diff --git a/.buildkite/pipeline.ml.yml b/.buildkite/pipeline.ml.yml
index 2c7925593e9f9..3b852da8f5121 100644
--- a/.buildkite/pipeline.ml.yml
+++ b/.buildkite/pipeline.ml.yml
@@ -544,8 +544,8 @@
     # (see https://github.com/ray-project/ray/pull/38432/)
     - pip install "transformers==4.30.2" "datasets==2.14.0"
     - ./ci/env/env_info.sh
-    - bazel test --config=ci $(./ci/run/bazel_export_options) --build_tests_only --test_tag_filters=-timeseries_libs,-external,-ray_air,-gpu,-post_wheel_build,-doctest,-datasets_train,-highly_parallel,-new_storage
-      --test_env=RAY_AIR_NEW_PERSISTENCE_MODE=0
+    - bazel test --config=ci $(./ci/run/bazel_export_options) --build_tests_only
+      --test_tag_filters=-timeseries_libs,-external,-ray_air,-gpu,-post_wheel_build,-doctest,-datasets_train,-highly_parallel
       doc/...
 
 - label: ":book: Doc tests and examples with time series libraries"
diff --git a/doc/source/ray-air/doc_code/hvd_trainer.py b/doc/source/ray-air/doc_code/hvd_trainer.py
index d0537fab73dc2..ca402189a42c8 100644
--- a/doc/source/ray-air/doc_code/hvd_trainer.py
+++ b/doc/source/ray-air/doc_code/hvd_trainer.py
@@ -1,3 +1,6 @@
+import os
+import tempfile
+
 import horovod.torch as hvd
 import ray
 from ray import train
@@ -55,10 +58,12 @@ def train_loop_per_worker():
             loss.backward()
             optimizer.step()
             print(f"epoch: {epoch}, loss: {loss.item()}")
-        train.report(
-            {},
-            checkpoint=Checkpoint.from_dict(dict(model=model.state_dict())),
-        )
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            torch.save(model.state_dict(), os.path.join(tmpdir, "model.pt"))
+            train.report(
+                {"loss": loss.item()}, checkpoint=Checkpoint.from_directory(tmpdir)
+            )
 
 
 train_dataset = ray.data.from_items([{"x": x, "y": x + 1} for x in range(32)])
diff --git a/doc/source/ray-air/doc_code/report_metrics_and_save_checkpoints.py b/doc/source/ray-air/doc_code/report_metrics_and_save_checkpoints.py
index cbdcde68bc10c..808bfb83d4442 100644
--- a/doc/source/ray-air/doc_code/report_metrics_and_save_checkpoints.py
+++ b/doc/source/ray-air/doc_code/report_metrics_and_save_checkpoints.py
@@ -2,6 +2,8 @@
 # isort: skip_file
 
 # __air_session_start__
+import os
+import tempfile
 
 import tensorflow as tf
 from ray import train
@@ -30,8 +32,9 @@ def train_func():
     else:
         model = build_model()
 
-    model.save("my_model", overwrite=True)
-    train.report(metrics={"iter": 1}, checkpoint=Checkpoint.from_directory("my_model"))
+    with tempfile.TemporaryDirectory() as tmpdir:
+        model.save(tmpdir, overwrite=True)
+        train.report(metrics={"iter": 1}, checkpoint=Checkpoint.from_directory(tmpdir))
 
 
 scaling_config = ScalingConfig(num_workers=2)
@@ -45,7 +48,7 @@ def train_func():
     train_loop_per_worker=train_func,
     scaling_config=scaling_config,
     # this is ultimately what is accessed through
-    # ``Session.get_checkpoint()``
+    # ``train.get_checkpoint()``
     resume_from_checkpoint=result.checkpoint,
 )
 result2 = trainer2.fit()
diff --git a/doc/source/ray-air/doc_code/torch_trainer.py b/doc/source/ray-air/doc_code/torch_trainer.py
index c609df090554d..422fba533970e 100644
--- a/doc/source/ray-air/doc_code/torch_trainer.py
+++ b/doc/source/ray-air/doc_code/torch_trainer.py
@@ -1,3 +1,6 @@
+import os
+import tempfile
+
 import torch
 import torch.nn as nn
 
@@ -48,12 +51,15 @@ def train_loop_per_worker():
             optimizer.step()
             print(f"epoch: {epoch}, loss: {loss.item()}")
 
-        train.report(
-            {},
-            checkpoint=Checkpoint.from_dict(
-                dict(epoch=epoch, model=model.state_dict())
-            ),
-        )
+        with tempfile.TemporaryDirectory() as tempdir:
+            torch.save(
+                {"epoch": epoch, "model": model.module.state_dict()},
+                os.path.join(tempdir, "checkpoint.pt"),
+            )
+            train.report(
+                {"loss": loss.item()},
+                checkpoint=Checkpoint.from_directory(tempdir),
+            )
 
 
 train_dataset = ray.data.from_items([{"x": x, "y": 2 * x + 1} for x in range(200)])
diff --git a/doc/source/ray-overview/doc_test/ray_train.py b/doc/source/ray-overview/doc_test/ray_train.py
index 0aad08e7f2936..d272f5cf8c2f0 100644
--- a/doc/source/ray-overview/doc_test/ray_train.py
+++ b/doc/source/ray-overview/doc_test/ray_train.py
@@ -1,8 +1,11 @@
+import os
+import tempfile
+
 import torch
 
 import ray.train as train
-from ray.train import ScalingConfig
-from ray.train.torch import TorchTrainer, LegacyTorchCheckpoint
+from ray.train import Checkpoint, ScalingConfig
+from ray.train.torch import TorchTrainer
 
 
 def train_func():
@@ -21,15 +24,21 @@ def train_func():
 
     # Train.
     for _ in range(5):
+        epoch_loss = []
         for X, y in dataloader:
             pred = model(X)
             loss = loss_fn(pred, y)
             optimizer.zero_grad()
             loss.backward()
             optimizer.step()
-            train.report({"loss": loss.item()})
-
-    train.report({}, checkpoint=LegacyTorchCheckpoint.from_model(model))
+            epoch_loss.append(loss.item())
+        train.report({"loss": sum(epoch_loss) / len(epoch_loss)})
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        torch.save(model.module.state_dict(), os.path.join(tmpdir, "model.pt"))
+        train.report(
+            {"loss": loss.item()}, checkpoint=Checkpoint.from_directory(tmpdir)
+        )
 
 
 trainer = TorchTrainer(train_func, scaling_config=ScalingConfig(num_workers=4))
diff --git a/doc/source/rllib/doc_code/checkpoints.py b/doc/source/rllib/doc_code/checkpoints.py
index 8b8c99014118f..8f0deab631cc9 100644
--- a/doc/source/rllib/doc_code/checkpoints.py
+++ b/doc/source/rllib/doc_code/checkpoints.py
@@ -16,7 +16,7 @@
 # Create standard (pickle-based) checkpoint.
 with tempfile.TemporaryDirectory() as pickle_cp_dir:
     # Note: `save()` always creates a pickle based checkpoint.
-    pickle_cp_dir = algo1.save(checkpoint_dir=pickle_cp_dir)
+    algo1.save(checkpoint_dir=pickle_cp_dir)
 
     # But we can convert this pickle checkpoint to a msgpack one using an RLlib utility
     # function.
diff --git a/doc/source/train/doc_code/accelerate_trainer.py b/doc/source/train/doc_code/accelerate_trainer.py
index 7b3ea61ff274d..c735c4e5f7c3a 100644
--- a/doc/source/train/doc_code/accelerate_trainer.py
+++ b/doc/source/train/doc_code/accelerate_trainer.py
@@ -1,3 +1,6 @@
+import os
+import tempfile
+
 import torch
 import torch.nn as nn
 
@@ -51,12 +54,15 @@ def train_loop_per_worker():
             optimizer.step()
             print(f"epoch: {epoch}, loss: {loss.item()}")
 
-        train.report(
-            metrics={"epoch": epoch, "loss": loss.item()},
-            checkpoint=Checkpoint.from_dict(
-                dict(epoch=epoch, model=accelerator.unwrap_model(model).state_dict())
-            ),
-        )
+        with tempfile.TemporaryDirectory() as tmpdir:
+            torch.save(
+                accelerator.unwrap_model(model).state_dict(),
+                os.path.join(tmpdir, "model.pt"),
+            )
+            train.report(
+                metrics={"epoch": epoch, "loss": loss.item()},
+                checkpoint=Checkpoint.from_directory(tmpdir),
+            )
 
 
 train_dataset = ray.data.from_items([{"x": x, "y": 2 * x + 1} for x in range(200)])
diff --git a/doc/source/train/doc_code/dl_guide.py b/doc/source/train/doc_code/dl_guide.py
index d2c0800bc6254..8187913af560f 100644
--- a/doc/source/train/doc_code/dl_guide.py
+++ b/doc/source/train/doc_code/dl_guide.py
@@ -3,11 +3,16 @@
 MOCK = True
 
 # __ft_initial_run_start__
+import os
+import tempfile
 from typing import Dict, Optional
 
+import torch
+
 import ray
 from ray import train
-from ray.train.torch import LegacyTorchCheckpoint, TorchTrainer
+from ray.train import Checkpoint
+from ray.train.torch import TorchTrainer
 
 
 def get_datasets() -> Dict[str, ray.data.Dataset]:
@@ -17,10 +22,16 @@ def get_datasets() -> Dict[str, ray.data.Dataset]:
 def train_loop_per_worker(config: dict):
     from torchvision.models import resnet18
 
+    model = resnet18()
+
     # Checkpoint loading
-    checkpoint: Optional[LegacyTorchCheckpoint] = train.get_checkpoint()
-    model = checkpoint.get_model() if checkpoint else resnet18()
-    ray.train.torch.prepare_model(model)
+    checkpoint: Optional[Checkpoint] = train.get_checkpoint()
+    if checkpoint:
+        with checkpoint.as_directory() as checkpoint_dir:
+            model_state_dict = torch.load(os.path.join(checkpoint_dir, "model.pt"))
+            model.load_state_dict(model_state_dict)
+
+    model = train.torch.prepare_model(model)
 
     train_ds = train.get_dataset_shard("train")
 
@@ -28,10 +39,9 @@ def train_loop_per_worker(config: dict):
         # Do some training...
 
         # Checkpoint saving
-        train.report(
-            {"epoch": epoch},
-            checkpoint=LegacyTorchCheckpoint.from_model(model),
-        )
+        with tempfile.TemporaryDirectory() as tmpdir:
+            torch.save(model.module.state_dict(), os.path.join(tmpdir, "model.pt"))
+            train.report({"epoch": epoch}, checkpoint=Checkpoint.from_directory(tmpdir))
 
 
 trainer = TorchTrainer(
@@ -39,8 +49,7 @@ def train_loop_per_worker(config: dict):
     datasets=get_datasets(),
     scaling_config=train.ScalingConfig(num_workers=2),
     run_config=train.RunConfig(
-        storage_path="~/ray_results",
-        name="dl_trainer_restore",
+        name="dl_trainer_restore", storage_path=os.path.expanduser("~/ray_results")
     ),
 )
 result = trainer.fit()
@@ -50,7 +59,7 @@ def train_loop_per_worker(config: dict):
 from ray.train.torch import TorchTrainer
 
 restored_trainer = TorchTrainer.restore(
-    path="~/ray_results/dl_trainer_restore",
+    path=os.path.expanduser("~/ray_results/dl_trainer_restore"),
     datasets=get_datasets(),
 )
 # __ft_restored_run_end__
@@ -78,11 +87,9 @@ def train_loop_per_worker(config: dict):
 
 
 # __ft_autoresume_start__
-if TorchTrainer.can_restore("~/ray_results/dl_restore_autoresume"):
-    trainer = TorchTrainer.restore(
-        "~/ray_results/dl_restore_autoresume",
-        datasets=get_datasets(),
-    )
+experiment_path = os.path.expanduser("~/ray_results/dl_restore_autoresume")
+if TorchTrainer.can_restore(experiment_path):
+    trainer = TorchTrainer.restore(experiment_path, datasets=get_datasets())
     result = trainer.fit()
 else:
     trainer = TorchTrainer(
@@ -90,7 +97,8 @@ def train_loop_per_worker(config: dict):
         datasets=get_datasets(),
         scaling_config=train.ScalingConfig(num_workers=2),
         run_config=train.RunConfig(
-            storage_path="~/ray_results", name="dl_restore_autoresume"
+            storage_path=os.path.expanduser("~/ray_results"),
+            name="dl_restore_autoresume",
         ),
     )
 result = trainer.fit()
diff --git a/doc/source/train/doc_code/key_concepts.py b/doc/source/train/doc_code/key_concepts.py
index 546f0157d8aea..5a38ad1801091 100644
--- a/doc/source/train/doc_code/key_concepts.py
+++ b/doc/source/train/doc_code/key_concepts.py
@@ -54,6 +54,10 @@ def train_fn(config):
 
 
 # __session_checkpoint_start__
+import json
+import os
+import tempfile
+
 from ray import train
 from ray.train import ScalingConfig, Checkpoint
 from ray.train.data_parallel_trainer import DataParallelTrainer
@@ -63,22 +67,33 @@ def train_fn(config):
     checkpoint = train.get_checkpoint()
 
     if checkpoint:
-        state = checkpoint.to_dict()
+        with checkpoint.as_directory() as checkpoint_dir:
+            with open(os.path.join(checkpoint_dir, "checkpoint.json"), "r") as f:
+                state = json.load(f)
+            state["step"] += 1
     else:
         state = {"step": 0}
 
     for i in range(state["step"], 10):
         state["step"] += 1
-        train.report(
-            metrics={"step": state["step"], "loss": (100 - i) / 100},
-            checkpoint=Checkpoint.from_dict(state),
-        )
+        with tempfile.TemporaryDirectory() as tempdir:
+            with open(os.path.join(tempdir, "checkpoint.json"), "w") as f:
+                json.dump(state, f)
+
+            train.report(
+                metrics={"step": state["step"], "loss": (100 - i) / 100},
+                checkpoint=Checkpoint.from_directory(tempdir),
+            )
+
 
+example_checkpoint_dir = tempfile.mkdtemp()
+with open(os.path.join(example_checkpoint_dir, "checkpoint.json"), "w") as f:
+    json.dump({"step": 4}, f)
 
 trainer = DataParallelTrainer(
     train_loop_per_worker=train_fn,
     scaling_config=ScalingConfig(num_workers=1),
-    resume_from_checkpoint=Checkpoint.from_dict({"step": 4}),
+    resume_from_checkpoint=Checkpoint.from_directory(example_checkpoint_dir),
 )
 trainer.fit()
 
@@ -108,7 +123,7 @@ def train_fn(config):
     # Name of the training run (directory name).
     name="my_train_run",
     # The experiment results will be saved to: storage_path/name
-    storage_path="~/ray_results",
+    storage_path=os.path.expanduser("~/ray_results"),
     # storage_path="s3://my_bucket/tune_results",
     # Custom and built-in callbacks
     callbacks=[WandbLoggerCallback()],
@@ -203,9 +218,9 @@ def train_fn(config):
 
 # TODO(justinvyu): Re-enable this after updating all of doc_code.
 # __result_restore_start__
-# from ray.train import Result
+from ray.train import Result
 
-# restored_result = Result.from_path(result_path)
+restored_result = Result.from_path(result_path)
 print("Restored loss", result.metrics["loss"])
 # __result_restore_end__
 
diff --git a/doc/source/train/doc_code/tuner.py b/doc/source/train/doc_code/tuner.py
index 5f3efa49f82d7..24d46601dbd87 100644
--- a/doc/source/train/doc_code/tuner.py
+++ b/doc/source/train/doc_code/tuner.py
@@ -77,6 +77,8 @@
 # __xgboost_end__
 
 # __torch_start__
+import os
+
 from ray import tune
 from ray.tune import Tuner
 from ray.train.examples.pytorch.torch_linear_example import (
@@ -104,7 +106,9 @@
 
 tuner = Tuner(
     trainable=trainer,
-    run_config=RunConfig(name="test_tuner", storage_path="~/ray_results"),
+    run_config=RunConfig(
+        name="test_tuner", storage_path=os.path.expanduser("~/ray_results")
+    ),
     param_space=param_space,
     tune_config=tune.TuneConfig(
         mode="min", metric="loss", num_samples=2, max_concurrent_trials=2
@@ -229,7 +233,9 @@ def get_another_dataset():
 
 # __tune_restore_start__
 tuner = Tuner.restore(
-    path="~/ray_results/test_tuner", trainable=trainer, restart_errored=True
+    path=os.path.expanduser("~/ray_results/test_tuner"),
+    trainable=trainer,
+    restart_errored=True,
 )
 tuner.fit()
 # __tune_restore_end__
diff --git a/doc/source/tune/doc_code/faq.py b/doc/source/tune/doc_code/faq.py
index 218d02f14d665..515aa97a78e8a 100644
--- a/doc/source/tune/doc_code/faq.py
+++ b/doc/source/tune/doc_code/faq.py
@@ -123,7 +123,7 @@ def train_func(config):
     metric = None
 
     # __modin_start__
-    def train_fn(config, checkpoint_dir=None):
+    def train_fn(config):
         # some Modin operations here
         # import modin.pandas as pd
         train.report({"metric": metric})
@@ -148,7 +148,7 @@ def train_fn(config, checkpoint_dir=None):
 import numpy as np
 
 
-def train_func(config, checkpoint_dir=None, num_epochs=5, data=None):
+def train_func(config, num_epochs=5, data=None):
     for i in range(num_epochs):
         for sample in data:
             # ... train on sample
@@ -337,6 +337,11 @@ def train_func(config):
 
 
 # __iter_experimentation_initial_start__
+import os
+import tempfile
+
+import torch
+
 from ray import train, tune
 from ray.train import Checkpoint
 import random
@@ -346,10 +351,14 @@ def trainable(config):
     for epoch in range(1, config["num_epochs"]):
         # Do some training...
 
-        train.report(
-            {"score": random.random()},
-            checkpoint=Checkpoint.from_dict({"model_state_dict": {"x": 1}}),
-        )
+        with tempfile.TemporaryDirectory() as tempdir:
+            torch.save(
+                {"model_state_dict": {"x": 1}}, os.path.join(tempdir, "model.pt")
+            )
+            train.report(
+                {"score": random.random()},
+                checkpoint=Checkpoint.from_directory(tempdir),
+            )
 
 
 tuner = tune.Tuner(
@@ -370,18 +379,19 @@ def trainable(config):
 
 def trainable(config):
     # Add logic to handle the initial checkpoint.
-    checkpoint_ref = config["start_from_checkpoint"]
-    checkpoint: Checkpoint = ray.get(checkpoint_ref)
-    model_state_dict = checkpoint.to_dict()["model_state_dict"]
+    checkpoint: Checkpoint = config["start_from_checkpoint"]
+    with checkpoint.as_directory() as checkpoint_dir:
+        model_state_dict = torch.load(os.path.join(checkpoint_dir, "model.pt"))
+
     # Initialize a model from the checkpoint...
+    # model = ...
+    # model.load_state_dict(model_state_dict)
 
     for epoch in range(1, config["num_epochs"]):
-        # Do some training...
+        # Do some more training...
+        ...
 
-        train.report(
-            {"score": random.random()},
-            checkpoint=Checkpoint.from_dict({"model_state_dict": {"x": 1}}),
-        )
+        train.report({"score": random.random()})
 
 
 new_tuner = tune.Tuner(
@@ -389,10 +399,7 @@ def trainable(config):
     param_space={
         "num_epochs": 10,
         "hyperparam": tune.grid_search([4, 5, 6]),
-        # Put the best checkpoint from above into the object store.
-        # This way, all trials will be able to access the checkpoint,
-        # regardless of which node they are on.
-        "start_from_checkpoint": ray.put(best_checkpoint),
+        "start_from_checkpoint": best_checkpoint,
     },
     tune_config=tune.TuneConfig(metric="score", mode="max"),
 )
diff --git a/doc/source/tune/doc_code/fault_tolerance.py b/doc/source/tune/doc_code/fault_tolerance.py
index 4266213a6a2c2..b390cd926620a 100644
--- a/doc/source/tune/doc_code/fault_tolerance.py
+++ b/doc/source/tune/doc_code/fault_tolerance.py
@@ -1,6 +1,8 @@
 # flake8: noqa
 
 # __ft_initial_run_start__
+import os
+
 from ray import train, tune
 from ray.train import Checkpoint
 
@@ -23,7 +25,8 @@ def trainable(config):
     trainable,
     param_space={"num_epochs": 10},
     run_config=train.RunConfig(
-        storage_path="~/ray_results", name="tune_fault_tolerance_guide"
+        storage_path=os.path.expanduser("~/ray_results"),
+        name="tune_fault_tolerance_guide",
     ),
 )
 tuner.fit()
@@ -31,7 +34,7 @@ def trainable(config):
 
 # __ft_restored_run_start__
 tuner = tune.Tuner.restore(
-    "~/ray_results/tune_fault_tolerance_guide",
+    os.path.expanduser("~/ray_results/tune_fault_tolerance_guide"),
     trainable=trainable,
     resume_errored=True,
 )
@@ -40,7 +43,7 @@ def trainable(config):
 
