This guide shows you how to serve a large language model (LLM) using Tensor Processing Units (TPUs) on Google Kubernetes Engine (GKE) with JetStream through PyTorch. In this guide, you download model weights to Cloud Storage and deploy them on a GKE Autopilot or Standard cluster using a container that runs JetStream.
If you need the scalability, resilience, and cost-effectiveness offered by Kubernetes features when deploying your model on JetStream, this guide is a good starting point.
This guide is intended for Generative AI customers who use PyTorch, new or existing users of GKE, ML Engineers, MLOps (DevOps) engineers, or platform administrators who are interested in using Kubernetes container orchestration capabilities for serving LLMs.
Background
By serving an LLM using TPUs on GKE with JetStream, you can build a robust, production-ready serving solution with all the benefits of managed Kubernetes, including cost-efficiency, scalability and higher availability. This section describes the key technologies used in this tutorial.
About TPUs
TPUs are Google's custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning and AI models built using frameworks such as TensorFlow, PyTorch, and JAX.
Before you use TPUs in GKE, we recommend that you complete the following learning path:
- Learn about current TPU version availability with the Cloud TPU system architecture.
- Learn about TPUs in GKE.
This tutorial covers serving various LLM models. GKE deploys the model on single-host TPUv5e nodes with TPU topologies configured based on the model requirements for serving prompts with low latency.
About JetStream
JetStream is an open source inference serving framework developed by Google. JetStream enables high-performance, high-throughput, and memory-optimized inference on TPUs and GPUs. JetStream provides advanced performance optimizations, including continuous batching, KV cache optimizations, and quantization techniques, to facilitate LLM deployment. JetStream enables PyTorch/XLA and JAX TPU serving to achieve optimal performance.
Continuous Batching
Continuous batching is a technique that dynamically groups incoming inference requests into batches, reducing latency and increasing throughput.
KV cache quantization
KV cache quantization involves compressing the key-value cache used in attention mechanisms, reducing memory requirements.
Int8 weight quantization
Int8 weight quantization reduces the precision of model weights from 32-bit floating point to 8-bit integers, leading to faster computation and reduced memory usage.
To learn more about these optimizations, refer to the JetStream PyTorch and JetStream MaxText project repositories.
About PyTorch
PyTorch is an open source machine learning framework developed by Meta and now part of the Linux Foundation umbrella. PyTorch provides high-level features such as tensor computation and deep neural networks.
Objectives
- Prepare a GKE Autopilot or Standard cluster with the recommended TPU topology based on the model characteristics.
- Deploy JetStream components on GKE.
- Get and publish your model.
- Serve and interact with the published model.
Architecture
This section describes the GKE architecture used in this tutorial. The architecture includes a GKE Autopilot or Standard cluster that provisions TPUs and hosts JetStream components to deploy and serve the models.
The following diagram shows you the components of this architecture:
This architecture includes the following components:
- A GKE Autopilot or Standard regional cluster.
- Two single-host TPU slice node pools that host the JetStream deployment.
- The Service component spreads inbound traffic to all
JetStream HTTP
replicas. JetStream HTTP
is an HTTP server which accepts requests as a wrapper to JetStream's required format and sends it to JetStream's GRPC client.JetStream-PyTorch
is a JetStream server that performs inferencing with continuous batching.
Before you begin
- Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
-
Make sure that billing is enabled for your Google Cloud project.
-
Enable the required API.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
-
Make sure that billing is enabled for your Google Cloud project.
-
Enable the required API.
-
Make sure that you have the following role or roles on the project: roles/container.admin, roles/iam.serviceAccountAdmin
Check for the roles
-
In the Google Cloud console, go to the IAM page.
Go to IAM - Select the project.
-
In the Principal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.
- For all rows that specify or include you, check the Role colunn to see whether the list of roles includes the required roles.
Grant the roles
-
In the Google Cloud console, go to the IAM page.
Go to IAM - Select the project.
- Click Grant access.
-
In the New principals field, enter your user identifier. This is typically the email address for a Google Account.
