From 69be05abfc4a6e4310e1a6e3a567c45cf1724cde Mon Sep 17 00:00:00 2001 From: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com> Date: Fri, 13 Dec 2024 11:32:22 +0100 Subject: [PATCH 01/13] Add `examples/gke/deploy-paligemma-2-with-tgi` example (WIP) --- .../gke/deploy-paligemma-2-with-tgi/README.md | 210 ++++++++++++++++++ .../config/deployment.yaml | 51 +++++ .../config/ingress.yaml | 18 ++ .../config/service.yaml | 12 + 4 files changed, 291 insertions(+) create mode 100644 examples/gke/deploy-paligemma-2-with-tgi/README.md create mode 100644 examples/gke/deploy-paligemma-2-with-tgi/config/deployment.yaml create mode 100644 examples/gke/deploy-paligemma-2-with-tgi/config/ingress.yaml create mode 100644 examples/gke/deploy-paligemma-2-with-tgi/config/service.yaml diff --git a/examples/gke/deploy-paligemma-2-with-tgi/README.md b/examples/gke/deploy-paligemma-2-with-tgi/README.md new file mode 100644 index 00000000..3f73d7e9 --- /dev/null +++ b/examples/gke/deploy-paligemma-2-with-tgi/README.md @@ -0,0 +1,210 @@ +--- +title: Deploy PaliGemma2 with TGI DLC on GKE +type: inference +--- + +# Deploy PaliGemma2 with TGI DLC on GKE + +PaliGemma 2 is an updated multilingual vision-language model that combines the Gemma 2 language model with the SigLIP vision model, enabling it to process both images and text inputs to generate text outputs for various tasks including captioning, visual question answering, and object detection. Text Generation Inference (TGI) is a toolkit developed by Hugging Face for deploying and serving LLMs, with high performance text generation. And, Google Kubernetes Engine (GKE) is a fully-managed Kubernetes service in Google Cloud that can be used to deploy and operate containerized applications at scale using Google Cloud infrastructure. + +This example showcases how to deploy Google PaliGemma2 from the Hugging Face Hub on a GKE Cluster, running a purpose-built container to deploy LLMs and VLMs in a secure and managed environment with the Hugging Face DLC for TGI. Additionally, this example also presents different scenarios or use-cases where PaliGemma2 can be used. + +## Setup / Configuration + +> [!NOTE] +> Some configuration steps such as the `gcloud`, `kubectl`, and `gke-cloud-auth-plugin` installation are not required if running the example within the Google Cloud Cloud Shell, as the spawned shell already comes with those dependencies installed; as well as logged in within the current account and project selected on Google Cloud. + +Optionally, to avoid duplicating the following values within this example, for convenience you should set the following environment variable with your own Google Cloud values: + +```bash +export PROJECT_ID=your-project-id +export LOCATION=your-location +export CLUSTER_NAME=your-cluster-name +``` + +### Requirements + +First, you need to install both `gcloud` and `kubectl` in your local machine, which are the command-line tools for Google Cloud and Kubernetes, respectively, to interact with the Google Cloud and the GKE Cluster. + +- To install `gcloud`, follow the instructions at [Cloud SDK Documentation - Install the gcloud CLI](https://cloud.google.com/sdk/docs/install). +- To install `kubectl`, follow the instructions at [Kubernetes Documentation - Install Tools](https://kubernetes.io/docs/tasks/tools/#kubectl). + +Additionally, to use `kubectl` with the GKE Cluster credentials, you also need to install the `gke-gcloud-auth-plugin`, that can be installed with `gcloud` as follows: + +```bash +gcloud components install gke-gcloud-auth-plugin +``` + +> [!NOTE] +> Installing the `gke-gcloud-auth-plugin` does not need to be installed via `gcloud` specifically, to read more about the alternative installation methods, please visit the [GKE Documentation - Install kubectl and configure cluster access](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl#install_plugin). + +### Login and API enablement + +Then you need to login into your Google Cloud account and set the project ID to the one you want to use for the deployment of the GKE Cluster. + +```bash +gcloud auth login +gcloud auth application-default login # Required for local development +gcloud config set project $PROJECT_ID +``` + +Once you are logged in, you need to enable the necessary service APIs in Google Cloud, such as the Google Kubernetes Engine API, the Google Container Registry API, and the Google Container File System API, which are necessary for the deployment of the GKE Cluster and the Hugging Face DLC for TGI. + +```bash +gcloud services enable container.googleapis.com +gcloud services enable containerregistry.googleapis.com +gcloud services enable containerfilesystem.googleapis.com +``` + +### PaliGemma2 gating