 # __ft_restore_options_start__
 tuner = tune.Tuner.restore(
-    "~/ray_results/tune_fault_tolerance_guide",
+    os.path.expanduser("~/ray_results/tune_fault_tolerance_guide"),
     trainable=trainable,
     resume_errored=True,
     restart_errored=False,
@@ -52,7 +55,7 @@ def trainable(config):
 import os
 from ray import train, tune
 
-storage_path = "~/ray_results"
+storage_path = os.path.expanduser("~/ray_results")
 exp_name = "tune_fault_tolerance_guide"
 path = os.path.join(storage_path, exp_name)
 
@@ -106,7 +109,7 @@ def train_fn(config):
     # Tune over the object references!
     param_space={"model_ref": tune.grid_search(model_refs)},
     run_config=train.RunConfig(
-        storage_path="~/ray_results", name="restore_object_refs"
+        storage_path=os.path.expanduser("~/ray_results"), name="restore_object_refs"
     ),
 )
 tuner.fit()
@@ -122,7 +125,7 @@ def train_fn(config):
 }
 
 tuner = tune.Tuner.restore(
-    "~/ray_results/restore_object_refs",
+    os.path.expanduser("~/ray_results/restore_object_refs"),
     trainable=train_fn,
     # Re-specify the `param_space` to update the object references.
     param_space=param_space,
@@ -138,7 +141,7 @@ def train_fn(config):
     trainable,
     param_space={"num_epochs": 10},
     run_config=train.RunConfig(
-        storage_path="~/ray_results",
+        storage_path=os.path.expanduser("~/ray_results"),
         name="trial_fault_tolerance",
         failure_config=train.FailureConfig(max_failures=3),
     ),
diff --git a/doc/source/tune/doc_code/trial_checkpoint.py b/doc/source/tune/doc_code/trial_checkpoint.py
index a02755f30efb1..244abe47ace96 100644
--- a/doc/source/tune/doc_code/trial_checkpoint.py
+++ b/doc/source/tune/doc_code/trial_checkpoint.py
@@ -88,97 +88,76 @@ def train_func(self):
 # __class_api_end_checkpointing_end__
 
 
-# __function_api_checkpointing_start__
-from ray import train, tune
-from ray.train import Checkpoint
-
-
-def train_func(config):
-    epochs = config.get("epochs", 2)
-    start = 0
-    loaded_checkpoint = train.get_checkpoint()
-    if loaded_checkpoint:
-        last_step = loaded_checkpoint.to_dict()["step"]
-        start = last_step + 1
-
-    for epoch in range(start, epochs):
-        # Model training here
-        # ...
-
-        # Report metrics and save a checkpoint
-        metrics = {"metric": "my_metric"}
-        checkpoint = Checkpoint.from_dict({"epoch": epoch})
-        train.report(metrics, checkpoint=checkpoint)
-
-
-tuner = tune.Tuner(train_func)
-results = tuner.fit()
-# __function_api_checkpointing_end__
-
-
 class MyModel:
-    pass
+    def state_dict(self) -> dict:
+        return {}
+
+    def load_state_dict(self, state_dict):
+        pass
 
 
 # __function_api_checkpointing_from_dir_start__
+import os
+import tempfile
+
 from ray import train, tune
 from ray.train import Checkpoint
 
 
-# like Keras, or pytorch save methods.
-def write_model_to_dir(model, dir_path):
-    pass
-
-
-def write_epoch_to_dir(epoch: int, dir_path: str):
-    with open(os.path.join(dir_path, "epoch"), "w") as f:
-        f.write(str(epoch))
-
-
-def get_epoch_from_dir(dir_path: str) -> int:
-    with open(os.path.join(dir_path, "epoch"), "r") as f:
-        return int(f.read())
-
-
 def train_func(config):
     start = 1
-    if train.get_checkpoint() is not None:
-        loaded_checkpoint = train.get_checkpoint()
-        with loaded_checkpoint.as_directory() as loaded_checkpoint_path:
-            start = get_epoch_from_dir(loaded_checkpoint_path) + 1
+    my_model = MyModel()
+
+    checkpoint = train.get_checkpoint()
+    if checkpoint:
+        with checkpoint.as_directory() as checkpoint_dir:
+            checkpoint_dict = torch.load(os.path.join(checkpoint_dir, "checkpoint.pt"))
+            start = checkpoint_dict["epoch"] + 1
+            my_model.load_state_dict(checkpoint_dict["model_state"])
 
     for epoch in range(start, config["epochs"] + 1):
         # Model training here
         # ...
 
-        my_model = MyModel()
-        metrics = {"metric": "my_metric"}
-        # some function to write model to directory
-        write_model_to_dir(my_model, "my_model")
-        write_epoch_to_dir(epoch, "my_model")
-        train.report(metrics=metrics, checkpoint=Checkpoint.from_directory("my_model"))
+        metrics = {"metric": 1}
+        with tempfile.TemporaryDirectory() as tempdir:
+            torch.save(
+                {"epoch": epoch, "model_state": my_model.state_dict()},
+                os.path.join(tempdir, "checkpoint.pt"),
+            )
+            train.report(metrics=metrics, checkpoint=Checkpoint.from_directory(tempdir))
 
 
+tuner = tune.Tuner(train_func, param_space={"epochs": 5})
+result_grid = tuner.fit()
 # __function_api_checkpointing_from_dir_end__
 
+assert not result_grid.errors
 
 # __function_api_checkpointing_periodic_start__
+NUM_EPOCHS = 12
+# checkpoint every three epochs.
+CHECKPOINT_FREQ = 3
+
+
 def train_func(config):
-    # checkpoint every three epochs.
-    checkpoint_freq = 3
     for epoch in range(1, config["epochs"] + 1):
         # Model training here
         # ...
 
         # Report metrics and save a checkpoint
         metrics = {"metric": "my_metric"}
-        if epoch % checkpoint_freq == 0:
-            checkpoint = Checkpoint.from_dict({"epoch": epoch})
-            train.report(metrics, checkpoint=checkpoint)
+        if epoch % CHECKPOINT_FREQ == 0:
+            with tempfile.TemporaryDirectory() as tempdir:
+                # Save a checkpoint in tempdir.
+                train.report(metrics, checkpoint=Checkpoint.from_directory(tempdir))
         else:
             train.report(metrics)
 
 
-tuner = tune.Tuner(train_func, param_space={"epochs": 12})
-
+tuner = tune.Tuner(train_func, param_space={"epochs": NUM_EPOCHS})
+result_grid = tuner.fit()
 # __function_api_checkpointing_periodic_end__
+
+assert not result_grid.errors
+assert len(result_grid[0].best_checkpoints) == NUM_EPOCHS // CHECKPOINT_FREQ
diff --git a/doc/source/tune/examples/BUILD b/doc/source/tune/examples/BUILD
index 0113c565e0051..8f51f9d223224 100644
--- a/doc/source/tune/examples/BUILD
+++ b/doc/source/tune/examples/BUILD
@@ -17,28 +17,21 @@ py_test_run_all_notebooks(
     size = "medium",
     include = ["*.ipynb"],
     exclude = [
+        # TODO(krfricke): Enable this test for new persistence path.
         "bohb_example.ipynb",
         "pbt_ppo_example.ipynb",
-        "pbt_guide.ipynb",
-        "tune-xgboost.ipynb",
         "tune-xgboost.ipynb",
+        "pbt_transformers.ipynb",  # Transformers uses legacy Tune APIs.
+        # TODO(justinvyu): tune_sklearn uses removed experiment analysis method
+        "tune-sklearn.ipynb",
+        # TODO(krfricke): This depends on the ptl callback PR landing.
+        "tune-vanilla-pytorch-lightning.ipynb",
         "sigopt_example.ipynb", # REGRESSION: no credentials
     ],
     data = ["//doc/source/tune/examples:tune_examples"],
     tags = ["exclusive", "team:ml"],
 )
 
