$ python3 ./deeprace.py --help
usage: deeprace [--version] [--help] [--verbose] [--loglevel <level>] <command> [<args>...]

options:
   -h, --help                           Show this help message
   -v, --version                        Print the version of deeprace
   -V, --verbose                        Run in verbose mode
   -L <level> --loglevel=<level>        logging level to use [default: info]

The most commonly used git commands are:
   list      list available models
   train     run training on a given model   
   describe  show available parameters for given model

See 'deeprace help <command>' for more information on a specific command.

$ python3 ./deeprace.py help train

usage: deeprace train [options] [--] <models>

options:
    -h, --help                                 print this help message
    -O <mopts> --meta-options=<mopts>          hyper-parameters for training, e.g. batch_size
    -D <dpath> --datapath=<dpath>              path used for temporary storage, e.g. for the input data, checkpoints etc [default: datasets]
    -R <rpath> --resultspath=<rpath>           path to store results or checkpoints [default: deeprace-results]
    -b <backe> --backend=<backe>               which backend to use [default: keras]
    -e <neps> --nepochs=<neps>                 number of epochs to train [default: 0]
    -d <ds> --dataset=<ds>                     the dataset to use (depends on the model of choice) [default: model_default]
    -f <dfrac> --datafraction=<dfrac>          fraction of the dataset to use, helpful for debugging/testing [default: 1.]
    -t <output> --timings=<output>             file to store the individual timings in [default: timings.tsv]
    -s <sep> --separator=<sep>                 seperator for the output data [default: 	]
    -c <cmt> --comment=<cmt>                   comment to add to the measurement

$ python3 ./deeprace.py train -t test.tsv resnet56v1

$ python3 ./deeprace.py train -e 5 -f 0.1 care_denoise2d
#...
[180509 12:27:09 talisker] :: wrote timings.tsv
[180509 12:27:09 talisker] :: Done.
$ cat timings.tsv
host	model	dataset	load_dur_sec	ntrain	ntest	datafraction	train_start	train_end	epoch	rel_epoch_start_sec	epoch_dur_sec	acc	loss	val_acc	val_loss	opts	n_model_params	versions	deeprace_version	comment
talisker-10g	care_denoise2d	care_2d	9.195996	768	0	0.1	20180509:122702	20180509:122709	0	0.000134	5.577799	0.00503091251148897	0.09069557444137685	0.0052471160888671875	0.0868075042963028	validation_split=0.1,dataset=care_2d,batch_size=32,n_dims=2,n_row=3,backend=keras,depth=2,version=1,n_gpus=1,checkpoint_epochs=False,n_depth=2,filter_base=16,epochs=5,n_conv_per_depth=2,scratchspace=/deeprace,n_col=3	83377	keras:2.1.5,backend:tensorflow:1.7.0	0.2.0+49.g9c8fa46.dirty	None
talisker-10g	care_denoise2d	care_2d	9.195996	768	0	0.1	20180509:122702	20180509:122709	1	5.578052	0.28374	0.005308263442095588	0.06664747700971715	0.0056743621826171875	0.04846179112792015	validation_split=0.1,dataset=care_2d,batch_size=32,n_dims=2,n_row=3,backend=keras,depth=2,version=1,n_gpus=1,checkpoint_epochs=False,n_depth=2,filter_base=16,epochs=5,n_conv_per_depth=2,scratchspace=/deeprace,n_col=3	83377	keras:2.1.5,backend:tensorflow:1.7.0	0.2.0+49.g9c8fa46.dirty	None
talisker-10g	care_denoise2d	care_2d	9.195996	768	0	0.1	20180509:122702	20180509:122709	2	5.861963	0.279709	0.005573272705078125	0.03972847233800327	0.005794525146484375	0.047284774482250214	validation_split=0.1,dataset=care_2d,batch_size=32,n_dims=2,n_row=3,backend=keras,depth=2,version=1,n_gpus=1,checkpoint_epochs=False,n_depth=2,filter_base=16,epochs=5,n_conv_per_depth=2,scratchspace=/deeprace,n_col=3	83377	keras:2.1.5,backend:tensorflow:1.7.0	0.2.0+49.g9c8fa46.dirty	None
talisker-10g	care_denoise2d	care_2d	9.195996	768	0	0.1	20180509:122702	20180509:122709	3	6.141824	0.282113	0.005601097555721507	0.044960127595592946	0.0057621002197265625	0.039718300104141235	validation_split=0.1,dataset=care_2d,batch_size=32,n_dims=2,n_row=3,backend=keras,depth=2,version=1,n_gpus=1,checkpoint_epochs=False,n_depth=2,filter_base=16,epochs=5,n_conv_per_depth=2,scratchspace=/deeprace,n_col=3	83377	keras:2.1.5,backend:tensorflow:1.7.0	0.2.0+49.g9c8fa46.dirty	None
talisker-10g	care_denoise2d	care_2d	9.195996	768	0	0.1	20180509:122702	20180509:122709	4	6.424077	0.279615	0.005590551039751838	0.03724859808297718	0.005687713623046875	0.04680463671684265	validation_split=0.1,dataset=care_2d,batch_size=32,n_dims=2,n_row=3,backend=keras,depth=2,version=1,n_gpus=1,checkpoint_epochs=False,n_depth=2,filter_base=16,epochs=5,n_conv_per_depth=2,scratchspace=/deeprace,n_col=3	83377	keras:2.1.5,backend:tensorflow:1.7.0	0.2.0+49.g9c8fa46.dirty	None

$ python3 ./deeprace.py list
Using TensorFlow backend.
[180509 12:26:02 talisker] :: found __init__.py but could not find a loader for it
[180509 12:26:02 talisker] :: available models and backends:
	[0] resnet20v1 resnet32v1 resnet44v1 resnet56v1 resnet110v1 resnet164v1 resnet29v2 resnet47v2 resnet65v2 resnet83v2 resnet164v2 resnet245v2 resnet1001v2
	     backend(s): keras,tensorflow.keras,tensorflow
	     datasets(s): cifar10
	[1] care_denoise_2Ddepth2
	     backend(s): keras,tensorflow.keras
	     datasets(s): care_2d

$ python3 ./deeprace.py describe resnet32v1
[180329 13:15:14 r02n12] :: importing resnet (from resnet32v1)
[180329 13:15:14 r02n12] :: found resnet implementation
[180329 13:15:14 r02n12] :: available options for resnet32v1
  num_classes          = 10
  batch_size           = 32
  scratchspace         = /home/steinbac
  checkpoint_epochs    = False
  subtract_pixel_mean  = True
  n_gpus               = 1
  epochs               = 200
  n                    = 5
  version              = 1
  data_augmentation    = True