What does it take to create an AI vision pipeline using modern tools on a Dell platform?
 
This blog describes how to implement object detection from a webcam video stream. The steps include:

• Install DeepStream software with a Docker container
• Process webcam Real Time Streaming Protocol (RTSP) output
• Detect objects (person, car, sign, bicycle) in each frame in near real time
• Draw bounding boxes with identifiers around the objects
• Stream the output using RTSP

NVIDIA Metropolis is an application framework with a set of developer tools that reside in a partner ecosystem. It features GPU-accelerated SDKs and tools to build, deploy, and scale AI-enabled video analytics and Internet of Things (IoT) applications optimally.

This blog focusses on NVIDIA DeepStream, which is one of the SDKs of the NVIDIA Metropolis stack. NVIDIA DeepStream SDK is a complete streaming analytics toolkit for AI-based multi-sensor processing, video, audio, and image understanding. Developers can use DeepStream SDK to create stream processing pipelines that incorporate neural networks and other complex processing tasks such as tracking, video encoding and decoding, IOT message brokers, and video rendering. DeepStream includes an open source Gstreamer project.

Metropolis-based components and solutions enable AI solutions that apply to a broad range of industries like manufacturing, retail, healthcare, and smart cities in the edge ecosystem.

The following figure shows the NVIDIA Metropolis framework:

The NVIDIA Metropolis framework consists of the following stages:

Generate─The stage in which images, video streams, and data originate. The data can be real-time data or synthetic data generated by using Synthetic Data Generation (SDG) tools. NVIDIA tools like NVIDIA Omniverse Replicator fit into this stage of the pipeline.

Train─The stage that uses the data from the Generate stage to feed into pretrained models and enables accelerated model tuning. Models developed from standard AI frameworks like TensorFlow and PyTorch are used in this stage and integrate into the Metropolis framework workflow. The NVIDIA Train, Adapt, and Optimize (TAO) toolkit is a low-code AI model development SDK that helps tune the pretrained models.

Build─The stage of the pipeline in which the core functionality of the Vision AI pipeline is performed. The Build stage of the pipeline includes the NVIDIA video storage toolkit, DeepStream, TensorRT, Triton, and Metropolis Microservices. The libraries and functions in these SDK components provide capabilities such as video codec, streaming analytics, inference optimization, runtime libraries, and inference services.

Deploy─The stage that deploys containerized AI solutions into the production environment at the edge or cloud. The deployment of containerized AI solutions uses industry-standard container orchestration technologies such as Kubernetes and Docker.

Test setup

The test setup includes the following hardware:

• Dell PowerEdge R740xd server with an NVIDIA A100 GPU
• Dell PowerEdge R750 server with an NVIDIA A16 GPU
• A 1080p webcam capable of streaming RTSP output and supporting H.264
• Client or laptop with VLC Media Player for viewing results

Note: Two servers are not required. We ran the demo on both servers to test different configurations. This hardware was available in the lab; we recommend the latest hardware for the best performance.

The test setup includes the following software:

• Ubuntu 20.04 server
• NVIDIA CUDA Toolkit and drivers
• Docker runtime

The following figure shows an example configuration:

Install NVIDIA CUDA

Enabling the CUDA toolkit on top of the base Ubuntu Linux operating system provides the necessary drivers and tools required to access the NVIDIA GPUs.  

The requirements for the CUDA toolkit installation include:

• A CUDA-capable GPU on the platform running the base Linux operating system
• A supported version of the GCC compiler and toolchain on the Linux operating system
• The CUDA Toolkit

1. Install the GCC compiler and other developer tool chains and libraries:

   ssudo apt-get update
   ssudo apt-get install build-essential

2. Verify that the installation is successful:

   gcc --version

3. Install the NVIDIA GPU CUDA toolkit and NVIDIA Container Toolkit:

   sudo sh NVIDIA-Linux-x86_64-515.76.run

   Note: For the PowerEdge system with an NVIDIA A16 GPU, the latest version of CUDA toolkit 12.2 did not function properly. After the installation, the nvidia-smi tool was unable to identify the GPU and activate the driver. Therefore, we chose an earlier version of the runfile (local installer) to install the CUDA toolkit package. We used CUDA Version 11.7 with driver version 515.76. The file used is NVIDIA-Linux-x86_64-515.76.run.
4. After installing the CUDA toolkit, see the nvidia-smi output for details about the GPU on the system:

   nvidia-smi

   

Install Docker Runtime

The following steps describe how to enable a Docker container runtime on top of the base operating system and enabling access to the GPUs from the container environment. With the release of Docker 19.03 and later, nvidia-docker2 packages are no longer required to access the NVIDIA GPUs from the Docker container environment as they are natively supported in Docker runtime.

