DSPy: Install, Setup, and Test.

TL;DR.

Update: Saturday 18th February 2025.

This post is a guide to installing, setting up, and testing DSPy (Declarative Self-improving Python) and will cover the following topics:

The prerequisites for assembling a DSPy development environment,
The installation of Ollama and Miniconda,
The creation of a Conda environment for DSPy,
The installation of the DSPy framework,
The installation and setup up of Jupyter Notebook,
Running the test code in the newly assembled DSPy development environment, and
Running the test code using Python.

The aim of this guide is to assemble, and test, a development environment for DSPy programming.

Attributions ↗:

https://dspy.ai/tutorials/rag/ ↗,

https://github.com/stanfordnlp/dspy ↗, and

https://dspy.ai/ ↗.

An Introduction.

DSPy is used to programmatically provide "a more systematic approach to solving hard tasks with advanced LMs." It exists to build simple Classifiers, sophisticated RAG pipelines, and Agentic processes.

The purpose of this post is to provide a procedure for creating a DSPy programming environment.

The Big Picture.

For classifiers, RAG (Retrieval-Augmented Generation), and agents, the main idea is to take advantage of advanced LM workflows, especially those systems that can retrieve relevant information while generating contextually accurate responses. By integrating retrieval mechanisms with generative models, these workflows aim to create intelligent systems that can autonomously navigate complex tasks, adapt to dynamic environments, and provide insightful solutions. This approach not only improves the accuracy and relevance of LM outputs but also empowers users to leverage AI for complex, problem-solving operations.

DSPy is a programmatic tool for creating advanced AI solutions that was originally designed to replace the clunky, and fragile, prompt engineering paradigm.

Prerequisites.

A Debian-based Linux distro (I use Ubuntu).

Updating my Base System.

From the (base) terminal, I update my (base) system:

sudo apt clean && \
sudo apt update && \
sudo apt dist-upgrade -y && \
sudo apt --fix-broken install && \
sudo apt autoclean && \
sudo apt autoremove -y

NOTE: The Ollama LLM manager is already installed on my (base) system.

What is Ollama?

Ollama is a tool that is used to download, set up, and run large language models on a local PC. It lets me use powerful models like Llama 2 and Mistral on my personal computer. Ollama natively runs on Linux, macOS, and Windows (in preview).

Installing Ollama.

From the terminal, I install Ollama:

curl https://ollama.ai/install.sh | sh

I list the LMs downloaded by Ollama:

ollama list

If the above command fails, I run Ollama as a background service:

ollama serve &

If the following error shows when running the previous command, that means Ollama is already running as a background service:

Error: listen tcp 127.0.0.1:11434: bind: address already in use

Pulling an Advanced LM.

I pull an advanced LM:

ollama pull deepseek-r1:14b

NOTE: This model has 14B parameters, requires 9GB of VRAM, and runs on my RTX 3060 12GB GPU.

What is Miniconda?

Miniconda, a bootstrap version of Anaconda, is a virtual environment manager that is small, FREE, and also includes the conda package manager, Python, and other packages that are required or useful to a developer, like pip and zlib.

Installing Miniconda.

I make the Miniconda directory:

mkdir -p ~/miniconda3

I download the installation payload:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh

I run the installation script:

bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3

I remove the installation script:

rm -rf ~/miniconda3/miniconda.sh

Initialising Miniconda.

I initialize Miniconda:

~/miniconda3/bin/conda init bash

Updating Miniconda.

I change the owner of the Miniconda directory to the current, logged-in account:

sudo chown -R $USER:$USER $HOME/miniconda3

I update Miniconda:

conda update -n base -c defaults conda

Using Conda to Create the DSPy Environment.

I use conda to display a list of Miniconda environments:

conda env list

I use conda to create, and activate, a new environment named (-n) (DSPy):

conda create -n DSPy python=3.11 -y && conda activate DSPy

NOTE: This command creates the (DSPy) environment, then activates the (DSPy) environment.

Creating the `DSPy` Home Directory.

