Merge branch 'main' into update-readme-files

README.md

@@ -1,6 +1,6 @@
 # 🤗 Hugging Face Deep Learning Containers for Google Cloud
 
-<img alt="Hugging Face x Google Cloud" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/Google-Cloud-Containers/thumbnail.png" />
+<img alt="Hugging Face x Google Cloud" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/google-cloud/thumbnail.png" />
 
 [Hugging Face Deep Learning Containers for Google Cloud](https://cloud.google.com/deep-learning-containers/docs/choosing-container#hugging-face) are a set of Docker images for training and deploying Transformers, Sentence Transformers, and Diffusers models on Google Cloud Vertex AI, Google Kubernetes Engine (GKE), and Google Cloud Run.
 
@@ -63,4 +63,4 @@ The [`examples`](./examples) directory contains examples for using the container
 | Vertex AI | [deploy-gemma-from-gcs-on-vertex-ai](./examples/vertex-ai/notebooks/deploy-gemma-from-gcs-on-vertex-ai)                   | Deploying Gemma 7B Instruct with Text Generation Inference (TGI) from a GCS Bucket on Vertex AI.                                                |
 | Vertex AI | [deploy-flux-on-vertex-ai](./examples/vertex-ai/notebooks/deploy-flux-on-vertex-ai)                                       | Deploying FLUX with Hugging Face PyTorch DLCs for Inference on Vertex AI.                                                                       |
 | Vertex AI | [deploy-llama-3-1-405b-on-vertex-ai](./examples/vertex-ai/notebooks/deploy-llama-405b-on-vertex-ai/vertex-notebook.ipynb) | Deploying Meta Llama 3.1 405B in FP8 with Hugging Face DLC for TGI on Vertex AI.                                                                |
-| Cloud Run | [tgi-deployment](./examples/cloud-run/tgi-deployment/README.md)                                                           | Deploying Meta Llama 3.1 8B with Text Generation Inference on Cloud Run.                                                                        |
+| Cloud Run | [tgi-deployment](./examples/cloud-run/tgi-deployment/README.md)                                                           | Deploying Meta Llama 3.1 8B with Text Generation Inference on Cloud Run.                                                                        |

docs/source/index.mdx

@@ -6,6 +6,8 @@ Hugging Face collaborates with Google across open science, open source, cloud, a
 
 Hugging Face enables new experiences for Google Cloud customers. They can easily train and deploy Hugging Face models on Google Kubernetes Engine (GKE) and Vertex AI, on any hardware available in Google Cloud using Hugging Face Deep Learning Containers (DLCs).
 
+If you have any issues using Hugging Face on Google Cloud, you can get community support by creating a new topic in the [Forum](https://discuss.huggingface.co/c/google-cloud/69/l/latest) dedicated to Google Cloud usage.
+
 ## Train and Deploy Models on Google Cloud with Hugging Face Deep Learning Containers
 
 Hugging Face built Deep Learning Containers (DLCs) for Google Cloud customers to run any of their machine learning workload in an optimized environment, with no configuration or maintenance on their part. These are Docker images pre-installed with deep learning frameworks and libraries such as 🤗 Transformers, 🤗 Datasets, and 🤗 Tokenizers. The DLCs allow you to directly serve and train any models, skipping the complicated process of building and optimizing your serving and training environments from scratch.

examples/gke/tei-deployment/README.md

@@ -1,6 +1,8 @@
 # Deploy Snowflake's Arctic Embed (M) with Text Embeddings Inference (TEI) on GKE
 
-Snowflake's Arctic Embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance, achieving state-of-the-art (SOTA) performance on the MTEB/BEIR leaderboard for each of their size variants. Text Embeddings Inference (TEI) is a toolkit developed by Hugging Face for deploying and serving open source text embeddings and sequence classification models; enabling high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5. 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 GCP's infrastructure. This post explains how to deploy a text embedding model from the Hugging Face Hub on a GKE Cluster running a purpose-built container to deploy text embedding models in a secure and managed environment with the Hugging Face DLC for TEI.
+Snowflake's Arctic Embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance, achieving state-of-the-art (SOTA) performance on the MTEB/BEIR leaderboard for each of their size variants. Text Embeddings Inference (TEI) is a toolkit developed by Hugging Face for deploying and serving open source text embeddings and sequence classification models; enabling high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5. 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 GCP's infrastructure.
+
+This example showcases how to deploy a text embedding model from the Hugging Face Hub on a GKE Cluster running a purpose-built container to deploy text embedding models in a secure and managed environment with the Hugging Face DLC for TEI.
 
 ## Setup / Configuration
 
@@ -47,7 +49,7 @@ gcloud components install gke-gcloud-auth-plugin
 Once everything is set up, you can proceed with the creation of the GKE Cluster and the node pool, which in this case will be a single CPU node as for most of the workloads CPU inference is enough to serve most of the text embeddings models, while it could benefit a lot from GPU serving.
 
 > [!NOTE]
-> CPU is being used to run the inference on top of the text embeddings models to showcase the current capabilities of TEI, but switching to GPU is as easy as replacing `spec.containers[0].image` with `us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-2.ubuntu2204`, and then updating the requested resources, as well as the `nodeSelector` requirements in the `deployment.yaml` file. For more information, please refer to the [`gpu-config`](./gpu-config/) directory that contains a pre-defined configuration for GPU serving in TEI with an NVIDIA Tesla T4 GPU (with a compute capability of 7.5 i.e. natively supported in TEI).
+> CPU is being used to run the inference on top of the text embeddings models to showcase the current capabilities of TEI, but switching to GPU is as easy as replacing `spec.containers[0].image` with `us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-4.ubuntu2204`, and then updating the requested resources, as well as the `nodeSelector` requirements in the `deployment.yaml` file. For more information, please refer to the [`gpu-config`](./gpu-config/) directory that contains a pre-defined configuration for GPU serving in TEI with an NVIDIA Tesla T4 GPU (with a compute capability of 7.5 i.e. natively supported in TEI).
 
 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. Alternatively, you can also use the "Standard" mode.
 
@@ -93,7 +95,7 @@ Now you can proceed to the Kubernetes deployment of the Hugging Face DLC for TEI
 The Hugging Face DLC for TEI will be deployed via `kubectl`, from the configuration files in either the `cpu-config/` or the `gpu-config/` directories depending on whether you want to use the CPU or GPU accelerators, respectively:
 
