xetrack 0.0.3

A simple tool for benchamrking and tracking machine learning models and experiments.

Xetrack

xetrack is a lightweight package to track experiments and benchmarks data using duckdb. It looks and feels like pandas and is very easy to use.

Each instance of the tracker has a "track_id" which is a unique identifier for a single run.

Features

• Simple
• Embedded
• Fast
• Pandas-like
• SQL-like

Installation

pip install xetrack

Quickstart

from xetrack import Tracker

tracker = Tracker('database.db', 
                  params={'model': 'resnet18'}, 
                  verbose=False)
tracker.track(accuracy=0.9, loss=0.1, epoch=1)
tracker.to_df(all=True)
                                    _id                              track_id                 date     model  loss  epoch  accuracy
0  3adfc3ca-e7a0-4955-a7c7-2dae1a2552e4  e5324c93-25b1-4344-a1e1-0b47e95761aa  16-08-2023 00:43:38  resnet18   0.1      1       0.9

Params are values which are added to every feature row:

tracker.set_params({'model': 'resnet18', 'dataset': 'cifar10'})

You can also set a value to an entire run with set_value:

tracker.set_value('test_accuracy', 0.9)

Track functions

You can track any function.

• The function must return a dictionary or None

tracker = Tracker('database.db', log_system_params=True, log_network_params=True, measurement_interval=0.1)
tracker.track_function(read_image, *args, **kwargs)
{'result': 571084, 'name': 'read_image', 'time': 0.30797290802001953, 'error': '', 'disk_percent': 0.6, 'p_memory_percent': 0.496507, 'cpu': 0.0, 'memory_percent': 32.874608, 'bytes_sent': 0.0078125, 'bytes_recv': 0.583984375}

Pandas-like

print(tracker)
                                    _id                              track_id                 date    b    a  accuracy
0  48154ec7-1fe4-4896-ac66-89db54ddd12a  fd0bfe4f-7257-4ec3-8c6f-91fe8ae67d20  16-08-2023 00:21:46  2.0  1.0       NaN
1  8a43000a-03a4-4822-98f8-4df671c2d410  fd0bfe4f-7257-4ec3-8c6f-91fe8ae67d20  16-08-2023 00:24:21  NaN  NaN       1.0

tracker['accuracy'] # get accuracy column
tracker.to_df() # get pandas dataframe of current run

SQL-like

You can filter the data using SQL-like syntax using duckdb:

tracker.conn.execute(f"SELECT * FROM {tracker.table_name} WHERE accuracy > 0.8").fetchall()

Analysis

To get the data of all runs in the database for analysis:
Use this for further analysis and plotting.

# retrieve all runs data
df = Tracker('database.db').to_df(all=True) 

Export

tracker.to_csv('tracker.csv', all=False)
tracker.to_parquet('tracker.parquet', all=True)

Merge two databases

If you have two databases, and you want to merge them into one, you can use the copy function:

python -c 'from xetrack import copy; copy(source="db1.db", target="db2.db")'