Meet Oscar

Your new (free) Data Scientist

He takes care of your ML hyperparameters research

Oscar loves simplicity.

Optimizing the hyperparameters of your machine learning algorithm is a hard and tedious task.

Describe your experiment to Oscar in a few lines and he will take care of it for you.


# Get Oscar
from Oscar import Oscar
scientist = Oscar(YOUR_ACCESS_TOKEN)

# Describe your experiment
experiment = {name:"Square", parameters:{x : {min: -10, max : 10}}}

for i in range(1, 10):
  # Get next parameters to try from Oscar
  job = scientist.suggest(experiment)

  # Run you complex, time-consuming algorithm
  loss = math.pow(job.x, 2)

  # Tell Oscar the result
  scientist:update(job, {loss : loss})
            
-- Get Oscar
local Oscar = require('Oscar')
local scientist = Oscar(YOUR_ACCESS_TOKEN)

-- Describe your experiment
local experiment = {name="Square", parameters={x = {min = -10, max = 10}}}

for i = 1, 10 do
  -- Get next parameters to try from Oscar
  local job = scientist:suggest(experiment)

  -- Run you complex, time-consuming algorithm
  local loss = math.pow(job.x, 2)

  -- Tell Oscar the result
  scientist:update(job, {loss : loss})
end

Oscar is smart and loves nice reports.

Getting lost with your experiments ?

Oscar automatically chooses the best next hyperparameters to try depending on all the previous results.

At any time, he gives you clear insights on the influence of each hyperparameter of your algorithm.

Oscar is a workaholic.

You want to get fast results ?

Completly hosted on the cloud, Oscar will happily manage hundreds of experiments running on your cluster.