Skip to main content

"Log as append-only source" logger

Project description

Log as append-only source package

Build Status

Logs as append-only source: write your ML training results in Python without having to worry about crashes. Loading is a breeze: the logs are native Python code. The package supports unstructured data. The data can easily be imported into Jupyter Notebooks or elsewhere.

Installation

To install using pip, use:

pip install blackhc.laaos

To run the tests, use:

python setup.py test

Append-only source logs

Storing training results as Python dictionaries or JSON files is problematic because the formats are not append-only, which means that you have to rewrite the file every time something changes. (Or you only write results at the end, which does not play well with interruptions or intermediate failures.)

Alternatively, we can simply write the operations that create a structure to a file in an append-only fashion. If the data structure itself is growing and not mutated, this only increases file-size by a constant factor.

The advantage of this library is that the file format is very simple: it's valid Python code.

The only requirement is that you only store primitive types, lists, sets, dicts and immutable types.

Custom wrappers can be added by registering TypeHandlers when creating a Store. See WeakEnumHandler and StrEnumHandler.

Example

from blackhc.laaos import create_file_store, safe_load_store
store = create_file_store('test', suffix='')

store['losses'] = []
losses = store['losses']

for i in range(1, 10):
    losses.append(1/i)

store.close()

The resulting file laaos/test.py contains valid Python code:

store = {}
store['losses']=[]
store['losses'].append(1.0)
store['losses'].append(0.5)
store['losses'].append(0.3333333333333333)
store['losses'].append(0.25)
store['losses'].append(0.2)
store['losses'].append(0.16666666666666666)
store['losses'].append(0.14285714285714285)
store['losses'].append(0.125)
store['losses'].append(0.1111111111111111)

It can be loaded either with:

form laaos.test import store

or with the more secure:

safe_load('laaos/test.py')

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

blackhc.laaos-1.1.0.tar.gz (6.4 kB view hashes)

Uploaded source

Built Distribution

blackhc.laaos-1.1.0-py3-none-any.whl (6.5 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page