and Hugging Face access token + +As [`google/paligemma2-3b-pt-224`](https://huggingface.co/google/paligemma2-3b-pt-224) is a gated model, as well as the rest of the PaliGemma2 released weights on the Hugging Face Hub (see them all in [the Google PaliGemma2 Collection on the Hub](https://huggingface.co/collections/google/paligemma-2-release-67500e1e1dbfdd4dee27ba48)), you need to first accept their gating / licensing in the model card, in order to be able to download the weights. + +![PaliGemma2 Gating on the Hugging Face Hub](./imgs/model-gating.png) + +Once you have been granted access to the PaliGemma2 model on the Hub, you should be able to generate either a fine-grained or a read-access token to be able to download [`google/paligemma2-3b-pt-224`](https://huggingface.co/google/paligemma2-3b-pt-224) model weights (or every model under the [`google`](https://huggingface.co/google) organization on the Hub), or to all the models your account has access to, respectively. To generate access tokens for the Hugging Face Hub you can follow the instructions at [Hugging Face Hub Documentation - User access tokens](https://huggingface.co/docs/hub/en/security-tokens). + +After the access token is generated, the recommended way of setting it is via the Python CLI `huggingface-cli` that comes with the `huggingface_hub` Python SDK, that can be installed as follows: + +```bash +pip install --upgrade --quiet huggingface_hub +``` + +And then login in with the generated access token with read-access over the gated/private model as: + +```bash +huggingface-cli login +``` + +## Create GKE Cluster + +To deploy the GKE Cluster, the "Autopilot" mode will be used as it is the recommended one for most of the workloads, since the underlying infrastructure is managed by Google; meaning that there's no need to create a node pool in advance or set up their ingress. Alternatively, you can also use the "Standard" mode, but that may require more configuration steps and being more aware / knowledgeable of Kubernetes. + +> [!NOTE] +> Before creating the GKE Autopilot Cluster on a different version than the one pinned below, you should read the [GKE Documentation - Optimize Autopilot Pod performance by choosing a machine series](https://cloud.google.com/kubernetes-engine/docs/how-to/performance-pods) page, as not all the Kubernetes versions available on GKE support GPU accelerators (e.g. `nvidia-l4` is not supported on GKE for Kubernetes 1.28.3 or lower). + +```bash +gcloud container clusters create-auto $CLUSTER_NAME \ + --project=$PROJECT_ID \ + --location=$LOCATION \ + --release-channel=stable \ + --cluster-version=1.30 \ + --no-autoprovisioning-enable-insecure-kubelet-readonly-port +``` + +> [!NOTE] +> If you want to change the Kubernetes version running on the GKE Cluster, you can do so, but make sure to check which are the latest supported Kubernetes versions in the location where you want to create the cluster on, with the following command: +> +> ```bash +> gcloud container get-server-config \ +> --flatten="channels" \ +> --filter="channels.channel=STABLE" \ +> --format="yaml(channels.channel,channels.defaultVersion)" \ +> --location=$LOCATION +> ``` +> +> Additionally, note that you can also use the "RAPID" channel instead of the "STABLE" if you require any Kubernetes feature not shipped yet within the latest Kubernetes version released on the "STABLE" channel, even though using the "STABLE" channel is recommended. For more information please visit . + +![GKE Cluster in the Google Cloud Console](./imgs/gke-cluster.png) + +## Get GKE Cluster Credentials + +Once the GKE Cluster is created, you need to get the credentials to access it via `kubectl`: + +```bash +gcloud container clusters get-credentials $CLUSTER_NAME --location=$LOCATION +``` + +Then you will be ready to use `kubectl` commands that will be calling the Kubernetes Cluster you just created on GKE. + +## Set Hugging Face Secrets on GKE + +As [`google/paligemma2-3b-pt-224`](https://huggingface.co/google/paligemma2-3b-pt-224) is a gated model and requires a Hugging Face Hub access token to download the weights [as mentioned before](#paligemma2-gating-and-hugging-face-access-token), you need to set a Kubernetes secret with the Hugging Face Hub token previously generated, with the following command (assuming that you have the `huggingface_hub` Python SDK installed): + +```bash +kubectl create secret generic hf-secret \ + --from-literal=hf_token=$(python -c "from huggingface_hub import get_token; print(get_token())") \ + --dry-run=client -o yaml | kubectl apply -f - +``` + +Alternatively, even if not recommended, you can also directly set the access token pasting it within the `kubectl` command as follows (make sure to replace that with your own token): + +```bash +kubectl create secret generic hf-secret \ + --from-literal=hf_token=hf_*** \ + --dry-run=client -o yaml | kubectl apply -f - +``` + +![GKE Secret in the Google Cloud Console](./imgs/gke-secrets.png) + +More information on how to set Kubernetes secrets in a GKE Cluster at . + +## Deploy TGI on GKE + +Now you can proceed to the Kubernetes deployment of the Hugging Face DLC for TGI, serving the [`google/paligemma2-3b-pt-224`](https://huggingface.co/google/paligemma2-3b-pt-224) model from the Hugging Face Hub. + +> [!NOTE] +> To explore all the models that can be served via TGI, you can explore the models tagged with `text-generation-inference` in the Hub at . + +The Hugging Face DLC for TGI will be deployed via `kubectl`, from the configuration files in the [`config/`](./config/) directory: + +- [`deployment.yaml`](./config/deployment.yaml): contains the deployment details of the pod including the reference to the Hugging Face DLC for TGI setting the `MODEL_ID` to [`google/paligemma2-3b-pt-224`](https://huggingface.co/google/paligemma2-3b-pt-224). +- [`service.yaml`](./config/service.yaml): contains the service details of the pod, exposing the port 8080 for the TGI service. +- (optional) [`ingress.yaml`](./config/ingress.yaml): contains the ingress details of the pod, exposing the service to the external world so that it can be accessed via the ingress IP. + +```bash +git clone https://github.com/huggingface/Google-Cloud-Containers +kubectl apply -f Google-Cloud-Containers/examples/gke/deploy-paligemma-2/config +``` + +![GKE Deployment in the Google Cloud Console](./imgs/gke-deployment.png) + +> [!NOTE] +> The Kubernetes deployment may take a few minutes to be ready, so you can check the status of the deployment with the following command: +> +> ```bash +> kubectl get pods +> ``` +> +> Alternatively, you can just wait for the deployment to be ready with the following command: +> +> ```bash +> kubectl wait --for=condition=Available --timeout=700s deployment/tgi +> ``` + +## Inference with TGI on GKE + +To run the inference over the deployed TGI service, you can either: + +- Port-forwarding the deployed TGI service to the port 8080, so as to access via `localhost` with the command: + + ```bash + kubectl port-forward service/tgi 8080:8080 + ``` + +- Accessing the TGI service via the external IP of the ingress, which is the default scenario here since you have defined the ingress configuration in the [`ingress.yaml`](./config/ingress.yaml) file (but it can be skipped in favour of the port-forwarding), that can be retrieved with the following command: + + ```bash + kubectl get ingress tgi -o jsonpath='{.status.loadBalancer.ingress[0].ip}' + ``` + +### Via cURL + +To send a POST request to the TGI service using `cURL`, you can run the following command: + +```bash +``` + +## Delete GKE Cluster + +Finally, once you are done using TGI on the GKE Cluster, you can safely delete the GKE Cluster to avoid incurring in unnecessary costs. + +```bash +gcloud container clusters delete $CLUSTER_NAME --location=$LOCATION +``` + +Alternatively, you can also downscale the replicas of the deployed pod to 0 in case you want to preserve the cluster, since the default GKE Cluster deployed with GKE Autopilot mode is running just a single `e2-small` instance. + +```bash +kubectl scale --replicas=0 deployment/tgi +``` diff --git a/examples/gke/deploy-paligemma-2-with-tgi/config/deployment.yaml b/examples/gke/deploy-paligemma-2-with-tgi/config/deployment.yaml new file mode 100644 index 00000000..d66b6112 --- /dev/null +++ b/examples/gke/deploy-paligemma-2-with-tgi/config/deployment.yaml @@ -0,0 +1,51 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: tgi +spec: + replicas: 1 + selector: + matchLabels: + app: tgi + template: + metadata: + labels: + app: tgi + hf.co/model: google--paligemma2-3b-pt-224 + hf.co/task: text-generation + spec: + containers: + - name: tgi + image: "us-central1-docker.pkg.dev/gcp-partnership-412108/deep-learning-images/huggingface-text-generation-inference-gpu.3.0.1" + # image: "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-generation-inference-cu124.3-0.ubuntu2204.py311" + resources: + requests: + nvidia.com/gpu: 1 + limits: + nvidia.com/gpu: 1 + env: + - name: MODEL_ID + value: google/paligemma2-3b-pt-224 + - name: NUM_SHARD + value: "1" + - name: PORT + value: "8080" + - name: HF_TOKEN + valueFrom: + secretKeyRef: + name: hf-secret + key: hf_token + volumeMounts: + - mountPath: /dev/shm + name: dshm + - mountPath: /tmp + name: tmp + volumes: + - name: dshm + emptyDir: + medium: Memory + sizeLimit: 1Gi + - name: tmp + emptyDir: {} + nodeSelector: + cloud.google.com/gke-accelerator: nvidia-l4 diff --git a/examples/gke/deploy-paligemma-2-with-tgi/config/ingress.yaml b/examples/gke/deploy-paligemma-2-with-tgi/config/ingress.yaml new file mode 100644 index 00000000..3f668d01 --- /dev/null +++ b/examples/gke/deploy-paligemma-2-with-tgi/config/ingress.yaml @@ -0,0 +1,18 @@ +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: tgi + # https://cloud.google.com/kubernetes-engine/docs/concepts/ingress + annotations: + kubernetes.io/ingress.class: "gce" +spec: + rules: + - http: + paths: + - path: / + pathType: Prefix + backend: + service: + name: tgi + port: + number: 8080 diff --git a/examples/gke/deploy-paligemma-2-with-tgi/config/service.yaml b/examples/gke/deploy-paligemma-2-with-tgi/config/service.yaml new file mode 100644 index 00000000..1dea9865 --- /dev/null +++ b/examples/gke/deploy-paligemma-2-with-tgi/config/service.yaml @@ -0,0 +1,12 @@ +apiVersion: v1 +kind: Service +metadata: + name: tgi +spec: + selector: + app: tgi + type: ClusterIP + ports: + - protocol: TCP + port: 8080 + targetPort: 8080 From 8709ae0be0febf70af928599005b0ba425e8e9b8 Mon Sep 17 00:00:00 2001 From: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com> Date: Fri, 13 Dec 2024 12:31:20 +0100 Subject: [PATCH 02/13] Update `examples/gke/deploy-paligemma-2-with-tgi/README.md` --- .../gke/deploy-paligemma-2-with-tgi/README.md | 123 ++++++++++++++++-- 1 file changed, 114 insertions(+), 9 deletions(-) diff --git a/examples/gke/deploy-paligemma-2-with-tgi/README.md b/examples/gke/deploy-paligemma-2-with-tgi/README.md index 3f73d7e9..2620d443 100644 --- a/examples/gke/deploy-paligemma-2-with-tgi/README.md +++ b/examples/gke/deploy-paligemma-2-with-tgi/README.md @@ -141,22 +141,120 @@ More information on how to set Kubernetes secrets in a GKE Cluster at [!NOTE] -> To explore all the models that can be served via TGI, you can explore the models tagged with `text-generation-inference` in the Hub at . +You can either deploy by copying the content above into a file named `deployment.yaml` and then deploy it with the following command: -The Hugging Face DLC for TGI will be deployed via `kubectl`, from the configuration files in the [`config/`](./config/) directory: +```bash +kubectl apply -f deployment.yaml +``` + +If you also want to deploy the Ingress to e.g. expose a public IP to access the Service, then you should then copy the following content into a file named `ingress.yaml`: + +```yaml +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: tgi + # https://cloud.google.com/kubernetes-engine/docs/concepts/ingress + annotations: + kubernetes.io/ingress.class: "gce" +spec: + rules: + - http: + paths: + - path: / + pathType: Prefix + backend: + service: + name: tgi + port: + number: 8080 +``` -- [`deployment.yaml`](./config/deployment.yaml): contains the deployment details of the pod including the reference to the Hugging Face DLC for TGI setting the `MODEL_ID` to [`google/paligemma2-3b-pt-224`](https://huggingface.co/google/paligemma2-3b-pt-224). -- [`service.yaml`](./config/service.yaml): contains the service details of the pod, exposing the port 8080 for the TGI service. -- (optional) [`ingress.yaml`](./config/ingress.yaml): contains the ingress details of the pod, exposing the service to the external world so that it can be accessed via the ingress IP. +And, then deploy it with the following command too: ```bash -git clone https://github.com/huggingface/Google-Cloud-Containers -kubectl apply -f Google-Cloud-Containers/examples/gke/deploy-paligemma-2/config +kubectl apply -f ingress.yaml ``` +> [!NOTE] +> Alternatively, you can just clone [the `huggingface/Google-Cloud-Containers` repository from GitHub](https://github.com/huggingface/Google-Cloud-Containers) and the apply the configuration including all the Kubernetes Manifests mentioned above as it follows: +> +> ```bash +> git clone https://github.com/huggingface/Google-Cloud-Containers +> kubectl apply -f Google-Cloud-Containers/examples/gke/deploy-paligemma-2-with-tgi/config +> ``` + ![GKE Deployment in the Google Cloud Console](./imgs/gke-deployment.png) > [!NOTE] @@ -193,8 +291,15 @@ To run the inference over the deployed TGI service, you can either: To send a POST request to the TGI service using `cURL`, you can run the following command: ```bash +curl http://localhost:8080/generate \ + -d '{"inputs":"![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)caption en\n","parameters":{"max_new_tokens":128}}' \ + -H 'Content-Type: application/json' ``` +| Image | Input | Output | +|------------------------------------------------------------------------------------------------------------|------------|-------------------------------| +| ![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png) | caption