Cogitare is a Modern, Fast, and Modular Deep Learning and Machine Learning framework in Python. A friendly interface for beginners and a powerful toolset for experts.
Cogitare Monitor is a web tool to analyze the model training in real time. Currently, is a working in progress, but the main objectives of cogitare monitor are:
- A unified tool to analyze the execution of multiple models and multiples instances of the same model in the same browser tab
- Support multiple clients connected at the same time
- Get real-time data about the machine resources (CPU/GPU/RAM usage, and others)
- Plot model loss and custom metrics
- Plot and analyze model weights
- Analyze, schedule, and run multiples instances of the same model with different parameters
- Debug the execution graph
- Analyze and debug the dataset. Check the loading time per bach, the cache usage (for AsyncLoader), and get raw samples to display in the monitor
- Interface to plot custom graphs using Bokeh. A tab to display user-defined graphs (a PlottingBokeh plugin) in the monitor.
Cogitare Monitor is a work in progress project, and any contribution is welcome.
You can contribute testing and providing bug reports, proposing feature ideas, fixing bugs, pushing code, etcs.
- You want to propose a new Feature and implement it
- post about your intended feature, and we shall discuss the design and implementation. Once we agree that the plan looks good, go ahead and implement it.
- You want to implement a feature or bug-fix for an outstanding issue
- Look at the outstanding issues here: https://github.com/cogitare-ai/monitor/issues
- Pick an issue and comment on the task that you want to work on this feature
- If you need more context on a particular issue, please ask and we shall provide.
Once you finish implementing a feature or bugfix, please send a Pull Request to https://github.com/cogitare-ai/monitor
If you are not familiar with creating a Pull Request, here are some guides: