Exploring Vultr GPU Stack | Generative AI Series

Jupyter Notebook Generative AI Vultr GPU Stack

Published on December 6, 2023•Updated on December 19, 2023

Exploring Vultr GPU Stack | Generative AI Series header image

Introduction

Llama 2 is an open-source large language model from Hugging Face. You can use the model in your application to perform natural language processing (NLP) tasks.

The Vultr GPU Stack is a preconfigured compute instance with all the essential components for developing and deploying AI and ML applications. In this tutorial, you'll explore the Vultr GPU Stack environment and run a Llama 2 model in a Docker container.

Prerequisites

Before you begin:

Deploy a new Ubuntu 22.04 A100 Vultr Cloud GPU Server with at least:
- 80 GB GPU RAM
- 12 vCPUs
- 120 GB Memory
Establish an SSH connection to the server.
Create a non-root user with sudo rights and switch to the account.
Create a HuggingFace account.
Create a Hugging Face user access token.

Explore the Vultr GPU Stack environment

The Vultr GPU stack has many packages that simplify AI model development. Follow the steps below to ensure your environment is up and running:

Check the configuration of the NVIDIA GPU server by running the nvidia-smi command.
console
```
$ nvidia-smi
```

Confirm the following output.

                            output
                            
                        
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.125.06   Driver Version: 525.125.06   CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GRID A100D-4C       On   | 00000000:06:00.0 Off |                    0 |
| N/A   N/A    P0    N/A /  N/A |      0MiB /  4096MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

The above output confirms that the NVIDIA driver is up and running and the GPU is accessible from the OS. The output also shows that the instance has an A100 GPU attached.

Inspect the docker runtime environment by running the following commands:

Check the Docker version.

                            console
                            
$ sudo docker version

Output.

Client: Docker Engine - Community
Version:           24.0.7
API version:       1.43
Go version:        go1.20.10
Git commit:        afdd53b
Built:             Thu Oct 26 09:07:41 2023
OS/Arch:           linux/amd64
Context:           default

Server: Docker Engine - Community
Engine:
 Version:          24.0.7
 API version:      1.43 (minimum version 1.12)
 Go version:       go1.20.10
 Git commit:       311b9ff
 Built:            Thu Oct 26 09:07:41 2023
 OS/Arch:          linux/amd64
 Experimental:     false
containerd:
 Version:          1.6.24
 GitCommit:        61f9fd88f79f081d64d6fa3bb1a0dc71ec870523
runc:
 Version:          1.1.9
 GitCommit:        v1.1.9-0-gccaecfc
docker-init:
 Version:          0.19.0
 GitCommit:        de40ad0

Display the Docker system information.

                            console
                            
$ sudo docker info

Output.

Client: Docker Engine - Community
 Version:    24.0.7
 Context:    default
 Debug Mode: false
 Plugins:
  buildx: Docker Buildx (Docker Inc.)
    Version:  v0.11.2
    Path:     /usr/libexec/docker/cli-plugins/docker-buildx
  compose: Docker Compose (Docker Inc.)
    Version:  v2.21.0
    Path:     /usr/libexec/docker/cli-plugins/docker-compose

Server:
...
 Runtimes: runc io.containerd.runc.v2 nvidia
 Default Runtime: runc
...
 Docker Root Dir: /var/lib/docker
 Debug Mode: false
 Experimental: false
 Insecure Registries:
  127.0.0.0/8
 Live Restore Enabled: false

The above output omits some settings for brevity. However, the critical aspect is the availability of the NVIDIA container runtime, which enables Docker containers to access the underlying GPU.

Run an Ubuntu image and execute the nvidia-smi command within the container.

                            console
                            
$ sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

Output.

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.125.06   Driver Version: 525.125.06   CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GRID A100D-4C       On   | 00000000:06:00.0 Off |                    0 |
| N/A   N/A    P0    N/A /  N/A |      0MiB /  4096MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

The above output confirms that Docker containers have access to the GPU. In the next step, you'll run LLama2 through a container.