en | image of a man in a spacesuit | + ## Delete GKE Cluster Finally, once you are done using TGI on the GKE Cluster, you can safely delete the GKE Cluster to avoid incurring in unnecessary costs. From 3902d13854e45011a38f6e9663ac712db890d7de Mon Sep 17 00:00:00 2001 From: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com> Date: Fri, 13 Dec 2024 12:36:46 +0100 Subject: [PATCH 03/13] Update `examples/gke/deploy-paligemma-2-with-tgi/README.md` --- examples/gke/deploy-paligemma-2-with-tgi/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/gke/deploy-paligemma-2-with-tgi/README.md b/examples/gke/deploy-paligemma-2-with-tgi/README.md index 2620d443..9e1e180b 100644 --- a/examples/gke/deploy-paligemma-2-with-tgi/README.md +++ b/examples/gke/deploy-paligemma-2-with-tgi/README.md @@ -5,7 +5,7 @@ type: inference # Deploy PaliGemma2 with TGI DLC on GKE -PaliGemma 2 is an updated multilingual vision-language model that combines the Gemma 2 language model with the SigLIP vision model, enabling it to process both images and text inputs to generate text outputs for various tasks including captioning, visual question answering, and object detection. Text Generation Inference (TGI) is a toolkit developed by Hugging Face for deploying and serving LLMs, with high performance text generation. And, Google Kubernetes Engine (GKE) is a fully-managed Kubernetes service in Google Cloud that can be used to deploy and operate containerized applications at scale using Google Cloud infrastructure. +PaliGemma 2 is the latest multilingual vision-language model released by Google, that combines the Gemma2 language model with the SigLIP vision model, enabling it to process both images and text inputs to generate text outputs for various tasks including captioning, visual question answering, and object detection. Text Generation Inference (TGI) is a toolkit developed by Hugging Face for deploying and serving LLMs, with high performance text generation. Google Kubernetes Engine (GKE) is a fully-managed Kubernetes service in Google Cloud that can be used to deploy and operate containerized applications at scale using Google Cloud infrastructure. This example showcases how to deploy Google PaliGemma2 from the Hugging Face Hub on a GKE Cluster, running a purpose-built container to deploy LLMs and VLMs in a secure and managed environment with the Hugging Face DLC for TGI. Additionally, this example also presents different scenarios or use-cases where PaliGemma2 can be used. From 7b8929870aa01a87e81f1f48070b13d7c36ec3a6 Mon Sep 17 00:00:00 2001 From: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com> Date: Fri, 13 Dec 2024 12:45:40 +0100 Subject: [PATCH 04/13] Comment manifest separator due to `doc-builder` conflicts --- examples/gke/deploy-paligemma-2-with-tgi/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/gke/deploy-paligemma-2-with-tgi/README.md b/examples/gke/deploy-paligemma-2-with-tgi/README.md index 9e1e180b..f7492339 100644 --- a/examples/gke/deploy-paligemma-2-with-tgi/README.md +++ b/examples/gke/deploy-paligemma-2-with-tgi/README.md @@ -197,7 +197,7 @@ spec: emptyDir: {} nodeSelector: cloud.google.com/gke-accelerator: nvidia-l4 ---- +# --- apiVersion: v1 kind: Service metadata: From a4168b629bdd3a942ef8fb4335207a7376dade2f Mon Sep 17 00:00:00 2001 From: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com> Date: Sun, 15 Dec 2024 12:04:40 +0100 Subject: [PATCH 05/13] Add notes on PaliGemma prompt formatting and inference --- .../gke/deploy-paligemma-2-with-tgi/README.md | 103 +++++++++++++++--- 1 file changed, 90 insertions(+), 13 deletions(-) diff --git a/examples/gke/deploy-paligemma-2-with-tgi/README.md b/examples/gke/deploy-paligemma-2-with-tgi/README.md index f7492339..e8b5a75c 100644 --- a/examples/gke/deploy-paligemma-2-with-tgi/README.md +++ b/examples/gke/deploy-paligemma-2-with-tgi/README.md @@ -197,7 +197,7 @@ spec: emptyDir: {} nodeSelector: cloud.google.com/gke-accelerator: nvidia-l4 -# --- +--- apiVersion: v1 kind: Service metadata: @@ -258,33 +258,70 @@ kubectl apply -f ingress.yaml ![GKE Deployment in the Google Cloud Console](./imgs/gke-deployment.png) > [!NOTE] -> The Kubernetes deployment may take a few minutes to be ready, so you can check the status of the deployment with the following command: +> The Kubernetes deployment may take a few minutes to be ready, so you can check the status of the pod/s being deployed on the default namespace with the following command: > > ```bash > kubectl get pods > ``` > -> Alternatively, you can just wait for the deployment to be ready with the following command: +> Alternatively, you can just wait (700 seconds) for the deployment to be ready with the