Perform these steps in Ubuntu 20.04:

1. Update the apt package index and allow Advanced Packaging Tool (APT) to use a repository over HTTPS:

   sudo apt-get update
   ssudo apt-get install ca-certificates curl gnupg

2. Add Docker's official GPG key:

   sudo install -m 0755 -d /etc/apt/keyrings
   curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
   sudo chmod a+r /etc/apt/keyrings/docker.gpg

3. Set up the repository:

   sudo echo\
    "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
    "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \
   sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

4. Update the apt package index:

   sudo apt-get update

5. Install the latest version of the Docker engine:

   sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

6. Verify that Docker is installed:

   sudo docker run hello-world

After the Docker engine is installed, install the NVIDIA Container Toolkit and enable the NVIDIA runtime to Docker runtime. This step makes the GPUs detectable to the Docker containers.  

1. Set up the package repository and the GPG key:

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
       && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
       && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

After installing the repository sources, perform the following steps:

1. Update the repository list:

   sudo apt-get update

2. Install the NVIDIA Container Toolkit:

   sudo apt-get install -y nvidia-container-toolkit

3. Configure the Docker daemon to recognize the NVIDIA Container Runtime:

   sudo nvidia-ctk runtime configure --runtime=docker

4. Set the default runtime and then restart the Docker daemon to complete the installation:

   sudo systemctl restart docker

5. Verify that the GPUs are visible from inside a container:

   sudo docker run –rm –runtime=nvidia –gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi

   The following figure shows the NVIDIA SMI output:
   

Run the DeepStream Docker Container

To run the DeepStream Docker Container, perform the following steps:

1. Obtain the DeepStream docker container:

   sudo docker pull nvcr.io/nvidia/deepstream:6.2-devel

   At the time of this blog, the latest version is v6.2. Because the container is large, we recommend that you pull it down first before using it. It takes a few minutes to fully download all the container layers.
2. When the container is fully downloaded, run:

   sudo docker run --gpus all -it --rm -p 8554:8554 nvcr.io/nvidia/deepstream:6.2-devel

   This command instructs Docker to use any GPU it detects, run interactively, delete itself at termination, and open port 8554 for the RTSP output stream.
   
   When the command runs, the following output indicates that the Docker container is accessible and in interactive mode:

   root@9cfa2cfeb11b:/opt/nvidia/deepstream/deepstream-6.2#

Configure DeepStream inside a Docker Container

In the Docker container, make configuration changes so that the demo runs properly.

1. Install the required dependencies:

   /opt/nvidia/deepstream/deepstream/user_additional_install.sh

   The resulting output is long. The following example shows the beginning of the output of a successful installation:

   Get:1 file:/var/nv-tensorrt-local-repo-ubuntu2004-8.5.2-cuda-11.8  InRelease [1575 B]
   Get:1 file:/var/nv-tensorrt-local-repo-ubuntu2004-8.5.2-cuda-11.8  InRelease [1575 B]
   Hit:2 http://archive.ubuntu.com/ubuntu focal InRelease

   The following example shows the end of the output of a successful installation:

   Setting up libavfilter7:amd64 (7:4.2.7-0ubuntu0.1) ...
   Setting up libavresample-dev:amd64 (7:4.2.7-0ubuntu0.1) ... 
   Processing triggers for libc-bin (2.31-0ubuntu9.9) ...

   When we did not perform this step and tried to run the demo, we received the following error message, which is a common error reported on message boards:

   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:29.078: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstchromaprint.so': libavcodec.so.58: cannot open shared object file: No such file or directory
   
