def run_experiment(dataset, network, model, proj_name, epoch, train_args):
print(f"Running experiment with network '{network}' and dataset '{dataset}''")
datasets_module = importlib.import_module("lab1.language_model.datasets.house_pred")
dataset_class_ = getattr(datasets_module, dataset)
# dataset_args = experiment_config.get("dataset_args", {})
models_module = importlib.import_module("lab1.language_model.models.base2")
model_class_ = getattr(models_module, model)
networks_module = importlib.import_module("lab1.language_model.networks.mlp")
network_fn = getattr(networks_module, network)
# save_net_artifact(project_name=proj_name, network=network_fn())
# network_args = experiment_config.get("network_args", {})
# mlflow.set_tracking_uri("sqlite:///mlruns.db")
model = model_class_(dataset_cls=dataset_class_, network_fn=network_fn)
# input_schema = Schema([TensorSpec(type=np.dtype(np.float32), shape=(-1, 13), name="house_attribs")])
# output_schema = Schema([TensorSpec(type=np.dtype(np.float32), shape=(-1, 1), name="predicted house price")])
# signature = ModelSignature(inputs=input_schema, outputs=output_schema)
# input_example = np.array([[1., 2.5, 3. , 1.7, 2.1, 1.3, .5, .75, .89, 1.9, 2.15, 2.2, .6]])
# mlflow.pyfunc.save_model(path="my_model", python_model=model, signature=signature, input_example=input_example )
config = dict(
dataset = dataset,
network = network,
model = model,
epoch = epoch,
train_args = train_args
)
net_config = dict(
input_shape=(13,),
output_shape=(1),
layer_size=64,
dropout_amount=0.2,
num_layers=3
)
save_net_artifact(project_name=proj_name, network_fn=network_fn)
save_data_raw_artifact(project_name=proj_name, data_class=dataset_class_)
save_data_processed_artifact(project_name=proj_name, data_class=dataset_class_)
with wandb.init(project=proj_name, config=config) as run:
config = wandb.config
model.fit(dataset=config.dataset, callbacks=[WandbCallback()])
def run_experiment(config_yaml, train_args):
exp_config = yaml_loader(config_yaml)
model = exp_config.get("model")
network = exp_config.get("network")
net_cl_name = network["name"]
net_config = network["network_args"]
dataset = exp_config.get("dataset")
data_cl_name = dataset["name"]
dataset_args = dataset["dataset_args"]
proj_name = exp_config.get("project_name")
print(
f"Running experiment with network '{net_cl_name}' and dataset '{data_cl_name}''"
)
datasets_module = importlib.import_module(
"lab1.language_model.datasets.house_pred")
dataset_class_ = getattr(datasets_module, data_cl_name)
models_module = importlib.import_module("lab1.language_model.models.base2")
model_class_ = getattr(models_module, model)
networks_module = importlib.import_module(
"lab1.language_model.networks.mlp")
network_fn = getattr(networks_module, net_cl_name)
model = model_class_(dataset_cls=dataset_class_,
network_fn=network_fn,
dataset_args=dataset_args,
network_args=net_config)
# mlflow.set_tracking_uri("sqlite:///mlruns.db")
# input_schema = Schema([TensorSpec(type=np.dtype(np.float32), shape=(-1, 13), name="house_attribs")])
# output_schema = Schema([TensorSpec(type=np.dtype(np.float32), shape=(-1, 1), name="predicted house price")])
# signature = ModelSignature(inputs=input_schema, outputs=output_schema)
# input_example = np.array([[1., 2.5, 3. , 1.7, 2.1, 1.3, .5, .75, .89, 1.9, 2.15, 2.2, .6]])
# mlflow.pyfunc.save_model(path="my_model", python_model=model, signature=signature, input_example=input_example )
save_net_artifact(project_name=proj_name, network_fn=network_fn)
save_data_raw_artifact(project_name=proj_name, data_class=dataset_class_)
save_data_processed_artifact(project_name=proj_name,
data_class=dataset_class_)
with wandb.init(project=proj_name, config=exp_config) as run:
config = wandb.config
model.fit(dataset=config.dataset, callbacks=[WandbCallback()])
def run_experiment(dataset, network, model, proj_name, epoch, train_args):
print(
f"Running experiment with network '{network}' and dataset '{dataset}''"
)
datasets_module = importlib.import_module(
"lab1.language_model.datasets.house_pred")
dataset_class_ = getattr(datasets_module, dataset)
# dataset_args = experiment_config.get("dataset_args", {})
models_module = importlib.import_module("lab1.language_model.models.base2")
model_class_ = getattr(models_module, model)
networks_module = importlib.import_module(
"lab1.language_model.networks.mlp")
network_fn = getattr(networks_module, network)
# save_net_artifact(project_name=proj_name, network=network_fn())
# network_args = experiment_config.get("network_args", {})
# mlflow.set_tracking_uri("sqlite:///mlruns.db")
model = model_class_(dataset_cls=dataset_class_, network_fn=network_fn)