NOTE: I will define the home directory with settings in the environment directory.

I create the DSPy home directory:

mkdir ~/DSPy

I make new directories within the (DSPy) environment:

mkdir -p ~/anaconda3/envs/DSPy/etc/conda/activate.d

I use the Nano text editor to create the set_working_directory.sh shell script:

sudo nano ~/anaconda3/envs/DSPy/etc/conda/activate.d/set_working_directory.sh

I add the following, save (CTRL + S) the changes, and exit (CTRL + X) the Nano text editor:

cd ~/DSPy

I activate the (base) environment:

conda activate

I activate the (DSPy) environment:

conda activate DSPy

NOTE: I should now, by default, be in the ~/DSPy home directory.

Installing DSPy.

From the (DSPy) terminal, I install the latest version of DSPy:

pip install git+https://github.com/stanfordnlp/dspy.git

I can also use pip to install DSPy directly from PyPI:

pip install -U dspy

Installing Jupyter Notebook

From the DSPy terminal, I install Jupyter Notebook:

pip install notebook

I generate a Jupyter Notebook configuration file:

jupyter notebook --generate-config

I open the configuration file with the Nano text editor:

sudo nano ~/.jupyter/jupyter_notebook_config.py

I set a default browser*:

c.ServerApp.browser = '/bin/brave-browser %s'

I save (CTRL + S) the changes and exit (CTRL + X) the Nano text editor,
I upgrade Jupyter Notebook:

pip install --upgrade jupyter

I upgrade a Jupyter Notebook dependency:

pip install --upgrade ipywidgets

I run the Jupyter Notebook on port 8091:

jupyter notebook --port 8091

In the file menu, I select "File > New > Notebook",
I choose the default "Python 3 (ipykernel)" kernel, select the "Always start the preferred kernel" tick box, and click the blue "Select" button,
In the file menu of the Notebook, I select "File > Rename...",
I rename the Notebook as "hello-world.ipynb" and click the blue "Rename" button.

Testing the DSPy Environment.

In the “hello-world“ Notebook, I define the local model that is used by DSPy:

import dspy

lm = dspy.LM(model='ollama/deepseek-r1:14b')
dspy.configure(lm=lm)

NOTE: Deepseek R1 uses chain-of-thought reasoning by default and, as a result, may take longer to compose a result. However, using this model results in a higher chance of generating a correct answer.

In a new cell, I declare a module that takes a question (of type str) as input and produces a response as an output:

qa = dspy.Predict('question: str -> response: str')
response = qa(question="What is Hello World?")

print(response.response)

Running the Code with Python.

From the (DSPy) terminal, I change to the DSPy directory:

cd ~/DSPy

I use the Nano text editor to create the hello_world.py file:

sudo nano hello-world.py

I add (CTRL + SHIFT + V) the following to the hello_world.py file:

import dspy

lm = dspy.LM(model='ollama/deepseek-r1:14b')
dspy.configure(lm=lm)

qa = dspy.Predict('question: str -> response: str')
response = qa(question="What is Hello World?")

print(response.response)

NOTE: The code above is exactly the same as the combined cells from the Notebook.

I save (CTRL + S) the changes and exit (CTRL + X) the Nano text editor.
I run the following command:

python3 hello_world.py

Advanced Testing: OPTIONAL

The previous “hello-world” test was to ensure the development software was installed correctly. These tests explore what tasks the deepseek-r1:14b LM can perform out of the box.

Also, these tests show a range of tasks that can be performed using DSPy programming techniques.

At the (DSPy) terminal, I change to the DSPy directory:

cd ~/DSPy

I install SGLang:

pip install "sglang[all]"

NOTE: SGLang is a fast serving framework for large language models and vision language models.

I install FlashInfer:

pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/

NOTE: FlashInfer is a library and kernel generator for Large Language Models that provides high-performance implementation of LLM GPU kernels such as FlashAttention, PageAttention and LoRA.

Maths.

