def main(config, experiments, num_cpus, num_gpus, redis_address):
print("config =", config.name)
print("experiments =", experiments)
print("num_gpus =", num_gpus)
print("num_cpus =", num_cpus)
print("redis_address =", redis_address)
# Use configuration file location as the project location.
project_dir = os.path.dirname(config.name)
project_dir = os.path.abspath(project_dir)
print("project_dir =", project_dir)
# Load and parse experiment configurations
configs = parse_config(config, experiments, globals_param=globals())
# Pre-download dataset
data_dir = os.path.join(project_dir, "data")
datasets.CIFAR10(data_dir, download=True, train=True)
# Initialize ray cluster
if redis_address is not None:
ray.init(redis_address=redis_address, include_webui=True)
num_cpus = 1
else:
ray.init(num_cpus=num_cpus,
num_gpus=num_gpus,
local_mode=num_cpus == 1)
# Run all experiments in parallel
results = []
for exp in configs:
config = configs[exp]
config["name"] = exp
# Make sure local directories are relative to the project location
path = config.get("path", None)
if path and not os.path.isabs(path):
config["path"] = os.path.join(project_dir, path)
data_dir = config.get("data_dir", "data")
if not os.path.isabs(data_dir):
config["data_dir"] = os.path.join(project_dir, data_dir)
# When running multiple hyperparameter searches on different experiments,
# ray.tune will run one experiment at the time. We use "ray.remote" to
# run each tune experiment in parallel as a "remote" function and wait until
# all experiments complete
results.append(
run_experiment.remote(config,
NotSoDenseTune,
num_cpus=num_cpus,
num_gpus=num_gpus / num_cpus))
# Wait for all experiments to complete
ray.get(results)
ray.shutdown()
def main(config, experiments, num_cpus, num_gpus, redis_address):
print("config =", config.name)
print("experiments =", experiments)
print("num_gpus =", num_gpus)
print("num_cpus =", num_cpus)
print("redis_address =", redis_address)
# Use configuration file location as the project location.
projectDir = os.path.dirname(config.name)
projectDir = os.path.abspath(projectDir)
print("projectDir =", projectDir)
# Load and parse experiment configurations
configs = parse_config(config, experiments, globals=globals())
# Pre-download dataset
data_dir = os.path.join(projectDir, "data")
datasets.CIFAR10(data_dir, download=True, train=True)
# Initialize ray cluster
if redis_address is not None:
ray.init(redis_address=redis_address, include_webui=True)
num_cpus = 1
else:
ray.init(num_cpus=num_cpus, num_gpus=num_gpus, local_mode=num_cpus == 1)
# Run all experiments in parallel
results = []
for exp in configs:
config = configs[exp]
config["name"] = exp
# Make sure local directories are relative to the project location
path = config.get("path", None)
if path and not os.path.isabs(path):
config["path"] = os.path.join(projectDir, path)
data_dir = config.get("data_dir", "data")
if not os.path.isabs(data_dir):
config["data_dir"] = os.path.join(projectDir, data_dir)
# When running multiple hyperparameter searches on different experiments,
# ray.tune will run one experiment at the time. We use "ray.remote" to
# run each tune experiment in parallel as a "remote" function and wait until
# all experiments complete
results.append(run_experiment.remote(config, MobileNetTune,
num_cpus=1,
num_gpus=num_gpus / num_cpus))
# Wait for all experiments to complete
ray.get(results)
ray.shutdown()
def main(config, experiment, show_list):
configs = parse_config(config, experiment, globals_param=globals())
if show_list:
print("Experiments:", list(configs.keys()))
return
project_dir = os.path.dirname(config.name)
project_dir = os.path.abspath(project_dir)
# Load dataset
data_dir = os.path.join(project_dir, "data")
datasets.CIFAR10(data_dir, download=True, train=True)
train_models(configs, project_dir=project_dir)
def main(config, experiment, show_list):
configs = parse_config(config, experiment, globals=globals())
if show_list:
print("Experiments:", list(configs.keys()))
return
projectDir = os.path.dirname(config.name)
projectDir = os.path.abspath(projectDir)
# Load dataset
data_dir = os.path.join(projectDir, "data")
datasets.CIFAR10(data_dir, download=True, train=True)
trainModels(configs, projectDir=projectDir)
help="run only selected experiments, by default run "
"all experiments in config file.")
return optparser.parse_args()
if __name__ == "__main__":
print("Using torch version", torch.__version__)
print("Torch device count=", torch.cuda.device_count())
# Load and parse command line option and experiment configurations
options = parse_options()
if options.config != "":
with open(options.config) as f:
configs = parse_config(f, options.experiments)
projectDir = os.path.dirname(options.config)
elif options.params != "":
with open(options.params) as f:
params = json.load(f)
params["data_dir"] = os.path.abspath(os.path.join(".", "data"))
params["path"] = os.path.abspath(os.path.dirname(options.params))
configs = {params["name"]: params}
projectDir = "."
else:
raise RuntimeError("Either a .cfg or a params .json file must be specified")
# Use configuration file location as the project location.
projectDir = os.path.abspath(projectDir)
dest="num_images",
default=1,
help="The number of images to test")
optparser.add_argument(
"-e",
"--experiment",
action="append",
dest="experiments",
help="run only selected experiments, by default run "
"all experiments in config file.")
return optparser.parse_args()
if __name__ == "__main__":
print("Torch device count=", torch.cuda.device_count())
# Load and parse command line option and experiment configurations
options = parse_options()
configs = parse_config(options.config, options.experiments)
# Use configuration file location as the project location.
projectDir = os.path.dirname(options.config.name)
projectDir = os.path.abspath(projectDir)
for exp in configs:
config = configs[exp]
config["name"] = exp
testModel(config, options, projectDir=projectDir)
dest="plot_gradients",
help="Plot gradients for debugging",
default=False)
optparser.add_argument("-v",
"--verbose",
dest="verbose",
help="Verbosity",
default=0)
return optparser.parse_args()
if __name__ == "__main__":
# Load and parse command line option and experiment configurations
options = parse_options()
configs = parse_config(options.config, options.experiments, globals(),
locals())
# Use configuration file location as the project location.
# Ray Tune default working directory is "~/ray_results"
project_dir = os.path.dirname(options.config.name)
project_dir = os.path.abspath(project_dir)
print("Using torch version", torch.__version__)
print("Torch device count=", torch.cuda.device_count())
# Initialize ray cluster
if "REDIS_ADDRESS" in os.environ:
ray.init(redis_address=os.environ["REDIS_ADDRESS"], include_webui=True)
else:
# Initialize ray cluster
ray.init(
help="run only selected experiments, by default run "
"all experiments in config file.",
)
return optparser.parse_args()
if __name__ == "__main__":
print("Using torch version", torch.__version__)
print("Torch device count=", torch.cuda.device_count())
# Load and parse command line option and experiment configurations
options = parse_options()
if options.config != "":
with open(options.config) as f:
configs = parse_config(f, options.experiments)
project_dir = os.path.dirname(options.config)
elif options.params != "":
with open(options.params) as f:
params = json.load(f)
params["data_dir"] = os.path.abspath(os.path.join(".", "data"))
params["path"] = os.path.abspath(os.path.dirname(options.params))
configs = {params["name"]: params}
project_dir = "."
else:
raise RuntimeError("Either a .cfg or a params .json file must be specified")
# Use configuration file location as the project location.
project_dir = os.path.abspath(project_dir)