 - `deployment.yaml`: contains the deployment details of the pod including the reference to the Hugging Face DLC for TEI setting the `MODEL_ID` to [`Snowflake/snowflake-arctic-embed-m`](https://huggingface.co/Snowflake/snowflake-arctic-embed-m).
-- `service.yaml`: contains the service details of the pod, exposing the port 80 for the TEI service.
+- `service.yaml`: contains the service details of the pod, exposing the port 8080 for the TEI service.
 - (optional) `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
@@ -156,7 +158,7 @@ curl http://localhost:8080/embed \
 Or send a POST request to the ingress IP instead:
 
 ```bash
-curl http://$(kubectl get ingress tei-ingress -o jsonpath='{.status.loadBalancer.ingress[0].ip}')/embed \
+curl http://$(kubectl get ingress tei-ingress -o jsonpath='{.status.loadBalancer.ingress[0].ip}'):8080/embed \
     -X POST \
     -d '{"inputs":"What is Deep Learning?"}' \
     -H 'Content-Type: application/json'

examples/gke/tei-deployment/cpu-config/deployment.yaml

@@ -16,7 +16,7 @@ spec:
     spec:
       containers:
         - name: tei-container
-          image: us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cpu.1-2:latest
+          image: us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cpu.1-4:latest
           resources:
             requests:
               cpu: "8"
@@ -38,5 +38,5 @@ spec:
         - name: data
           emptyDir: {}
       nodeSelector:
-         cloud.google.com/compute-class: "Performance"
-         cloud.google.com/machine-family: "c2"
+        cloud.google.com/compute-class: "Performance"
+        cloud.google.com/machine-family: "c2"

examples/gke/tei-deployment/cpu-config/ingress.yaml

@@ -7,11 +7,11 @@ metadata:
 spec:
   rules:
     - http:
-        paths:
-          - path: /
-            pathType: Prefix
-            backend:
-              service:
-                name: tei-service
-                port:
-                  number: 8080
+      paths:
+        - path: /
+          pathType: Prefix
+          backend:
+            service:
+              name: tei-service
+              port:
+                number: 8080

examples/gke/tei-deployment/cpu-config/service.yaml

@@ -7,6 +7,6 @@ spec:
     app: tei-server
   type: ClusterIP
   ports:
-  - protocol: TCP
-    port: 8080
-    targetPort: 8080
+    - protocol: TCP
+      port: 8080
+      targetPort: 8080

examples/gke/tei-deployment/gpu-config/deployment.yaml

@@ -16,7 +16,7 @@ spec:
     spec:
       containers:
         - name: tei-container
-          image: us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-2.ubuntu2204:latest
+          image: us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-4.ubuntu2204:latest
           resources:
             requests:
               nvidia.com/gpu: 1

examples/gke/tei-deployment/gpu-config/ingress.yaml

@@ -7,11 +7,11 @@ metadata:
 spec:
   rules:
     - http:
-        paths:
-          - path: /
-            pathType: Prefix
-            backend:
-              service:
-                name: tei-service
-                port:
-                  number: 8080
+      paths:
+        - path: /
+          pathType: Prefix
+          backend:
+            service:
+              name: tei-service
+              port:
+                number: 8080

examples/gke/tei-deployment/gpu-config/service.yaml

@@ -7,6 +7,6 @@ spec:
     app: tei-server
   type: ClusterIP
   ports:
-  - protocol: TCP
-    port: 8080
-    targetPort: 8080
+    - protocol: TCP
+      port: 8080
+      targetPort: 8080

examples/gke/tei-from-gcs-deployment/README.md

@@ -1,6 +1,8 @@
 # Deploy BGE Base v1.5 (English) with Text Embeddings Inference (TEI) from a GCS Bucket on GKE
 
-BGE, standing for BAAI General Embedding, is a collection of embedding models released by BAAI, which is an English base model for general embedding tasks ranked in the MTEB Leaderboard. Text Embeddings Inference (TEI) is a toolkit developed by Hugging Face for deploying and serving open source text embeddings and sequence classification models; enabling high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5. 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 GCP's infrastructure. This post explains how to deploy a text embedding model from a Google Cloud Storage (GCS) Bucket on a GKE Cluster running a purpose-built container to deploy text embedding models in a secure and managed environment with the Hugging Face DLC for TEI.
+BGE, standing for BAAI General Embedding, is a collection of embedding models released by BAAI, which is an English base model for general embedding tasks ranked in the MTEB Leaderboard. Text Embeddings Inference (TEI) is a toolkit developed by Hugging Face for deploying and serving open source text embeddings and sequence classification models; enabling high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5. 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 GCP's infrastructure.
+
+This example showcases how to deploy a text embedding model from a Google Cloud Storage (GCS) Bucket on a GKE Cluster running a purpose-built container to deploy text embedding models in a secure and managed environment with the Hugging Face DLC for TEI.
 
 ## Setup / Configuration
 
@@ -48,7 +50,7 @@ gcloud components install gke-gcloud-auth-plugin