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:29.110: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstmpeg2dec.so': libmpeg2.so.0: cannot open shared object file: No such file or directory
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:29.111: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstmpeg2enc.so': libmpeg2encpp-2.1.so.0: cannot open shared object file: No such file or directory
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:29.112: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstmpg123.so': libmpg123.so.0: cannot open shared object file: No such file or directory
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:29.117: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstopenmpt.so': libmpg123.so.0: cannot open shared object file: No such file or directory
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:31.675: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_inferserver.so': libtritonserver.so: cannot open shared object file: No such file or directory
   (gst-plugin-scanner:12): GStreamer-WARNING **: 18:35:31.699: Failed to load plugin '/usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream/libnvdsgst_udp.so': librivermax.so.0: cannot open shared object file: No such file or directory
   ** ERROR: <create_udpsink_bin:644>: Failed to create 'sink_sub_bin_encoder1'
   ** ERROR: <create_udpsink_bin:719>: create_udpsink_bin failed
   ** ERROR: <create_sink_bin:828>: create_sink_bin failed
   ** ERROR: <create_processing_instance:884>: create_processing_instance failed
   ** ERROR: <create_pipeline:1485>: create_pipeline failed
   ** ERROR: <main:697>: Failed to create pipeline
   Quitting
   App run failed

2. Change directories and edit the configuration file:

   cd samples
   
   vim configs/deepstream-app/source30_1080p_dec_infer-resnet_tiled_display_int8.txt

3. Find the following entries:

   [tiled-display]
   enable=1

4. Change enable=1 to enable=0.
   A nontiled display makes it easier to compare the before and after webcam video streams.
5. Find the following entries:

   [source0] 
   enable=1 
   #Type - 1=CameraV4L2 2=URI 3=MultiURI 4=RTSP 
   type=3
   uri=file://../../streams/sample_1080p_h264.mp4 

6. Change:
   • type=3 to type=4
   • uri to uri=rtsp://192.168.10.210:554/s0
   Note: This URI is to the webcam that is streaming output.
7. Find the following entries:|

   [source1] 
   enable=1

8. Change enable=1 to enable=0.
9. Find the following entries:

   [sink0]
   enable=1

10. Change enable=1 to enable=0.
11. Find the following entries:

    [sink2] 
    enable=0 
    #Type - 1=FakeSink 2=EglSink 3=File 4=RTSPStreaming 
    type=4
    #1=h264 2=h265 
    codec=1
    #encoder type 0=Hardware 1=Software 
    enc-type=0

12. Change:
    • enable=0 to enable=1
    •enc-type=0 to enc-type=1
    Note: The enc-type=1 entry changes the configuration to use software encoders instead of the hardware. We changed the entry because our demo system has an NVIDIA A100 GPU that has no hardware encoders. Ideally, keep this entry as enc-type=0 if hardware encoders are available. With the NVIDIA A16 GPU, we used enc-type=0 entry. The Video Encode and Decode GPU Support Matrix at https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new shows the GPU hardware and encoder support.
    
    If you do not change the enc-type=1 entry (software encoder), the following error message might be displayed:

    ERROR from sink_sub_bin_encoder1: Could not get/set settings from/on resource.
    Debug info: gstv4l2object.c(3511): gst_v4l2_object_set_format_full ():
    /GstPipeline:pipeline/GstBin:processing_bin_0/GstBin:sink_bin/GstBin:sink_sub_bin1/nvv4l2h264enc:sink_sub_bin_encoder1:
    Device is in streaming mode

13. Save the file and exit the editor.

Running the Demo

To run the demo:

1. In the container, start DeepStream to run with the new configuration. This command must be on one line.

   deepstream-app -c configs/deepstream-app/source30_1080p_dec_infer-resnet_tiled_display_int8.txt

2. Find the following text in the warning messages that are displayed:

   *** DeepStream: Launched RTSP Streaming at rtsp://localhost:8554/ds-test ***

   Even though the message indicates that DeepStream is bound to localhost, it is accessible remotely due to the Docker port command that was used earlier.
   After more text and warning messages are displayed, the following output indicates that the software has started and is processing video input from the webcam:

   Runtime commands: 
           h: Print this help
           q: Quit 
           p: Pause
           r: Resume
   
   **PERF:  FPS 0 (Avg) 
   **PERF:  0.00 (0.00) 
   ** INFO: <bus_callback:239>: Pipeline ready 
   
   ** ERROR: <cb_newpad3:510>: Failed to link depay loader to rtsp src 
   ** INFO: <bus_callback:225>: Pipeline running 
   
   **PERF:   30.89 (30.89) 
   **PERF:   30.00 (30.43)
   **PERF:   30.00 (30.28)

Viewing the Demo

To view the demo:

1. On a laptop, start the media player. We use VLC media player.
2. Click Media, and then in the dropdown list, select Open Network Stream… , as shown in the following figure:
   
3. Enter the IP address of the Linux system on which the container is running. 
   Note: The IP address in the following figure is an example. Use the appropriate IP address of your deployment.
4. Click Play.

