Python is one of the best programming languages for Machine learning because of its advanced ecosystem tailored for data science and artificial intelligence. In the previous article, I laid the foundation by delving into fundamental programming concepts in Python. These are essential building blocks for any programmer, especially those venturing into machine learning.
I will teach you how to use Python tools and best practices for machine learning projects in this article. We''ll cover the importance of organizing your projects, using virtual environments, and managing dependencies. I''ll also introduce you to Anaconda and Jupyter Notebooks, two valuable tools for prototyping models. Finally, we''ll discuss the hardware requirements for machine learning projects and cloud platforms that you can use to develop your models.
Organizing Your Machine Learning Project
Before you start writing code for a machine learning model, it''s crucial to organize your project. A well-structured project will be easier to maintain, collaborate, and understand. A good practice is creating a dedicated folder for each project, with subfolders for data, code, and other relevant assets. This organization will make it easier to manage your project as it grows. It also helps in accessing the data using code in Python easily.
All the libraries, including Python itself, are constantly being updated, and some significant updates may break your old code. Some may change the import path of a module or remove the method you used previously. To avoid these problems, you can use a "virtual environment" that can act like an isolated container for your project.
Creating Virtual Environments for Projects
A virtual environment is a special Python environment that lets you keep your project''s dependencies separate from other Python projects. You can use different versions of the same library for different projects without worrying about conflicts. Your virtual environment will have its own Python version, which won''t be updated when you update Python in your base environment (not until you update it in the virtual environment).
Python has a tool called venv that you can use to create lightweight virtual environments. To create a virtual environment, run the following command:
python -m venv /path/to/my_venv
The above command will create a new folder at the specified location with all the dependencies like pip and python itself.
When you activate a virtual environment, it changes your system''s PATH variable to include the Python and Pip executables for that virtual environment. The versions installed in your virtual environment will be used when you run python or pip. Use the below OS-specific commands to activate your venv. Activate your virtual environment with the following commands:
On Windows:
.\my_venv\Scripts\activate
On Mac/Linux:
source my_venv/bin/activate
If you want to deactivate the virtual environment and return to your system''s Python environment, simply type:
deactivate
Virtual environments are a great way to manage project dependencies. Learn more about venv in Python here.
Streamlining Dependency Management with pip
Once your virtual environment is set up, you can use pip, Python''s package manager, to manage project dependencies. pip makes it easy to install, upgrade, and remove packages. Pip installs packages from the Python Package Index (PyPI) by default. PyPI is the primary repository for Python packages and libraries, and it provides many resources for developers to build, publish, or install third-party Python modules into their projects.
You can install a specific package by specifying its name like this:
pip install <package-name>
Using a requirements.txt File
Python virtual environments are not portable by default, so you can''t copy and paste them to another location. Instead, you can delete the old environment and create a new one. To avoid installing every dependency one by one, you should use a requirements.txt file. This file lists all the libraries and their versions your project needs, making it easy to recreate the environment when you share your code or deploy it to a different environment.
To generate a requirements.txt file, use pip as follows.
pip freeze > requirements.txt
This command will save a list of all the installed packages with their version to the file requirements.txt.
You can also create a requirements.txt file manually by adding a line for each dependency, including the package name and version. For example:
numpy==1.24.2
pandas==1.5.3
To install all the dependencies listed in your requirements.txt file, run the following command:
pip install -r requirements.txt
This command will install all the packages listed in the file, along with their versions (if specified). This file ensures that all the dependencies your project needs are installed and the exact versions are installed on every machine.
Popular Editors for Machine Learning
You can use your existing VS Code for machine learning by installing some extensions. But if you want a get more serious with data science, consider installing Anaconda. It''s a pre-configured software with all the necessary tools for data science, including Jupyter, Spyder IDE, and RStudio. It also has its own package managers, Conda and Mamba, which make it easy to install, update, and manage Python packages.
Prototyping Models with Jupyter Notebooks
Jupyter Notebook is a powerful tool for data exploration and model prototyping. It is an interactive environment where you can write and run code, visualize data, and document your work.
To get started with Jupyter Notebook, install it in your virtual environment with pip:
pip install jupyter
Once the jupyter installation is complete, you can start a notebook by running the following command:
jupyter notebook
This command will open a web browser window displaying the Jupyter interface, where you can create, open, and run your notebooks.
Jupyter Notebook combines Markdown and Python code in one file. It divides the notebook into cells containing code, text, or Markdown. You can run any cell anytime and see the output below it. You can create a new notebook using the UI or add a new file with the .ipynb extension.
You can use it with popular editors like VS Code, and it comes pre-installed with Anaconda.
Jupyter can be a powerful companion as you dive further into your data science adventure. Learn more about using Jupyter notebooks here.
Hardware Essentials for Machine Learning
Machine learning needs powerful hardware for good performance and efficiency. As you progress towards deep learning, you''ll need even better hardware to handle the computational demands. Here are some recommended requirements for an optimal machine learning system:
- CPU: A powerful CPU is essential for machine learning, as it handles most calculations. Look for a CPU with at least 4 cores.
- RAM: Machine learning algorithms can use a lot of RAM, so it''s essential to have plenty. You need a minimum of 8GB of RAM. However, 16GB or more is even better.
- GPU: Without GPU, you have to wait for hours for your deep learning models to complete training. Look for a GPU with at least 4GB of VRAM.
- Storage: Machine learning datasets can be massive, so you''ll need plenty of storage space. Make use of your storage effectively based on your budget. At least 512GB is preferable.
Cloud-Based Environments for Machine Learning
If you don''t have GPU-supporting systems, you can use cloud environments like Google Colab and GitHub codespaces for machine learning project development. They offer free plans with good CPUs and essential GPUs, and you can also upgrade to more powerful GPUs or TPUs for a subscription. With cloud environments, you can start working on projects faster and access them anywhere.
Here is one of my projects using free resources from Google Colab. You can also upgrade to a high-RAM runtime for larger projects.
Google Colab is an excellent resource for learning machine learning because it provides free access to powerful GPUs. The Colab notebook is similar to Jupyter Lab and is user-friendly. You have to change the "Notebook settings" for using "T4 GPU" in the "Edit" menu of your project.
GitHub Codespaces is a cloud-based development environment similar to using VS Code locally. It is a VS Code project running in a development container hosted on a virtual machine in the cloud. You can use the jupyter notebooks using extensions or installing them manually. Codespaces offers 60 hours of free usage per month, up to a 4-core CPU with 16 GB of RAM and 32 GB of storage. However, if you are a student, you can take advantage of GitHub Student Developer Pack to get 90 hours of usage monthly and up to 16-core CPU with 32 GB RAM and 128 GB of storage.
You can install CUDA GPUs in Codespaces to accelerate your machine learning workloads. Find out how here.
Conclusion: Your Journey in Machine Learning
To summarize, Python is all you need for your machine-learning projects. Organize them clearly and use a virtual environment. You can use your existing VS Code setup with some extensions or install Anaconda for a better experience. You can use Jupyter Notebooks for interactive development, and cloud platforms like Google Colab and GitHub Codespaces offer generous free plans with access to powerful computing resources. Students can use the GitHub Student Developer Pack for the best resources provided by GitHub. Remember that the right tools and practices can empower your machine learning journey.
Check out the essential Python libraries for machine learning in my next post.
