
+      block: 'true'
+      num-gpus: '0'
+    replicas: 2
+    template:
+      metadata:
+        annotations:
+          key: value
+      spec:
+        containers:
+        - env:
+          - name: MY_POD_IP
+            valueFrom:
+              fieldRef:
+                fieldPath: status.podIP
+          - name: RAY_USE_TLS
+            value: '0'
+          - name: RAY_TLS_SERVER_CERT
+            value: /home/ray/workspace/tls/server.crt
+          - name: RAY_TLS_SERVER_KEY
+            value: /home/ray/workspace/tls/server.key
+          - name: RAY_TLS_CA_CERT
+            value: /home/ray/workspace/tls/ca.crt
+          image: quay.io/project-codeflare/ray:2.5.0-py38-cu116
+          lifecycle:
+            preStop:
+              exec:
+                command:
+                - /bin/sh
+                - -c
+                - ray stop
+          name: machine-learning
+          resources:
+            limits:
+              cpu: 1
+              memory: 16G
+              nvidia.com/gpu: 0
+            requests:
+              cpu: 1
+              memory: 16G
+              nvidia.com/gpu: 0
+        imagePullSecrets: []
+        initContainers:
+        - command:
+          - sh
+          - -c
+          - until nslookup $RAY_IP.$(cat /var/run/secrets/kubernetes.io/serviceaccount/namespace).svc.cluster.local;
+            do echo waiting for myservice; sleep 2; done
+          image: busybox:1.28
+          name: init-myservice