Configure Experiments with wandb.config
Use a dictionary-like object to save your experiment configuration
Set the
wandb.config object in your script to save your training configuration: hyperparameters, input settings like dataset name or model type, and any other independent variables for your experiments. This is useful for analyzing your experiments and reproducing your work in the future. You'll be able to group by config values in the web interface, comparing the settings of different runs and seeing how these affect the output. Note that output metrics or dependent variables (like loss and accuracy) should be saved with wandb.loginstead.You can send us a nested dictionary in config, and we'll flatten the names using dots in our backend. We recommend that you avoid using dots in your config variable names, and use a dash or underscore instead. Once you've created your wandb config dictionary, if your script accesses wandb.config keys below the root, use
[ ] syntax instead of . syntax.wandb.config.epochs = 4
wandb.config.batch_size = 32
# you can also initialize your run with a config
wandb.init(config={"epochs": 4})
You can treat
wandb.config as a dictionary, updating multiple values at a time.wandb.init(config={"epochs": 4, "batch_size": 32})
# later
wandb.config.update({"lr": 0.1, "channels": 16})
You can pass in the arguments returned by
argparse. This is convenient for quickly testing different hyperparameter values from the command line.wandb.init(config={"lr": 0.1})
wandb.config.epochs = 4
parser = argparse.ArgumentParser()
parser.add_argument('-b', '--batch-size', type=int, default=8, metavar='N',
help='input batch size for training (default: 8)')
args = parser.parse_args()
wandb.config.update(args) # adds all of the arguments as config variables
flags.DEFINE_string("model", None, "model to run") # name, default, help
wandb.config.update(flags.FLAGS) # adds all absl flags to config
If you create a file called
config-defaults.yaml, and it will automatically be loaded into wandb.config.configs-default.yaml
# sample config defaults file
epochs:
desc: Number of epochs to train over
value: 100
batch_size:
desc: Size of each mini-batch
value: 32
These values can then be over-written inside your script by passing values to the
config argument of wandb.init.You can also load different config files with the command line argument
--configs.One example use case: you have a YAML file with some metadata for the run, and then a dictionary of hyperparameters in your Python script. You can save both in the nested
config object:hyperparameter_defaults = dict(
dropout=0.5,
batch_size=100,
learning_rate=0.001,
)
config_dictionary = dict(
yaml=my_yaml_file,
params=hyperparameter_defaults,
)
wandb.init(config=config_dictionary)
You can add a unique identifier (like a hash or other identifier) in your run's configuration for your dataset by tracking it as input to your experiment using
wandb.configwandb.config.update({"dataset": "ab131"})
You can use the public API to add values your
config file, even after the run has finished.import wandb
api = wandb.Api()
run = api.run("username/project/run_id")
run.config["foo"] = 32
run.update()
You can log any key-value pairs into
wandb.config. They can be different for every type of model you are training, e.g.wandb.config.update({"my_param": 10, "learning_rate": 0.3, "model_architecture": "B"}).You can pass TensorFlow flags into the
wandb.config object directly.wandb.init()
wandb.config.epochs = 4
flags = tf.app.flags
flags.DEFINE_string("data_dir", "/tmp/data")
flags.DEFINE_integer("batch_size", 128, "Batch size.")
wandb.config.update(flags.FLAGS) # adds all of the tensorflow flags as config
Last modified 1yr ago