!pip install --upgrade --quiet google-cloud-aiplatform

%env PROJECT_ID=your-project-id
%env LOCATION=your-location
%env BUCKET_URI=gs://your-bucket
%env ARTIFACT_NAME=google--gemma-7b-it
%env CONTAINER_URI=us-docker.pkg.dev/deeplearning-platform-release/gcr.io/huggingface-text-generation-inference-cu124.2-3.ubuntu2204.py311

!gcloud auth login
!gcloud auth application-default login  # For local development
!gcloud config set project $PROJECT_ID

!gcloud services enable aiplatform.googleapis.com
!gcloud services enable compute.googleapis.com
!gcloud services enable container.googleapis.com
!gcloud services enable containerregistry.googleapis.com
!gcloud services enable containerfilesystem.googleapis.com

!gcloud components install gsutil
!pip install --upgrade --quiet crcmod

%%bash

# Parse the bucket from the provided $BUCKET_URI path i.e. given gs://bucket-name/dir, extract bucket-name
BUCKET_NAME=$(echo $BUCKET_URI | cut -d'/' -f3)
# Check if the bucket exists, if not create it
if [ -z "$(gsutil ls | grep gs://$BUCKET_NAME)" ]; then
    gcloud storage buckets create gs://$BUCKET_NAME --project=$PROJECT_ID --location=$LOCATION --default-storage-class=STANDARD --uniform-bucket-level-access
fi

%%bash
# Upload the model to Google Cloud Storage
LOCAL_DIR=~/.cache/huggingface/hub/models--google--gemma-7b-it/snapshots/8adab6a35fdbcdae0ae41ab1f711b1bc8d05727e
if [ -d "$LOCAL_DIR" ]; then
    gsutil -o GSUtil:parallel_composite_upload_threshold=150M -m cp -r $LOCAL_DIR/* $BUCKET_URI/$ARTIFACT_NAME
fi

!pip install "huggingface_hub[hf_transfer]" --upgrade --quiet

from huggingface_hub import interpreter_login

interpreter_login()

%%bash
# Ensure the necessary environment variables are set
export HF_HUB_ENABLE_HF_TRANSFER=1

# # Create a local directory to store the downloaded models
LOCAL_DIR="tmp/google--gemma-7b-it"
mkdir -p $LOCAL_DIR

# # Download models from HuggingFace, excluding certain file types
huggingface-cli download google/gemma-7b-it --exclude "*.bin" "*.pth" "*.gguf" ".gitattributes" --local-dir $LOCAL_DIR

# Upload the downloaded models to Google Cloud Storage
gsutil -o GSUtil:parallel_composite_upload_threshold=150M -m cp -e -r $LOCAL_DIR/* $BUCKET_URI/$ARTIFACT_NAME

# Remove all files and hidden files in the target directory
rm -rf tmp/

import os
from google.cloud import aiplatform

aiplatform.init(
    project=os.getenv("PROJECT_ID"),
    location=os.getenv("LOCATION"),
    staging_bucket=os.getenv("BUCKET_URI"),
)

model = aiplatform.Model.upload(
    display_name="google--gemma-7b-it",
    artifact_uri=f"{os.getenv('BUCKET_URI')}/{os.getenv('ARTIFACT_NAME')}",
    serving_container_image_uri=os.getenv("CONTAINER_URI"),
    serving_container_environment_variables={
        "NUM_SHARD": "1",
        "MAX_INPUT_TOKENS": "512",
        "MAX_TOTAL_TOKENS": "1024",
        "MAX_BATCH_PREFILL_TOKENS": "1512",
    },
    serving_container_ports=[8080],
)
model.wait()

endpoint = aiplatform.Endpoint.create(display_name="google--gemma-7b-it-endpoint")

deployed_model = model.deploy(
    endpoint=endpoint,
    machine_type="g2-standard-4",
    accelerator_type="NVIDIA_L4",
    accelerator_count=1,
)