-py_test_run_all_notebooks(
-    size = "medium",
-    include = [
-        "bohb_example.ipynb",
-        "pbt_guide.ipynb"
-    ],
-    exclude = [],
-    data = ["//doc/source/tune/examples:tune_examples"],
-    tags = ["exclusive", "team:ml", "new_storage"],
-)
-
 # GPU tests
 py_test_run_all_notebooks(
     size = "large",
diff --git a/doc/source/tune/examples/pbt_guide.ipynb b/doc/source/tune/examples/pbt_guide.ipynb
index d23e2b9ba2988..70cf245750826 100644
--- a/doc/source/tune/examples/pbt_guide.ipynb
+++ b/doc/source/tune/examples/pbt_guide.ipynb
@@ -62,6 +62,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
     "import torch\n",
     "import torch.optim as optim\n",
     "\n",
@@ -83,10 +86,13 @@
     "        momentum=config.get(\"momentum\", 0.9),\n",
     "    )\n",
     "\n",
-    "    # If `train.get_checkpoint()` is not None, then we are resuming from a checkpoint.\n",
-    "    if train.get_checkpoint():\n",
+    "    # If `train.get_checkpoint()` is populated, then we are resuming from a checkpoint.\n",
+    "    checkpoint = train.get_checkpoint()\n",
+    "    if checkpoint:\n",
+    "        with checkpoint.as_directory() as checkpoint_dir:\n",
+    "            checkpoint_dict = torch.load(os.path.join(checkpoint_dir, \"checkpoint.pt\"))\n",
+    "\n",
     "        # Load model state and iteration step from checkpoint.\n",
-    "        checkpoint_dict = train.get_checkpoint().to_dict()\n",
     "        model.load_state_dict(checkpoint_dict[\"model_state_dict\"])\n",
     "        # Load optimizer state (needed since we're using momentum),\n",
     "        # then set the `lr` and `momentum` according to the config.\n",
@@ -104,20 +110,24 @@
     "    while True:\n",
     "        ray.tune.examples.mnist_pytorch.train_func(model, optimizer, train_loader)\n",
     "        acc = test_func(model, test_loader)\n",
-    "        checkpoint = None\n",
+    "        metrics = {\"mean_accuracy\": acc, \"lr\": config[\"lr\"]}\n",
+    "\n",
+    "        # Every `checkpoint_interval` steps, checkpoint our current state.\n",
     "        if step % config[\"checkpoint_interval\"] == 0:\n",
-    "            # Every `checkpoint_interval` steps, checkpoint our current state.\n",
-    "            checkpoint = Checkpoint.from_dict({\n",
-    "                \"step\": step,\n",
-    "                \"model_state_dict\": model.state_dict(),\n",
-    "                \"optimizer_state_dict\": optimizer.state_dict(),\n",
-    "            })\n",
-    "\n",
-    "        train.report(\n",
-    "            {\"mean_accuracy\": acc, \"lr\": config[\"lr\"]},\n",
-    "            checkpoint=checkpoint\n",
-    "        )\n",
-    "        step += 1"
+    "            with tempfile.TemporaryDirectory() as tmpdir:\n",
+    "                torch.save(\n",
+    "                    {\n",
+    "                        \"step\": step,\n",
+    "                        \"model_state_dict\": model.state_dict(),\n",
+    "                        \"optimizer_state_dict\": optimizer.state_dict(),\n",
+    "                    },\n",
+    "                    os.path.join(tmpdir, \"checkpoint.pt\"),\n",
+    "                )\n",
+    "                train.report(metrics, checkpoint=Checkpoint.from_directory(tmpdir))\n",
+    "        else:\n",
+    "            train.report(metrics)\n",
+    "\n",
+    "        step += 1\n"
    ]
   },
   {
@@ -154,7 +164,7 @@
     "        # allow perturbations within this set of categorical values\n",
     "        \"momentum\": [0.8, 0.9, 0.99],\n",
     "    },\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -214,7 +224,7 @@
     "    },\n",
     ")\n",
     "\n",
-    "results_grid = tuner.fit()"
+    "results_grid = tuner.fit()\n"
    ]
   },
   {
@@ -276,12 +286,12 @@
     "best_result = results_grid.get_best_result(metric=\"mean_accuracy\", mode=\"max\")\n",
     "\n",
     "# Print `log_dir` where checkpoints are stored\n",
-    "print('Best result logdir:', best_result.log_dir)\n",
+    "print(\"Best result logdir:\", best_result.log_dir)\n",
     "\n",
     "# Print the best trial `config` reported at the last iteration\n",
     "# NOTE: This config is just what the trial ended up with at the last iteration.\n",
     "# See the next section for replaying the entire history of configs.\n",
-    "print('Best final iteration hyperparameter config:\\n', best_result.config)\n",
+    "print(\"Best final iteration hyperparameter config:\\n\", best_result.config)\n",
     "\n",
     "# Plot the learning curve for the best trial\n",
     "df = best_result.metrics_dataframe\n",
@@ -290,7 +300,7 @@
     "df.plot(\"training_iteration\", \"mean_accuracy\")\n",
     "plt.xlabel(\"Training Iterations\")\n",
     "plt.ylabel(\"Test Accuracy\")\n",
-    "plt.show()"
+    "plt.show()\n"
    ]
   },
   {
@@ -463,7 +473,7 @@
     "    tune_config=tune.TuneConfig(scheduler=replay),\n",
     "    run_config=train.RunConfig(stop={\"training_iteration\": 50}),\n",
     ")\n",
-    "results_grid = tuner.fit()"
+    "results_grid = tuner.fit()\n"
    ]
   },
   {
@@ -558,7 +568,8 @@
     "\n",
     "from ray.tune.examples.pbt_dcgan_mnist.common import Net\n",
     "from ray.tune.examples.pbt_dcgan_mnist.pbt_dcgan_mnist_func import (\n",
-    "    dcgan_train, download_mnist_cnn\n",
+    "    dcgan_train,\n",
+    "    download_mnist_cnn,\n",
     ")\n",
     "\n",
     "# Load the pretrained mnist classification model for inception_score\n",
@@ -600,7 +611,7 @@
     "        \"checkpoint_interval\": perturbation_interval,\n",
     "    },\n",
     ")\n",
-    "results_grid = tuner.fit()"
+    "results_grid = tuner.fit()\n"
    ]
   },
   {
@@ -658,7 +669,7 @@
     "plt.title(\"Inception Score During Training\")\n",
     "plt.xlabel(\"Training Iterations\")\n",
     "plt.ylabel(\"Inception Score\")\n",
-    "plt.show()"
+    "plt.show()\n"
    ]
   },
   {
@@ -704,7 +715,7 @@
     "axs[1].set_xlabel(\"Training Iterations\")\n",
     "axs[1].set_ylabel(\"Discriminator Loss\")\n",
     "\n",
-    "plt.show()"
+    "plt.show()\n"
    ]
   },
   {
@@ -728,7 +739,7 @@
     "from ray.tune.examples.pbt_dcgan_mnist.common import demo_gan\n",
     "\n",
     "with best_result.checkpoint.as_directory() as best_checkpoint:\n",
-    "    demo_gan([best_checkpoint])"
+    "    demo_gan([best_checkpoint])\n"
    ]
   },
   {
diff --git a/doc/source/tune/examples/tune_analyze_results.ipynb b/doc/source/tune/examples/tune_analyze_results.ipynb
index db9227df5d0e6..83cb86adadf5d 100644
--- a/doc/source/tune/examples/tune_analyze_results.ipynb
+++ b/doc/source/tune/examples/tune_analyze_results.ipynb
@@ -30,12 +30,151 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 1,
             "id": "8479d7d2",
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div class=\"tuneStatus\">\n",
+                            "  <div style=\"display: flex;flex-direction: row\">\n",
+                            "    <div style=\"display: flex;flex-direction: column;\">\n",
+                            "      <h3>Tune Status</h3>\n",
+                            "      <table>\n",
+                            "<tbody>\n",
+                            "<tr><td>Current time:</td><td>2023-08-25 17:42:39</td></tr>\n",
+                            "<tr><td>Running for: </td><td>00:00:12.43        </td></tr>\n",
+                            "<tr><td>Memory:      </td><td>27.0/64.0 GiB      </td></tr>\n",
+                            "</tbody>\n",
+                            "</table>\n",
+                            "    </div>\n",
+                            "    <div class=\"vDivider\"></div>\n",
+                            "    <div class=\"systemInfo\">\n",
+                            "      <h3>System Info</h3>\n",
+                            "      Using FIFO scheduling algorithm.<br>Logical resource usage: 1.0/10 CPUs, 0/0 GPUs\n",
+                            "    </div>\n",
+                            "    \n",
+                            "  </div>\n",
+                            "  <div class=\"hDivider\"></div>\n",
+                            "  <div class=\"trialStatus\">\n",
+                            "    <h3>Trial Status</h3>\n",
+                            "    <table>\n",
+                            "<thead>\n",
+                            "<tr><th>Trial name             </th><th>status    </th><th>loc            </th><th style=\"text-align: right;\">        lr</th><th style=\"text-align: right;\">  momentum</th><th style=\"text-align: right;\">     acc</th><th style=\"text-align: right;\">  iter</th><th style=\"text-align: right;\">  total time (s)</th></tr>\n",
+                            "</thead>\n",
+                            "<tbody>\n",
+                            "<tr><td>train_mnist_6e465_00000</td><td>TERMINATED</td><td>127.0.0.1:94903</td><td style=\"text-align: right;\">0.0188636 </td><td style=\"text-align: right;\">      0.8 </td><td style=\"text-align: right;\">0.925   </td><td style=\"text-align: right;\">   100</td><td style=\"text-align: right;\">         8.81282</td></tr>\n",
+                            "<tr><td>train_mnist_6e465_00001</td><td>TERMINATED</td><td>127.0.0.1:94904</td><td style=\"text-align: right;\">0.0104137 </td><td style=\"text-align: right;\">      0.9 </td><td style=\"text-align: right;\">0.9625  </td><td style=\"text-align: right;\">   100</td><td style=\"text-align: right;\">         8.6819 </td></tr>\n",
+                            "<tr><td>train_mnist_6e465_00002</td><td>TERMINATED</td><td>127.0.0.1:94905</td><td style=\"text-align: right;\">0.00102317</td><td style=\"text-align: right;\">      0.99</td><td style=\"text-align: right;\">0.953125</td><td style=\"text-align: right;\">   100</td><td style=\"text-align: right;\">         8.67491</td></tr>\n",
+                            "<tr><td>train_mnist_6e465_00003</td><td>TERMINATED</td><td>127.0.0.1:94906</td><td style=\"text-align: right;\">0.0103929 </td><td style=\"text-align: right;\">      0.8 </td><td style=\"text-align: right;\">0.94375 </td><td style=\"text-align: right;\">   100</td><td style=\"text-align: right;\">         8.92996</td></tr>\n",
+                            "<tr><td>train_mnist_6e465_00004</td><td>TERMINATED</td><td>127.0.0.1:94907</td><td style=\"text-align: right;\">0.00808686</td><td style=\"text-align: right;\">      0.9 </td><td style=\"text-align: right;\">0.95625 </td><td style=\"text-align: right;\">   100</td><td style=\"text-align: right;\">         8.75311</td></tr>\n",
+                            "<tr><td>train_mnist_6e465_00005</td><td>TERMINATED</td><td>127.0.0.1:94908</td><td style=\"text-align: right;\">0.00172525</td><td style=\"text-align: right;\">      0.99</td><td style=\"text-align: right;\">0.95625 </td><td style=\"text-align: right;\">   100</td><td style=\"text-align: right;\">         8.76523</td></tr>\n",
+                            "<tr><td>train_mnist_6e465_00006</td><td>TERMINATED</td><td>127.0.0.1:94909</td><td style=\"text-align: right;\">0.0507692 </td><td style=\"text-align: right;\">      0.8 </td><td style=\"text-align: right;\">0.946875</td><td style=\"text-align: right;\">   100</td><td style=\"text-align: right;\">         8.94565</td></tr>\n",
+                            "<tr><td>train_mnist_6e465_00007</td><td>TERMINATED</td><td>127.0.0.1:94910</td><td style=\"text-align: right;\">0.00978134</td><td style=\"text-align: right;\">      0.9 </td><td style=\"text-align: right;\">0.965625</td><td style=\"text-align: right;\">   100</td><td style=\"text-align: right;\">         8.77776</td></tr>\n",
+                            "<tr><td>train_mnist_6e465_00008</td><td>TERMINATED</td><td>127.0.0.1:94911</td><td style=\"text-align: right;\">0.00368709</td><td style=\"text-align: right;\">      0.99</td><td style=\"text-align: right;\">0.934375</td><td style=\"text-align: right;\">   100</td><td style=\"text-align: right;\">         8.8495 </td></tr>\n",
+                            "</tbody>\n",
+                            "</table>\n",
+                            "  </div>\n",
+                            "</div>\n",
+                            "<style>\n",
+                            ".tuneStatus {\n",
+                            "  color: var(--jp-ui-font-color1);\n",
+                            "}\n",
+                            ".tuneStatus .systemInfo {\n",
+                            "  display: flex;\n",
+                            "  flex-direction: column;\n",
+                            "}\n",
+                            ".tuneStatus td {\n",
+                            "  white-space: nowrap;\n",
+                            "}\n",
+                            ".tuneStatus .trialStatus {\n",
+                            "  display: flex;\n",
+                            "  flex-direction: column;\n",
+                            "}\n",
