Create the model run with the config
example_config = {
"learning_rate": 0.001,
"checkpoint_path": "/path/to/checkpoint/file",
"early_stopping": False,
"batch_size": 32,
"optimizer": {
"adam": {
"beta1": 0.899999976158,
"beta2": 0.999000012875,
"epsilon": 9.99999993923e-9
}
},
"ngpu": 1,
}
model = client.get_model("<MODEL_ID>")
# create a model with the config
model_run = model.create_model_run(name="run 1", config=example_config)
# alternatively, create a model run first and update the model config field.
model_run = model.create_model_run(name="run 2")
model_run.update_config(example_config)
Get model run config
model_run_parameters = model_run.get_config()
Update the model run config
The update will replace the previous model run config with the new JSON input.
model_run = client.get_model_run("<MODEL_RUN_ID>")
model_run.update_config({"batch_size": 16})
Delete the model run config
model_run.reset_config()