following command: > > ```bash > kubectl wait --for=condition=Available --timeout=700s deployment/tgi > ``` +## Accessing TGI on GKE + +To access the deployed TGI service, you have two options: + +1. Port-forwarding the service +2. Using the ingress (if configured) + +### Port-forwarding + +You can port-forward the deployed TGI service to port 8080 on your local machine using the following command: + +```bash +kubectl port-forward service/tgi 8080:8080 +``` + +This allows you to access the service via `localhost:8080`. + +### Accessing via Ingress + +If you've configured the ingress (as defined in the [`ingress.yaml`](./config/ingress.yaml) file), you can access the service using the external IP of the ingress. Retrieve the external IP with this command: + +```bash +kubectl get ingress tgi -o jsonpath='{.status.loadBalancer.ingress.ip}' +``` + ## Inference with TGI on GKE -To run the inference over the deployed TGI service, you can either: +First of all, you need to make sure that the service is healthy and reachable via either `localhost` or the ingress IP (depending on how you exposed the service as of the step above), with the following `curl` command: + +```bash +curl http://localhost:8080/health +``` + +Then before sending the `curl` request for inference, you need to note that the PaliGemma variant that you are serving is [`google/paligemma2-3b-pt-224`](https://huggingface.co/google/paligemma2-3b-pt-224) i.e. the pre-trained variant, meaning that's not particularly usable out of the box for any task, but just to transfer well to other tasks after the fine-tuning; anyway, it's pre-trained on a set of given tasks following the previous [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://arxiv.org/abs/2209.06794) works, which are the following and, so on, the supported prompt formats that will work out of the box via the `/generate` endpoint: + +- `caption {lang}`: Simple captioning objective on datasets like WebLI and CC3M-35L +- `ocr`: Transcription of text on the image using a public OCR system +- `answer en {question}`: Generated VQA on CC3M-35L and object-centric questions on OpenImages +- `question {lang} {English answer}`: Generated VQG on CC3M-35L in 35 languages for given English answers +- `detect {thing} ; {thing} ; ...`: Multi-object detection on generated open-world data +- `segment {thing} ; {thing} ; ...`: Multi-object instance segmentation on generated open-world data +- `caption `: Grounded captioning of content within a specified box -- Port-forwarding the deployed TGI service to the port 8080, so as to access via `localhost` with the command: +The PaliGemma and PaliGemma2 papers use the `\n` i.e. the line-break, as the separator token from the image(s) + suffix (input) and the prefix (output); which is automatically included within the `PaliGemmaProcessor` in Transformers, but needs to be manually provided to the `/generate` endpoint on TGI. Besides that, the images should be provided following the Markdown formatting for image rendering i.e. `![]()`, and the image needs to be publicly accessible; or provided as its base64 encoding if not hosted within a publicly accessible URL. This means that the prompt formatting expected on the `/generate` method is either e.g. `![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)caption en\n` if the image is provided via URL, or `![](data:image/png;base64,...)caption en\n` if the image is provided as its base64 encoding. - ```bash - kubectl port-forward service/tgi 8080:8080 - ``` +Read more information about the technical details and implementation of PaliGemma on the papers / technical reports released by Google: -- Accessing the TGI service via the external IP of the ingress, which is the default scenario here since you have defined the ingress configuration in the [`ingress.yaml`](./config/ingress.yaml) file (but it can be skipped in favour of the port-forwarding), that can be retrieved with the following command: +- [PaliGemma: A versatile 3B VLM for transfer](https://arxiv.org/abs/2407.07726) +- [PaliGemma 2: A Family of Versatile VLMs for Transfer](https://arxiv.org/abs/2412.03555) - ```bash - kubectl get ingress tgi -o jsonpath='{.status.loadBalancer.ingress[0].ip}' - ``` +> [!NOTE] +> Note that the `/v1/chat/completions` endpoint cannot be used, and will result in a "chat template error not found", as the model is pre-trained and not fine-tuned for e.g. chat conversations, and does not have a chat template defined that can be applied within the `v1/chat/completions` endpoint following the OpenAI OpenAPI specification. ### Via cURL @@ -292,7 +329,7 @@ To send a POST request to the TGI service using `cURL`, you can run the followin ```bash curl