Introducing Comet.ml’s Python API Client

Programmatically access your machine learning system of record Accessing your model weights, metrics, hyperparameters, images, and other workflow artifacts should be easy for 10 or 10,000 experiments. Our new Python API client allows you to programmatically access your Comet.ml workspace, project, and experiment data. By using the API Client, you can reduce the amount of code…

Read More

Comet.ml cheat sheet: supercharge your machine learning experiment management

Comet.ml allows you to automatically track your machine learning code, experiments, hyperparameters, and results to achieve reproducibility, transparency, and more efficient iteration cycles. We built it after seeing many data scientists trying to grapple with disjointed scripts, notebooks (both Jupyter and paper ones), and complex file structures to remember what they ran previously. Comet.ml has…

Read More

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.…

Read More

Introducing Comet.ml Project Visualizations

Compare across your model iterations efficiently with rich visualizations to identify your champion model At Comet.ml, we believe that machine learning should be highly iterative, collaborative, and reproducible. Comet.ml allows data science teams to automatically track their datasets, code changes, experimentation history and models creating efficiency, transparency, and reproducibility. One of our most popular features have…

Read More