# input_schema = Schema([TensorSpec(type=np.dtype(np.float32), shape=(-1, 13), name="house_attribs")])
# output_schema = Schema([TensorSpec(type=np.dtype(np.float32), shape=(-1, 1), name="predicted house price")])
# signature = ModelSignature(inputs=input_schema, outputs=output_schema)
# input_example = np.array([[1., 2.5, 3. , 1.7, 2.1, 1.3, .5, .75, .89, 1.9, 2.15, 2.2, .6]])
# mlflow.pyfunc.save_model(path="my_model", python_model=model, signature=signature, input_example=input_example )
config = dict(dataset=dataset,
network=network,
model=model,
epoch=epoch,
train_args=train_args)
net_config = dict(input_shape=(13, ),
output_shape=(1),
layer_size=64,
dropout_amount=0.2,
num_layers=3)
# save_net_artifact(project_name=proj_name, network_fn=network_fn)
# save_data_raw_artifact(project_name=proj_name, data_class=dataset_class_)
# save_data_processed_artifact(project_name=proj_name, data_class=dataset_class_)
with wandb.init(project=proj_name, config=config) as run:
config = wandb.config
save_net_artifact(project_name=proj_name, network_fn=network_fn)
save_data_raw_artifact(project_name=proj_name,
data_class=dataset_class_)
save_data_processed_artifact(project_name=proj_name,
data_class=dataset_class_)
# Add model artifact
# model_artifact = wandb.Artifact("convnet", type="model", description="Simple AlexNet style CNN", metadata=dict(net_config))
# model.network.save("initialized_model.keras")
# model_artifact.new_file("initialized_model.keras")
# wandb.save("initialized_model.keras")
# run.log_artifact(model_artifact)
# Add data artifact
# raw data
# raw_data = wandb.Artifact(
# "mnist-raw", type="dataset",
# description="sklearn.datasets.load_boston",
# metadata={"source": "keras.datasets.mnist",
# #"size (rows)": [model.dataset.X.shape[0]]
# })
# with raw_data.new_file("raw" + ".npz", mode="wb") as file:
# np.savez(file, x=model.data.X, y=model.data.y)
# run.log_artifact(raw_data)
# preprocessed data
# preprocessed_data = wandb.Artifact(
# "mnist-processed", type="dataset",
# description="sklearn.datasets.load_boston",
# metadata={"source": "keras.datasets.mnist",
# #"size (rows)": [model.dataset.X.shape[0]]
# })
# with preprocessed_data.new_file("training" + ".npz", mode="wb") as file:
# np.savez(file, x=model.data.X_tr, y=model.data.y_tr)
# run.log_artifact(preprocessed_data)
model.fit(dataset=config.dataset, callbacks=[WandbCallback()])
def run_experiment(exp_config_yaml, epochs, activation_fn, optimizer):
"""
Run a single experiment.
Parameters:
----------
exp_config: File path
Path to a yaml file specifying the configuration for this
experiment. The includes model the model to use along with
it's hyperparamters and other args.
sweep_config: File path
Path to a yaml file specifying some information needed to
run a sweep using wandb
"""
exp_config = yaml_loader(exp_config_yaml)
exp_config["network"]["network_args"]["hyperparams"]["epochs"] = epochs
exp_config["network"]["network_args"]["hyperparams"][
"activation_fn"] = activation_fn
# exp_config["network"]["network_args"]["hyperparams"]["optimizer"] = optimizer
yaml_dump(exp_config_yaml, exp_config)
model = exp_config.get("model")
network = exp_config.get("network")
net_cl_name = network["name"]
net_config = network["network_args"]
dataset = exp_config.get("dataset")
data_cl_name = dataset["name"]
dataset_args = dataset["dataset_args"]
proj_name = exp_config.get("project_name")
print(
f"Running experiment with network '{net_cl_name}' and dataset '{data_cl_name}''"
)
datasets_module = importlib.import_module(
"lab1.language_model.datasets.house_pred")
dataset_class_ = getattr(datasets_module, data_cl_name)
models_module = importlib.import_module("lab1.language_model.models.base2")
model_class_ = getattr(models_module, model)
networks_module = importlib.import_module(
"lab1.language_model.networks.mlp")
network_fn = getattr(networks_module, net_cl_name)
model = model_class_(dataset_cls=dataset_class_,
network_fn=network_fn,
dataset_args=dataset_args,
network_args=net_config)
# mlflow.set_tracking_uri("sqlite:///mlruns.db")
# input_schema = Schema([TensorSpec(type=np.dtype(np.float32), shape=(-1, 13), name="house_attribs")])
# output_schema = Schema([TensorSpec(type=np.dtype(np.float32), shape=(-1, 1), name="predicted house price")])
# signature = ModelSignature(inputs=input_schema, outputs=output_schema)
# input_example = np.array([[1., 2.5, 3. , 1.7, 2.1, 1.3, .5, .75, .89, 1.9, 2.15, 2.2, .6]])
# mlflow.pyfunc.save_model(path="my_model", python_model=model, signature=signature, input_example=input_example )
save_net_artifact(project_name=proj_name,
network_fn=network_fn,
net_config=net_config)
save_data_raw_artifact(project_name=proj_name, data_class=dataset_class_)
save_data_processed_artifact(project_name=proj_name,
data_class=dataset_class_)
with wandb.init(project=proj_name, config=exp_config):
config = wandb.config
model.fit(dataset=config.dataset, callbacks=[WandbCallback()])
data = dataset_class_()