+                            ".tuneStatus h3 {\n",
+                            "  font-weight: bold;\n",
+                            "}\n",
+                            ".tuneStatus .hDivider {\n",
+                            "  border-bottom-width: var(--jp-border-width);\n",
+                            "  border-bottom-color: var(--jp-border-color0);\n",
+                            "  border-bottom-style: solid;\n",
+                            "}\n",
+                            ".tuneStatus .vDivider {\n",
+                            "  border-left-width: var(--jp-border-width);\n",
+                            "  border-left-color: var(--jp-border-color0);\n",
+                            "  border-left-style: solid;\n",
+                            "  margin: 0.5em 1em 0.5em 1em;\n",
+                            "}\n",
+                            "</style>\n"
+                        ],
+                        "text/plain": [
+                            "<IPython.core.display.HTML object>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "2023-08-25 17:42:27,603\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94906)\u001b[0m StorageContext on SESSION (rank=None):\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94906)\u001b[0m StorageContext<\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94906)\u001b[0m   storage_path=/tmp/ray_results\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94906)\u001b[0m   storage_local_path=/Users/justin/ray_results\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94906)\u001b[0m   storage_filesystem=<pyarrow._fs.LocalFileSystem object at 0x149b763b0>\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94906)\u001b[0m   storage_fs_path=/tmp/ray_results\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94906)\u001b[0m   experiment_dir_name=tune_analyzing_results\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94906)\u001b[0m   trial_dir_name=train_mnist_6e465_00003_3_lr=0.0104,momentum=0.8000_2023-08-25_17-42-27\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94906)\u001b[0m   current_checkpoint_index=0\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94906)\u001b[0m >\n",
+                        "\u001b[2m\u001b[36m(train_mnist pid=94907)\u001b[0m Checkpoint successfully created at: Checkpoint(filesystem=local, path=/tmp/ray_results/tune_analyzing_results/train_mnist_6e465_00004_4_lr=0.0081,momentum=0.9000_2023-08-25_17-42-27/checkpoint_000000)\n",
+                        "2023-08-25 17:42:30,460\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:30,868\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:31,252\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:31,684\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:32,050\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:32,422\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:32,836\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:33,238\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:33,599\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:33,987\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:34,358\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:34,768\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94905)\u001b[0m StorageContext on SESSION (rank=None):\u001b[32m [repeated 8x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)\u001b[0m\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94905)\u001b[0m StorageContext<\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94905)\u001b[0m   storage_path=/tmp/ray_results\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94905)\u001b[0m   storage_local_path=/Users/justin/ray_results\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94905)\u001b[0m   storage_filesystem=<pyarrow._fs.LocalFileSystem object at 0x13e75e070>\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94905)\u001b[0m   storage_fs_path=/tmp/ray_results\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94905)\u001b[0m   experiment_dir_name=tune_analyzing_results\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94905)\u001b[0m   current_checkpoint_index=0\u001b[32m [repeated 16x across cluster]\u001b[0m\n",
+                        "\u001b[2m\u001b[36m(ImplicitFunc pid=94905)\u001b[0m >\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+                        "2023-08-25 17:42:35,127\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "\u001b[2m\u001b[36m(train_mnist pid=94906)\u001b[0m Checkpoint successfully created at: Checkpoint(filesystem=local, path=/tmp/ray_results/tune_analyzing_results/train_mnist_6e465_00003_3_lr=0.0104,momentum=0.8000_2023-08-25_17-42-27/checkpoint_000050)\u001b[32m [repeated 455x across cluster]\u001b[0m\n",
+                        "2023-08-25 17:42:35,508\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:35,899\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:36,277\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:36,662\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:37,065\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:37,455\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:37,857\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:38,237\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:38,639\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:39,019\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:39,400\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:39,773\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:39,879\tWARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.\n",
+                        "2023-08-25 17:42:39,882\tINFO tune.py:1147 -- Total run time: 12.52 seconds (12.42 seconds for the tuning loop).\n"
+                    ]
+                }
+            ],
             "source": [
-                "from ray import tune, air\n",
+                "import os\n",
+                "\n",
+                "from ray import train, tune\n",
                 "from ray.tune.examples.mnist_pytorch import train_mnist\n",
                 "from ray.tune import ResultGrid\n",
                 "\n",
@@ -48,10 +187,10 @@
                 "        \"momentum\": tune.grid_search([0.8, 0.9, 0.99]),\n",
                 "        \"should_checkpoint\": True,\n",
                 "    },\n",
-                "    run_config=air.RunConfig(\n",
+                "    run_config=train.RunConfig(\n",
                 "        name=exp_name,\n",
                 "        stop={\"training_iteration\": 100},\n",
-                "        checkpoint_config=air.CheckpointConfig(\n",
+                "        checkpoint_config=train.CheckpointConfig(\n",
                 "            checkpoint_score_attribute=\"mean_accuracy\",\n",
                 "            num_to_keep=5,\n",
                 "        ),\n",
@@ -75,7 +214,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 19,
+            "execution_count": 2,
             "id": "92ded070",
             "metadata": {},
             "outputs": [
@@ -85,17 +224,10 @@
                     "text": [
                         "Loading results from /tmp/ray_results/tune_analyzing_results...\n"
                     ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "2022-10-17 16:04:54,189\tINFO experiment_analysis.py:795 -- No `self.trials`. Drawing logdirs from checkpoint file. This may result in some information that is out of sync, as checkpointing is periodic.\n"
-                    ]
                 }
             ],
             "source": [
-                "experiment_path = f\"{storage_path}/{exp_name}\"\n",
+                "experiment_path = os.path.join(storage_path, exp_name)\n",
                 "print(f\"Loading results from {experiment_path}...\")\n",
                 "\n",
                 "restored_tuner = tune.Tuner.restore(experiment_path, trainable=train_mnist)\n",
@@ -182,15 +314,15 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Trial #0 finished successfully with a mean accuracy metric of: 0.96875\n",
-                        "Trial #1 finished successfully with a mean accuracy metric of: 0.925\n",
-                        "Trial #2 finished successfully with a mean accuracy metric of: 0.946875\n",
-                        "Trial #3 finished successfully with a mean accuracy metric of: 0.86875\n",
-                        "Trial #4 finished successfully with a mean accuracy metric of: 0.94375\n",
-                        "Trial #5 finished successfully with a mean accuracy metric of: 0.971875\n",
-                        "Trial #6 finished successfully with a mean accuracy metric of: 0.91875\n",
-                        "Trial #7 finished successfully with a mean accuracy metric of: 0.965625\n",
-                        "Trial #8 finished successfully with a mean accuracy metric of: 0.740625\n"
+                        "Trial #0 finished successfully with a mean accuracy metric of: 0.953125\n",
+                        "Trial #1 finished successfully with a mean accuracy metric of: 0.9625\n",
+                        "Trial #2 finished successfully with a mean accuracy metric of: 0.95625\n",
+                        "Trial #3 finished successfully with a mean accuracy metric of: 0.946875\n",
+                        "Trial #4 finished successfully with a mean accuracy metric of: 0.925\n",
+                        "Trial #5 finished successfully with a mean accuracy metric of: 0.934375\n",
+                        "Trial #6 finished successfully with a mean accuracy metric of: 0.965625\n",
+                        "Trial #7 finished successfully with a mean accuracy metric of: 0.95625\n",
+                        "Trial #8 finished successfully with a mean accuracy metric of: 0.94375\n"
                     ]
                 }
             ],
@@ -252,47 +384,47 @@
                             "    <tr>\n",
                             "      <th>0</th>\n",
                             "      <td>100</td>\n",
-                            "      <td>0.968750</td>\n",
+                            "      <td>0.953125</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>1</th>\n",
                             "      <td>100</td>\n",
-                            "      <td>0.925000</td>\n",
+                            "      <td>0.962500</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>2</th>\n",
                             "      <td>100</td>\n",
-                            "      <td>0.946875</td>\n",
+                            "      <td>0.956250</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>3</th>\n",
                             "      <td>100</td>\n",
-                            "      <td>0.868750</td>\n",
+                            "      <td>0.946875</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>4</th>\n",
                             "      <td>100</td>\n",
-                            "      <td>0.943750</td>\n",
+                            "      <td>0.925000</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>5</th>\n",
                             "      <td>100</td>\n",
-                            "      <td>0.971875</td>\n",
+                            "      <td>0.934375</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>6</th>\n",
                             "      <td>100</td>\n",
-                            "      <td>0.918750</td>\n",
+                            "      <td>0.965625</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>7</th>\n",
                             "      <td>100</td>\n",
-                            "      <td>0.965625</td>\n",
+                            "      <td>0.956250</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>8</th>\n",
                             "      <td>100</td>\n",
-                            "      <td>0.740625</td>\n",
+                            "      <td>0.943750</td>\n",
                             "    </tr>\n",
                             "  </tbody>\n",
                             "</table>\n",
@@ -300,15 +432,15 @@
                         ],
                         "text/plain": [
                             "   training_iteration  mean_accuracy\n",
-                            "0                 100       0.968750\n",
-                            "1                 100       0.925000\n",
-                            "2                 100       0.946875\n",
-                            "3                 100       0.868750\n",
-                            "4                 100       0.943750\n",
-                            "5                 100       0.971875\n",
-                            "6                 100       0.918750\n",
-                            "7                 100       0.965625\n",
-                            "8                 100       0.740625"
+                            "0                 100       0.953125\n",
