Contrary to the popular notion, model training in machine learning is not simply a black box activity. For the machine learning (ML) solution to consistently perform well, the developers have to deep dive into each model to find the right fit with the data and the business use case.
In simple terms, a machine learning model is a simple statistical equation that is developed over time based on the data at hand. This learning process, also known as training, ranges from simple to complex processes. A model training tool is an interface that enables easy interaction between the developer and the complexities of machine learning models.
How to choose the right model training tool
In machine learning, there is no “jack of all trades”- no one tool can fix all problems because of vast variations in real-world problems and data. But there are model training tools that can fit you like a glove – you and your requirements specifically.
To be able to choose a primary model training tool for your solution, you need to assess your existing development process, production infrastructure, skills level of your team, compliance restrictions, and similar vital details to be able to pin down the right tool.
However, one key feature that is often overlooked, leading to a weak foundation and an unstable series of solutions, in the long run, is the ability of the model training tool to either track metadata or the ability to integrate seamlessly with a metadata store and monitoring tools.
Model metadata involves assets such as training parameters, experiment metrics, data versions, pipeline configurations, weight reference files, and much more. This data is powerful and cuts down both production and model recovery time. To choose the right metadata store, your team can do a cost-benefit analysis between building new vs. buying existing solutions.
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Top 10 tools for ML model training
Below is a list of the top ten model training tools in the ML marketspace that you could use to estimate if your requirements match the features offered by the tool.
I remember coming across TensorFlow as an intern and being clearly intimidated after having barely explored scikit-learn. Looking back, it seems imminent because TensorFlow is a low-level library and requires working closely with the model code. Developers can achieve full control and train models from scratch with TensorFlow.
However, TensorFlow also offers some pre-built models that can be used for simpler solutions. One of the most desirable features of TensorFlow is dataflow graphs that come in handy especially when complex models are under development.
TensorFlow supports a wide range of solutions including NLP, computer vision, predictive ML solutions, and reinforcement learning. Being an open-source tool from Google, TensorFlow is constantly evolving due to a community of over 380,000 contributors worldwide.
👉 Check how to keep track of TensorFlow/Keras model training.
PyTorch is another popular open-source tool that offers tough competition to TensorFlow. PyTorch has two significant features – tensor computing with accelerated processing on GPU and neural networks built on a tape-based auto diff system.
Additionally, PyTorch supports a host of ML libraries and tools that can support a variety of solutions. Some examples include AllenNLP and ELF which is a game research platform. PyTorch also supports C++ and Java in addition to Python.
One leading difference between PyTorch and TensorFlow is that PyTorch supports dynamic dataflow graphs whereas TensorFlow is limited to static graphs. Compared to TensorFlow, PyTorch is easier to learn and implement since TensorFlow needs heavy code work.
👉 Check how to keep track of PyTorch model training.
3. PyTorch Lightning
PyTorch Lightning is a wrapper on top of PyTorch, built primarily to redirect focus on research instead of on engineering or redundant tasks. It abstracts the underlying complexities of the model and common code structures so the developer can focus on multiple models in a short span.
The two strengths of PyTorch Lightning, as the name partially suggests, are speed and scale. It supports TPU integration and removes barriers to using multiple GPUs. For scale, PyTorch Lightning allows experiments to run in parallel on multiple virtual machines through grid.ai.
PyTorch Lightning has significantly less need for code because of high-level wrappers. However, that does not restrict the flexibility since the primary objective of PyTorch is to reduce the need for redundant boilerplate code. Developers can still modify and deep dive into areas that need customization.
👉 Check how to keep track of PyTorch Lightning model training.
Scikit-learn is one of the top open-source frameworks ideal for getting started with machine learning. It has high-level wrappers which enable users to play around with multiple algorithms and explore the wide range of classification, clustering, and regression models.
For the curious mind, scikit-learn can also be a great way to gain deeper insight into the models simply by unwrapping the code and following the dependencies. Scikit-learn’s documentation is highly detailed and easily readable by both beginners and experts.
Scikit-learn is great for ML solutions with a limited time and resource allotment. It is strictly machine learning-focused and has been an instrumental part of predictive solutions from popular brands over the last few years.
👉 Check how to keep track of Scikit-learn model training.
Catalyst is another PyTorch framework built specifically for deep learning solutions. Catalyst is research-friendly and takes care of engineering tasks such as code reusability and reproducibility, facilitating rapid experimentation.
Deep learning has always been considered as complex and Catalyst enables developers to execute deep learning models with a few lines of code. It supports some of the top deep learning models such as ranger optimizer, stochastic weight averaging, and one-cycle training.
Catalyst saves source code and environment variables to enable reproducible experiments. Some other notable features include model checkpointing, callbacks, and early stopping.
👉 Check how to keep track of Catalyst model training.
XGBoost is a tree-based model training algorithm that uses gradient boosting to optimize performance. It is an ensemble learning technique which means several tree-based algorithms are used to achieve the optimal model sequence.
With gradient boosting, XGBoost grows the trees one after the other so that the following trees can learn from the weakness of the previous ones. It gradually moderates the weights of weak and strong learners by borrowing information from the preceding tree model.
To enhance speed XGBoost supports parallel model boosting across distributed environments such as Hadoop or MPI. XGBoost is well suited for large training datasets and combinations of numeric and categorical features.
👉 Check how to keep track of XGBoost model training.
LightGBM, like XGBoost, is also a gradient boosting algorithm that uses tree-based models. But when it comes to speed, LightGBM has an upper hand over XGBoost. LightGBM is best suited for large datasets that otherwise would consume a lot of training time with other models.