http://localhost:8080/generate \ - -d '{"inputs":"![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)caption en\n","parameters":{"max_new_tokens":128}}' \ + -d '{"inputs":"![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)caption en\n","parameters":{"max_new_tokens":128,"seed":42}}' \ -H 'Content-Type: application/json' ``` @@ -300,6 +337,46 @@ curl http://localhost:8080/generate \ |------------------------------------------------------------------------------------------------------------|------------|-------------------------------| | ![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png) | caption en | image of a man in a spacesuit | +### Via Python + +You can install it via pip as `pip install --upgrade --quiet huggingface_hub`, and then run the following snippet to mimic the cURL command above i.e. sending requests to the Generate API: + +```python +from huggingface_hub import InferenceClient + +client = InferenceClient("http://localhost:8080", api_key="-") + +generation = client.text_generation( + prompt="![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)caption en\n", + max_new_tokens=128, + seed=42, +) +``` + +Or, if you don't have a public URL with the image hosted, you can also send the base64 encoding of the image from the image file as it follows: + +```python +import base64 +from huggingface_hub import InferenceClient + +client = InferenceClient("http://localhost:8080", api_key="-") + +with open("/path/to/image.png", "rb") as f: + b64_image = base64.b64encode(f.read()).decode("utf-8") + +generation = client.text_generation( + prompt=f"![](data:image/png;base64,{b64_image})caption en\n", + max_new_tokens=128, + seed=42, +) +``` + +Both producing the following output: + +```json +{"generated_text": "image of a man in a spacesuit"} +``` + ## Delete GKE Cluster Finally, once you are done using TGI on the GKE Cluster, you can safely delete the GKE Cluster to avoid incurring in unnecessary costs. From 980a217cf16b96911dbb304845ec03a9257fe0e1 Mon Sep 17 00:00:00 2001 From: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com> Date: Sun, 15 Dec 2024 12:19:09 +0100 Subject: [PATCH 06/13] Update `examples/gke/deploy-paligemma-2-with-tgi/README.md` --- .../gke/deploy-paligemma-2-with-tgi/README.md | 29 +++++++++++++------ 1 file changed, 20 insertions(+), 9 deletions(-) diff --git a/examples/gke/deploy-paligemma-2-with-tgi/README.md b/examples/gke/deploy-paligemma-2-with-tgi/README.md index e8b5a75c..2777c809 100644 --- a/examples/gke/deploy-paligemma-2-with-tgi/README.md +++ b/examples/gke/deploy-paligemma-2-with-tgi/README.md @@ -103,7 +103,7 @@ gcloud container clusters create-auto $CLUSTER_NAME \ > --location=$LOCATION > ``` > -> Additionally, note that you can also use the "RAPID" channel instead of the "STABLE" if you require any Kubernetes feature not shipped yet within the latest Kubernetes version released on the "STABLE" channel, even though using the "STABLE" channel is recommended. For more information please visit . +> Additionally, note that you can also use the "RAPID" channel instead of the "STABLE" if you require any Kubernetes feature not shipped yet within the latest Kubernetes version released on the "STABLE" channel, even though using the "STABLE" channel is recommended. For more information please visit the [GKE Documentation - Specifying cluster version](https://cloud.google.com/kubernetes-engine/versioning#specifying_cluster_version). ![GKE Cluster in the Google Cloud Console](./imgs/gke-cluster.png) @@ -137,7 +137,7 @@ kubectl create secret generic hf-secret \ ![GKE Secret in the Google Cloud Console](./imgs/gke-secrets.png) -More information on how to set Kubernetes secrets in a GKE Cluster at . +More information on how to set Kubernetes secrets in a GKE Cluster check the [GKE Documentation - Specifying cluster version](https://cloud.google.com/secret-manager/docs/secret-manager-managed-csi-component). ## Deploy TGI on GKE @@ -218,7 +218,7 @@ You can either deploy by copying the content above into a file named `deployment kubectl apply -f deployment.yaml ``` -If you also want to deploy the Ingress to e.g. expose a public IP to access the Service, then you should then copy the following content into a file named `ingress.yaml`: +Optionally, if you also want to deploy the Ingress to e.g. expose a public IP to access the Service, then you should then copy the following content into a file named `ingress.yaml`: ```yaml apiVersion: networking.k8s.io/v1 @@ -241,14 +241,14 @@ spec: number: 8080 ``` -And, then deploy it with the following command too: +And, then deploy it with the following command: ```bash kubectl apply -f ingress.yaml ``` > [!NOTE] -> Alternatively, you can just clone [the `huggingface/Google-Cloud-Containers` repository from GitHub](https://github.com/huggingface/Google-Cloud-Containers) and the apply the configuration including all the Kubernetes Manifests mentioned above as it follows: +> Alternatively, you can just clone the [`huggingface/Google-Cloud-Containers`](https://github.com/huggingface/Google-Cloud-Containers) repository from GitHub and the apply the configuration including all the Kubernetes Manifests mentioned above as it follows: > > ```bash > git clone https://github.com/huggingface/Google-Cloud-Containers @@ -295,15 +295,19 @@ If you've configured the ingress (as defined in the [`ingress.yaml`](./config/in kubectl get ingress tgi -o jsonpath='{.status.loadBalancer.ingress.ip}' ``` -## Inference with TGI on GKE +--- -First of all, you need to make sure that the service is healthy and reachable via either `localhost` or the ingress IP (depending on how you exposed the service as of the step above), with the following `curl` command: +Finally, to make sure that the service is healthy and reachable via either `localhost` or the ingress IP (depending on how you exposed the service as of the step above), you can send the following `curl` command: ```bash curl http://localhost:8080/health ``` -Then before sending the `curl` request for inference, you need to note that the PaliGemma variant that you are serving is [`google/paligemma2-3b-pt-224`](https://huggingface.co/google/paligemma2-3b-pt-224) i.e. the pre-trained variant, meaning that's not particularly usable out of the box for any task, but just to transfer well to other tasks after the fine-tuning; anyway, it's pre-trained on a set of given tasks following the previous [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://arxiv.org/abs/2209.06794) works, which are the following and, so on, the supported prompt formats that will work out of the box via the `/generate` endpoint: +And that's it, TGI is now reachable and healthy on GKE! + +## Inference with TGI on GKE + +Before sending the `curl` request for inference, you need to note that the PaliGemma variant that you are serving is [`google/paligemma2-3b-pt-224`](https://huggingface.co/google/paligemma2-3b-pt-224) i.e. the pre-trained variant, meaning that's not particularly usable out of the box for any task, but just to transfer well to other tasks after the fine-tuning; anyway, it's pre-trained on a set of given tasks following the previous [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://arxiv.org/abs/2209.06794) works, which are the following and, so on, the supported prompt formats that will work out of the box via the `/generate` endpoint: - `caption {lang}`: Simple captioning objective on datasets like WebLI and CC3M-35L - `ocr`: Transcription of text on the image using a public OCR system @@ -313,7 +317,14 @@ Then before sending the `curl` request for inference, you need to note that the - `segment {thing} ; {thing} ; ...`: Multi-object instance segmentation on generated open-world data - `caption `: Grounded captioning of content within a specified box -The PaliGemma and PaliGemma2 papers use the `\n` i.e. the line-break, as the separator token from the image(s) + suffix (input) and the prefix (output); which is automatically included within the `PaliGemmaProcessor` in Transformers, but needs to be manually provided to the `/generate` endpoint on TGI. Besides that, the images should be provided following the Markdown formatting for image rendering i.e. `![]()`, and the image needs to be publicly accessible; or provided as its base64 encoding if not hosted within a publicly accessible URL. This means that the prompt formatting expected on the `/generate` method is either e.g. `![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)caption en\n` if the image is provided via URL, or `![](data:image/png;base64,...)caption en\n` if the image is provided as its base64 encoding. +The PaliGemma and PaliGemma2 papers use the `\n` i.e. the line-break, as the separator token from the image(s) + suffix (input) and the prefix (output); which is automatically included within the `PaliGemmaProcessor` in Transformers, but needs to be manually provided to the `/generate` endpoint on TGI. + +Besides that, the images should be provided following the Markdown formatting for image rendering i.e. `![]()`, and the image needs to be publicly accessible; or provided as its base64 encoding if not hosted within a publicly accessible URL. + +This means that the prompt formatting expected on the `/generate` method is either: + +- `![]()\n` if the image is provided via URL. +- `![](data:image/png;base64,)\n` if the image is provided as its base64 encoding. 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