Monitor machine learning experiments
Project description
# HTUNEML - machine learning experiments monitoring and tuning
Quickstart: pip install htuneml. See the “Installing” section for more details.
Project links:
Examples
See the examples/ directory in the repository root for usage examples:
Requirements
To use all of the functionality of the library, you should have:
Python 2.6, 2.7, or 3.3+ (required)
PyAudio 0.2.11+ (required only if you need to use microphone input, Microphone)
Quick start
Register on website http://registru.ml, copy the api_key:
import htuneml as ht
job = Job('api_key')
@job.monitor
def train(par1=2,par2=2):
for i in range(par1):
#do training here
job.log({'loss':i*4,'ep':i})
job.setName('l2')
#job.debug()# uncomment and no experiment will be created and no logs sent
train(10, 2)
This will print out something like the following:
make experiment got key experimnet 5c5c8eaacbcfb9146641367a
Also it is possible to sent the parameters from the web app. First on gpu/cpu set the lisener:
import htuneml as ht
job = Job('api_key')
def train(par1=2,par2=2):
for i in range(par1):
#do training here
job.log({'loss':i*4,'ep':i})
job.sentParams(train)#sent the parameters list to the app
job.waitTask(train)#wait for parameters from app
Project details
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