| import re | |
| import time | |
| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| from bs4 import BeautifulSoup | |
| from urllib.parse import urljoin, urlparse | |
| import vertexai | |
| from vertexai.generative_models import GenerativeModel | |
| import vertexai.preview.generative_models as generative_models |
gcloud config set project sascha-playground-doit export PROJECT_ID=$(gcloud config get project) export REGION=us-central1 export CLUSTER_NAME=vllm export HF_TOKEN=XXX
gcloud container clusters create-auto ${CLUSTER_NAME}
--project=${PROJECT_ID}
--region=${REGION}
--release-channel=rapid \
Question: how can I deploy a LLM to Google cloud? The model size is around 80GB.
Answer: Deploying an 80GB LLM to Google Cloud is a great goal! It's a bit like moving a large piece of furniture - you need the right tools and plan to make it smooth. Here's a breakdown of how to do it:
Vertex AI: This is Google Cloud's managed machine learning platform. It's designed for deploying and managing models, including large ones. Vertex AI offers:
Model Serving: You can deploy your LLM as a model endpoint, allowing you to send requests for predictions.
Question: How can I create text embeddings with google cloud?
Answer: You can create text embeddings with Google Cloud using the Text Embeddings API on Vertex AI. This API allows you to generate numerical representations of text, which can be used for various tasks like:
Semantic Search: Finding documents similar in meaning to a given query. Recommendation Systems: Suggesting relevant content based on user preferences. Clustering: Grouping similar documents together. Question Answering: Finding answers to questions based on a knowledge base.
| import vertexai | |
| from vertexai.language_models import TextGenerationModel | |
| from google.cloud import translate_v2 as translate | |
| vertexai.init(project="sascha-playground-doit", location="us-central1") | |
| parameters = { | |
| "candidate_count": 1, | |
| "max_output_tokens": 1024, | |
| "temperature": 0.9, | |
| "top_p": 1 |
| response = index_endpoint.match( | |
| deployed_index_id="similar_text_deployed", | |
| queries=[embedding[0]], | |
| num_neighbors=5 | |
| ) |
| { | |
| "predictions": [ | |
| { | |
| "confidence": 0.9409326314926147, | |
| "sentiment": "POSITIVE" | |
| } | |
| ] | |
| } |
| training_data_artifact = aiplatform.Artifact.create( | |
| schema_title='system.Dataset', | |
| uri='gs://doit-vertex-demo/higgs/training.csv', | |
| display_name='data') | |
| with aiplatform.start_execution( | |
| schema_title="system.ContainerExecution", | |
| display_name='training' | |
| ) as execution: |
| experiment_run = aiplatform.ExperimentRun( | |
| run_name='run-1668456760', | |
| experiment='experiment-demo', | |
| ) | |
| print(experiment_run.get_artifacts()) | |
| print(experiment_run.get_metrics()) | |
| print(experiment_run.get_params()) | |
| print(experiment_run.get_time_series_data_frame()) | |
| print(experiment_run.get_classification_metrics()) |
| experiment_df = aiplatform.get_experiment_df() | |
| experiment_df = experiment_df[experiment_df.experiment_name == 'experiment-demo'] | |
| experiment_df |
