Monitoring machine learning model results live from Jupyter notebooks
Tracking and saving your model results just got that much easier with Comet.ml
For many data scientists, Jupyter notebooks have become the tool of choice. Its ability to combine software code, computational output, explanatory text, and multimedia into a single document has helped countless users easily create tutorials, iterate more quickly, and showcase their work externally.
A recent Nature article cites a Github analysis that counted “more than 2.5 million public Jupyter notebooks in September 2018, up from 200,000 or so in 2015.”
The larger Project Jupyter ecosystem extends beyond the notebook — Jupyter’s newest release called JupyterLab extends the notebook framework with features such as file browsers, chat functionality, and text editors. Companies have also released tools based on notebooks where kernels reside on the cloud — most prominently, Google with their Colaboratory project.
At Comet.ml, we agree that Jupyter notebooks and tools will continue to play a key role within the data science community — which is why we’re very excited to release full Jupyter support with Comet.ml!
Comet.ml improves the notebook experience through:
- Instant Feedback — track your model’s results instantly and in real time
- Low Overhead — eliminate the need to write manual code to create complex plots
- Collaboration — share your model results easily with teammates or collaborate through teams
- Rich Visualizations — compare results across model iterations with visualizations like bar, line, and parallel coordinates charts.
Comet is doing for machine learning what GitHub did for software. We allow data science teams to automatically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.
Try a sample notebook with a Keras MNIST model
Code running from Jupyter notebooks, Jupyter Lab, or any other Jupyter-based tool (such as Google Colab) now has our full support, just like your scripts!
Feel free to experiment with this sample Jupyter notebook below where we train a simple Keras model on the MNIST dataset in order to conduct image classification (i.e. classify a handwritten 7 as a 7). This notebook demonstrates how to use Comet to automatically track the model code, results, and more.
You can also play with the full public Comet project here with a browser view.
With Comet.ml, you can finally:
- have a tight feedback loop between EDA and modeling
- view real-time model results plotted as nice visualizations
- easily collaborate with other data scientists
Try with your own Jupyter Notebook
Sign up for your free account at www.comet.ml and explore Comet with your own Jupyter-based tools! We’re very excited about this new full integration and look forward to hearing your feedback on Github or Slack.
Found this article useful? Follow us (Comet.ml) on Medium and check out some other relevant articles below! Please 👏 this article to share it!
- Comet.ml Release Notes — updated daily with new features and fixes!
- Real-Time Model Performance Visualizations with Comet.ml
- Building Reliable Machine Learning Models with Cross Validation