In a few seconds, the webcam streams video that identifies objects with bounding boxes applied in near real time. This demo detects people, cars, signs, and bicycles. 

The following figure is an example that shows the video output of recognized objects:

Note: The model is not trained to detect animals and correctly detects people and cars.

Summary

In this blog, we reviewed the Metropolis DeepStream hardware configuration test setup, the software installation steps, and how to use DeepStream to create a common vision AI pipeline with a Dell server. We included detailed instructions so that you can gain a deeper understanding of the configuration and ease of use.

We hope you enjoyed following our DeepStream journey. 

Check back regularly for upcoming AI blogs. From Dell data center servers to rugged edge devices, Dell Technologies provides optimized solutions for running your AI workloads.

Introduction

The past few years have been an exciting adventure in the field of AI, as increasingly sophisticated AI models continue to be developed to analyze and comprehend vast amounts of data. From predicting protein structures to charting the neural anatomy of a fruit fly to creating optimized math operations that accelerate supercomputers, AI has achieved impressive feats across varied disciplines. Large language models (LLMs) are no exception. Although producing human-like language garners attention, large language models were also created for code generation. By training on massive code datasets, these systems can write code by forecasting the next token in a sequence.

Companies like Google, Microsoft, OpenAI, and Anthropic are developing commercial LLMs for coding such as Codex, GitHub Copilot, and Claude. In contrast to closed systems, Meta has opened up its AI coding tool Code Llama, an AI coding assistant that aims to boost software developer productivity. It is released under a permissive license for both commercial and research use. Code Llama is an LLM capable of generating code and natural language descriptions of code from both code snippets and user-engineered prompts to automate repetitive coding tasks and enhance developer workflows. The open-source release allows the wider tech community to build upon Meta’s foundational model in natural language processing and code intelligence. For more information about the release, see Meta’s blog Introducing Code Llama, an AI Tool for Coding.

Code Llama is a promising new AI assistant for programmers. It can autocomplete code, search through codebases, summarize code, translate between languages, and even fix issues. This impressive range of functions makes Code Llama seem almost magical—like a programmer's dream come true! With coding assistants like Code Llama building applications could become far easier. Instead of writing every line of code, we may one day be able to describe what we want the program to do in a natural language prompt and the model can generate the necessary code for us. This workflow of the future could allow programmers to focus on the high-level logic and architecture of applications, without getting bogged down in implementation details.

Getting up and running with Code Llama was straightforward and fast. Meta released a 7 B, 13 B, and 34 B version of the model including instruction models that were trained with fill-in-the-middle (FIM) capability. This allows the models to insert into existing code, perform code completion, and accept natural language prompts. Using a Dell PowerEdge R740XD equipped with a single Nvidia A100 40GB GPU experimented with the smaller 7 billion parameter model, CodeLlama-Instruct-7 B. We used our Rattler Cluster in the HPC AI Innovation Lab to take advantage of PowerEdge XE8545 servers each equipped with four Nvidia A100 40 GB GPUs for the larger 34 billion parameter model (CodeLlama-Instruct-34 B). The examples provided by Meta were running a few minutes after downloading the model files and we began experimenting with natural language prompts to generate code. By engineering the prompts in a strategic manner, we aimed to produce the scaffolding for a web service wrapping the Code Llama model with an API that could be accessed over the web.

Accept user input from command line

The examples provided in example_instructions.py  must be edited manually to add wanted user prompts. To make Code Llama a bit easier to use, we copied example_instructions.py into test_instructions.py and added the ability to send in a prompt from the command line.

The full file with changes: 

# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from typing import Optional
import fire
from llama import Llama
def main(
    ckpt_dir: str,
    tokenizer_path: str,
    content:str,
    temperature: float = 0.2,
    top_p: float = 0.95,
    max_seq_len: int = 512,
    max_batch_size: int = 8,
    max_gen_len: Optional[int] = None,
):
    generator = Llama.build(
        ckpt_dir=ckpt_dir,
         tokenizer_path=tokenizer_path,
         max_seq_len=max_seq_len,
         max_batch_size=max_batch_size,
    )
 
    instructions = [
        [
            {
                 "role": "user",
                 "content": content,
            }
        ]
    ]
    results = generator.chat_completion(
        instructions,  # type: ignore
         max_gen_len=max_gen_len,
         temperature=temperature,
        top_p=top_p,
    )
 
    for instruction, result in zip(instructions, results):
        for msg in instruction:
             print(f"{msg['role'].capitalize()}: {msg['content']}\n")
        print(
            f"> {result['generation']['role'].capitalize()}: {result['generation']['content']}"
        )
         print("\n==================================\n")
 
if __name__ == "__main__":
    fire.Fire(main)
 

It is possible to run the standard torchrun command with a --content option to pass in the prompt. Rather than evaluating the model on complex tasks, we started with simple coding challenges. To test the model, we designed a few experiments involving straightforward coding prompts posing basic programming problems. The manual process of directly prompting the model allows us to evaluate the strengths and weaknesses in generating code with this model.

