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Use non-Comet optimizers

Comet provides its own powerful hyperparameter optimizer designed for experiment management and different search algorithms (including random, grid, and bayes).

You can also use your own, or any third-party optimizer. You need only make sure that all of the hyperparameters are logged, and log these additional "other" items, as part of Experiments, so:

import json, os
# parameters is a dict of hyperparameter names/values
# experiment is a Comet experiment
experiment.log_other("optimizer_metric", "loss") # name of metric
experiment.log_other("optimizer_metric_value", 0.7) 
experiment.log_other("optimizer_version", "diy-1.0") # your own info
experiment.log_other("optimizer_process", os.getpid()) # if you wish
experiment.log_other("optimizer_count", 23) # the combination count so far
experiment.log_other("optimizer_objective", objective) # "minimum" or "maximum"
experiment.log_other("optimizer_parameters", json.dumps(parameters)) # string of parameters
experiment.log_other("optimizer_name", "optional-name") # string

By creating an Experiment with Comet's Optimizer, you also get the support of Comet's related built-in tools, including the Optimizer Report Panel and Parallel Coordinate Report:

Optimizer Report

Below is an example search algorithm. Note that it has many limitations, including:

  • When selecting a combination randomly, all combinations are in memory and could crash your computer if there are too many.
  • If a training example doesn't complete (for example, it crashes) then there is no mechanism to try the combination again.
  • It only runs one trial for each combination.
  • It can't be used in a distributed manner (you need a centralized server to provide combinations).
  • It only does random and grid search, not a sophisticated Bayes search.

You can substitute a proper hyperparameter search for this simple version. Of course, Comet's Optimizer solves all of the issues listed.

# A DIY Hyperparameter search
from comet_ml import Experiment
import itertools
import json
import random
import os
def get_parameters(params, shuffle=True):
    Return a dictionary of parameter settings given
    lists of possible values for each.
        params: dict of lists of possible values
        shuffle: bool; if True, shuffle the value orders

    Returns a dict of key/value for each combination.
    if shuffle:
        combinations = []
        for values in itertools.product(*params.values()):
            combinations.append(dict(zip(params, values)))
        for combo in combinations:
            yield combo
        for values in itertools.product(*params.values()):
            yield dict(zip(params, values))
hyperparameters = {
    "learning-rate": [0.001, 0.1, 0.2, 0.5, 0.9],
    "batch-size": [16, 32, 64],
    "hidden-layer-size": [5, 10, 15],
    "optimizer": ["adam", "sgd"],
def train(**hyperparams):
    # a dummy training function that returns loss
    return random.random()
count = 0
metric_name = "loss"
objective = "minimize"
for parameters in get_parameters(hyperparameters):
    count += 1
    experiment = Experiment(project_name="diy-search")
    metric_value = train(**parameters)
    experiment.log_metric(metric_name, metric_value, step=0)
    experiment.log_other("optimizer_metric", metric_name)
    experiment.log_other("optimizer_metric_value", metric_value)
    experiment.log_other("optimizer_version", "diy-1.0")
    experiment.log_other("optimizer_process", os.getpid())
    experiment.log_other("optimizer_count", count)
    experiment.log_other("optimizer_objective", objective)
    experiment.log_other("optimizer_parameters", json.dumps(parameters))
    experiment.log_other("optimizer_name", "diy-optimizer-001")

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May. 24, 2022