- In the Select a role list, select a role.
- To grant additional roles, click Add another role and add each additional role.
- Click Save.
-
- Ensure that you have sufficient quota for eight TPU v5e PodSlice Lite chips. In this tutorial, you use on-demand instances.
- Create a Hugging Face token, if you don't already have one.
Get access to the model
Get access to various models on Hugging Face for deployment to GKE
Gemma 7B-it
To get access to the Gemma model for deployment to GKE, you must first sign the license consent agreement.
- Access the Gemma model consent page on Hugging Face
- Login to Hugging Face if you haven't done so already.
- Review and accept the model Terms and Conditions.
Llama 3 8B
To get access to the Llama 3 model for deployment to GKE, you must first sign the license consent agreement.
- Access the Llama 3 model consent page on Hugging Face
- Login to Hugging Face if you haven't done so already.
- Review and accept the model Terms and Conditions.
Prepare the environment
In this tutorial, you use Cloud Shell to manage resources hosted on
Google Cloud. Cloud Shell comes preinstalled with the software you'll need
for this tutorial, including
kubectl
and
gcloud CLI.
To set up your environment with Cloud Shell, follow these steps:
In the Google Cloud console, launch a Cloud Shell session by clicking Activate Cloud Shell in the Google Cloud console. This launches a session in the bottom pane of Google Cloud console.
Set the default environment variables:
gcloud config set project PROJECT_ID export PROJECT_ID=$(gcloud config get project) export CLUSTER_NAME=CLUSTER_NAME export BUCKET_NAME=BUCKET_NAME export REGION=REGION export LOCATION=LOCATION export CLUSTER_VERSION=CLUSTER_VERSION
Replace the following values:
PROJECT_ID
: your Google Cloud project ID.CLUSTER_NAME
: the name of your GKE cluster.BUCKET_NAME
: the name of your Cloud Storage bucket. You don't need to specify thegs://
prefix.REGION
: the region where your GKE cluster, Cloud Storage bucket, and TPU nodes are located. The region contains zones where TPU v5e machine types are available (for example,us-west1
,us-west4
,us-central1
,us-east1
,us-east5
, oreurope-west4
). For Autopilot clusters, ensure that you have sufficient TPU v5e zonal resources for your region of choice.- (Standard cluster only)
LOCATION
: the zone where the TPU resources are available (for example,us-west4-a
). For Autopilot clusters, you don't need to specify the zone, only the region. CLUSTER_VERSION
: the GKE version, which must support the machine type that you want to use. Note that the default GKE version might not have availability for your target TPU. For a list of minimum GKE versions available by TPU machine type, see TPU availability in GKE.
Create and configure Google Cloud resources
Follow these instructions to create the required resources.
Create a GKE cluster
You can serve Gemma on TPUs in a GKE Autopilot or Standard cluster. We recommend that you use a Autopilot cluster for a fully managed Kubernetes experience. To choose the GKE mode of operation that's the best fit for your workloads, see Choose a GKE mode of operation.
Autopilot
Create an Autopilot GKE cluster:
gcloud container clusters create-auto CLUSTER_NAME \
--project=PROJECT_ID \
--region=REGION \
--cluster-version=CLUSTER_VERSION
Standard
Create a regional GKE Standard cluster that uses Workload Identity Federation for GKE:
gcloud container clusters create CLUSTER_NAME \ --enable-ip-alias \ --machine-type=e2-standard-4 \ --num-nodes=2 \ --cluster-version=CLUSTER_VERSION \ --workload-pool=PROJECT_ID.svc.id.goog \ --location=REGION
The cluster creation might take several minutes.
Create a TPU v5e node pool with a
2x4
topology and two nodes:gcloud container node-pools create tpu-nodepool \ --cluster=CLUSTER_NAME \ --machine-type=ct5lp-hightpu-8t \ --project=PROJECT_ID \ --num-nodes=2 \ --region=REGION \ --node-locations=LOCATION
Create a Cloud Storage bucket
Create a Cloud Storage bucket to store the converted checkpoint:
gcloud storage buckets create gs://BUCKET_NAME --location=REGION
Generate your Hugging Face CLI token in Cloud Shell
Generate a new Hugging Face token if you don't already have one:
- Click Your Profile > Settings > Access Tokens.
