demo-notebooks/guided-demos/notebook-ex-outputs/1_cluster_job_client.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this demo we will go over the basics of the Ray Job Submission Client in the SDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pieces from codeflare-sdk\n",
    "from codeflare_sdk import Cluster, ClusterConfiguration, TokenAuthentication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create authentication object for user permissions\n",
    "# IF unused, SDK will automatically check for default kubeconfig, then in-cluster config\n",
    "# KubeConfigFileAuthentication can also be used to specify kubeconfig path manually\n",
    "auth = TokenAuthentication(\n",
    "    token = \"XXXXX\",\n",
    "    server = \"XXXXX\",\n",
    "    skip_tls=False\n",
    ")\n",
    "auth.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create and configure our cluster object\n",
    "# The SDK will try to find the name of your default local queue based on the annotation \"kueue.x-k8s.io/default-queue\": \"true\" unless you specify the local queue manually below\n",
    "cluster = Cluster(ClusterConfiguration(\n",
    "    name='jobtest',\n",
    "    namespace='default', # Update to your namespace\n",
    "    num_workers=2,\n",
    "    min_cpus=1,\n",
    "    max_cpus=1,\n",
    "    min_memory=4,\n",
    "    max_memory=4,\n",
    "    num_gpus=1,\n",
    "    head_gpus=1,\n",
    "    image=\"quay.io/project-codeflare/ray:latest-py39-cu118\",\n",
    "    write_to_file=False, # When enabled Ray Cluster yaml files are written to /HOME/.codeflare/resources \n",
    "    # local_queue=\"local-queue-name\" # Specify the local queue manually\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bring up the cluster\n",
    "cluster.up()\n",
    "cluster.wait_ready()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.details()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ray Job Submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Initialise the Cluster Job Client \n",
    "* Provide an entrypoint command directed to your job script\n",
    "* Set up your runtime environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the Job Submission Client\n",
    "\"\"\"\n",
    "The SDK will automatically gather the dashboard address and authenticate using the Ray Job Submission Client\n",
    "\"\"\"\n",
    "client = cluster.job_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submit an example mnist job using the Job Submission Client\n",
    "submission_id = client.submit_job(\n",
    "    entrypoint=\"python mnist_fashion.py\",\n",
    "    runtime_env={\"working_dir\": \"./\",\"pip\": \"requirements.txt\"},\n",
    ")\n",
    "print(submission_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the job's logs\n",
    "client.get_job_logs(submission_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the job's status\n",
    "client.get_job_status(submission_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get job related info\n",
    "client.get_job_info(submission_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List all existing jobs\n",
    "client.list_jobs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the logs of a job \n",
    "async for lines in client.tail_job_logs(submission_id):\n",
    "    print(lines, end=\"\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete a job\n",
    "# Can run client.cancel_job(submission_id) first if job is still running\n",
    "client.delete_job(submission_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.down()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "auth.logout()"
   ]
  }
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