!pip install --upgrade --quiet transformers

from huggingface_hub import get_token
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it", token=get_token())

messages = [
    {"role": "user", "content": "What's Deep Learning?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
# <bos><start_of_turn>user\nWhat's Deep Learning?<end_of_turn>\n<start_of_turn>model\n

output = deployed_model.predict(
    instances=[
        {
            "inputs": "<bos><start_of_turn>user\nWhat's Deep Learning?<end_of_turn>\n<start_of_turn>model\n",
            "parameters": {
                "max_new_tokens": 256,
                "do_sample": True,
                "top_p": 0.95,
                "temperature": 1.0,
            },
        },
    ]
)
print(output.predictions[0])

Prediction(predictions=['\n\nDeep learning is a type of machine learning that uses artificial neural networks to learn from large amounts of data, making it a powerful tool for various tasks, including image recognition, natural language processing, and speech recognition.\n\n**Key Concepts:**\n\n* **Artificial Neural Networks (ANNs):** Structures that mimic the interconnected neurons in the brain.\n* **Deep Learning Architectures:** Multi-layered ANNs that learn hierarchical features from data.\n* **Transfer Learning:** Reusing learned features from one task to improve performance on another.\n\n**Types of Deep Learning:**\n\n* **Supervised Learning:** Models are trained on labeled data, where inputs are paired with corresponding outputs.\n* **Unsupervised Learning:** Models learn patterns from unlabeled data, such as clustering or dimensionality reduction.\n* **Reinforcement Learning:** Models learn through trial-and-error by interacting with an environment to optimize a task.\n\n**Benefits:**\n\n* **High Accuracy:** Deep learning models can achieve high accuracy on complex tasks.\n* **Adaptability:** Deep learning models can adapt to new data and tasks.\n* **Scalability:** Deep learning models can handle large amounts of data.\n\n**Applications:**\n\n* Image recognition\n* Natural language processing (NLP)\n'], deployed_model_id='***', metadata=None, model_version_id='1', model_resource_name='projects/***/locations/us-central1/models/***', explanations=None)

import os
from google.cloud import aiplatform

aiplatform.init(project=os.getenv("PROJECT_ID"), location=os.getenv("LOCATION"))

endpoint_display_name = "google--gemma-7b-it-endpoint"  # TODO: change to your endpoint display name

# Iterates over all the Vertex AI Endpoints within the current project and keeps the first match (if any), otherwise set to None
ENDPOINT_ID = next(
    (endpoint.name for endpoint in aiplatform.Endpoint.list() if endpoint.display_name == endpoint_display_name), None
)
assert ENDPOINT_ID, (
    "`ENDPOINT_ID` is not set, please make sure that the `endpoint_display_name` is correct at "
    f"https://console.cloud.google.com/vertex-ai/online-prediction/endpoints?project={os.getenv('PROJECT_ID')}"
)

endpoint = aiplatform.Endpoint(
    f"projects/{os.getenv('PROJECT_ID')}/locations/{os.getenv('LOCATION')}/endpoints/{ENDPOINT_ID}"
)
output = endpoint.predict(
    instances=[
        {
            "inputs": "<bos><start_of_turn>user\nWhat's Deep Learning?<end_of_turn>\n<start_of_turn>model\n",
            "parameters": {
                "max_new_tokens": 128,
                "do_sample": True,
                "top_p": 0.95,
                "temperature": 0.7,
            },
        },
    ],
)
print(output.predictions[0])

{
    "instances": [
        {
            "inputs": "<bos><start_of_turn>user\nWhat's Deep Learning?<end_of_turn>\n<start_of_turn>model\n",
            "parameters": {
                "max_new_tokens": 128,
                "do_sample": true,
                "top_p": 0.95,
                "temperature": 0.7
            }
        }
    ]
}

deployed_model.undeploy_all()
deployed_model.delete()
model.delete()

!gcloud storage rm -r $BUCKET_URI