- Click New Token.
- Specify a Name of your choice and a Role of at least
Read
. - Click Generate a token.
- Edit permissions to your access token to have read access to your model's Hugging Face repository.
- Copy the generated token to your clipboard.
Create a Kubernetes Secret for Hugging Face credentials
In Cloud Shell, do the following:
Configure
kubectl
to communicate with your cluster:gcloud container clusters get-credentials CLUSTER_NAME --location=REGION
Create a Secret to store the Hugging Face credentials:
kubectl create secret generic huggingface-secret \ --from-literal=HUGGINGFACE_TOKEN=HUGGINGFACE_TOKEN
Replace
HUGGINGFACE_TOKEN
with your Hugging Face token.
Configure your workloads access using Workload Identity Federation for GKE
Assign a Kubernetes ServiceAccount to the application and configure that Kubernetes ServiceAccount to act as an IAM service account.
Create an IAM service account for your application:
gcloud iam service-accounts create wi-jetstream
Add an IAM policy binding for your IAM service account to manage Cloud Storage:
gcloud projects add-iam-policy-binding PROJECT_ID \ --member "serviceAccount:wi-jetstream@PROJECT_ID.iam.gserviceaccount.com" \ --role roles/storage.objectUser gcloud projects add-iam-policy-binding PROJECT_ID \ --member "serviceAccount:wi-jetstream@PROJECT_ID.iam.gserviceaccount.com" \ --role roles/storage.insightsCollectorService
Allow the Kubernetes ServiceAccount to impersonate the IAM service account by adding an IAM policy binding between the two service accounts. This binding allows the Kubernetes ServiceAccount to act as the IAM service account:
gcloud iam service-accounts add-iam-policy-binding wi-jetstream@PROJECT_ID.iam.gserviceaccount.com \ --role roles/iam.workloadIdentityUser \ --member "serviceAccount:PROJECT_ID.svc.id.goog[default/default]"
Annotate the Kubernetes service account with the email address of the IAM service account:
kubectl annotate serviceaccount default \ iam.gke.io/gcp-service-account=wi-jetstream@PROJECT_ID.iam.gserviceaccount.com
Deploy JetStream
Deploy the JetStream container to serve your model:
Save the following manifest as jetstream-pytorch-deployment.yaml
:
Gemma 7B-it
Llama 3 8B
The manifest sets the following key properties:
model_id
: the model name from Hugging Face (google/gemma-7b-it
,meta-llama/Meta-Llama-3-8B
) (see the supported models).override_batch_size
: the decoding batch size per device, where one TPU chip equals one device. This value defaults to30
.working_dir
: the working directory where the model checkpoint is or will be stored. Since the Cloud Storage bucket is mounted on your node, your workload can access checkpoints atgs://BUCKET_NAME/pytorch/org/repo
. The JetStream-PyTorch server downloads the model weights from Hugging Face to the working directory upon first use, taking a few minutes for a 7B or 8B model to load. For subsequent deployments, the JetStream-PyTorch server loads the model weights from the working directory, taking less time to load. For pre-existing checkpoints, ensure the working directory uses the same path convention.enable_model_warmup
: this setting enables model warmup after the model server has started. This value defaults toFalse
.
You can optionally set these properties:
max_input_length
: the maximum input sequence length. This value defaults to1024
.max_output_length
: the maximum output decode length, this value defaults to1024
.quantize_weights
: whether the checkpoint is quantized. This value defaults to0
; set it to 1 to enableint8
quantization.internal_jax_compilation_cache
: the directory for the JAX compilation cache. This value defaults to~/jax_cache
; set it togs://BUCKET_NAME/jax_cache
for remote caching.
