Add prediction via callback

.env

@@ -0,0 +1 @@
+PYTORCH_ENABLE_MPS_FALLBACK=1

.gitignore

@@ -104,7 +104,7 @@ celerybeat.pid
 *.sage.py
 
 # Environments
-.env
+# .env
 .venv
 env/
 venv/

CHANGELOG.md

@@ -1,6 +1,15 @@
 # Release Notes
 
-## v0.10.4 UNRELEASED (xx/xx/xxxx)
+## v1.0.0 Update to pytorch 2.0 (xx/xx/xxxx)
+
+
+### Breaking Changes
+
+- Upgraded to pytorch 2.0 and lightning 2.0. This brings a couple of changes, such as configuration of trainers. See the [lightning upgrade guide](https://lightning.ai/docs/pytorch/latest/upgrade/migration_guide.html). For PyTorch Forecasting, this particularly means if you are developing own models, the class method `epoch_end` has been renamed to `on_epoch_end` and replacing `model.summarize()` with `ModelSummary(model, max_depth=-1)` and `Tuner(trainer)` is its own class, so `trainer.tuner` needs replacing. (#1280)
+
+### Changes
+
+- The predict method is now using the lightning predict functionality and allows writing results to disk (#1280).
 
 ### Fixed
 
@@ -402,7 +411,7 @@ This release has only one purpose: Allow usage of PyTorch Lightning 1.0 - all te
   - Using `LearningRateMonitor` instead of `LearningRateLogger`
   - Use `EarlyStopping` callback in trainer `callbacks` instead of `early_stopping` argument
   - Update metric system `update()` and `compute()` methods
-  - Use `trainer.tuner.lr_find()` instead of `trainer.lr_find()` in tutorials and examples
+  - Use `Tuner(trainer).lr_find()` instead of `trainer.lr_find()` in tutorials and examples
 - Update poetry to 1.1.0
 
 ---

README.md

@@ -90,6 +90,7 @@ from lightning.pytorch.loggers import TensorBoardLogger
 from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor
 # import dataset, network to train and metric to optimize
 from pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer, QuantileLoss
+from lightning.pytorch.tuner import Tuner
 
 # load data: this is pandas dataframe with at least a column for
 # * the target (what you want to predict)
@@ -159,7 +160,7 @@ tft = TemporalFusionTransformer.from_dataset(
 print(f"Number of parameters in network: {tft.size()/1e3:.1f}k")
 
 # find the optimal learning rate
-res = trainer.lr_find(
+res = Tuner(trainer).lr_find(
     tft, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, early_stop_threshold=1000.0, max_lr=0.3,
 )
 # and plot the result - always visually confirm that the suggested learning rate makes sense

docs/source/getting-started.rst

@@ -67,7 +67,7 @@ Example
 
     import lightning.pytorch as pl
     from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor
-
+    from lightning.pytorch.tuner import Tuner
     from pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer
 
     # load data
@@ -127,7 +127,7 @@ Example
     print(f"Number of parameters in network: {tft.size()/1e3:.1f}k")
 
     # find optimal learning rate (set limit_train_batches to 1.0 and log_interval = -1)
-    res = trainer.tuner.lr_find(
+    res = Tuner(trainer).lr_find(
         tft, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, early_stop_threshold=1000.0, max_lr=0.3,
     )