Getting Started

First, install the package with the GUI with pip:

pip install deepboard[full]

The deepboard package is divided into two main components: the experiment tracking one called ResultTable, and the GUI application. You will interact with the ResultTable api in your code to log your experiments, and then use the GUI to visualize and analyze them.

Creating a new experiment

from deepboard.resultTable import ResultTable

# Step 1: Create / load the result table
rTable = ResultTable()

# Step 2: Create a new run
run = rTable.new_run("My Experiment")

# Step 3: Write the final results
run.write_result(accuracy=0.98, loss= 0.0257)

Here, we first create a new ResultTable instance. If no database exist at the specified path, a new result table, a database, will be created. Then, we create a new entry in the table with the new run command. We can run our experiment, then write the final results to the result table. By default, it creates a file named result_table.db. You can visualize the results in the GUI by running and opening in your web browser.

deepboard result_table.db

Experiment uniqueness and debug mode

Note that if you try to run the previous script again, it will raise an error saying that the experiment has already been run with runID 1. This is because each experiment must be unique in the result table. To avoid getting this constraint, you can run in debug mode, which will give the same runID (-1) to every run, and each run will overwrite the previous one. You can simply call the new_debug_run method instead of new_run, and the rest of the api remains the same, so we can use the same code as before.

run = rTable.new_debug_run("My Experiment")

The Run ID

As every run must be unique, each run is given a unique id called the RunID. You can retrieve this id by accessing the run_id attribute of the LogWriter object (the run variable in the previous examples). Since this id is unique, you can use it for whatever you want, for example, you could save checkpoints to a directory, where each checkpoint file or directory is named with the corresponding RunID.

Note:
When an experiment fails (an exception is raised), the ResultTable will detect it, and the run won't be considered when checking uniqueness. This means that you can re-run failed experiments without changing anything, and the run will be accepted as a new unique experiment.

It is useful to log final results of experiments to compare them later, but we often want to log intermediate results during training. This topic is explored in the next chapter.