In the manifest, a startup probe
is configured to ensure that the model server is labeled Ready
after the
model has been loaded and warmup has completed. Liveness and readiness probes are configured to ensure the healthiness of the model server.
Replace
BUCKET_NAME
with your GSBucket created earlier:sed -i "s|BUCKET_NAME|BUCKET_NAME|g" jetstream-pytorch-deployment.yaml
Apply the manifest:
kubectl apply -f jetstream-pytorch-deployment.yaml
Verify the Deployment:
kubectl get deployment
The output is similar to the following:
NAME READY UP-TO-DATE AVAILABLE AGE jetstream-pytorch-server 0/2 2 0 ##s
For Autopilot clusters, it may take a few minutes to provision the required TPU resources.
View the JetStream-PyTorch server logs to check that the model weights have been loaded and model warmup has completed. It might take the server a few minutes to complete this operation.
kubectl logs deploy/jetstream-pytorch-server -f -c jetstream-pytorch-server
The output is similar to the following:
Started jetstream_server.... 2024-04-12 04:33:37,128 - root - INFO - ---------Generate params 0 loaded.---------
Verify the Deployment is ready:
kubectl get deployment
The output is similar to the following:
NAME READY UP-TO-DATE AVAILABLE AGE jetstream-pytorch-server 2/2 2 2 ##s
It might take several minutes for the
healthcheck
endpoint to register.
Serve the model
In this section, you interact with the model.
Set up port forwarding
You can access the JetStream Deployment through the ClusterIP Service that you created in the preceding step. The ClusterIP Services are only reachable from within the cluster. Therefore, to access the Service from outside the cluster, complete the following steps:
To establish a port forwarding session, run the following command:
kubectl port-forward svc/jetstream-svc 8000:8000
Interact with the model using curl
Verify that you can access the JetStream HTTP server by opening a new terminal and running the following command:
curl --request POST \ --header "Content-type: application/json" \ -s \ localhost:8000/generate \ --data \ '{ "prompt": "What are the top 5 programming languages", "max_tokens": 200 }'
The initial request can take several seconds to complete due to model warmup. The output is similar to the following:
{ "response": " for data science in 2023?\n\n**1. Python:**\n- Widely used for data science due to its readability, extensive libraries (pandas, scikit-learn), and integration with other tools.\n- High demand for Python programmers in data science roles.\n\n**2. R:**\n- Popular choice for data analysis and visualization, particularly in academia and research.\n- Extensive libraries for statistical modeling and data wrangling.\n\n**3. Java:**\n- Enterprise-grade platform for data science, with strong performance and scalability.\n- Widely used in data mining and big data analytics.\n\n**4. SQL:**\n- Essential for data querying and manipulation, especially in relational databases.\n- Used for data analysis and visualization in various industries.\n\n**5. Scala:**\n- Scalable and efficient for big data processing and machine learning models.\n- Popular in data science for its parallelism and integration with Spark and Spark MLlib." }
You've successfully done the following:
- Deployed the JetStream-PyTorch model server on GKE using TPUs.
- Created a checkpoint at
gs://BUCKET_NAME/pytorch/org/repo
. - Served and interacted with the model.
Troubleshoot issues
- If you get the message
Empty reply from server
, it's possible the container has not finished downloading the model data. Check the Pod's logs again for theConnected
message which indicates that the model is ready to serve. - If you see
Connection refused
, verify that your port forwarding is active.
Clean up
To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.
Delete the deployed resources
To avoid incurring charges to your Google Cloud account for the resources that you created in this guide, run the following commands and follow the prompts:
gcloud container clusters delete CLUSTER_NAME --region=REGION
gcloud iam service-accounts delete wi-jetstream@PROJECT_ID.iam.gserviceaccount.com
gcloud storage rm --recursive gs://BUCKET_NAME
What's next
- Discover how you can run Gemma models on GKE and how to run optimized AI/ML workloads with GKE platform orchestration capabilities.
- Learn more about TPUs in GKE.
- Explore the JetStream GitHub repository.
- Explore the Vertex AI Model Garden.