Our first prompt: “Show an example of recursion in Python” 

Command:

torchrun test_instructions.py \
--nproc_per_node 1 \
--ckpt_dir CodeLlama-7b-Instruct/ \
--tokenizer_path CodeLlama-7b-Instruct/tokenizer.model \
--max_seq_len 512 \
--max_batch_size 1 \
--content "Show an example of recursion in Python"

Output:

> Assistant:  Sure! Here's an example of recursion in Python:
```
def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n-1)
```
In this example, the `factorial` function takes an integer `n` as input and returns its factorial. The function calls itself with the argument `n-1` until the base case is reached, where `n` is equal to 0. The base case returns 1, and the recursive calls return the factorial of `n-1`. The final result is the product of `n` and the factorial of `n-1`.
Here's an example of how the function would work for different values of `n`: 
* `factorial(5)` would call `factorial(4)`, which would call `factorial(3)`, which would call `factorial(2)`, which would call `factorial(1)`, which would return 1. The final result would be 5! = 120.
* `factorial(0)` would return 1.
* `factorial(-1)` would raise a `ValueError` because negative numbers are not allowed.
Recursion can be a powerful tool for solving problems that have a recursive structure, but it can also be difficult to understand and debug. It's important to use recursion judiciously and to make sure that it's the best approach for the problem you're trying to solve.
==================================

The model not only generates example code, but it also explains how the code works through examples. By providing both code snippets and clarifying descriptions, the model enables a deeper understanding of the functionality. Users can both see concrete implementations and learn the rationale behind them. Now that we have made it easier to provide a prompt to the model, let’s build an API endpoint by prompting Code Llama to build its own server.

Create An API endpoint for Code Llama

RESTful APIs are a popular way to build backend services that can be consumed by various applications over a network with tools such as curl. However, instead of manually coding this, we asked Code Llama to write the code for its own REST server.

Our process for using Code Llama to produce a web service of itself:

• Step 1: Ask Code Llama to create a REST server for itself
• Step 2: Store the generated code in a file
• Step 3: Run the REST app on same GPU enabled server
• Step 4: Send prompts using HTTP POST requests to Code Llama service on the local host
• Step 5: REST server runs torchrun command with user prompt
• Step 6: REST server returns response to user

We needed to add some context to the prompt for Code Llama to generate the correct code, as the engineered prompt is a bit more complex and includes how to call torchrun with the correct parameters:

torchrun \
--nproc_per_node 1 \
test_instructions.py \
--ckpt_dir CodeLlama-7b-Instruct/ \
--tokenizer_path CodeLlama-7b-Instruct/tokenizer.model\
 --max_seq_len 512 \
--max_batch_size 1 \
 --content "Create a REST server in Python with a POST /codellama URL, expect a JSON payload with a 'content' field, use a subprocess.run(['torchrun', '--nproc_per_node', '1', 'test_instructions.py', '--ckpt_dir', 'CodeLlama-7b-Instruct/', '--tokenizer_path', 'CodeLlama-7b-Instruct/tokenizer.model', '--max_seq_len', '512', '--max_batch_size', '1', '--content', content], stdout=subprocess.PIPE) and return stdout from subprocess.run"

All torchrun command line options, except for --content, are defined by Code Llama itself. Let’s break down what our engineered prompt is requesting:

• “Create a REST server in Python” – No framework was defined; Code Llama chose Flask
• “with a POST /codellama URL” – Expect to receive HTTP POSTS to the /codellama URL
• “expect a JSON payload with a ‘content’ field” – Expect a ‘content’ key in the POST JSON. This holds the user prompt.
• “use a subprocess.run(…)” – This is where we tell Code Llama how to call itself
• “return stdout from subprocess.run” – Return any text that Code Llama produces over HTTP

 The resulting output:

> Assistant:  Here is an example of how you can create a REST server in Python with a POST /codellama URL that expects a JSON payload with a 'content' field, and uses a subprocess.run command to run the torchrun command with the specified arguments:
```
import json
import subprocess
from flask import Flask, request
 
app = Flask(__name__)
@app.route('/codellama', methods=['POST'])
def codellama():
    if request.method == 'POST':
        data = request.get_json()
        content = data['content']
        args = ['torchrun', '--nproc_per_node', '1', 'test_instructions.py', '--ckpt_dir', 'CodeLlama-7b-Instruct/', '--tokenizer_path', 'CodeLlama-7b-Instruct/tokenizer.model', '--max_seq_len', '512', '--max_batch_size', '1', '--content', content]
        result = subprocess.run(args, stdout=subprocess.PIPE)
        return result.stdout
    else:
        return 'Invalid request'
 
if __name__ == '__main__':
    app.run(debug=True)
```
This code defines a Flask application with a single route, '/codellama', that handles POST requests. The route expects a JSON payload with a 'content' field, and uses the subprocess.run command to run the torchrun command with the specified arguments. The result of the
==================================

We clipped the Code Llama generated code and pasted it into server.py:

import json
import subprocess
from flask import Flask, request
app = Flask(__name__)
@app.route('/codellama', methods=['POST'])
def codellama():
    if request.method == 'POST':
        data = request.get_json()
        content = data['content']
        args = ['torchrun', '--nproc_per_node', '1', 'test_instructions.py', '--ckpt_dir', 'CodeLlama-7b-Instruct/', '--tokenizer_path', 'CodeLlama-7b-Instruct/tokenizer.model', '--max_seq_len', '512', '--max_batch_size', '1', '--content', content]
        result = subprocess.run(args, stdout=subprocess.PIPE)
        return result.stdout
    else:
        return 'Invalid request' 
if __name__ == '__main__':
    app.run(debug=True)

After running the code:

python server.py

… the REST-like server will be running and waiting for commands.

Using curl, we can send commands over the network to Code Llama now:

curl -X POST -H "Content-Type: application/json" -d '{"content": " Show an example of recursion in Python"}' http://localhost:5000/codellama

… and will receive a result like:

> Assistant:  Sure! Here's an example of recursion in Python:
```
def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n-1)
```
In this example, the `factorial` function takes an integer `n` as input and returns its factorial. The function calls itself with the argument `n-1` until the base case is reached, where `n` is equal to 0. The base case returns 1, and the recursive calls return the factorial of `n-1`. The final result is the product of `n` and the factorial of `n-1`.
Here's an example of how the function would work for different values of `n`:
* `factorial(5)` would call `factorial(4)`, which would call `factorial(3)`, which would call `factorial(2)`, which would call `factorial(1)`, which would return 1. The final result would be 5! = 120.
* `factorial(0)` would return 1.
* `factorial(-1)` would raise a `ValueError` because negative numbers are not allowed 
Recursion can be a powerful tool for solving problems that have a recursive structure, but it can also be difficult to understand and debug. It's important to use recursion judiciously and to make sure that it's the best approach for the problem you're trying to solve.
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Conclusion

The capabilities of AI and LLMs continue to rapidly evolve. What we find most compelling about an open source model like Code Llama is the potential for customization and data privacy. Unlike closed, proprietary models, companies can run Code Llama on their own servers and fine-tune it using internal code examples and data.  This allows enforcement of coding styles and best practices while keeping code and data private. Rather than relying on external sources and APIs, teams can query a customized expert trained on their unique data in their own data center. Whatever your use cases, we found standing up an instance of Code Llama using Dell servers accelerated with Nvidia GPUs a simple and powerful solution that enables an exciting innovation for development teams and enterprises alike.  

AI coding assistants such as Code Llama have the potential to transform software development in the future. By automating routine coding tasks, these tools could save developers significant time that can be better spent on higher-level system design and logic. With the ability to check for errors and inconsistencies, AI coders may also contribute to improved code quality and reduced technical debt. However, our experiments reaffirm that generative AI is still prone to limitations like producing low-quality or non-functional code. Now that we have a local API endpoint for Code Llama, we plan to conduct more thorough testing to further evaluate its capabilities and limitations. We encourage developers to try out Code Llama themselves using the resources we provided here. Getting experience with this open-sourced model is a great way to start exploring the possibilities of AI for code generation while contributing to its ongoing improvement.