Run Llama2 Model on a Vultr GPU Stack

In this step, you'll launch the Hugging Face Text Generation Inference container to expose the Llama-2-7b-chat-hf parameter model through an API. Follow the steps below:

Fill out the Llama 2 model request form.
Use the same email address to sign up for a Hugging Face account and create an access token.
Request access to the Llama-2-7b-chat-hf repository.
Run the following command on your SSH interface to initialize some environment variables. Replace YOUR_HF_TOKEN with the correct Hugging Face token.
console
```
model=meta-llama/Llama-2-7b-chat-hf
volume=$PWD/data
token=YOUR_HF_TOKEN
```
Create a data directory to store the model's artifacts in your home directory.
console
```
$ mkdir data
```

Run the command below to launch the ghcr.io/huggingface/text-generation-inference:1.1.0 Docker container and initialize the Llama 2 model.

                            console
                            
                        
$ sudo docker run -d \
    --name hf-tgi \
    --runtime=nvidia \
    --gpus all \
    -e HUGGING_FACE_HUB_TOKEN=$token \
    -p 8080:80 \
    -v $volume:/data \
    ghcr.io/huggingface/text-generation-inference:1.1.0 --model-id $model

Output.

Digest: sha256:55...45871608f903f7f71d7d
Status: Downloaded newer image for ghcr.io/huggingface/text-generation-inference:1.1.0
78a39...f3e1dca928e00f859

Wait for the container to start and check the logs.
console
```
$ sudo docker logs -f hf-tgi
```
The last few lines below indicate the host is now listening to incoming HTTP connections, and the API is ready.
```
 ...
 ...Connected
 ...Invalid hostname, defaulting to 0.0.0.0
```

Run the following curl command to query the API.

                            console
                            
                        
$ curl 127.0.0.1:8080/generate \
    -X POST \
    -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":128}}' \
    -H 'Content-Type: application/json'

Output.

                            json
                            
{"generated_text":"\n\nDeep learning (also known as deep structured learning) is part of a broader family of machine learning techniques based on artificial neural networks—specifically, on the representation and processing of data using multiple layers of neural networks. Learning can be supervised, semi-supervised, or unsupervised.\n\nDeep-learning architectures such as Deep Neural Networks, Deep Belief Networks, and Deep Reinforcement Learning have been applied to fields including visual recognition, natural language processing, speech recognition, and expert system.\n\nDeep learning has been described as a \"paradigm shift\""}

The output confirms that the LLM is running.

Query the Llama2 Model Using Jupyter Notebook

Use the Python client to invoke the model from a Jupyter Notebook by following the steps below:

Install the HF Text Generation client by running the command below.
console
```
$ pip install text-generation
```

Run the Jupyter Lab and retrieve the access token.

$ jupyter lab --ip 0.0.0.0 --port 8890

Output.

http://YOUR_SERVER_HOST_NAME:8890/lab?token=b7ab2bdscb366edsddssfsff0faeb5fa68b6b0cf

Allow port 8890 through the firewall.

                            console
                            
                        
$ sudo ufw allow 8890
$ sudo ufw reload

Access the Jupyter Lab on a browser and replace YOUR_SERVER_IP with the public IP address of the GPU instance.
```
https://YOUR_SERVER_IP:8888/lab?token=YOUR_JUPYTER_LAB_TOKEN
```

Click Python 3 ipykernel under Notebook and paste the following Python code.

                            python
                            
from text_generation import Client

URI='http://localhost:8080'

tgi_client = Client(URI)
prompt='What is the most important tourist attraction in Paris?'

print(tgi_client.generate(prompt, max_new_tokens=100).generated_text.strip())

Run the above code. The LLM responds to your query and displays the following response.

Paris, the City of Light, is known for its iconic landmarks, cultural institutions, and historical significance. As one of the most popular tourist destinations in the world, Paris has a plethora of attractions that draw visitors from all over the globe. While opinions may vary, some of the most important tourist attractions in Paris include:

1. The Eiffel Tower: The most iconic symbol of Paris, the Eiffel Tower

Conclusion

This tutorial walked you through running the LLama 2 model in a container on the Vultr GPU Stack. In the next section, you'll explore other advanced LLM models.

Exploring Vultr GPU Stack | Generative AI Series

Introduction

Prerequisites

Explore the Vultr GPU Stack environment

Run Llama2 Model on a Vultr GPU Stack

Query the Llama2 Model Using Jupyter Notebook

Conclusion

Products

Features

Marketplace

Resources

Company