I run the Jupyter Notebook:

jupyter notebook --port 8091

In the file menu, I select "File > New > Notebook",
I choose the default "Python 3 (ipykernel)" kernel, select the "Always start the preferred kernel" tick box, and click the blue "Select" button,
In the file menu of the Notebook, I select "File > Rename...",
I rename the Notebook as "maths.ipynb" and click the blue "Rename" button.
In the Notebook, I define the local model that is used by DSPy:

import dspy

lm = dspy.LM(model='ollama/deepseek-r1:14b')
dspy.configure(lm=lm)

In a new cell, I add the following:

maths = dspy.ChainOfThought("question -> answer: float")
maths(question="Two dice are tossed. What is the probability that the sum equals two?")

ANSWER: 0.0277778.

RAG.

In the Jupyter Notebook file menu, I select "File > New > Notebook",
I choose the default "Python 3 (ipykernel)" kernel, select the "Always start the preferred kernel" tick box, and click the blue "Select" button,
In the file menu of the Notebook, I select "File > Rename...",
I rename the Notebook as "rag.ipynb" and click the blue "Rename" button.
In the Notebook, I define the local model that is used by DSPy:

import dspy

lm = dspy.LM(model='ollama/deepseek-r1:14b')
dspy.configure(lm=lm)

In a new cell, I add the following:

def search_wikipedia(query: str) -> list[str]:
    results = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')(query, k=3)
    return [x['text'] for x in results]

rag = dspy.ChainOfThought('context, question -> response')

question = "What's the name of the castle that David Gregory inherited?"
rag(context=search_wikipedia(question), question=question)

ANSWER: Kinnairdy Castle.

Classification.

In the Jupyter Notebook file menu, I select "File > New > Notebook",
I choose the default "Python 3 (ipykernel)" kernel, select the "Always start the preferred kernel" tick box, and click the blue "Select" button,
In the file menu of the Notebook, I select "File > Rename...",
I rename the Notebook as "classification.ipynb" and click the blue "Rename" button.
In the Notebook, I define the local model that is used by DSPy:

import dspy

lm = dspy.LM(model='ollama/deepseek-r1:14b')
dspy.configure(lm=lm)

In a new cell, I add the following:

from typing import Literal

class Classify(dspy.Signature):
    """Classify sentiment of a given sentence."""

    sentence: str = dspy.InputField()
    sentiment: Literal['positive', 'negative', 'neutral'] = dspy.OutputField()
    confidence: float = dspy.OutputField()

classify = dspy.Predict(Classify)
classify(sentence="This book was super fun to read, though not the last chapter.")

ANSWER: sentiment='positive', confidence=0.75.

Information Extraction.

In the Jupyter Notebook file menu, I select "File > New > Notebook",
I choose the default "Python 3 (ipykernel)" kernel, select the "Always start the preferred kernel" tick box, and click the blue "Select" button,
In the file menu of the Notebook, I select "File > Rename...",
I rename the Notebook as "extraction.ipynb" and click the blue "Rename" button.
In the Notebook, I define the local model that is used by DSPy:

import dspy

lm = dspy.LM(model='ollama/deepseek-r1:14b')
dspy.configure(lm=lm)

In a new cell, I add the following:

class ExtractInfo(dspy.Signature):
    """Extract structured information from text."""

    text: str = dspy.InputField()
    title: str = dspy.OutputField()
    headings: list[str] = dspy.OutputField()
    entities: list[dict[str, str]] = dspy.OutputField(desc="a list of entities and their metadata")

module = dspy.Predict(ExtractInfo)

text = "Apple Inc. announced its latest iPhone 14 today." \
    "The CEO, Tim Cook, highlighted its new features in a press release."
response = module(text=text)

print(response.title)
print(response.headings)
print(response.entities)

ANSWER: Apple Announces iPhone 14 ['Announcement Details', 'Press Release Highlights', 'New Features'] [{'EntityType': 'Company', 'Description': 'Technology company known for iPhones'}, {'EntityType': 'Person', 'Description': 'CEO of Apple'}]

Agents.