While most tree-based algorithms split the tree level or depth-wise, LightGBM comes in with the unique technique of leaf or breadth-wise splits which has proven to increase performance. Even though this tends to overfit the model, the developer can avoid the situation by tweaking the max_depth parameter.
LightGBM requires low memory space in spite of working with heavy datasets since it replaces continuous values with discrete bins. It also supports parallel learning which is again a major time saver.
👉 Check how to keep track of LightGBM model training.
CatBoost is a gradient boosting algorithm that provides best-in-class results with minimal training compared to most machine learning models. It is an open-source tool and has become a popular favorite because of its ease of use.
CatBoost cuts down preprocessing efforts since it can directly and optimally handle categorical data. It does so by generating numerical encodings and by experimenting with various combinations in the background.
Even though CatBoost offers the scope of tuning extensively with a range of multiple hyperparameters, it does not require much tuning and can produce results without overfitting the training data. It is well-suited for both low and high-volume data.
Fast.ai’s catchy tagline says it all – “making neural nets uncool again”. Fast.ai aims to make deep learning accessible across multiple languages, operating systems, and small datasets. It was developed on the idea that transfer learning is a key strength in deep learning and can cut down a huge amount of redundant engineering work.
It offers an easy-to-use high-level interface for deep learning models and also allows users to download a set of pre-trained models. Fast.ai has multiple wrappers that hide the complexities of the underlying model architecture. This allows developers to focus on data intelligence and process breakthroughs.
Fast.ai is also extremely popular for sharing their free online course, “Practical Deep Learning for Coders”, which does not demand any pre-requisite yet dives deep into deep learning concepts and illustrates how to make it easy through fast.ai.
👉 Check how to keep track of fast.ai model training.
10. PyTorch Ignite
PyTorch Ignite is a wrapper built on top of PyTorch and is quite similar to PyTorch Lightning. Both offer an abstraction of model complexities and an easy-to-use interface to expand research abilities and diminish redundant code.
Architecture-wise, there is a subtle difference between the two. While PyTorch Lightning has a standard reproducible interface, Ignite does not have any standard version.
While it cannot support highly advanced features, Ignite works well with an ecosystem of integrations to support the machine learning solution whereas Lightning supports state-of-the-art solutions, advanced features, and distributed training.
👉 Check how to keep track of PyTorch Ignite model training.
Other model training tools
There are several other options that might not be as popular as the above choices but are great for specific model training requirements.
- If high speed with limited GPU resources is your priority, Theano takes the lead.
- For .NET and C# capabilities, Accord would be ideal. It also has a host of audio and image processing libraries.
- ML.NET is another tool for .NET developers.
- Other options for NLP-specific and computer vision solutions include Gensim and Caffe respectively.
Conclusively, it is always better to do thorough market research before selecting the right fit for your specific solutions. It might not be the most popular or a well-known tool, but it can definitely be the right one for you.
As suggested earlier, no one tool has to be the solution for every business case or machine learning problem. Even if none of the tools seem like a perfect fit for you, a combination of them can be the ideal way to go since most of them are compatible with each other.
The trick is to first list down some of the best tools in the space, which we have already done for you, and then explore the shortlisted ones to arrive at the right match gradually. The tools shared here are easy to install and have extensive documentation on their respective sites for an easy kickstart!
15 Best Tools for ML Experiment Tracking and Management
10 mins read | Author Patrycja Jenkner | Updated August 25th, 2021
While working on a machine learning project, getting good results from a single model-training run is one thing. But keeping all of your machine learning experiments well organized and having a process that lets you draw valid conclusions from them is quite another.
The answer to these needs is experiment tracking. In machine learning, experiment tracking is the process of saving all experiment-related information that you care about for every experiment you run.
ML teams implement experiment tracking in different ways, may it be by using spreadsheets, GitHub, or self-built platforms. Yet, the most effective option is to do it with tools designed specifically for tracking and managing ML experiments.
In this article, we overview and compare the 15 best tools that will allow you to track and manage your ML experiments. You’ll get to know their main features and see how they are different from each other. Hopefully, this will help you evaluate them and choose the right one for your needs.
How to evaluate an experiment tracking tool?
There’s no one answer to the question “what is the best experiment tracking tool?”. Your motivation and needs may be completely different when you work individually or in a team. And, depending on your role, you may be looking for various functionalities.
If you’re a Data Scientist or a Researcher, you should consider:
- If the tool comes with a web UI or it’s console-based;
- If you can integrate the tool with your preferred model training frameworks;
- What metadata you can log, display, and compare (code, text, audio, video, etc.);
- Can you easily compare multiple runs? If so, in what format – only table, or also charts;
- If organizing and searching through experiments is user-friendly;
- If you can customize metadata structure and dashboards;
- If the tool lets you track hardware consumption;
- How easy it is to collaborate with other team members – can you just share a link to the experiment or you have to use screenshots as a workaround?
As an ML Engineer, you should check if the tool lets you:
- Easily reproduce and re-run experiments;
- Track and search through experiment lineage (data/models/experiments used downstream);
- Save, fetch, and cache datasets for experiments;
- Integrate it with your CI/CD pipeline;
- Easily collaborate and share work with your colleagues.
Finally, as an ML team lead, you’ll be interested in:
- General business-related stuff like pricing model, security, and support;
- How much infrastructure the tool requires, how easy it is to integrate it into your current workflow;
- Is the product delivered as commercial software, open-source software, or a managed cloud service?
- What collaboration, sharing, and review feature it has.
I made sure to keep these motivations in mind when reviewing the tools that are on the market. So let’s take a closer look at them.Continue reading ->