Deploy serverless GPU applications on your AWS account
Built with developer experience in mind, Tensorkube simplifies the process of deploying serverless GPU apps. In this guide,
we will walk you through the process of deploying jina-embeddings-v2-base-code model on your private cloud.Jina-embeddings-v2-base-code is an multilingual embedding model speaks English and 30 widely used programming languages
Each tensorkube deployment requires two things - your code and your environment (as a Dockerfile).
While deploying machine learning models, it is beneficial if your model is also a part of your container image. This reduces cold-start times by a significant margin.We are using the Huggingface Text Embeddings Inference toolkit to make our models utilise the full GPU capacity. You can try any of the supported model here.
We will use an nginx server to start our app. We will configure the /readiness endpoint to return a 200 status code. Remember that Tensorfuse uses this endpoint to check the health of your deployment.The Huggingface TEI toolkit serves embeddings at /embed and hence we configure all other endpoints to route to the TEI toolkit which is running on port 8000.
nginx.conf
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worker_processes auto;error_log /var/log/nginx/error.log warn;pid /var/run/nginx.pid;events { worker_connections 1024;}http { include /etc/nginx/mime.types; default_type application/octet-stream; log_format main '$remote_addr - $remote_user [$time_local] "$request" ' '$status $body_bytes_sent "$http_referer" ' '"$http_user_agent" "$http_x_forwarded_for"'; access_log /var/log/nginx/access.log main; sendfile on; keepalive_timeout 65; server { listen 80; client_max_body_size 200M; location /readiness { return 200 'true'; add_header Content-Type text/plain; } location / { # You may need to adjust this if your application is not running on localhost:8000 proxy_pass http://127.0.0.1:8000; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Forwarded-Proto $scheme; } }}
Jina is now ready to be deployed on Tensorkube. Navigate to your project root and run the following command:
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tensorkube deploy --gpus 1 --gpu-type a10g
Jina embedding model is now deployed on your AWS account. You can access your app at the URL provided in the output or using the following command:
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tensorkube list deployments
And that’s it! You have successfully deployed Jina embedding model on serverless GPUs using Tensorkube. 🚀To test it out you can run the following command by replacing the URL with the one provided in the output:
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curl -X POST -H "Content-Type: application/json" -d '{"inputs":"&int x = &y"}' <YOUR_URL_HERE>/embed
You can also send multiple inputs in a batch. For example,
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curl <YOUR_URL_HERE>/embed \ -X POST \ -d '{"inputs":["Today is a nice day", "I like you"]}' \ -H 'Content-Type: application/json'
You can also use the readiness endpoint to wake up your nodes in case you are expecting incoming traffic