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Merged
Borda merged 3 commits into from
Dec 16, 2024
Merged

example: simplify the simple example #23

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71d8dca
example: simplify the simple example
Borda Dec 13, 2024
5f43276
Merge branch 'main' into example/simple
Borda Dec 16, 2024
cf49c01
Merge branch 'main' into example/simple
Borda Dec 16, 2024
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52 changes: 18 additions & 34 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,10 @@ from lightning.pytorch.callbacks import ModelCheckpoint
from litmodels import upload_model
from litmodels.demos import BoringModel

# Define the model name - this should be unique to your model
# The format is <organization>/<teamspace>/<model-name>
MY_MODEL_NAME = "jirka/kaggle/lit-boring-model"


class LitModel(BoringModel):
def training_step(self, batch, batch_idx: int):
Expand All @@ -48,32 +52,14 @@ class LitModel(BoringModel):
return {"loss": loss}


# Define the model name - this should be unique to your model
# The format is <organization>/<teamspace>/<model-name>
MY_MODEL_NAME = "jirka/kaggle/lit-boring-model"

# Define the model
model = LitModel()
# Save the best model based on validation loss
checkpoint_callback = ModelCheckpoint(
monitor="train_loss", # Metric to monitor
save_top_k=1, # Only save the best model (use -1 to save all)
mode="min", # 'min' for loss, 'max' for accuracy
save_last=True, # Additionally save the last checkpoint
dirpath="my_checkpoints/", # Directory to save checkpoints
filename="{epoch:02d}-{val_loss:.2f}", # Custom checkpoint filename
)

# Train the model
trainer = Trainer(
max_epochs=2,
callbacks=[checkpoint_callback],
)
trainer.fit(model)
# Configure Lightning Trainer
trainer = Trainer(max_epochs=2)
# Define the model and train it
trainer.fit(LitModel())

# Upload the best model to cloud storage
print(f"last: {vars(checkpoint_callback)}")
upload_model(model=checkpoint_callback.last_model_path, name=MY_MODEL_NAME)
checkpoint_path = getattr(trainer.checkpoint_callback, "best_model_path")
upload_model(model=checkpoint_path, name=MY_MODEL_NAME)
```

To load the model, use the `download_model` function.
Expand All @@ -84,6 +70,11 @@ from litmodels import download_model
from litmodels.demos import BoringModel


# Define the model name - this should be unique to your model
# The format is <organization>/<teamspace>/<model-name>:<model-version>
MY_MODEL_NAME = "jirka/kaggle/lit-boring-model:latest"


class LitModel(BoringModel):
def training_step(self, batch, batch_idx: int):
loss = self.step(batch)
Expand All @@ -92,20 +83,13 @@ class LitModel(BoringModel):
return {"loss": loss}


# Define the model name - this should be unique to your model
# The format is <organization>/<teamspace>/<model-name>:<model-version>
MY_MODEL_NAME = "jirka/kaggle/lit-boring-model:latest"

# Load the model from cloud storage
model_path = download_model(name=MY_MODEL_NAME, download_dir="my_models")
print(f"model: {model_path}")
checkpoint_path = download_model(name=MY_MODEL_NAME, download_dir="my_models")
print(f"model: {checkpoint_path}")

# Train the model with extended training period
trainer = Trainer(max_epochs=4)
trainer.fit(
LitModel(),
ckpt_path=model_path,
)
trainer.fit(LitModel(), ckpt_path=checkpoint_path)
```

You can also use model store together with [LitLogger](https://github.com/gridai/lit-logger) to log your model to the cloud storage.
Expand Down
20 changes: 4 additions & 16 deletions examples/train-simple.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,6 @@
import torch.utils.data as data
import torchvision as tv
from lightning import Trainer
from lightning.pytorch.callbacks import ModelCheckpoint
from litmodels import upload_model
from sample_model import LitAutoEncoder

Expand All @@ -15,24 +14,13 @@
train, val = data.random_split(dataset, [55000, 5000])

autoencoder = LitAutoEncoder()
# Save the best model based on validation loss
checkpoint_callback = ModelCheckpoint(
monitor="val_loss", # Metric to monitor
save_top_k=1, # Only save the best model (use -1 to save all)
mode="min", # 'min' for loss, 'max' for accuracy
save_last=True, # Additionally save the last checkpoint
dirpath="my_checkpoints/", # Directory to save checkpoints
filename="{epoch:02d}-{val_loss:.2f}", # Custom checkpoint filename
)

trainer = Trainer(
max_epochs=2,
callbacks=[checkpoint_callback],
)
trainer = Trainer(max_epochs=2)
trainer.fit(
autoencoder,
data.DataLoader(train, batch_size=256),
data.DataLoader(val, batch_size=256),
)
print(f"last: {vars(checkpoint_callback)}")
upload_model(model=checkpoint_callback.last_model_path, name=MY_MODEL_NAME)
checkpoint_path = getattr(trainer.checkpoint_callback, "best_model_path")
print(f"best: {checkpoint_path}")
upload_model(model=checkpoint_path, name=MY_MODEL_NAME)
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