 Once everything is set up, you can proceed with the creation of the GKE Cluster and the node pool, which in this case will be a single CPU node as for most of the workloads CPU inference is enough to serve most of the text embeddings models, while it could benefit a lot from GPU serving.
 
 > [!NOTE]
-> CPU is being used to run the inference on top of the text embeddings models to showcase the current capabilities of TEI, but switching to GPU is as easy as replacing `spec.containers[0].image` with `us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-2.ubuntu2204`, and then updating the requested resources, as well as the `nodeSelector` requirements in the `deployment.yaml` file. For more information, please refer to the [`gpu-config`](./gpu-config/) directory that contains a pre-defined configuration for GPU serving in TEI with an NVIDIA Tesla T4 GPU (with a compute capability of 7.5 i.e. natively supported in TEI).
+> CPU is being used to run the inference on top of the text embeddings models to showcase the current capabilities of TEI, but switching to GPU is as easy as replacing `spec.containers[0].image` with `us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-4.ubuntu2204`, and then updating the requested resources, as well as the `nodeSelector` requirements in the `deployment.yaml` file. For more information, please refer to the [`gpu-config`](./gpu-config/) directory that contains a pre-defined configuration for GPU serving in TEI with an NVIDIA Tesla T4 GPU (with a compute capability of 7.5 i.e. natively supported in TEI).
 
 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. Alternatively, you can also use the "Standard" mode.
 

examples/gke/tei-from-gcs-deployment/cpu-config/deployment.yaml

@@ -33,7 +33,7 @@ spec:
               cpu: 8.0
       containers:
         - name: tei-container
-          image: us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cpu.1-2:latest
+          image: us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cpu.1-4:latest
           resources:
             requests:
               cpu: "8"
@@ -58,11 +58,11 @@ spec:
           ephemeral:
             volumeClaimTemplate:
               spec:
-                accessModes: [ "ReadWriteOnce" ]
+                accessModes: ["ReadWriteOnce"]
                 storageClassName: ssd
                 resources:
                   requests:
                     storage: 48Gi
       nodeSelector:
-         cloud.google.com/compute-class: "Performance"
-         cloud.google.com/machine-family: "c2"
+        cloud.google.com/compute-class: "Performance"
+        cloud.google.com/machine-family: "c2"

examples/gke/tei-from-gcs-deployment/cpu-config/ingress.yaml

@@ -8,11 +8,11 @@ metadata:
 spec:
   rules:
     - http:
-        paths:
-          - path: /
-            pathType: Prefix
-            backend:
-              service:
-                name: tei-service
-                port:
-                  number: 8080
+      paths:
+        - path: /
+          pathType: Prefix
+          backend:
+            service:
+              name: tei-service
+              port:
+                number: 8080

examples/gke/tei-from-gcs-deployment/cpu-config/service.yaml

@@ -8,6 +8,6 @@ spec:
     app: tei-server
   type: ClusterIP
   ports:
-  - protocol: TCP
-    port: 8080
-    targetPort: 8080
+    - protocol: TCP
+      port: 8080
+      targetPort: 8080

examples/gke/tei-from-gcs-deployment/gpu-config/deployment.yaml

@@ -33,7 +33,7 @@ spec:
               cpu: 8.0
       containers:
         - name: tei-container
-          image: us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-2.ubuntu2204:latest
+          image: us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-4.ubuntu2204:latest
           resources:
             requests:
               nvidia.com/gpu: 1
@@ -60,7 +60,7 @@ spec:
           ephemeral:
             volumeClaimTemplate:
               spec:
-                accessModes: [ "ReadWriteOnce" ]
+                accessModes: ["ReadWriteOnce"]
                 storageClassName: ssd
                 resources:
                   requests:

examples/gke/tei-from-gcs-deployment/gpu-config/ingress.yaml

@@ -8,11 +8,11 @@ metadata:
 spec:
   rules:
     - http:
-        paths:
-          - path: /
-            pathType: Prefix
-            backend:
-              service:
-                name: tei-service
-                port:
-                  number: 8080
+      paths:
+        - path: /
+          pathType: Prefix
+          backend:
+            service:
+              name: tei-service
+              port:
+                number: 8080

examples/gke/tei-from-gcs-deployment/gpu-config/service.yaml

@@ -8,6 +8,6 @@ spec:
     app: tei-server
   type: ClusterIP
   ports:
-  - protocol: TCP
-    port: 8080
-    targetPort: 8080
+    - protocol: TCP
+      port: 8080
+      targetPort: 8080

examples/vertex-ai/notebooks/deploy-embedding-on-vertex-ai/vertex-notebook.ipynb

@@ -66,7 +66,7 @@
    "source": [
     "%env PROJECT_ID=your-project-id\n",
     "%env LOCATION=your-location\n",
-    "%env CONTAINER_URI=us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-2.ubuntu2204"
+    "%env CONTAINER_URI=us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-embeddings-inference-cu122.1-4.ubuntu2204"
    ]
   },
   {