+                            "1                 100       0.962500\n",
+                            "2                 100       0.956250\n",
+                            "3                 100       0.946875\n",
+                            "4                 100       0.925000\n",
+                            "5                 100       0.934375\n",
+                            "6                 100       0.965625\n",
+                            "7                 100       0.956250\n",
+                            "8                 100       0.943750"
                         ]
                     },
                     "execution_count": 6,
@@ -331,8 +463,8 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Shortest training time: 28.826712369918823\n",
-                        "Longest training time: 31.22410249710083\n"
+                        "Shortest training time: 8.674914598464966\n",
+                        "Longest training time: 8.945653676986694\n"
                     ]
                 }
             ],
@@ -384,48 +516,48 @@
                             "  <tbody>\n",
                             "    <tr>\n",
                             "      <th>0</th>\n",
-                            "      <td>81</td>\n",
-                            "      <td>0.978125</td>\n",
+                            "      <td>50</td>\n",
+                            "      <td>0.968750</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>1</th>\n",
-                            "      <td>44</td>\n",
-                            "      <td>0.953125</td>\n",
+                            "      <td>55</td>\n",
+                            "      <td>0.975000</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>2</th>\n",
-                            "      <td>96</td>\n",
-                            "      <td>0.953125</td>\n",
+                            "      <td>95</td>\n",
+                            "      <td>0.975000</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>3</th>\n",
-                            "      <td>94</td>\n",
-                            "      <td>0.925000</td>\n",
+                            "      <td>71</td>\n",
+                            "      <td>0.978125</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>4</th>\n",
-                            "      <td>87</td>\n",
-                            "      <td>0.975000</td>\n",
+                            "      <td>65</td>\n",
+                            "      <td>0.959375</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>5</th>\n",
-                            "      <td>92</td>\n",
-                            "      <td>0.978125</td>\n",
+                            "      <td>77</td>\n",
+                            "      <td>0.965625</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>6</th>\n",
-                            "      <td>77</td>\n",
-                            "      <td>0.959375</td>\n",
+                            "      <td>82</td>\n",
+                            "      <td>0.975000</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>7</th>\n",
-                            "      <td>59</td>\n",
-                            "      <td>0.971875</td>\n",
+                            "      <td>80</td>\n",
+                            "      <td>0.968750</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>8</th>\n",
-                            "      <td>10</td>\n",
-                            "      <td>0.896875</td>\n",
+                            "      <td>92</td>\n",
+                            "      <td>0.975000</td>\n",
                             "    </tr>\n",
                             "  </tbody>\n",
                             "</table>\n",
@@ -433,15 +565,15 @@
                         ],
                         "text/plain": [
                             "   training_iteration  mean_accuracy\n",
-                            "0                  81       0.978125\n",
-                            "1                  44       0.953125\n",
-                            "2                  96       0.953125\n",
-                            "3                  94       0.925000\n",
-                            "4                  87       0.975000\n",
-                            "5                  92       0.978125\n",
-                            "6                  77       0.959375\n",
-                            "7                  59       0.971875\n",
-                            "8                  10       0.896875"
+                            "0                  50       0.968750\n",
+                            "1                  55       0.975000\n",
+                            "2                  95       0.975000\n",
+                            "3                  71       0.978125\n",
+                            "4                  65       0.959375\n",
+                            "5                  77       0.965625\n",
+                            "6                  82       0.975000\n",
+                            "7                  80       0.968750\n",
+                            "8                  92       0.975000"
                         ]
                     },
                     "execution_count": 8,
@@ -512,7 +644,7 @@
                 {
                     "data": {
                         "text/plain": [
-                            "{'lr': 0.0034759400828981743, 'momentum': 0.99, 'should_checkpoint': True}"
+                            "{'lr': 0.009781335971854077, 'momentum': 0.9, 'should_checkpoint': True}"
                         ]
                     },
                     "execution_count": 10,
@@ -530,7 +662,7 @@
             "id": "403111f9",
             "metadata": {},
             "source": [
-                "Next, we can access the trial's log directory via `Result.log_dir`. The result `log_dir` gives the trial level directory that contains checkpoints (if you had checkpointing enabled) and logged metrics to load manually or inspect using a tool like Tensorboard (see `result.json`, `progress.csv`)."
+                "Next, we can access the trial directory via `Result.path`. The result `path` gives the trial level directory that contains checkpoints (if you reported any) and logged metrics to load manually or inspect using a tool like Tensorboard (see `result.json`, `progress.csv`)."
             ]
         },
         {
@@ -542,7 +674,7 @@
                 {
                     "data": {
                         "text/plain": [
-                            "PosixPath('/tmp/ray_results/tune_analyzing_results/train_mnist_daaa1_00005_5_lr=0.0035,momentum=0.9900_2022-10-17_16-03-12')"
+                            "'/tmp/ray_results/tune_analyzing_results/train_mnist_6e465_00007_7_lr=0.0098,momentum=0.9000_2023-08-25_17-42-27'"
                         ]
                     },
                     "execution_count": 11,
@@ -551,7 +683,7 @@
                 }
             ],
             "source": [
-                "best_result.log_dir"
+                "best_result.path"
             ]
         },
         {
@@ -572,7 +704,7 @@
                 {
                     "data": {
                         "text/plain": [
-                            "LegacyTorchCheckpoint(local_path=/tmp/ray_results/tune_analyzing_results/train_mnist_daaa1_00005_5_lr=0.0035,momentum=0.9900_2022-10-17_16-03-12/checkpoint_000099)"
+                            "Checkpoint(filesystem=local, path=/tmp/ray_results/tune_analyzing_results/train_mnist_6e465_00007_7_lr=0.0098,momentum=0.9000_2023-08-25_17-42-27/checkpoint_000099)"
                         ]
                     },
                     "execution_count": 12,
@@ -581,7 +713,7 @@
                 }
             ],
             "source": [
-                "# Get the last Ray AIR Checkpoint associated with the best-performing trial\n",
+                "# Get the last Checkpoint associated with the best-performing trial\n",
                 "best_result.checkpoint"
             ]
         },
@@ -596,39 +728,34 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 13,
+            "execution_count": 15,
             "id": "52d4b99c",
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/plain": [
-                            "{'mean_accuracy': 0.971875,\n",
-                            " 'time_this_iter_s': 0.23050832748413086,\n",
+                            "{'mean_accuracy': 0.965625,\n",
+                            " 'timestamp': 1693010559,\n",
                             " 'should_checkpoint': True,\n",
                             " 'done': True,\n",
-                            " 'timesteps_total': None,\n",
-                            " 'episodes_total': None,\n",
                             " 'training_iteration': 100,\n",
-                            " 'trial_id': 'daaa1_00005',\n",
-                            " 'experiment_id': 'a15f57f8a3f84b1d823c2cf65c37aece',\n",
-                            " 'date': '2022-10-17_16-03-45',\n",
-                            " 'timestamp': 1666047825,\n",
-                            " 'time_total_s': 29.587023496627808,\n",
-                            " 'pid': 3699,\n",
-                            " 'hostname': 'ip-172-31-113-120',\n",
-                            " 'node_ip': '172.31.113.120',\n",
-                            " 'config': {'lr': 0.0034759400828981743,\n",
-                            "  'momentum': 0.99,\n",
+                            " 'trial_id': '6e465_00007',\n",
+                            " 'date': '2023-08-25_17-42-39',\n",
+                            " 'time_this_iter_s': 0.08028697967529297,\n",
+                            " 'time_total_s': 8.77775764465332,\n",
+                            " 'pid': 94910,\n",
+                            " 'node_ip': '127.0.0.1',\n",
+                            " 'config': {'lr': 0.009781335971854077,\n",
+                            "  'momentum': 0.9,\n",
                             "  'should_checkpoint': True},\n",
-                            " 'time_since_restore': 29.587023496627808,\n",
-                            " 'timesteps_since_restore': 0,\n",
+                            " 'time_since_restore': 8.77775764465332,\n",
                             " 'iterations_since_restore': 100,\n",
-                            " 'warmup_time': 0.003263711929321289,\n",
-                            " 'experiment_tag': '5_lr=0.0035,momentum=0.9900'}"
+                            " 'checkpoint_dir_name': 'checkpoint_000099',\n",
+                            " 'experiment_tag': '7_lr=0.0098,momentum=0.9000'}"
                         ]
                     },
-                    "execution_count": 13,
+                    "execution_count": 15,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -649,7 +776,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 14,
+            "execution_count": 16,
             "id": "ca87204f",
             "metadata": {},
             "outputs": [
@@ -683,32 +810,32 @@
                             "    <tr>\n",
                             "      <th>0</th>\n",
                             "      <td>1</td>\n",
-                            "      <td>0.121875</td>\n",
-                            "      <td>1.874643</td>\n",
+                            "      <td>0.168750</td>\n",
+                            "      <td>0.111393</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>1</th>\n",
                             "      <td>2</td>\n",
-                            "      <td>0.340625</td>\n",
-                            "      <td>2.110028</td>\n",
+                            "      <td>0.609375</td>\n",
+                            "      <td>0.195086</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>2</th>\n",
                             "      <td>3</td>\n",
-                            "      <td>0.321875</td>\n",
-                            "      <td>2.332039</td>\n",
+                            "      <td>0.800000</td>\n",
+                            "      <td>0.283543</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>3</th>\n",
                             "      <td>4</td>\n",
-                            "      <td>0.521875</td>\n",
-                            "      <td>2.621943</td>\n",
+                            "      <td>0.840625</td>\n",
+                            "      <td>0.388538</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>4</th>\n",
                             "      <td>5</td>\n",
-                            "      <td>0.684375</td>\n",
-                            "      <td>2.958664</td>\n",
+                            "      <td>0.840625</td>\n",
+                            "      <td>0.479402</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>...</th>\n",
@@ -719,32 +846,32 @@
