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MLflow vs PyTorch: What are the differences?
Introduction:
In the field of machine learning and artificial intelligence, MLflow and PyTorch are two popular frameworks that are used for different purposes. MLflow is an open-source platform that helps manage the machine learning lifecycle, while PyTorch is a deep learning framework that allows developers to create neural network models. Although both tools have their own unique features, there are several key differences between MLflow and PyTorch.
Deployment and Management: MLflow provides comprehensive functionality for deploying and managing machine learning models. It supports various deployment options such as REST API, batch inference, and serverless functions. On the other hand, PyTorch focuses primarily on model training and inference. It does not have built-in functionalities for model deployment and management.
Experiment Tracking: MLflow excels in experiment tracking, allowing users to record and compare parameters, metrics, and artifacts associated with different models. It provides a centralized repository for storing experiments, making it easier to collaborate and reproduce results. While PyTorch also supports basic experiment tracking capabilities, it does not offer the same level of comprehensive tracking features as MLflow.
Model Registry and Versioning: MLflow provides a model registry feature that allows users to store, manage, and version models. It enables easy collaboration and sharing of models within an organization. PyTorch, on the other hand, does not have a built-in model registry or versioning system. Users need to implement their own versioning mechanisms if they want to manage and track model versions.
Integration with Other Frameworks: MLflow is designed to be framework-agnostic and supports integration with various machine learning and deep learning frameworks, including PyTorch. This allows users to leverage MLflow's tracking and deployment capabilities while using PyTorch for model training and inference. PyTorch, on the other hand, is a deep learning framework that is specifically tailored for neural networks and does not offer direct integration with other frameworks.
Ease of Use and Learning Curve: PyTorch provides a high-level interface that is easy to use and understand, especially for developers familiar with Python. It offers a dynamic computation graph and intuitive debugging capabilities, making it easier to build and debug models. MLflow, on the other hand, has a slightly steeper learning curve due to its broader scope and more extensive feature set. It requires some additional effort to learn and understand all the different components of MLflow.
Maturity and Community Support: PyTorch is a mature and widely adopted deep learning framework with a large and active community. It has extensive documentation, tutorials, and a wide range of third-party resources available. MLflow is a relatively newer framework compared to PyTorch and has a smaller community. While MLflow is gaining traction, the community support and ecosystem around PyTorch are more extensive and well-established.
In summary, MLflow provides comprehensive capabilities for model management, experiment tracking, and deployment, while PyTorch focuses primarily on deep learning model training and inference. MLflow excels in experiment tracking, model registry, and deployment functionalities, while PyTorch offers ease of use, a high-level interface, and a mature community.
Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.
I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.
For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.
We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.
For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.
A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.
Pros of MLflow
- Code First5
- Simplified Logging4
Pros of PyTorch
- Easy to use15
- Developer Friendly11
- Easy to debug10
- Sometimes faster than TensorFlow7
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Cons of MLflow
Cons of PyTorch
- Lots of code3
- It eats poop1