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pip install test_tube

from test-tube import Experiment
import torch

exp = Experiment('/some/path')
exp.tag({'learning_rate': 0.02, 'layers': 4})    

# exp is superclass of SummaryWriter
features = torch.Tensor(100, 784)
writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))

# simulate training
for n_iter in range(2000):
    e.log({'testtt': n_iter * np.sin(n_iter)})

# save and close
exp.save()
exp.close()

pip install tensorflow   

tensorboard --logdir /some/path

from test_tube.hpc import SlurmCluster

# hyperparameters is a test-tube hyper params object
hyperparams = args.parse()

# init cluster
cluster = SlurmCluster(
    hyperparam_optimizer=hyperparams,
    log_path='/path/to/log/results/to',
    python_cmd='python3'
)

# let the cluster know where to email for a change in job status (ie: complete, fail, etc...)
cluster.notify_job_status(email='[email protected]', on_done=True, on_fail=True)

# set the job options. In this instance, we'll run 20 different models
# each with its own set of hyperparameters giving each one 1 GPU (ie: taking up 20 GPUs)
cluster.per_experiment_nb_gpus = 1
cluster.per_experiment_nb_nodes = 1

# run the models on the cluster
cluster.optimize_parallel_cluster_gpu(train, nb_trials=20, job_name='first_tt_batch', job_display_name='my_batch')   

# we just ran 20 different hyperparameters on 20 GPUs in the HPC cluster!!    

from test_tube import HyperOptArgumentParser

# subclass of argparse
parser = HyperOptArgumentParser(strategy='random_search')
parser.add_argument('--learning_rate', default=0.002, type=float, help='the learning rate')

# let's enable optimizing over the number of layers in the network
parser.opt_list('--nb_layers', default=2, type=int, tunable=True, options=[2, 4, 8])

# and tune the number of units in each layer
parser.opt_range('--neurons', default=50, type=int, tunable=True, low=100, high=800, nb_samples=10)

# compile (because it's argparse underneath)
hparams = parser.parse_args()

# optimize across 4 gpus
# use 2 gpus together and the other two separately
hparams.optimize_parallel_gpu(MyModel.fit, gpu_ids=['1', '2,3', '0'], nb_trials=192, nb_workers=4)

hparams.optimize_parallel_cpu(MyModel.fit, nb_trials=192, nb_workers=12)

from test_tube import HyperOptArgumentParser

# subclass of argparse
parser = HyperOptArgumentParser(strategy='random_search')

# Randomly searches over the (log-transformed) range [100,800).

parser.opt_range('--neurons', default=50, type=int, tunable=True, low=100, high=800, nb_samples=10, log_base=10)


# compile (because it's argparse underneath)
hparams = parser.parse_args()

# run 20 trials of random search over the hyperparams
for hparam_trial in hparams.trials(20):
    train_network(hparam_trial)

import argparse
from test_tube import HyperOptArgumentParser

# these lines are equivalent
parser = argparse.ArgumentParser(description='Process some integers.')
parser = HyperOptArgumentParser(description='Process some integers.', strategy='grid_search')

# do normal argparse stuff
...

# name must have either jpg, png or jpeg in it
img = np.imread('a.jpg')
exp.log('test_jpg': img, 'val_err': 0.2)

# saves image to ../exp/version/media/test_0.jpg
# csv has file path to that image in that cell

@misc{Falcon2017,
  author = {Falcon, W.A.},
  title = {Test Tube},
  year = {2017},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/williamfalcon/test-tube}}
}