                             "    <tr>\n",
                             "      <th>95</th>\n",
                             "      <td>96</td>\n",
-                            "      <td>0.953125</td>\n",
-                            "      <td>28.516581</td>\n",
+                            "      <td>0.946875</td>\n",
+                            "      <td>8.415694</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>96</th>\n",
                             "      <td>97</td>\n",
-                            "      <td>0.959375</td>\n",
-                            "      <td>28.819717</td>\n",
+                            "      <td>0.943750</td>\n",
+                            "      <td>8.524299</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>97</th>\n",
                             "      <td>98</td>\n",
-                            "      <td>0.934375</td>\n",
-                            "      <td>29.085851</td>\n",
+                            "      <td>0.956250</td>\n",
+                            "      <td>8.606126</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>98</th>\n",
                             "      <td>99</td>\n",
-                            "      <td>0.968750</td>\n",
-                            "      <td>29.356515</td>\n",
+                            "      <td>0.934375</td>\n",
+                            "      <td>8.697471</td>\n",
                             "    </tr>\n",
                             "    <tr>\n",
                             "      <th>99</th>\n",
                             "      <td>100</td>\n",
-                            "      <td>0.971875</td>\n",
-                            "      <td>29.587023</td>\n",
+                            "      <td>0.965625</td>\n",
+                            "      <td>8.777758</td>\n",
                             "    </tr>\n",
                             "  </tbody>\n",
                             "</table>\n",
@@ -753,22 +880,22 @@
                         ],
                         "text/plain": [
                             "    training_iteration  mean_accuracy  time_total_s\n",
-                            "0                    1       0.121875      1.874643\n",
-                            "1                    2       0.340625      2.110028\n",
-                            "2                    3       0.321875      2.332039\n",
-                            "3                    4       0.521875      2.621943\n",
-                            "4                    5       0.684375      2.958664\n",
+                            "0                    1       0.168750      0.111393\n",
+                            "1                    2       0.609375      0.195086\n",
+                            "2                    3       0.800000      0.283543\n",
+                            "3                    4       0.840625      0.388538\n",
+                            "4                    5       0.840625      0.479402\n",
                             "..                 ...            ...           ...\n",
-                            "95                  96       0.953125     28.516581\n",
-                            "96                  97       0.959375     28.819717\n",
-                            "97                  98       0.934375     29.085851\n",
-                            "98                  99       0.968750     29.356515\n",
-                            "99                 100       0.971875     29.587023\n",
+                            "95                  96       0.946875      8.415694\n",
+                            "96                  97       0.943750      8.524299\n",
+                            "97                  98       0.956250      8.606126\n",
+                            "98                  99       0.934375      8.697471\n",
+                            "99                 100       0.965625      8.777758\n",
                             "\n",
                             "[100 rows x 3 columns]"
                         ]
                     },
-                    "execution_count": 14,
+                    "execution_count": 16,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -791,23 +918,23 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 15,
+            "execution_count": 17,
             "id": "1ff489ec",
             "metadata": {},
             "outputs": [
                 {
                     "data": {
                         "text/plain": [
-                            "<AxesSubplot: xlabel='training_iteration'>"
+                            "<AxesSubplot:xlabel='training_iteration'>"
                         ]
                     },
-                    "execution_count": 15,
+                    "execution_count": 17,
                     "metadata": {},
                     "output_type": "execute_result"
                 },
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -831,7 +958,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 16,
+            "execution_count": 18,
             "id": "54b78da6",
             "metadata": {},
             "outputs": [
@@ -841,13 +968,13 @@
                             "Text(0, 0.5, 'Mean Test Accuracy')"
                         ]
                     },
-                    "execution_count": 16,
+                    "execution_count": 18,
                     "metadata": {},
                     "output_type": "execute_result"
                 },
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -876,25 +1003,26 @@
             "source": [
                 "## Accessing checkpoints and loading for test inference\n",
                 "\n",
-                "We noticed earlier that `Result` contains the last checkpoint associated with a trial. Let's see how we can use this checkpoint to load a model for performing inference on some sample MNIST images.\n",
-                "\n",
-                "If you are running a Tune experiment with Ray AIR Trainers, the checkpoints saved may be framework-specific checkpoints such as `LegacyTorchCheckpoint`. Refer to [documentation on framework-specific integrations](air-trainer-ref) to learn how to load from these types of checkpoints."
+                "We saw earlier that `Result` contains the last checkpoint associated with a trial. Let's see how we can use this checkpoint to load a model for performing inference on some sample MNIST images."
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": 17,
+            "execution_count": 19,
             "id": "50d3acff",
             "metadata": {},
             "outputs": [],
             "source": [
-                "from ray.train.torch import LegacyTorchCheckpoint, TorchPredictor\n",
+                "import torch\n",
+                "\n",
                 "from ray.tune.examples.mnist_pytorch import ConvNet, get_data_loaders\n",
                 "\n",
-                "checkpoint: LegacyTorchCheckpoint = best_result.checkpoint\n",
+                "model = ConvNet()\n",
                 "\n",
-                "# Create a Predictor using the best result's checkpoint\n",
-                "predictor = TorchPredictor.from_checkpoint(checkpoint, ConvNet())"
+                "with best_result.checkpoint.as_directory() as checkpoint_dir:\n",
+                "    # The model state dict was saved under `model.pt` by the training function\n",
+                "    # imported from `ray.tune.examples.mnist_pytorch`\n",
+                "    model.load_state_dict(torch.load(os.path.join(checkpoint_dir, \"model.pt\")))"
             ]
         },
         {
@@ -910,7 +1038,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 18,
+            "execution_count": 21,
             "id": "eb8f6942",
             "metadata": {},
             "outputs": [
@@ -918,12 +1046,22 @@
                     "name": "stdout",
                     "output_type": "stream",
                     "text": [
-                        "Predicted Class = 4\n"
+                        "Predicted Class = 9\n"
                     ]
                 },
                 {
                     "data": {
-                        "image/png": "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",
+                        "text/plain": [
+                            "<matplotlib.image.AxesImage at 0x31ddd2fd0>"
+                        ]
+                    },
+                    "execution_count": 21,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                },
+                {
+                    "data": {
+                        "image/png": "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",
                         "text/plain": [
                             "<Figure size 200x200 with 1 Axes>"
                         ]
@@ -934,17 +1072,17 @@
             ],
             "source": [
                 "import matplotlib.pyplot as plt\n",
-                "import numpy as np\n",
                 "\n",
                 "_, test_loader = get_data_loaders()\n",
                 "test_img = next(iter(test_loader))[0][0]\n",
+                "\n",
+                "predicted_class = torch.argmax(model(test_img)).item()\n",
+                "print(\"Predicted Class =\", predicted_class)\n",
+                "\n",
                 "# Need to reshape to (batch_size, channels, width, height)\n",
                 "test_img = test_img.numpy().reshape((1, 1, 28, 28))\n",
                 "plt.figure(figsize=(2, 2))\n",
-                "plt.imshow(test_img.reshape((28, 28)))\n",
-                "\n",
-                "predicted_class = np.argmax(predictor.predict(test_img))\n",
-                "print(\"Predicted Class =\", predicted_class)"
+                "plt.imshow(test_img.reshape((28, 28)))\n"
             ]
         },
         {
diff --git a/doc/source/tune/examples/tune_mnist_keras.ipynb b/doc/source/tune/examples/tune_mnist_keras.ipynb
index 271defbccf3fe..962775887379b 100644
--- a/doc/source/tune/examples/tune_mnist_keras.ipynb
+++ b/doc/source/tune/examples/tune_mnist_keras.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3b05af3b",
    "metadata": {},
@@ -1244,9 +1245,9 @@
     "from tensorflow.keras.datasets import mnist\n",
     "\n",
     "import ray\n",
-    "from ray import air, tune\n",
+    "from ray import train, tune\n",
     "from ray.tune.schedulers import AsyncHyperBandScheduler\n",
-    "from ray.tune.integration.keras import TuneReportCallback\n",
+    "from ray.air.integrations.keras import ReportCheckpointCallback\n",
     "\n",
     "\n",
     "def train_mnist(config):\n",
@@ -1282,11 +1283,11 @@
     "        epochs=epochs,\n",
     "        verbose=0,\n",
     "        validation_data=(x_test, y_test),\n",
-    "        callbacks=[TuneReportCallback({\"mean_accuracy\": \"accuracy\"})],\n",
+    "        callbacks=[ReportCheckpointCallback(metrics={\"mean_accuracy\": \"accuracy\"})],\n",
     "    )\n",
     "\n",
     "\n",
-    "def tune_mnist(num_training_iterations):\n",
+    "def tune_mnist():\n",
     "    sched = AsyncHyperBandScheduler(\n",
     "        time_attr=\"training_iteration\", max_t=400, grace_period=20\n",
     "    )\n",
@@ -1299,9 +1300,9 @@
     "            scheduler=sched,\n",
     "            num_samples=10,\n",
     "        ),\n",
-    "        run_config=air.RunConfig(\n",
+    "        run_config=train.RunConfig(\n",
     "            name=\"exp\",\n",
-    "            stop={\"mean_accuracy\": 0.99, \"training_iteration\": num_training_iterations},\n",
+    "            stop={\"mean_accuracy\": 0.99},\n",
     "        ),\n",
     "        param_space={\n",
     "            \"threads\": 2,\n",
@@ -1314,20 +1315,11 @@
     "\n",
     "    print(\"Best hyperparameters found were: \", results.get_best_result().config)\n",
     "\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    parser = argparse.ArgumentParser()\n",
-    "    parser.add_argument(\n",
-    "        \"--smoke-test\", action=\"store_true\", help=\"Finish quickly for testing\"\n",
-    "    )\n",
-    "    args, _ = parser.parse_known_args()\n",
-    "    if args.smoke_test:\n",
-    "        ray.init(num_cpus=4)\n",
-    "\n",
-    "    tune_mnist(num_training_iterations=5 if args.smoke_test else 300)\n"
+    "tune_mnist()\n"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "d7e46189",
    "metadata": {},
diff --git a/doc/source/tune/tutorials/tune-trial-checkpoints.rst b/doc/source/tune/tutorials/tune-trial-checkpoints.rst
index 2e4257e1e9175..d0f0d992d802c 100644
--- a/doc/source/tune/tutorials/tune-trial-checkpoints.rst
+++ b/doc/source/tune/tutorials/tune-trial-checkpoints.rst
@@ -27,21 +27,10 @@ To create a checkpoint, one can either use :meth:`~ray.train.Checkpoint.from_dic
     checkpoint is synced to driver node or the cloud. We are planning to work on it to address the
     issue.
 
