Skip to main content

Dam4ML client library

Project description

This is the dam4ml client library.


pip install dam4ml

(Ugh, pretty is, uh?)

Basic usage

from dam4ml import client
from dam4ml import transforms

# Login to DAM4ML
dataset = client.connect("mnist", api_key="")

# (optional) Pre-load the whole dataset for offline performance.
# This will take a while but will improve further performance.

# (optional) You can pre-filter your dataset. See DAM4ML website
# for more information about how to build your filter
filter = {
    "tag_slug": "test",

# Iterate through all dataset items
for item in dataset.as_dict(**filter):
    # ...process each dataset item here.

# Convert dataset to a pynum array

# Even better, simulate what Keras' load_dataset() method would do:
pn_dataset = dataset.as_pynum()
(x_train, y_train) = pn_dataset[]
(x_val, y_val) = pn_dataset[]

Project details

Download files

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

Files for dam4ml, version 1.0.0
Filename, size File type Python version Upload date Hashes
Filename, size dam4ml-1.0.0-py3-none-any.whl (4.2 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size dam4ml-1.0.0.tar.gz (3.5 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page