In the Jupyter Notebook file menu, I select "File > New > Notebook",
I choose the default "Python 3 (ipykernel)" kernel, select the "Always start the preferred kernel" tick box, and click the blue "Select" button,
In the file menu of the Notebook, I select "File > Rename...",
I rename the Notebook as "agents.ipynb" and click the blue "Rename" button.
In the Notebook, I define the local model that is used by DSPy:

import dspy

lm = dspy.LM(model='ollama/deepseek-r1:14b')
dspy.configure(lm=lm)

In a new cell, I add the following:

def evaluate_math(expression: str):
    return dspy.PythonInterpreter({}).execute(expression)

def search_wikipedia(query: str):
    results = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')(query, k=3)
    return [x['text'] for x in results]

react = dspy.ReAct("question -> answer: float", tools=[evaluate_math, search_wikipedia])

pred = react(question="What is 9362158 divided by the year of birth of David Gregory of Kinnairdy castle?")
print(pred.answer)

ANSWER: 5761.328

The Results.

Setting up DSPy involves creating a robust, development environment where complex tasks can leverage the capabilities of advanced, and powerful, language models. By following the outlined process, from installing Ollama and Miniconda, to creating a dedicated Conda environment, while setting up a Jupyter Notebook, I can use the resulting environment to efficiently manage (and execute) DSPy programmes. The whole purpose of this post is to setup a local workspace for DSPy development. With DSPy, I will be equipped to explore, and implement, advanced LM capabilities that provide leading, innovative solutions.

In Conclusion.

I am now ready to revolutionize my approach to complex tasks with advanced LMs. Meet DSPy (Declarative Self-improving Python) – my new best friend in systematic problem-solving!

Setting up DSPy is a breeze with a few essential steps. I start by installing Ollama and Miniconda, then I create a dedicated environment for DSPy.

Next, set up Jupyter Notebook to streamline my workflow and enhance productivity. With DSPy, I’m paving the way for innovative solutions across numerous domains.

By following this structured setup, I can efficiently manage and execute DSPy procedures, unlocking advanced LM capabilities.

Are you ready to explore the potential of DSPy in your projects? How do you plan to leverage advanced LMs for your next big idea? Let's discuss below!

Until next time: Be safe, be kind, be awesome.

Tags.

#DSPy #Python #LanguageModels #Innovation #ProblemSolving

DSPy: Install, Setup, and Test.

Comments (1)

The Dev Series

Is NZ Ready for the Next Pandemic?

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System Tools for Ubuntu Desktop.

Installing vLLM onto EVO-X2.

Installing ROCm & Llama.cpp for CPUs & APUs.

Installing Ruby & VS Code

Running Hermes Agent in YOLO Mode.

TL;DR.

An Introduction.

The Big Picture.

Prerequisites.

Updating my Base System.

What is Ollama?

Installing Ollama.

Pulling an Advanced LM.

What is Miniconda?

Installing Miniconda.

Initialising Miniconda.

Updating Miniconda.

Using Conda to Create the DSPy Environment.

Creating the `DSPy` Home Directory.

Installing DSPy.

Installing Jupyter Notebook

Testing the DSPy Environment.

Running the Code with Python.

Advanced Testing: OPTIONAL

Maths.

RAG.

Classification.

Information Extraction.

Agents.

The Results.

In Conclusion.

Tags.

Command Palette

Comments (1)

The Dev Series

Is NZ Ready for the Next Pandemic?

More from this blog

TL;DR.

An Introduction.

The Big Picture.

Prerequisites.

Updating my Base System.

What is Ollama?

Installing Ollama.

Pulling an Advanced LM.

What is Miniconda?

Installing Miniconda.

Initialising Miniconda.

Updating Miniconda.

Using Conda to Create the DSPy Environment.

Creating the DSPy Home Directory.

Installing DSPy.

Installing Jupyter Notebook

Testing the DSPy Environment.

Running the Code with Python.

Advanced Testing: OPTIONAL

Maths.

RAG.

Classification.

Information Extraction.

Agents.

The Results.

In Conclusion.

Tags.

Creating the `DSPy` Home Directory.