-.. tab-set::
-
-    .. tab-item:: Checkpoint a dictionary
-
-        .. literalinclude:: /tune/doc_code/trial_checkpoint.py
-            :language: python
-            :start-after: __function_api_checkpointing_start__
-            :end-before: __function_api_checkpointing_end__
-
-    .. tab-item:: Checkpoint a directory
-
-        .. literalinclude:: /tune/doc_code/trial_checkpoint.py
-            :language: python
-            :start-after: __function_api_checkpointing_from_dir_start__
-            :end-before: __function_api_checkpointing_from_dir_end__
+.. literalinclude:: /tune/doc_code/trial_checkpoint.py
+    :language: python
+    :start-after: __function_api_checkpointing_from_dir_start__
+    :end-before: __function_api_checkpointing_from_dir_end__
 
 In the above code snippet:
 
@@ -63,8 +52,6 @@ In the above code snippet:
 
 
 See :class:`here for more information on creating checkpoints <ray.train.Checkpoint>`.
-If using framework-specific trainers from Ray AIR, see :ref:`here <air-trainer-ref>` for
-references to framework-specific checkpoints such as `TensorflowCheckpoint`.
 
 
 .. _tune-class-trainable-checkpointing:
diff --git a/python/ray/train/base_trainer.py b/python/ray/train/base_trainer.py
index 8fd2964ef7a97..7240fb8af2e2e 100644
--- a/python/ray/train/base_trainer.py
+++ b/python/ray/train/base_trainer.py
@@ -257,8 +257,8 @@ def restore(
             from ray.train.trainer import BaseTrainer
 
             experiment_name = "unique_experiment_name"
-            local_dir = "~/ray_results"
-            experiment_dir = os.path.join(local_dir, experiment_name)
+            storage_path = os.path.expanduser("~/ray_results")
+            experiment_dir = os.path.join(storage_path, experiment_name)
 
             # Define some dummy inputs for demonstration purposes
             datasets = {"train": ray.data.from_items([{"a": i} for i in range(10)])}
@@ -269,15 +269,14 @@ def training_loop(self):
 
             if CustomTrainer.can_restore(experiment_dir):
                 trainer = CustomTrainer.restore(
-                    experiment_dir,
-                    datasets=datasets,
+                    experiment_dir, datasets=datasets
                 )
             else:
                 trainer = CustomTrainer(
                     datasets=datasets,
                     run_config=train.RunConfig(
                         name=experiment_name,
-                        local_dir=local_dir,
+                        storage_path=storage_path,
                         # Tip: You can also enable retries on failure for
                         # worker-level fault tolerance
                         failure_config=train.FailureConfig(max_failures=3),
diff --git a/python/ray/tune/BUILD b/python/ray/tune/BUILD
index 9801d1e53477f..92268ceb9529b 100644
--- a/python/ray/tune/BUILD
+++ b/python/ray/tune/BUILD
@@ -655,7 +655,7 @@ py_test(
     size = "small",
     srcs = ["examples/mnist_pytorch.py"],
     deps = [":tune_lib"],
-    tags = ["team:ml", "exclusive", "example", "pytorch"],
+    tags = ["team:ml", "exclusive", "example", "pytorch", "new_storage"],
     args = ["--smoke-test"]
 )
 
@@ -754,7 +754,7 @@ py_test(
     size = "medium",
     srcs = ["examples/pbt_dcgan_mnist/pbt_dcgan_mnist_func.py"],
     deps = [":tune_lib"],
-    tags = ["team:ml", "exclusive", "example"],
+    tags = ["team:ml", "exclusive", "example", "new_storage"],
     args = ["--smoke-test"]
 )
 
@@ -763,7 +763,7 @@ py_test(
     size = "medium",
     srcs = ["examples/pbt_dcgan_mnist/pbt_dcgan_mnist_trainable.py"],
     deps = [":tune_lib"],
-    tags = ["team:ml", "exclusive", "example"],
+    tags = ["team:ml", "exclusive", "example", "new_storage"],
     args = ["--smoke-test"]
 )
 
diff --git a/python/ray/tune/examples/mnist_pytorch.py b/python/ray/tune/examples/mnist_pytorch.py
index d599e581dd3e6..6a2752287fb57 100644
--- a/python/ray/tune/examples/mnist_pytorch.py
+++ b/python/ray/tune/examples/mnist_pytorch.py
@@ -3,6 +3,7 @@
 import os
 import argparse
 from filelock import FileLock
+import tempfile
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -11,7 +12,7 @@
 
 import ray
 from ray import train, tune
-from ray.train.torch import LegacyTorchCheckpoint
+from ray.train import Checkpoint
 from ray.tune.schedulers import AsyncHyperBandScheduler
 
 # Change these values if you want the training to run quicker or slower.
@@ -104,11 +105,15 @@ def train_mnist(config):
     while True:
         train_func(model, optimizer, train_loader, device)
         acc = test_func(model, test_loader, device)
-        checkpoint = None
+        metrics = {"mean_accuracy": acc}
+
+        # Report metrics (and possibly a checkpoint)
         if should_checkpoint:
-            checkpoint = LegacyTorchCheckpoint.from_state_dict(model.state_dict())
-        # Report metrics (and possibly a checkpoint) to Tune
-        train.report({"mean_accuracy": acc}, checkpoint=checkpoint)
+            with tempfile.TemporaryDirectory() as tempdir:
+                torch.save(model.state_dict(), os.path.join(tempdir, "model.pt"))
+                train.report(metrics, checkpoint=Checkpoint.from_directory(tempdir))
+        else:
+            train.report(metrics)
 
 
 if __name__ == "__main__":
@@ -150,3 +155,5 @@ def train_mnist(config):
     results = tuner.fit()
 
     print("Best config is:", results.get_best_result().config)
+
+    assert not results.errors
diff --git a/python/ray/tune/examples/pbt_dcgan_mnist/common.py b/python/ray/tune/examples/pbt_dcgan_mnist/common.py
index bf2b21eed9224..791a5aac3c4af 100644
--- a/python/ray/tune/examples/pbt_dcgan_mnist/common.py
+++ b/python/ray/tune/examples/pbt_dcgan_mnist/common.py
@@ -1,5 +1,4 @@
 import ray
-from ray.train import Checkpoint
 
 import os
 import torch
@@ -269,7 +268,8 @@ def demo_gan(checkpoint_paths):
     img_list = []
     fixed_noise = torch.randn(64, nz, 1, 1)
     for path in checkpoint_paths:
-        checkpoint_dict = Checkpoint.from_directory(path).to_dict()
+        checkpoint_dict = torch.load(os.path.join(path, "checkpoint.pt"))
+
         loadedG = Generator()
         loadedG.load_state_dict(checkpoint_dict["netGmodel"])
         with torch.no_grad():
diff --git a/python/ray/tune/examples/pbt_dcgan_mnist/pbt_dcgan_mnist_func.py b/python/ray/tune/examples/pbt_dcgan_mnist/pbt_dcgan_mnist_func.py
index e58408dbe24e3..89956b00b486a 100644
--- a/python/ray/tune/examples/pbt_dcgan_mnist/pbt_dcgan_mnist_func.py
+++ b/python/ray/tune/examples/pbt_dcgan_mnist/pbt_dcgan_mnist_func.py
@@ -10,6 +10,7 @@
 import argparse
 import os
 from filelock import FileLock
+import tempfile
 import torch
 import torch.nn as nn
 import torch.nn.parallel
@@ -50,8 +51,10 @@ def dcgan_train(config):
         dataloader = get_data_loader()
 
     step = 1
-    if train.get_checkpoint():
-        checkpoint_dict = train.get_checkpoint().to_dict()
+    checkpoint = train.get_checkpoint()
+    if checkpoint:
+        with checkpoint.as_directory() as checkpoint_dir:
+            checkpoint_dict = torch.load(os.path.join(checkpoint_dir, "checkpoint.pt"))
         netD.load_state_dict(checkpoint_dict["netDmodel"])
         netG.load_state_dict(checkpoint_dict["netGmodel"])
         optimizerD.load_state_dict(checkpoint_dict["optimD"])
@@ -84,21 +87,24 @@ def dcgan_train(config):
             device,
             config["mnist_model_ref"],
         )
-        checkpoint = None
+        metrics = {"lossg": lossG, "lossd": lossD, "is_score": is_score}
+
         if step % config["checkpoint_interval"] == 0:
-            checkpoint = Checkpoint.from_dict(
-                {
-                    "netDmodel": netD.state_dict(),
-                    "netGmodel": netG.state_dict(),
-                    "optimD": optimizerD.state_dict(),
-                    "optimG": optimizerG.state_dict(),
-                    "step": step,
-                }
-            )
-        train.report(
-            {"lossg": lossG, "lossd": lossD, "is_score": is_score},
-            checkpoint=checkpoint,
-        )
+            with tempfile.TemporaryDirectory() as tmpdir:
+                torch.save(
+                    {
+                        "netDmodel": netD.state_dict(),
+                        "netGmodel": netG.state_dict(),
+                        "optimD": optimizerD.state_dict(),
+                        "optimG": optimizerG.state_dict(),
+                        "step": step,
+                    },
+                    os.path.join(tmpdir, "checkpoint.pt"),
+                )
+                train.report(metrics, checkpoint=Checkpoint.from_directory(tmpdir))
+        else:
+            train.report(metrics)
+
         step += 1
 
 
diff --git a/python/ray/tune/trainable/trainable.py b/python/ray/tune/trainable/trainable.py
index 63a512a71cb70..f9968e8183fcc 100644
--- a/python/ray/tune/trainable/trainable.py
+++ b/python/ray/tune/trainable/trainable.py
@@ -477,8 +477,11 @@ def _create_checkpoint_dir(
         if _use_storage_context():
             # NOTE: There's no need to supply the checkpoint directory inside
             # the local trial dir, since it'll get persisted to the right location.
-            checkpoint_dir = tempfile.mkdtemp()
-            return checkpoint_dir
+            if checkpoint_dir:
+                os.makedirs(checkpoint_dir, exist_ok=True)
+                return checkpoint_dir
+            else:
+                return tempfile.mkdtemp()
 
         # Create checkpoint_xxxxx directory and drop checkpoint marker
         checkpoint_dir = TrainableUtil.make_checkpoint_dir(
@@ -533,6 +536,8 @@ def save(
 
                 local_checkpoint = NewCheckpoint.from_directory(checkpoint_dir)
 
+                metrics = self._last_result.copy() if self._last_result else {}
+
                 if self._storage:
                     persisted_checkpoint = self._storage.persist_current_checkpoint(
                         local_checkpoint
@@ -543,8 +548,7 @@ def save(
                     self._storage.current_checkpoint_index += 1
 
                     checkpoint_result = _TrainingResult(
-                        checkpoint=persisted_checkpoint,
-                        metrics=self._last_result.copy(),
+                        checkpoint=persisted_checkpoint, metrics=metrics
                     )
                     # Persist trial artifacts to storage.
                     self._storage.persist_artifacts(
@@ -557,7 +561,7 @@ def save(
                     # is to just not upload anything and report a local checkpoint.
                     # This is fine for the main use case of local debugging.
                     checkpoint_result = _TrainingResult(
-                        checkpoint=local_checkpoint, metrics=self._last_result.copy()
+                        checkpoint=local_checkpoint, metrics=metrics
                     )
             else:
                 checkpoint_result: _TrainingResult = checkpoint_dict_or_path
diff --git a/python/ray/tune/tuner.py b/python/ray/tune/tuner.py
index 88ff6c2416dfe..e27d33de5bc62 100644
--- a/python/ray/tune/tuner.py
+++ b/python/ray/tune/tuner.py
@@ -294,22 +294,22 @@ def train_fn(config):
                 pass
 
             name = "exp_name"
-            local_dir = "~/ray_results"
-            exp_dir = os.path.join(local_dir, name)
+            storage_path = os.path.expanduser("~/ray_results")
+            exp_dir = os.path.join(storage_path, name)
 
             if Tuner.can_restore(exp_dir):
                 tuner = Tuner.restore(exp_dir, trainable=train_fn, resume_errored=True)
             else:
                 tuner = Tuner(
                     train_fn,
-                    run_config=RunConfig(name=name, local_dir=local_dir),
+                    run_config=RunConfig(name=name, storage_path=storage_path),
                 )
             tuner.fit()
 
         Args:
             path: The path to the experiment directory of the Tune experiment.
-                This can be either a local directory (e.g. ~/ray_results/exp_name)
-                or a remote URI (e.g. s3://bucket/exp_name).
+                This can be either a local directory or a remote URI
+                (e.g. s3://bucket/exp_name).
 
         Returns:
             bool: True if this path exists and contains the Tuner state to resume from
@@ -351,7 +351,15 @@ def fit(self) -> ResultGrid:
 
         .. code-block:: python
 
-            tuner = Tuner.restore("~/ray_results/tuner_resume", trainable=trainable)
+            import os
+            from ray.tune import Tuner
+
+            trainable = ...
+
+            tuner = Tuner.restore(
+                os.path.expanduser("~/ray_results/tuner_resume"),
+                trainable=trainable
+            )
             tuner.fit()
 
         Raises: