From 71eb15a5eab6f21e3dd4d132082f5ddc51c4e6dd Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Thu, 23 Jun 2022 15:38:29 -0700
Subject: [PATCH 01/28] [air] use session.report.

---
 README.rst                                    |   4 +-
 doc/source/ray-contribute/docs.ipynb          |   4 +-
 doc/source/ray-overview/doc_test/ray_tune.py  |   3 +-
 doc/source/tune/api_docs/env.rst              |   2 +-
 doc/source/tune/api_docs/schedulers.rst       |   2 +-
 doc/source/tune/api_docs/trainable.rst        |  57 ++++----
 doc/source/tune/doc_code/faq.py               |   7 +-
 doc/source/tune/doc_code/key_concepts.py      |   3 +-
 doc/source/tune/doc_code/pytorch_optuna.py    |   3 +-
 doc/source/tune/examples/ax_example.ipynb     |   9 +-
 .../tune/examples/bayesopt_example.ipynb      |   7 +-
 doc/source/tune/examples/bohb_example.ipynb   |   5 +-
 .../tune/examples/dragonfly_example.ipynb     |   5 +-
 doc/source/tune/examples/flaml_example.ipynb  |   4 +-
 doc/source/tune/examples/hebo_example.ipynb   |   6 +-
 .../tune/examples/hyperopt_example.ipynb      |   7 +-
 .../tune/examples/lightgbm_example.ipynb      |   7 +-
 .../tune/examples/nevergrad_example.ipynb     |   5 +-
 doc/source/tune/examples/optuna_example.ipynb |  12 +-
 doc/source/tune/examples/sigopt_example.ipynb |   9 +-
 doc/source/tune/examples/skopt_example.ipynb  |   5 +-
 doc/source/tune/examples/tune-comet.ipynb     |   3 +-
 doc/source/tune/examples/tune-mlflow.ipynb    |   5 +-
 .../tune/examples/tune-pytorch-cifar.ipynb    |  29 ++--
 .../examples/tune-pytorch-lightning.ipynb     | 126 +++++++++++++-----
 .../tune-serve-integration-mnist.ipynb        |  46 ++++---
 doc/source/tune/examples/tune-wandb.ipynb     |   5 +-
 doc/source/tune/examples/tune-xgboost.ipynb   |   5 +-
 doc/source/tune/examples/zoopt_example.ipynb  |   5 +-
 doc/source/tune/faq.rst                       |  20 ++-
 doc/source/tune/getting-started.rst           |   2 +-
 doc/source/tune/key-concepts.rst              |   8 +-
 .../tune/tutorials/tune-checkpoints.rst       |   8 +-
 doc/source/tune/tutorials/tune-lifecycle.rst  |   2 +-
 doc/source/tune/tutorials/tune-metrics.rst    |  11 +-
 doc/source/tune/tutorials/tune-output.rst     |  10 +-
 doc/source/tune/tutorials/tune-stopping.rst   |   2 +-
 python/ray/air/examples/custom_trainer.py     |   4 +-
 python/ray/train/base_trainer.py              |   7 +-
 python/ray/train/data_parallel_trainer.py     |   3 +-
 .../tune/examples/async_hyperband_example.py  |   3 +-
 python/ray/tune/examples/ax_example.py        |   9 +-
 python/ray/tune/examples/bayesopt_example.py  |   3 +-
 .../ray/tune/examples/blendsearch_example.py  |   3 +-
 python/ray/tune/examples/cfo_example.py       |   3 +-
 python/ray/tune/examples/cifar10_pytorch.py   |  38 +++---
 .../examples/custom_func_checkpointing.py     |  32 ++---
 python/ray/tune/examples/dragonfly_example.py |   3 +-
 python/ray/tune/examples/genetic_example.py   |   3 +-
 python/ray/tune/examples/hebo_example.py      |   3 +-
 .../examples/hyperband_function_example.py    |  10 +-
 ...peropt_conditional_search_space_example.py |   3 +-
 python/ray/tune/examples/logging_example.py   |   3 +-
 python/ray/tune/examples/mlflow_example.py    |   5 +-
 python/ray/tune/examples/mnist_pytorch.py     |   3 +-
 .../tune/examples/mnist_pytorch_lightning.py  |   4 +-
 python/ray/tune/examples/nevergrad_example.py |   3 +-
 .../examples/optuna_define_by_run_example.py  |   3 +-
 python/ray/tune/examples/optuna_example.py    |   3 +-
 .../examples/optuna_multiobjective_example.py |   9 +-
 .../examples/pbt_convnet_function_example.py  |  46 ++++---
 .../pbt_dcgan_mnist/pbt_dcgan_mnist_func.py   |  51 ++++---
 python/ray/tune/examples/pbt_function.py      |  38 +++---
 python/ray/tune/examples/sigopt_example.py    |   3 +-
 .../sigopt_multi_objective_example.py         |   3 +-
 .../examples/sigopt_prior_beliefs_example.py  |   3 +-
 python/ray/tune/examples/skopt_example.py     |   3 +-
 .../ray/tune/examples/tune_basic_example.py   |   3 +-
 .../ray/tune/examples/tune_cifar10_gluon.py   |   3 +-
 python/ray/tune/examples/wandb_example.py     |   5 +-
 .../xgboost_dynamic_resources_example.py      |   5 +-
 python/ray/tune/examples/zoopt_example.py     |   3 +-
 python/ray/tune/session.py                    |  33 +++++
 python/ray/tune/utils/trainable.py            |   3 +-
 74 files changed, 490 insertions(+), 325 deletions(-)

diff --git a/README.rst b/README.rst
index 8cf4a608e034..03a06664e776 100644
--- a/README.rst
+++ b/README.rst
@@ -118,7 +118,7 @@ This example runs a parallel grid search to optimize an example objective functi
 
 .. code-block:: python
 
-    from ray import tune
+    from ray.air import session
 
 
     def objective(step, alpha, beta):
@@ -132,7 +132,7 @@ This example runs a parallel grid search to optimize an example objective functi
             # Iterative training function - can be any arbitrary training procedure.
             intermediate_score = objective(step, alpha, beta)
             # Feed the score back back to Tune.
-            tune.report(mean_loss=intermediate_score)
+            session.report({"mean_loss": intermediate_score})
 
 
     analysis = tune.run(
diff --git a/doc/source/ray-contribute/docs.ipynb b/doc/source/ray-contribute/docs.ipynb
index 106fce761785..e18864d31171 100644
--- a/doc/source/ray-contribute/docs.ipynb
+++ b/doc/source/ray-contribute/docs.ipynb
@@ -189,7 +189,7 @@
    "outputs": [],
    "source": [
     "# __function_api_start__\n",
-    "from ray import tune\n",
+    "from ray.air import session\n",
     "\n",
     "\n",
     "def objective(x, a, b):  # Define an objective function.\n",
@@ -201,7 +201,7 @@
     "    for x in range(20):  # \"Train\" for 20 iterations and compute intermediate scores.\n",
     "        score = objective(x, config[\"a\"], config[\"b\"])\n",
     "\n",
-    "        tune.report(score=score)  # Send the score to Tune.\n",
+    "        session.report({\"score\": score})  # Send the score to Tune.\n",
     "\n",
     "\n",
     "# __function_api_end__"
diff --git a/doc/source/ray-overview/doc_test/ray_tune.py b/doc/source/ray-overview/doc_test/ray_tune.py
index 0b4efffe7909..28df86a2c73c 100644
--- a/doc/source/ray-overview/doc_test/ray_tune.py
+++ b/doc/source/ray-overview/doc_test/ray_tune.py
@@ -1,4 +1,5 @@
 from ray import tune
+from ray.air import session
 
 
 def objective(step, alpha, beta):
@@ -12,7 +13,7 @@ def training_function(config):
         # Iterative training function - can be any arbitrary training procedure.
         intermediate_score = objective(step, alpha, beta)
         # Feed the score back back to Tune.
-        tune.report(mean_loss=intermediate_score)
+        session.report({"mean_loss": intermediate_score})
 
 
 analysis = tune.run(
diff --git a/doc/source/tune/api_docs/env.rst b/doc/source/tune/api_docs/env.rst
index 041cc740a707..73151ea5ffde 100644
--- a/doc/source/tune/api_docs/env.rst
+++ b/doc/source/tune/api_docs/env.rst
@@ -24,7 +24,7 @@ These are the environment variables Ray Tune currently considers:
   directories when the name is not specified explicitly or the trainable isn't passed
   as a string. Setting this environment variable to ``1`` disables adding these date strings.
 * **TUNE_DISABLE_STRICT_METRIC_CHECKING**: When you report metrics to Tune via
-  ``tune.report()`` and passed a ``metric`` parameter to ``tune.run()``, a scheduler,
+  ``session.report()`` and passed a ``metric`` parameter to ``tune.run()``, a scheduler,
   or a search algorithm, Tune will error
   if the metric was not reported in the result. Setting this environment variable
   to ``1`` will disable this check.
diff --git a/doc/source/tune/api_docs/schedulers.rst b/doc/source/tune/api_docs/schedulers.rst
index 07e4405097db..8abd694209ec 100644
--- a/doc/source/tune/api_docs/schedulers.rst
+++ b/doc/source/tune/api_docs/schedulers.rst
@@ -245,7 +245,7 @@ It wraps around another scheduler and uses its decisions.
     by calling ``Trainable.trial_resources``.
 
 * If you are using the functional API for tuning, the current trial resources can be
-    obtained by calling `tune.get_trial_resources()` inside the training function.
+    obtained by calling `session.get_trial_resources()` inside the training function.
     The function should be able to :ref:`load and save checkpoints <tune-checkpoint-syncing>`
     (the latter preferably every iteration).
 
diff --git a/doc/source/tune/api_docs/trainable.rst b/doc/source/tune/api_docs/trainable.rst
index 8ed3302101cc..26733583fd39 100644
--- a/doc/source/tune/api_docs/trainable.rst
+++ b/doc/source/tune/api_docs/trainable.rst
@@ -4,10 +4,10 @@
     API does not really have a signature to just describe.
 .. TODO: Reusing actors and advanced resources allocation seem ill-placed.
 
-Training (tune.Trainable, tune.report)
+Training (tune.Trainable, session.report)
 ======================================
 
-Training can be done with either a **Class API** (``tune.Trainable``) or **function API** (``tune.report``).
+Training can be done with either a **Class API** (``tune.Trainable``) or **function API** (``session.report``).
 
 For the sake of example, let's maximize this objective function:
 
@@ -21,7 +21,7 @@ For the sake of example, let's maximize this objective function:
 Function API
 ------------
 
-With the Function API, you can report intermediate metrics by simply calling ``tune.report`` within the provided function.
+With the Function API, you can report intermediate metrics by simply calling ``session.report`` within the provided function.
 
 .. code-block:: python
 
@@ -31,7 +31,7 @@ With the Function API, you can report intermediate metrics by simply calling ``t
         for x in range(20):
             intermediate_score = objective(x, config["a"], config["b"])
 
-            tune.report(score=intermediate_score)  # This sends the score to Tune.
+            session.report({"score": intermediate_score})  # This sends the score to Tune.
 
     analysis = tune.run(
         trainable,
@@ -40,7 +40,7 @@ With the Function API, you can report intermediate metrics by simply calling ``t
 
     print("best config: ", analysis.get_best_config(metric="score", mode="max"))
 
-.. tip:: Do not use ``tune.report`` within a ``Trainable`` class.
+.. tip:: Do not use ``session.report`` within a ``Trainable`` class.
 
 Tune will run this function on a separate thread in a Ray actor process.
 
@@ -49,7 +49,7 @@ such as ``iterations_since_restore``. See :ref:`tune-autofilled-metrics` for an
 
 Function API return and yield values
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Instead of using ``tune.report()``, you can also use Python's ``yield``
+Instead of using ``session.report()``, you can also use Python's ``yield``
 statement to report metrics to Ray Tune:
 
 
@@ -70,9 +70,9 @@ statement to report metrics to Ray Tune:
 
     print("best config: ", analysis.get_best_config(metric="score", mode="max"))
 
-If you yield a dictionary object, this will work just as ``tune.report()``.
+If you yield a dictionary object, this will work just as ``session.report()``.
 If you yield a number, if will be reported to Ray Tune with the key ``_metric``, i.e.
-as if you had called ``tune.report(_metric=value)``.
+as if you had called ``session.report({"_metric": value})``.
 
 Ray Tune supports the same functionality for return values if you only
 report metrics at the end of each run:
@@ -102,30 +102,27 @@ Function API Checkpointing
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Many Tune features rely on checkpointing, including the usage of certain Trial Schedulers and fault tolerance.
-To use Tune's checkpointing features, you must expose a ``checkpoint_dir`` argument in the function signature,
-and call ``tune.checkpoint_dir`` :
+You can save and load checkpoint in Ray Tune in the following manner:
 
 .. code-block:: python
 
         import time
         from ray import tune
+        from ray.air import session
+        from ray.air.checkpoint import Checkpoint
 
-        def train_func(config, checkpoint_dir=None):
-            start = 0
-            if checkpoint_dir:
-                with open(os.path.join(checkpoint_dir, "checkpoint")) as f:
-                    state = json.loads(f.read())
-                    start = state["step"] + 1
+        def train_func(config):
+            step = 0
+            loaded_checkpoint = session.get_checkpoint()
+            if loaded_checkpoint:
+                last_step = loaded_checkpoint.to_dict()["step"]
+                step = last_step + 1
 
-            for iter in range(start, 100):
+            for iter in range(step, 100):
                 time.sleep(1)
 
-                with tune.checkpoint_dir(step=step) as checkpoint_dir:
-                    path = os.path.join(checkpoint_dir, "checkpoint")
-                    with open(path, "w") as f:
-                        f.write(json.dumps({"step": start}))
-
-                tune.report(hello="world", ray="tune")
+                checkpoint = Checkpoint.from_dict({"step": step})
+                session.report({"message": "Hello world Ray Tune!"}, checkpoint=checkpoint)
 
         tune.run(train_func)
 
@@ -153,7 +150,7 @@ it is important not to depend on absolute paths in the implementation of ``save`
 Trainable Class API
 -------------------
 
-.. caution:: Do not use ``tune.report`` within a ``Trainable`` class.
+.. caution:: Do not use ``session.report`` within a ``Trainable`` class.
 
 The Trainable **class API** will require users to subclass ``ray.tune.Trainable``. Here's a naive example of this API:
 
@@ -343,18 +340,18 @@ It is also possible to specify memory (``"memory"``, in bytes) and custom resour
 
 .. _tune-function-docstring:
 
-tune.report / tune.checkpoint (Function API)
+session (Function API)
 --------------------------------------------
 
-.. autofunction:: ray.tune.report
+.. autofunction:: ray.air.session.report
 
-.. autofunction:: ray.tune.checkpoint_dir
+.. autofunction:: ray.air.session.get_checkpoint
 
-.. autofunction:: ray.tune.get_trial_dir
+.. autofunction:: ray.air.session.get_trial_name
 
-.. autofunction:: ray.tune.get_trial_name
+.. autofunction:: ray.air.session.get_trial_id
 
-.. autofunction:: ray.tune.get_trial_id
+.. autofunction:: ray.air.session.get_trial_resources
 
 tune.Trainable (Class API)
 --------------------------
diff --git a/doc/source/tune/doc_code/faq.py b/doc/source/tune/doc_code/faq.py
index 3a2b0c4e3e7e..9fbecb9d88ab 100644
--- a/doc/source/tune/doc_code/faq.py
+++ b/doc/source/tune/doc_code/faq.py
@@ -3,6 +3,7 @@
 # __reproducible_start__
 import numpy as np
 from ray import tune
+from ray.air import session
 
 
 def train(config):
@@ -12,7 +13,7 @@ def train(config):
     # is the same.
     np.random.seed(config["seed"])
     random_result = np.random.uniform(0, 100, size=1).item()
-    tune.report(result=random_result)
+    session.report({"result": random_result})
 
 
 # Set seed for Ray Tune's random search.
@@ -54,7 +55,7 @@ def _iter():
 
 def train(config):
     random_result = np.random.uniform(0, 100, size=1).item()
-    tune.report(result=random_result)
+    session.report({"result": random_result})
 
 
 train_fn = train
@@ -90,7 +91,7 @@ def train(config):
     def train_fn(config, checkpoint_dir=None):
         # some Modin operations here
         # import modin.pandas as pd
-        tune.report(metric=metric)
+        session.report({"metric": metric})
 
     tune.run(
         train_fn,
diff --git a/doc/source/tune/doc_code/key_concepts.py b/doc/source/tune/doc_code/key_concepts.py
index 96e302ebb446..13bf2ee9c1f2 100644
--- a/doc/source/tune/doc_code/key_concepts.py
+++ b/doc/source/tune/doc_code/key_concepts.py
@@ -2,6 +2,7 @@
 
 # __function_api_start__
 from ray import tune
+from ray.air import session
 
 
 def objective(x, a, b):  # Define an objective function.
@@ -13,7 +14,7 @@ def trainable(config):  # Pass a "config" dictionary into your trainable.
     for x in range(20):  # "Train" for 20 iterations and compute intermediate scores.
         score = objective(x, config["a"], config["b"])
 
-        tune.report(score=score)  # Send the score to Tune.
+        session.report({"score": score})  # Send the score to Tune.
 
 
 # __function_api_end__
diff --git a/doc/source/tune/doc_code/pytorch_optuna.py b/doc/source/tune/doc_code/pytorch_optuna.py
index 76fbe1c22193..d96be9d01c9c 100644
--- a/doc/source/tune/doc_code/pytorch_optuna.py
+++ b/doc/source/tune/doc_code/pytorch_optuna.py
@@ -81,6 +81,7 @@ def forward(self, x):
 # 1. Wrap your PyTorch model in an objective function.
 import torch
 from ray import tune
+from ray.air import session
 from ray.tune.suggest.optuna import OptunaSearch
 
 
@@ -95,7 +96,7 @@ def objective(config):
     while True:
         train(model, optimizer, train_loader)  # Train the model
         acc = test(model, test_loader)  # Compute test accuracy
-        tune.report(mean_accuracy=acc)  # Report to Tune
+        session.report({"mean_accuracy": acc})  # Report to Tune
 
 
 # 2. Define a search space and initialize the search algorithm.
diff --git a/doc/source/tune/examples/ax_example.ipynb b/doc/source/tune/examples/ax_example.ipynb
index 307aaed42e68..977f881a7a1f 100644
--- a/doc/source/tune/examples/ax_example.ipynb
+++ b/doc/source/tune/examples/ax_example.ipynb
@@ -54,6 +54,7 @@
     "\n",
     "import ray\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest.ax import AxSearch"
    ]
   },
@@ -112,7 +113,7 @@
    "metadata": {},
    "source": [
     "Next, our `objective` function takes a Tune `config`, evaluates the `landscape` of our experiment in a training loop,\n",
-    "and uses `tune.report` to report the `landscape` back to Tune."
+    "and uses `session.report` to report the `landscape` back to Tune."
    ]
   },
   {
@@ -125,8 +126,8 @@
     "def objective(config):\n",
     "    for i in range(config[\"iterations\"]):\n",
     "        x = np.array([config.get(\"x{}\".format(i + 1)) for i in range(6)])\n",
-    "        tune.report(\n",
-    "            timesteps_total=i, landscape=landscape(x), l2norm=np.sqrt((x ** 2).sum())\n",
+    "        session.report(\n",
+    "            {\"timesteps_total\": i, \"landscape\": landscape(x), \"l2norm\": np.sqrt((x ** 2).sum()})\n",
     "        )\n",
     "        time.sleep(0.02)"
    ]
@@ -250,7 +251,7 @@
    "id": "91076c5a",
    "metadata": {},
    "source": [
-    "Finally, we run the experiment to find the global minimum of the provided landscape (which contains 5 false minima). The argument to metric, `\"landscape\"`, is provided via the `objective` function's `tune.report`. The experiment `\"min\"`imizes the \"mean_loss\" of the `landscape` by searching within `search_space` via `algo`, `num_samples` times or when `\"timesteps_total\": stop_timesteps`. This previous sentence is fully characterizes the search problem we aim to solve. With this in mind, notice how efficient it is to execute `tune.run()`."
+    "Finally, we run the experiment to find the global minimum of the provided landscape (which contains 5 false minima). The argument to metric, `\"landscape\"`, is provided via the `objective` function's `session.report`. The experiment `\"min\"`imizes the \"mean_loss\" of the `landscape` by searching within `search_space` via `algo`, `num_samples` times or when `\"timesteps_total\": stop_timesteps`. This previous sentence is fully characterizes the search problem we aim to solve. With this in mind, notice how efficient it is to execute `tune.run()`."
    ]
   },
   {
diff --git a/doc/source/tune/examples/bayesopt_example.ipynb b/doc/source/tune/examples/bayesopt_example.ipynb
index 7a4d0493ad87..4a7dd084da0f 100644
--- a/doc/source/tune/examples/bayesopt_example.ipynb
+++ b/doc/source/tune/examples/bayesopt_example.ipynb
@@ -51,8 +51,9 @@
     "import time\n",
     "\n",
     "import ray\n",
-    "from ray import tune\n",
     "from ray.tune.suggest import ConcurrencyLimiter\n",
+    "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest.bayesopt import BayesOptSearch"
    ]
   },
@@ -85,7 +86,7 @@
    "metadata": {},
    "source": [
     "Next, our ``objective`` function takes a Tune ``config``, evaluates the `score` of your experiment in a training loop,\n",
-    "and uses `tune.report` to report the `score` back to Tune."
+    "and uses `session.report` to report the `score` back to Tune."
    ]
   },
   {
@@ -98,7 +99,7 @@
     "def objective(config):\n",
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"])\n",
-    "        tune.report(iterations=step, mean_loss=score)"
+    "        session.report({\"iterations\": step, \"mean_loss\": score})"
    ]
   },
   {
diff --git a/doc/source/tune/examples/bohb_example.ipynb b/doc/source/tune/examples/bohb_example.ipynb
index dea0ce1cdd0b..39ccf171e294 100644
--- a/doc/source/tune/examples/bohb_example.ipynb
+++ b/doc/source/tune/examples/bohb_example.ipynb
@@ -70,6 +70,7 @@
     "\n",
     "import ray\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest import ConcurrencyLimiter\n",
     "from ray.tune.schedulers.hb_bohb import HyperBandForBOHB\n",
     "from ray.tune.suggest.bohb import TuneBOHB\n",
@@ -106,7 +107,7 @@
    "metadata": {},
    "source": [
     "Next, our `objective` function takes a Tune `config`, evaluates the `score` of your\n",
-    "experiment in a training loop, and uses `tune.report` to report the `score` back to Tune."
+    "experiment in a training loop, and uses `session.report` to report the `score` back to Tune."
    ]
   },
   {
@@ -119,7 +120,7 @@
     "def objective(config):\n",
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
-    "        tune.report(iterations=step, mean_loss=score)"
+    "        session.report({\"iterations\": step, \"mean_loss\": score})"
    ]
   },
   {
diff --git a/doc/source/tune/examples/dragonfly_example.ipynb b/doc/source/tune/examples/dragonfly_example.ipynb
index 6779a3eaf79f..3c0eeea2f3a7 100644
--- a/doc/source/tune/examples/dragonfly_example.ipynb
+++ b/doc/source/tune/examples/dragonfly_example.ipynb
@@ -76,6 +76,7 @@
     "\n",
     "import ray\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest import ConcurrencyLimiter\n",
     "from ray.tune.suggest.dragonfly import DragonflySearch"
    ]
@@ -96,7 +97,7 @@
     "4 ingredients-- these proportions should be considered as hyperparameters.\n",
     "Our `objective` function will take a Tune `config`, evaluates the `conductivity` of\n",
     "our experiment in a training loop,\n",
-    "and uses `tune.report` to report the `conductivity` back to Tune."
+    "and uses `session.report` to report the `conductivity` back to Tune."
    ]
   },
   {
@@ -118,7 +119,7 @@
     "        vol4 = 10 - (vol1 + vol2 + vol3)  # Water\n",
     "        conductivity = vol1 + 0.1 * (vol2 + vol3) ** 2 + 2.3 * vol4 * (vol1 ** 1.5)\n",
     "        conductivity += np.random.normal() * 0.01\n",
-    "        tune.report(timesteps_total=i, objective=conductivity)\n",
+    "        session.report({\"timesteps_total\": i, \"objective\": conductivity})\n",
     "        time.sleep(0.02)"
    ]
   },
diff --git a/doc/source/tune/examples/flaml_example.ipynb b/doc/source/tune/examples/flaml_example.ipynb
index 307c40c80b4a..b516f1608384 100644
--- a/doc/source/tune/examples/flaml_example.ipynb
+++ b/doc/source/tune/examples/flaml_example.ipynb
@@ -91,7 +91,7 @@
    "metadata": {},
    "source": [
     "Next, our `objective` function takes a Tune `config`, evaluates the `score` of your\n",
-    "experiment in a training loop, and uses `tune.report` to report the `score` back to Tune."
+    "experiment in a training loop, and uses `session.report` to report the `score` back to Tune."
    ]
   },
   {
@@ -104,7 +104,7 @@
     "def objective(config):\n",
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
-    "        tune.report(iterations=step, mean_loss=score)"
+    "        session.report({\"iterations\": step, \"mean_loss\": score})"
    ]
   },
   {
diff --git a/doc/source/tune/examples/hebo_example.ipynb b/doc/source/tune/examples/hebo_example.ipynb
index fa3e10c6be32..e041b37436d9 100644
--- a/doc/source/tune/examples/hebo_example.ipynb
+++ b/doc/source/tune/examples/hebo_example.ipynb
@@ -53,7 +53,7 @@
     "import time\n",
     "\n",
     "import ray\n",
-    "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest.hebo import HEBOSearch"
    ]
   },
@@ -87,7 +87,7 @@
    "metadata": {},
    "source": [
     "Next, our ``objective`` function takes a Tune ``config``, evaluates the `score` of your experiment in a training loop,\n",
-    "and uses `tune.report` to report the `score` back to Tune."
+    "and uses `session.report` to report the `score` back to Tune."
    ]
   },
   {
@@ -100,7 +100,7 @@
     "def objective(config):\n",
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
-    "        tune.report(iterations=step, mean_loss=score)"
+    "        session.report({\"iterations\": step, \"mean_loss\": score})"
    ]
   },
   {
diff --git a/doc/source/tune/examples/hyperopt_example.ipynb b/doc/source/tune/examples/hyperopt_example.ipynb
index bf7e16ae5771..eb62d75ee4de 100644
--- a/doc/source/tune/examples/hyperopt_example.ipynb
+++ b/doc/source/tune/examples/hyperopt_example.ipynb
@@ -69,6 +69,7 @@
     "\n",
     "import ray\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest import ConcurrencyLimiter\n",
     "from ray.tune.suggest.hyperopt import HyperOptSearch\n",
     "from hyperopt import hp"
@@ -103,7 +104,7 @@
    "metadata": {},
    "source": [
     "Next, our ``objective`` function takes a Tune ``config``, evaluates the `score` of your experiment in a training loop,\n",
-    "and uses `tune.report` to report the `score` back to Tune."
+    "and uses `session.report` to report the `score` back to Tune."
    ]
   },
   {
@@ -116,7 +117,7 @@
     "def objective(config):\n",
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"])\n",
-    "        tune.report(iterations=step, mean_loss=score)"
+    "        session.report({\"iterations\": step, \"mean_loss\": score})"
    ]
   },
   {
@@ -298,7 +299,7 @@
     "    \n",
     "    for step in range(config[\"steps\"]):\n",
     "        intermediate_score = evaluation_fn(step, width, height, mult)\n",
-    "        tune.report(iterations=step, mean_loss=intermediate_score)\n",
+    "        session.report({\"iterations\": step, \"mean_loss\": intermediate_score})\n",
     "        time.sleep(0.1)"
    ]
   },
diff --git a/doc/source/tune/examples/lightgbm_example.ipynb b/doc/source/tune/examples/lightgbm_example.ipynb
index 0faa3eaec9d4..79da6f7fcc9c 100644
--- a/doc/source/tune/examples/lightgbm_example.ipynb
+++ b/doc/source/tune/examples/lightgbm_example.ipynb
@@ -37,6 +37,7 @@
     "from sklearn.model_selection import train_test_split\n",
     "\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.schedulers import ASHAScheduler\n",
     "from ray.tune.integration.lightgbm import TuneReportCheckpointCallback\n",
     "\n",
@@ -64,9 +65,9 @@
     "    )\n",
     "    preds = gbm.predict(test_x)\n",
     "    pred_labels = np.rint(preds)\n",
-    "    tune.report(\n",
-    "        mean_accuracy=sklearn.metrics.accuracy_score(test_y, pred_labels), done=True\n",
-    "    )\n",
+    "    session.report({\n",
+    "        \"mean_accuracy\": sklearn.metrics.accuracy_score(test_y, pred_labels), \"done\": True\n",
+    "    })\n",
     "\n",
     "\n",
     "if __name__ == \"__main__\":\n",
diff --git a/doc/source/tune/examples/nevergrad_example.ipynb b/doc/source/tune/examples/nevergrad_example.ipynb
index feeaa431b8c5..624b241d5f9f 100644
--- a/doc/source/tune/examples/nevergrad_example.ipynb
+++ b/doc/source/tune/examples/nevergrad_example.ipynb
@@ -55,6 +55,7 @@
     "import ray\n",
     "import nevergrad as ng\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest import ConcurrencyLimiter\n",
     "from ray.tune.suggest.nevergrad import NevergradSearch"
    ]
@@ -89,7 +90,7 @@
    "metadata": {},
    "source": [
     "Next, our `objective` function takes a Tune `config`, evaluates the `score` of your experiment in a training loop,\n",
-    "and uses `tune.report` to report the `score` back to Tune."
+    "and uses `session.report` to report the `score` back to Tune."
    ]
   },
   {
@@ -102,7 +103,7 @@
     "def objective(config):\n",
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
-    "        tune.report(iterations=step, mean_loss=score)"
+    "        session.report({\"iterations\": step, \"mean_loss\": score})"
    ]
   },
   {
diff --git a/doc/source/tune/examples/optuna_example.ipynb b/doc/source/tune/examples/optuna_example.ipynb
index 4e9369e60f7b..10d6a28e3dbc 100644
--- a/doc/source/tune/examples/optuna_example.ipynb
+++ b/doc/source/tune/examples/optuna_example.ipynb
@@ -57,6 +57,7 @@
     "\n",
     "import ray\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest import ConcurrencyLimiter\n",
     "from ray.tune.suggest.optuna import OptunaSearch"
    ]
@@ -106,7 +107,7 @@
    "metadata": {},
    "source": [
     "Next, our `objective` function to be optimized takes a Tune `config`, evaluates the `score` of your experiment in a training loop,\n",
-    "and uses `tune.report` to report the `score` back to Tune."
+    "and uses `session.report` to report the `score` back to Tune."
    ]
   },
   {
@@ -121,8 +122,7 @@
     "def objective(config):\n",
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
-    "        tune.report(iterations=step, mean_loss=score)\n",
-    "              "
+    "        session.report({\"iterations\": step, \"mean_loss\": score})\n",
    ]
   },
   {
@@ -483,9 +483,9 @@
     "        # Iterative training function - can be any arbitrary training procedure\n",
     "        intermediate_score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
     "        # Feed the score back back to Tune.\n",
-    "        tune.report(\n",
-    "           iterations=step, loss=intermediate_score, gain=intermediate_score * width\n",
-    "        )"
+    "        session.report({\n",
+    "           \"iterations\": step, \"loss\": intermediate_score, \"gain\": intermediate_score * width\n",
+    "        })"
    ]
   },
   {
diff --git a/doc/source/tune/examples/sigopt_example.ipynb b/doc/source/tune/examples/sigopt_example.ipynb
index 89c4b4f00dd5..31f7200b4aaf 100644
--- a/doc/source/tune/examples/sigopt_example.ipynb
+++ b/doc/source/tune/examples/sigopt_example.ipynb
@@ -54,6 +54,7 @@
     "import ray\n",
     "import numpy as np\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest.sigopt import SigOptSearch\n",
     "\n",
     "if \"SIGOPT_KEY\" not in os.environ:\n",
@@ -93,7 +94,7 @@
    "metadata": {},
    "source": [
     "Next, our ``objective`` function takes a Tune ``config``, evaluates the `score` of your experiment in a training loop,\n",
-    "and uses `tune.report` to report the `score` back to Tune."
+    "and uses `session.report` to report the `score` back to Tune."
    ]
   },
   {
@@ -106,7 +107,7 @@
     "def objective(config):\n",
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
-    "        tune.report(iterations=step, mean_loss=score)"
+    "        session.report({\"iterations\": step, \"mean_loss\": score})"
    ]
   },
   {
@@ -306,7 +307,7 @@
     "    w2 = config[\"total_weight\"] - w1\n",
     "    \n",
     "    average, std = evaluate(w1, w2)\n",
-    "    tune.report(average=average, std=std, sharpe=average / std)\n",
+    "    session.report({\"average\": average, \"std\": std, \"sharpe\": average / std})\n",
     "    time.sleep(0.1)"
    ]
   },
@@ -425,7 +426,7 @@
     "    w3 = 1 - total\n",
     "    \n",
     "    average, std = evaluate(w1, w2, w3)\n",
-    "    tune.report(average=average, std=std)"
+    "    session.report({\"average\": average, \"std\": std})"
    ]
   },
   {
diff --git a/doc/source/tune/examples/skopt_example.ipynb b/doc/source/tune/examples/skopt_example.ipynb
index 1278a5c3ac5d..600f1593f618 100644
--- a/doc/source/tune/examples/skopt_example.ipynb
+++ b/doc/source/tune/examples/skopt_example.ipynb
@@ -57,6 +57,7 @@
     "\n",
     "import skopt\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest import ConcurrencyLimiter\n",
     "from ray.tune.suggest.skopt import SkOptSearch"
    ]
@@ -102,7 +103,7 @@
    "metadata": {},
    "source": [
     "Next, our `objective` function to be optimized takes a Tune `config`, evaluates the `score` of your experiment in a training loop,\n",
-    "and uses `tune.report` to report the `score` back to Tune."
+    "and uses `session.report` to report the `score` back to Tune."
    ]
   },
   {
@@ -115,7 +116,7 @@
     "def objective(config):\n",
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
-    "        tune.report(iterations=step, mean_loss=score)"
+    "        session.report({\"iterations\": step, \"mean_loss\": score})"
    ]
   },
   {
diff --git a/doc/source/tune/examples/tune-comet.ipynb b/doc/source/tune/examples/tune-comet.ipynb
index 8ad25a18dc6b..6f458cb2d00f 100644
--- a/doc/source/tune/examples/tune-comet.ipynb
+++ b/doc/source/tune/examples/tune-comet.ipynb
@@ -40,12 +40,13 @@
    "source": [
     "import numpy as np\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "\n",
     "\n",
     "def train_function(config, checkpoint_dir=None):\n",
     "    for i in range(30):\n",
     "        loss = config[\"mean\"] + config[\"sd\"] * np.random.randn()\n",
-    "        tune.report(loss=loss)"
+    "        session.report({\"loss\": loss})"
    ]
   },
   {
diff --git a/doc/source/tune/examples/tune-mlflow.ipynb b/doc/source/tune/examples/tune-mlflow.ipynb
index 9e5a71140fb9..7d79a1ce5931 100644
--- a/doc/source/tune/examples/tune-mlflow.ipynb
+++ b/doc/source/tune/examples/tune-mlflow.ipynb
@@ -62,6 +62,7 @@
     "import mlflow\n",
     "\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.integration.mlflow import MLflowLoggerCallback, mlflow_mixin"
    ]
   },
@@ -94,7 +95,7 @@
     "        # Iterative training function - can be any arbitrary training procedure\n",
     "        intermediate_score = evaluation_fn(step, width, height)\n",
     "        # Feed the score back to Tune.\n",
-    "        tune.report(iterations=step, mean_loss=intermediate_score)\n",
+    "        session.report({\"iterations\": step, \"mean_loss\": intermediate_score})\n",
     "        time.sleep(0.1)"
    ],
    "metadata": {
@@ -175,7 +176,7 @@
     "        # Log the metrics to mlflow\n",
     "        mlflow.log_metrics(dict(mean_loss=intermediate_score), step=step)\n",
     "        # Feed the score back to Tune.\n",
-    "        tune.report(iterations=step, mean_loss=intermediate_score)\n",
+    "        session.report({\"iterations\": step, \"mean_loss\": intermediate_score})\n",
     "        time.sleep(0.1)"
    ],
    "metadata": {
diff --git a/doc/source/tune/examples/tune-pytorch-cifar.ipynb b/doc/source/tune/examples/tune-pytorch-cifar.ipynb
index b871b4d5f3fb..ab8aa84cac9e 100644
--- a/doc/source/tune/examples/tune-pytorch-cifar.ipynb
+++ b/doc/source/tune/examples/tune-pytorch-cifar.ipynb
@@ -74,6 +74,8 @@
     "import torchvision.transforms as transforms\n",
     "import ray\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
+    "from ray.air.checkpoint import Checkpoint\n",
     "from ray.tune.schedulers import ASHAScheduler"
    ]
   },
@@ -177,7 +179,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def train_cifar(config, checkpoint_dir=None):\n",
+    "def train_cifar(config):\n",
     "    net = Net(config[\"l1\"], config[\"l2\"])\n",
     "\n",
     "    device = \"cpu\"\n",
@@ -190,11 +192,11 @@
     "    criterion = nn.CrossEntropyLoss()\n",
     "    optimizer = optim.SGD(net.parameters(), lr=config[\"lr\"], momentum=0.9)\n",
     "\n",
-    "    # The `checkpoint_dir` parameter gets passed by Ray Tune when a checkpoint\n",
-    "    # should be restored.\n",
-    "    if checkpoint_dir:\n",
-    "        checkpoint = os.path.join(checkpoint_dir, \"checkpoint\")\n",
-    "        model_state, optimizer_state = torch.load(checkpoint)\n",
+    "    # To restore a checkpoint, use `session.get_checkpoint()`.\n",
+    "    loaded_checkpoint = session.get_checkpoint()\n",
+    "    if loaded_checkpoint:\n",
+    "        with loaded_checkpoint.as_directory() as loaded_checkpoint_dir:\n",
+    "           model_state, optimizer_state = torch.load(os.path.join(loaded_checkpoint_dir, \"checkpoint.pt\"))\n",
     "        net.load_state_dict(model_state)\n",
     "        optimizer.load_state_dict(optimizer_state)\n",
     "\n",
@@ -261,14 +263,13 @@
     "                val_steps += 1\n",
     "\n",
     "        # Here we save a checkpoint. It is automatically registered with\n",
-    "        # Ray Tune and will potentially be passed as the `checkpoint_dir`\n",
-    "        # parameter in future iterations.\n",
-    "        with tune.checkpoint_dir(step=epoch) as checkpoint_dir:\n",
-    "            path = os.path.join(checkpoint_dir, \"checkpoint\")\n",
-    "            torch.save(\n",
-    "                (net.state_dict(), optimizer.state_dict()), path)\n",
-    "\n",
-    "        tune.report(loss=(val_loss / val_steps), accuracy=correct / total)\n",
+    "        # Ray Tune and can be accessed through `session.get_checkpoint()`\n",
+    "        # API in future iterations.\n",
+    "        os.makedirs(\"my_model\", exist_ok=True)\n",
+    "        torch.save(\n",
+    "            (net.state_dict(), optimizer.state_dict()), \"my_model/checkpoint.pt\")\n",
+    "        checkpoint = Checkpoint.from_directory(\"my_model\")\n",
+    "        session.report({\"loss\": (val_loss / val_steps), \"accuracy\": correct / total}, checkpoint=checkpoint)\n",
     "    print(\"Finished Training\")"
    ]
   },
diff --git a/doc/source/tune/examples/tune-pytorch-lightning.ipynb b/doc/source/tune/examples/tune-pytorch-lightning.ipynb
index f46654ce055f..cd8b95e3611b 100644
--- a/doc/source/tune/examples/tune-pytorch-lightning.ipynb
+++ b/doc/source/tune/examples/tune-pytorch-lightning.ipynb
@@ -59,7 +59,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "e6e77570",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "import math\n",
@@ -92,7 +96,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "48b20f48",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "class LightningMNISTClassifier(pl.LightningModule):\n",
@@ -209,7 +217,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "86df3d39",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "def train_mnist_no_tune():\n",
@@ -243,11 +255,16 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "34faeb3b",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "from pytorch_lightning.loggers import TensorBoardLogger\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune import CLIReporter\n",
     "from ray.tune.schedulers import ASHAScheduler, PopulationBasedTraining\n",
     "from ray.tune.integration.pytorch_lightning import TuneReportCallback, \\\n",
@@ -274,7 +291,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "4bab80bc",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "TuneReportCallback(\n",
@@ -312,7 +333,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "74e7d1c2",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "def train_mnist_tune(config, num_epochs=10, num_gpus=0, data_dir=\"~/data\"):\n",
@@ -323,7 +348,7 @@
     "        # If fractional GPUs passed in, convert to int.\n",
     "        gpus=math.ceil(num_gpus),\n",
     "        logger=TensorBoardLogger(\n",
-    "            save_dir=tune.get_trial_dir(), name=\"\", version=\".\"),\n",
+    "            save_dir=os.getcwd(), name=\"\", version=\".\"),\n",
     "        enable_progress_bar=False,\n",
     "        callbacks=[\n",
     "            TuneReportCallback(\n",
@@ -354,7 +379,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "a50645e9",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "config = {\n",
@@ -382,7 +411,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "a2596b01",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "num_epochs = 10\n",
@@ -409,7 +442,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "cd605a16",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "reporter = CLIReporter(\n",
@@ -433,7 +470,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "332668dc",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "gpus_per_trial = 0\n",
@@ -465,7 +506,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "dc402716",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "resources_per_trial = {\"cpu\": 1, \"gpu\": gpus_per_trial}"
@@ -500,7 +545,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "ea182330",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "def tune_mnist_asha(num_samples=10, num_epochs=10, gpus_per_trial=0, data_dir=\"~/data\"):\n",
@@ -586,7 +635,7 @@
     "\n",
     "First, we need to introduce\n",
     "another callback to save model checkpoints. Since Tune requires a call to\n",
-    "`tune.report()` after creating a new checkpoint to register it, we will use\n",
+    "`session.report()` after creating a new checkpoint to register it, we will use\n",
     "a combined reporting and checkpointing callback:"
    ]
   },
@@ -594,7 +643,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "7f86e4d8",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "TuneReportCheckpointCallback(\n",
@@ -621,7 +674,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "746e962a",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "def train_mnist_tune_checkpoint(config,\n",
@@ -635,7 +692,7 @@
     "        # If fractional GPUs passed in, convert to int.\n",
     "        \"gpus\": math.ceil(num_gpus),\n",
     "        \"logger\": TensorBoardLogger(\n",
-    "            save_dir=tune.get_trial_dir(), name=\"\", version=\".\"),\n",
+    "            save_dir=os.getcwd(), name=\"\", version=\".\"),\n",
     "        \"enable_progress_bar\": False,\n",
     "        \"callbacks\": [\n",
     "            TuneReportCheckpointCallback(\n",
@@ -672,7 +729,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "e12a1bd5",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "def tune_mnist_pbt(num_samples=10, num_epochs=10, gpus_per_trial=0, data_dir=\"~/data\"):\n",
@@ -733,22 +794,31 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
     "data_dir = \"~/data/\"\n",
     "\n",
     "tune_mnist_asha(num_samples=1, num_epochs=6, gpus_per_trial=0, data_dir=data_dir)\n",
     "tune_mnist_pbt(num_samples=1, num_epochs=6, gpus_per_trial=0, data_dir=data_dir)"
-   ],
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": false,
     "pycharm": {
-     "name": "#%%\n"
+     "name": "#%% md\n"
     }
-   }
-  },
-  {
-   "cell_type": "markdown",
+   },
    "source": [
     "If you have more resources available (e.g. a GPU), you can modify the above parameters accordingly.\n",
     "\n",
@@ -790,13 +860,7 @@
     "- {ref}`A walkthrough tutorial for using Ray Tune with Pytorch-Lightning <tune-pytorch-lightning-ref>`.\n",
     "- {doc}`/tune/examples/includes/mlflow_ptl_example`: Example for using [MLflow](https://github.com/mlflow/mlflow/)\n",
     "  and [Pytorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) with Ray Tune."
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
+   ]
   }
  ],
  "metadata": {
diff --git a/doc/source/tune/examples/tune-serve-integration-mnist.ipynb b/doc/source/tune/examples/tune-serve-integration-mnist.ipynb
index d3dad824c173..fc2ae4f0fb3d 100644
--- a/doc/source/tune/examples/tune-serve-integration-mnist.ipynb
+++ b/doc/source/tune/examples/tune-serve-integration-mnist.ipynb
@@ -150,6 +150,8 @@
     "import torch.optim as optim\n",
     "import ray\n",
     "from ray import tune, serve\n",
+    "from ray.air import session\n",
+    "from ray.air.checkpoint import Checkpoint\n",
     "from ray.serve.exceptions import RayServeException\n",
     "from ray.tune import CLIReporter\n",
     "from ray.tune.schedulers import ASHAScheduler\n",
@@ -315,7 +317,6 @@
     "def train_mnist(\n",
     "    config,\n",
     "    start_model=None,\n",
-    "    checkpoint_dir=None,\n",
     "    num_epochs=10,\n",
     "    use_gpus=False,\n",
     "    data_fn=None,\n",
@@ -333,17 +334,21 @@
     "\n",
     "    # Load checkpoint, or load start model if no checkpoint has been\n",
     "    # passed and a start model is specified\n",
-    "    load_dir = None\n",
-    "    if checkpoint_dir:\n",
-    "        load_dir = checkpoint_dir\n",
-    "    elif start_model:\n",
-    "        load_dir = start_model\n",
-    "\n",
-    "    if load_dir:\n",
-    "        model_state, optimizer_state = torch.load(os.path.join(load_dir, \"checkpoint\"))\n",
+    "    \n",
+    "    model_state = None\n",
+    "    optimizer_state = None\n",
+    "\n",
+    "    if start_model:  # load start model if provided\n",
+    "        model_state, optimizer_state = torch.load(os.path.join(load_dir, \"checkpoint.pt\"))\n",
+    "    elif session.get_checkpoint():  # load from previous checkpoint\n",
+    "        with session.get_checkpoint().as_directory() as loaded_checkpoint_dir:\n",
+    "            model_state, optimizer_state = torch.load(os.path.join(loaded_checkpoint_dir, \"checkpoint.pt\"))\n",
+    "\n",
+    "    if model_state: \n",
     "        model.load_state_dict(model_state)\n",
     "        optimizer.load_state_dict(optimizer_state)\n",
     "\n",
+    "\n",
     "    # Get full training datasets\n",
     "    train_dataset, validation_dataset = data_fn(day=day)\n",
     "\n",
@@ -359,14 +364,15 @@
     "        train(model, optimizer, train_loader, device)\n",
     "        acc = test(model, validation_loader, device)\n",
     "        if i == num_epochs - 1:\n",
-    "            with tune.checkpoint_dir(step=i) as checkpoint_dir:\n",
-    "                torch.save(\n",
-    "                    (model.state_dict(), optimizer.state_dict()),\n",
-    "                    os.path.join(checkpoint_dir, \"checkpoint\"),\n",
-    "                )\n",
-    "            tune.report(mean_accuracy=acc, done=True)\n",
+    "            os.makedirs(\"my_model\", exist_ok=True)\n",
+    "            torch.save(\n",
+    "                (model.state_dict(), optimizer.state_dict()),\n",
+    "                os.path.join(\"my_model/checkpoint.pt\"),\n",
+    "            )\n",
+    "            checkpoint = Checkpoint.from_directory(\"my_model\")\n",
+    "            session.report({\"mean_accuracy\": acc, \"done\": True}, checkpoint=checkpoint)\n",
     "        else:\n",
-    "            tune.report(mean_accuracy=acc)"
+    "            session.report({\"mean_accuracy\": acc})"
    ]
   },
   {
@@ -559,7 +565,7 @@
     "        model = ConvNet(layer_size=self.config[\"layer_size\"]).to(self.device)\n",
     "\n",
     "        model_state, optimizer_state = torch.load(\n",
-    "            os.path.join(self.checkpoint_dir, \"checkpoint\"), map_location=self.device\n",
+    "            os.path.join(self.checkpoint_dir, \"checkpoint.pt\"), map_location=self.device\n",
     "        )\n",
     "        model.load_state_dict(model_state)\n",
     "\n",
@@ -608,7 +614,7 @@
     "    move the checkpoint to a shared storage, like Amazon S3, instead.\"\"\"\n",
     "    os.makedirs(model_dir, 0o755, exist_ok=True)\n",
     "\n",
-    "    checkpoint_path = os.path.join(model_dir, \"checkpoint\")\n",
+    "    checkpoint_path = os.path.join(model_dir, \"checkpoint.pt\")\n",
     "    meta_path = os.path.join(model_dir, \"meta.json\")\n",
     "\n",
     "    if os.path.exists(checkpoint_path):\n",
@@ -641,7 +647,7 @@
    "outputs": [],
    "source": [
     "def get_current_model(model_dir):\n",
-    "    checkpoint_path = os.path.join(model_dir, \"checkpoint\")\n",
+    "    checkpoint_path = os.path.join(model_dir, \"checkpoint.pt\")\n",
     "    meta_path = os.path.join(model_dir, \"meta.json\")\n",
     "\n",
     "    if not os.path.exists(checkpoint_path) or not os.path.exists(meta_path):\n",
@@ -874,4 +880,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/doc/source/tune/examples/tune-wandb.ipynb b/doc/source/tune/examples/tune-wandb.ipynb
index b430dcb6f506..5470a4c2add9 100644
--- a/doc/source/tune/examples/tune-wandb.ipynb
+++ b/doc/source/tune/examples/tune-wandb.ipynb
@@ -54,6 +54,7 @@
     "import wandb\n",
     "\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune import Trainable\n",
     "from ray.tune.integration.wandb import (\n",
     "    WandbLoggerCallback,\n",
@@ -81,7 +82,7 @@
     "def objective(config, checkpoint_dir=None):\n",
     "    for i in range(30):\n",
     "        loss = config[\"mean\"] + config[\"sd\"] * np.random.randn()\n",
-    "        tune.report(loss=loss)"
+    "        session.report({\"loss\": loss})"
    ],
    "metadata": {
     "collapsed": false,
@@ -148,7 +149,7 @@
     "def decorated_objective(config, checkpoint_dir=None):\n",
     "    for i in range(30):\n",
     "        loss = config[\"mean\"] + config[\"sd\"] * np.random.randn()\n",
-    "        tune.report(loss=loss)\n",
+    "        session.report({\"loss\": loss})\n",
     "        wandb.log(dict(loss=loss))"
    ],
    "metadata": {
diff --git a/doc/source/tune/examples/tune-xgboost.ipynb b/doc/source/tune/examples/tune-xgboost.ipynb
index e15163dbfd74..5cbf566c70fc 100644
--- a/doc/source/tune/examples/tune-xgboost.ipynb
+++ b/doc/source/tune/examples/tune-xgboost.ipynb
@@ -345,7 +345,7 @@
     "         verbose_eval=False)\n",
     "     # Return prediction accuracy\n",
     "     accuracy = 1. - results[\"eval\"][\"error\"][-1]\n",
-    "     tune.report(mean_accuracy=accuracy, done=True)\n",
+    "     session.report({\"mean_accuracy\": accuracy, \"done\": True})\n",
     "\n",
     "\n",
     "if __name__ == \"__main__\":\n",
@@ -374,7 +374,7 @@
    "cell_type": "markdown",
    "source": [
     "As you can see, the changes in the actual training function are minimal. Instead of\n",
-    "returning the accuracy value, we report it back to Tune using `tune.report()`.\n",
+    "returning the accuracy value, we report it back to Tune using `session.report()`.\n",
     "Our `config` dictionary only changed slightly. Instead of passing hard-coded\n",
     "parameters, we tell Tune to choose values from a range of valid options. There are\n",
     "a number of options we have here, all of which are explained in\n",
@@ -485,6 +485,7 @@
     "import xgboost as xgb\n",
     "\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.integration.xgboost import TuneReportCheckpointCallback\n",
     "\n",
     "\n",
diff --git a/doc/source/tune/examples/zoopt_example.ipynb b/doc/source/tune/examples/zoopt_example.ipynb
index 0619608b3d98..d58f5f68ed43 100644
--- a/doc/source/tune/examples/zoopt_example.ipynb
+++ b/doc/source/tune/examples/zoopt_example.ipynb
@@ -52,6 +52,7 @@
     "\n",
     "import ray\n",
     "from ray import tune\n",
+    "from ray.air import session\n",
     "from ray.tune.suggest.zoopt import ZOOptSearch\n",
     "from zoopt import ValueType"
    ]
@@ -85,7 +86,7 @@
    "metadata": {},
    "source": [
     "Next, our ``objective`` function takes a Tune ``config``, evaluates the `score` of your experiment in a training loop,\n",
-    "and uses `tune.report` to report the `score` back to Tune."
+    "and uses `session.report` to report the `score` back to Tune."
    ]
   },
   {
@@ -98,7 +99,7 @@
     "def objective(config):\n",
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"])\n",
-    "        tune.report(iterations=step, mean_loss=score)"
+    "        session.report({\"iterations\": step, \"mean_loss\": score})"
    ]
   },
   {
diff --git a/doc/source/tune/faq.rst b/doc/source/tune/faq.rst
index ffbaedaf2779..b61447d29fd0 100644
--- a/doc/source/tune/faq.rst
+++ b/doc/source/tune/faq.rst
@@ -56,7 +56,7 @@ results per each added tree in GBDTs, etc.) using early stopping usually allows
 more configurations, as unpromising trials are pruned before they run their full course.
 Please note that not all search algorithms can use information from pruned trials.
 Early stopping cannot be used without incremental results - in case of the functional API,
-that means that ``tune.report()`` has to be called more than once - usually in a loop.
+that means that ``session.report()`` has to be called more than once - usually in a loop.
 
 **If your model is small**, you can usually try to run many different configurations.
 A **random search** can be used to generate configurations. You can also grid search
@@ -171,7 +171,7 @@ the a and b variables and use them afterwards.
 How does early termination (e.g. Hyperband/ASHA) work?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Early termination algorithms look at the intermediately reported values,
-e.g. what is reported to them via ``tune.report()`` after each training
+e.g. what is reported to them via ``session.report()`` after each training
 epoch. After a certain number of steps, they then remove the worst
 performing trials and keep only the best performing trials. Goodness of a trial
 is determined by ordering them by the objective metric, for instance accuracy
@@ -188,8 +188,8 @@ Why are all my trials returning "1" iteration?
 
 **This is most likely applicable for the Tune function API.**
 
-Ray Tune counts iterations internally every time ``tune.report()`` is
-called. If you only call ``tune.report()`` once at the end of the training,
+Ray Tune counts iterations internally every time ``session.report()`` is
+called. If you only call ``session.report()`` once at the end of the training,
 the counter has only been incremented once. If you're using the class API,
 the counter is increased after calling ``step()``.
 
@@ -203,7 +203,7 @@ What are all these extra outputs?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 You'll notice that Ray Tune not only reports hyperparameters (from the
-``config``) or metrics (passed to ``tune.report()``), but also some other
+``config``) or metrics (passed to ``session.report()``), but also some other
 outputs.
 
 .. code-block:: bash
@@ -436,15 +436,11 @@ dictionary should only contain primitive types, like numbers or strings.
 **The Trial result is very large**
 
 This is the case if you return objects, data, or other large objects via the return value of ``step()`` in
-your class trainable or to ``tune.report()`` in your function trainable. The effect is the same as above:
+your class trainable or to ``session.report()`` in your function trainable. The effect is the same as above:
 The results are repeatedly serialized and written to disk, and this can take a long time.
 
-**Solution**: Usually you should be able to write data to the trial directory instead. You can then pass a
-filename back (or assume it is a known location). The trial dir is usually the current working directory. Class
-trainables have the ``Trainable.logdir`` property and function trainables the :func:`ray.tune.get_trial_dir`
-function to retrieve the logdir. If you really have to, you can also ``ray.put()`` an object to the Ray
-object store and retrieve it with ``ray.get()`` on the other side. Generally, your result dictionary
-should only contain primitive types, like numbers or strings.
+**Solution**: Use checkpoint by writing data to the trainable's current working directory instead. There are various ways
+to do that depending on whether you are using class or functional Trainable API. 
 
 **You are training a large number of trials on a cluster, or you are saving huge checkpoints**
 
diff --git a/doc/source/tune/getting-started.rst b/doc/source/tune/getting-started.rst
index b4d92748359e..7cd34336836d 100644
--- a/doc/source/tune/getting-started.rst
+++ b/doc/source/tune/getting-started.rst
@@ -63,7 +63,7 @@ Below, we define a function that trains the Pytorch model for multiple epochs.
 This function will be executed on a separate :ref:`Ray Actor (process) <actor-guide>` underneath the hood,
 so we need to communicate the performance of the model back to Tune (which is on the main Python process).
 
-To do this, we call :ref:`tune.report <tune-function-docstring>` in our training function,
+To do this, we call :ref:`session.report <tune-function-docstring>` in our training function,
 which sends the performance value back to Tune. Since the function is executed on the separate process,
 make sure that the function is :ref:`serializable by Ray <serialization-guide>`.
 
diff --git a/doc/source/tune/key-concepts.rst b/doc/source/tune/key-concepts.rst
index cfaa445947ef..20ce8ec9c61f 100644
--- a/doc/source/tune/key-concepts.rst
+++ b/doc/source/tune/key-concepts.rst
@@ -47,11 +47,11 @@ Given concrete choices for ``a``, ``b`` and ``x`` we can evaluate the objective
         :start-after: __function_api_start__
         :end-before: __function_api_end__
 
-    Note that we use ``tune.report(...)`` to report the intermediate ``score`` in the training loop, which can be useful
+    Note that we use ``session.report(...)`` to report the intermediate ``score`` in the training loop, which can be useful
     in many machine learning tasks.
     If you just want to report the final ``score`` outside of this loop, you can simply return the score at the
     end of the ``trainable`` function with ``return {"score": score}``.
-    You can also use ``yield {"score": score}`` instead of ``tune.report()``.
+    You can also use ``yield {"score": score}`` instead of ``session.report()``.
 
 .. tabbed:: Class API
 
@@ -62,7 +62,7 @@ Given concrete choices for ``a``, ``b`` and ``x`` we can evaluate the objective
         :start-after: __class_api_start__
         :end-before: __class_api_end__
 
-    .. tip:: ``tune.report`` can't be used within a ``Trainable`` class.
+    .. tip:: ``session.report`` can't be used within a ``Trainable`` class.
 
 Learn more about the details of :ref:`Trainables here<trainable-docs>`
 and :ref:`have a look at our examples <tune-general-examples>`.
@@ -266,7 +266,7 @@ Schedulers
 ----------
 
 To make your training process more efficient, you can use a :ref:`Trial Scheduler <tune-schedulers>`.
-For instance, in our ``trainable`` example minimizing a function in a training loop, we used ``tune.report()``.
+For instance, in our ``trainable`` example minimizing a function in a training loop, we used ``session.report()``.
 This reported `incremental` results, given a hyperparameter configuration selected by a search algorithm.
 Based on these reported results, a Tune scheduler can decide whether to stop the trial early or not.
 If you don't specify a scheduler, Tune will use a first-in-first-out (FIFO) scheduler by default, which simply
diff --git a/doc/source/tune/tutorials/tune-checkpoints.rst b/doc/source/tune/tutorials/tune-checkpoints.rst
index eeda9efc76c5..7bcaa6b15f11 100644
--- a/doc/source/tune/tutorials/tune-checkpoints.rst
+++ b/doc/source/tune/tutorials/tune-checkpoints.rst
@@ -189,8 +189,10 @@ Your ``my_trainable`` is either a:
 
 2. **Custom training function**
 
-  * All this means is that your function has to expose a ``checkpoint_dir`` argument in the function signature,
-    and call ``tune.checkpoint_dir``. See :doc:`this example </tune/examples/includes/custom_func_checkpointing>`,
+  * All this means is that your function needs to take care of saving and loading from checkpoint.
+    For saving, this is done through ``session.report()`` API, which can take in a ``Checkpoint`` object.
+    For loading, your function can access existing checkpoint through ``Session.get_checkpoint()`` API. 
+    See :doc:`this example </tune/examples/includes/custom_func_checkpointing>`,
     it's quite simple to do.
 
 Let's assume for this example you're running this script from your laptop, and connecting to your remote Ray cluster
@@ -319,7 +321,7 @@ On a multinode cluster, Tune automatically creates a copy of all trial checkpoin
 This requires the Ray cluster to be started with the :ref:`cluster launcher <cluster-cloud>` and also
 requires rsync to be installed.
 
-Note that you must use the ``tune.checkpoint_dir`` API to trigger syncing
+Note that you must use the ``session.report`` API to trigger syncing
 (or use a model type with a built-in Ray Tune integration as described here).
 See :doc:`/tune/examples/includes/custom_func_checkpointing` for an example.
 
diff --git a/doc/source/tune/tutorials/tune-lifecycle.rst b/doc/source/tune/tutorials/tune-lifecycle.rst
index 8aa2bad0a519..6b929ac6a52c 100644
--- a/doc/source/tune/tutorials/tune-lifecycle.rst
+++ b/doc/source/tune/tutorials/tune-lifecycle.rst
@@ -56,7 +56,7 @@ After each invocation, the driver is notified that a "result dict" is ready.
 The driver will then pull the result via ``ray.get``.
 
 If the trainable is a callable or a function, it will be executed on the Ray actor process on a separate execution thread.
-Whenever ``tune.report`` is called, the execution thread is paused and waits for the driver to pull a
+Whenever ``session.report`` is called, the execution thread is paused and waits for the driver to pull a
 result (see `function_runner.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/function_runner.py>`__.
 After pulling, the actor’s execution thread will automatically resume.
 
diff --git a/doc/source/tune/tutorials/tune-metrics.rst b/doc/source/tune/tutorials/tune-metrics.rst
index bc59ccc10a53..8a8be276df87 100644
--- a/doc/source/tune/tutorials/tune-metrics.rst
+++ b/doc/source/tune/tutorials/tune-metrics.rst
@@ -15,6 +15,7 @@ This simple callback just prints a metric each time a result is received:
 
     from ray import tune
     from ray.tune import Callback
+    from ray.air import session
 
 
     class MyCallback(Callback):
@@ -24,7 +25,7 @@ This simple callback just prints a metric each time a result is received:
 
     def train(config):
         for i in range(10):
-            tune.report(metric=i)
+            session.report(metric=i)
 
 
     tune.run(
@@ -46,7 +47,7 @@ You can log arbitrary values and metrics in both Function and Class training API
     def trainable(config):
         for i in range(num_epochs):
             ...
-            tune.report(acc=accuracy, metric_foo=random_metric_1, bar=metric_2)
+            session.report(acc=accuracy, metric_foo=random_metric_1, bar=metric_2)
 
     class Trainable(tune.Trainable):
         def step(self):
@@ -56,7 +57,7 @@ You can log arbitrary values and metrics in both Function and Class training API
 
 
 .. tip::
-    Note that ``tune.report()`` is not meant to transfer large amounts of data, like models or datasets.
+    Note that ``session.report()`` is not meant to transfer large amounts of data, like models or datasets.
     Doing so can incur large overheads and slow down your Tune run significantly.
 
 Which metrics get automatically filled in?
@@ -73,7 +74,7 @@ All of these can be used as stopping conditions or passed as a parameter to Tria
 * ``experiment_id``: Unique experiment ID
 * ``experiment_tag``: Unique experiment tag (includes parameter values)
 * ``hostname``: Hostname of the worker
-* ``iterations_since_restore``: The number of times ``tune.report()/trainable.train()`` has been
+* ``iterations_since_restore``: The number of times ``session.report`` has been
   called after restoring the worker from a checkpoint
 * ``node_ip``: Host IP of the worker
 * ``pid``: Process ID (PID) of the worker process
@@ -84,7 +85,7 @@ All of these can be used as stopping conditions or passed as a parameter to Tria
 * ``timestamp``: Timestamp when the result was processed
 * ``timesteps_since_restore``: Number of timesteps since restoring from a checkpoint
 * ``timesteps_total``: Total number of timesteps
-* ``training_iteration``: The number of times ``tune.report()`` has been
+* ``training_iteration``: The number of times ``session.report()`` has been
   called
 * ``trial_id``: Unique trial ID
 
diff --git a/doc/source/tune/tutorials/tune-output.rst b/doc/source/tune/tutorials/tune-output.rst
index 3fbd0f45a1b1..e85198e45123 100644
--- a/doc/source/tune/tutorials/tune-output.rst
+++ b/doc/source/tune/tutorials/tune-output.rst
@@ -183,6 +183,7 @@ You can do this in the trainable, as shown below:
     ``library`` refers to whatever 3rd party logging library you are using.
 
     .. code-block:: python
+        from ray.air import session
 
         def trainable(config):
             library.init(
@@ -191,12 +192,12 @@ You can do this in the trainable, as shown below:
                 resume=trial_id,
                 reinit=True,
                 allow_val_change=True)
-            library.set_log_path(tune.get_trial_dir())
+            library.set_log_path(os.getcwd())
 
             for step in range(100):
                 library.log_model(...)
                 library.log(results, step=step)
-                tune.report(results)
+                session.report(results)
 
 
 .. tabbed:: Class API
@@ -212,7 +213,7 @@ You can do this in the trainable, as shown below:
                     resume=trial_id,
                     reinit=True,
                     allow_val_change=True)
-                library.set_log_path(self.logdir)
+                library.set_log_path(os.getcwd())
 
             def step(self):
                 library.log_model(...)
@@ -227,7 +228,8 @@ You can do this in the trainable, as shown below:
                 library.log(res_dict, step=step)
 
 
-    Use ``self.logdir`` (only for Class API) or ``tune.get_trial_dir()`` (only for Function API) for the trial log directory.
+Note: For both functional and class trainables, the current working directory is changed to something
+specific to that trainable once it's launched on a remote actor.
 
 In the distributed case, these logs will be sync'ed back to the driver under your logger path.
 This will allow you to visualize and analyze logs of all distributed training workers on a single machine.
diff --git a/doc/source/tune/tutorials/tune-stopping.rst b/doc/source/tune/tutorials/tune-stopping.rst
index ca1164ef9b8e..aefda5c1c622 100644
--- a/doc/source/tune/tutorials/tune-stopping.rst
+++ b/doc/source/tune/tutorials/tune-stopping.rst
@@ -62,7 +62,7 @@ We've just covered the case in which you manually interrupt a Tune run.
 But you can also control when trials are stopped early by passing the ``stop`` argument to ``tune.run``.
 This argument takes, a dictionary, a function, or a :class:`Stopper <ray.tune.stopper.Stopper>` class as an argument.
 
-If a dictionary is passed in, the keys may be any field in the return result of ``tune.report`` in the
+If a dictionary is passed in, the keys may be any field in the return result of ``session.report`` in the
 Function API or ``step()`` (including the results from ``step`` and auto-filled metrics).
 
 Stopping with a dictionary
diff --git a/python/ray/air/examples/custom_trainer.py b/python/ray/air/examples/custom_trainer.py
index d82888c71623..b43f19b46ff8 100644
--- a/python/ray/air/examples/custom_trainer.py
+++ b/python/ray/air/examples/custom_trainer.py
@@ -6,7 +6,7 @@
 import torch
 
 from ray.train.trainer import BaseTrainer
-from ray import tune
+from ray.air import session
 
 
 class MyPytorchTrainer(BaseTrainer):
@@ -42,7 +42,7 @@ def training_loop(self):
 
             # Use Tune functions to report intermediate
             # results.
-            tune.report(loss=loss, epoch=epoch_idx)
+            session.report({"loss": loss, "epoch": epoch_idx})
 
 
 # __custom_trainer_end__
diff --git a/python/ray/train/base_trainer.py b/python/ray/train/base_trainer.py
index 3176a8898767..bf5884e38167 100644
--- a/python/ray/train/base_trainer.py
+++ b/python/ray/train/base_trainer.py
@@ -70,6 +70,7 @@ class BaseTrainer(abc.ABC):
 
         from ray.train.trainer import BaseTrainer
         from ray import tune
+        from ray.air import session
 
 
         class MyPytorchTrainer(BaseTrainer):
@@ -106,7 +107,7 @@ def training_loop(self):
 
                     # Use Tune functions to report intermediate
                     # results.
-                    tune.report(loss=loss, epoch=epoch_idx)
+                    session.report({"loss": loss, "epoch": epoch_idx})
 
     **How do I use an existing Trainer or one of my custom Trainers?**
 
@@ -283,7 +284,7 @@ def training_loop(self) -> None:
         ``self.datasets`` have already been preprocessed by ``self.preprocessor``.
 
         You can use the :ref:`Tune Function API functions <tune-function-docstring>`
-        (``tune.report()`` and ``tune.save_checkpoint()``) inside
+        (``session.report()`` and ``session.get_checkpoint()``) inside
         this training loop.
 
         Example:
@@ -295,7 +296,7 @@ class MyTrainer(BaseTrainer):
                     def training_loop(self):
                         for epoch_idx in range(5):
                             ...
-                            tune.report(epoch=epoch_idx)
+                            session.report({"epoch": epoch_idx})
 
         """
         raise NotImplementedError
diff --git a/python/ray/train/data_parallel_trainer.py b/python/ray/train/data_parallel_trainer.py
index 59ed5cb0be4c..3e448dfed553 100644
--- a/python/ray/train/data_parallel_trainer.py
+++ b/python/ray/train/data_parallel_trainer.py
@@ -5,7 +5,6 @@
 from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Type, Union
 
 import ray
-from ray import tune
 from ray.air import session
 from ray.air.checkpoint import Checkpoint
 from ray.air.config import DatasetConfig, RunConfig, ScalingConfig
@@ -362,7 +361,7 @@ def training_loop(self) -> None:
             # TODO(ml-team): add ability to report results from multiple workers.
             first_worker_results = results[0]
 
-            tune.report(**first_worker_results)
+            session.report(**first_worker_results)
 
         # Shutdown workers.
         backend_executor.shutdown()
diff --git a/python/ray/tune/examples/async_hyperband_example.py b/python/ray/tune/examples/async_hyperband_example.py
index 7d9cd8e2fead..654684d3abdd 100644
--- a/python/ray/tune/examples/async_hyperband_example.py
+++ b/python/ray/tune/examples/async_hyperband_example.py
@@ -5,6 +5,7 @@
 
 import ray
 from ray import tune
+from ray.air import session
 from ray.tune.schedulers import AsyncHyperBandScheduler
 
 
@@ -21,7 +22,7 @@ def easy_objective(config):
         # Iterative training function - can be an arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/examples/ax_example.py b/python/ray/tune/examples/ax_example.py
index b7eb02463d07..8c203f26e6e1 100644
--- a/python/ray/tune/examples/ax_example.py
+++ b/python/ray/tune/examples/ax_example.py
@@ -8,6 +8,7 @@
 import time
 
 from ray import tune
+from ray.air import session
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.ax import AxSearch
 
@@ -42,8 +43,12 @@ def hartmann6(x):
 def easy_objective(config):
     for i in range(config["iterations"]):
         x = np.array([config.get("x{}".format(i + 1)) for i in range(6)])
-        tune.report(
-            timesteps_total=i, hartmann6=hartmann6(x), l2norm=np.sqrt((x ** 2).sum())
+        session.report(
+            {
+                "timesteps_total": i,
+                "hartmann6": hartmann6(x),
+                "l2norm": np.sqrt((x ** 2).sum()),
+            }
         )
         time.sleep(0.02)
 
diff --git a/python/ray/tune/examples/bayesopt_example.py b/python/ray/tune/examples/bayesopt_example.py
index b1b985b57e32..65543fd784a4 100644
--- a/python/ray/tune/examples/bayesopt_example.py
+++ b/python/ray/tune/examples/bayesopt_example.py
@@ -7,6 +7,7 @@
 import time
 
 from ray import tune
+from ray.air import session
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest import ConcurrencyLimiter
 from ray.tune.suggest.bayesopt import BayesOptSearch
@@ -24,7 +25,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
         time.sleep(0.1)
 
 
diff --git a/python/ray/tune/examples/blendsearch_example.py b/python/ray/tune/examples/blendsearch_example.py
index 441dbfb7a88c..d2bae4eb0865 100644
--- a/python/ray/tune/examples/blendsearch_example.py
+++ b/python/ray/tune/examples/blendsearch_example.py
@@ -8,6 +8,7 @@
 
 import ray
 from ray import tune
+from ray.air import session
 from ray.tune.suggest import ConcurrencyLimiter
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.flaml import BlendSearch
@@ -25,7 +26,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
         time.sleep(0.1)
 
 
diff --git a/python/ray/tune/examples/cfo_example.py b/python/ray/tune/examples/cfo_example.py
index b6192165fafc..3d87ac66aaea 100644
--- a/python/ray/tune/examples/cfo_example.py
+++ b/python/ray/tune/examples/cfo_example.py
@@ -8,6 +8,7 @@
 
 import ray
 from ray import tune
+from ray.air import session
 from ray.tune.suggest import ConcurrencyLimiter
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.flaml import CFO
@@ -25,7 +26,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
         time.sleep(0.1)
 
 
diff --git a/python/ray/tune/examples/cifar10_pytorch.py b/python/ray/tune/examples/cifar10_pytorch.py
index 3e56f8bef729..2c08df70e878 100644
--- a/python/ray/tune/examples/cifar10_pytorch.py
+++ b/python/ray/tune/examples/cifar10_pytorch.py
@@ -15,6 +15,8 @@
 import torchvision.transforms as transforms
 import ray
 from ray import tune
+from ray.air import session
+from ray.air.checkpoint import Checkpoint
 from ray.tune.schedulers import ASHAScheduler
 # __import_end__
 
@@ -63,7 +65,7 @@ def forward(self, x):
 
 
 # __train_begin__
-def train_cifar(config, checkpoint_dir=None):
+def train_cifar(config):
     net = Net(config["l1"], config["l2"])
 
     device = "cpu"
@@ -76,13 +78,13 @@ def train_cifar(config, checkpoint_dir=None):
     criterion = nn.CrossEntropyLoss()
     optimizer = optim.SGD(net.parameters(), lr=config["lr"], momentum=0.9)
 
-    # The `checkpoint_dir` parameter gets passed by Ray Tune when a checkpoint
-    # should be restored.
-    if checkpoint_dir:
-        checkpoint = os.path.join(checkpoint_dir, "checkpoint")
-        model_state, optimizer_state = torch.load(checkpoint)
-        net.load_state_dict(model_state)
-        optimizer.load_state_dict(optimizer_state)
+    # Load existing checkpoint through `session.get_checkpoint()` API.
+    if session.get_checkpoint():
+        loaded_checkpoint = session.get_checkpoint()
+        with loaded_checkpoint.as_directory() as loaded_checkpoint_dir:
+            model_state, optimizer_state = torch.load(os.path.join(loaded_checkpoint_dir, "checkpoint.pt"))
+            net.load_state_dict(model_state)
+            optimizer.load_state_dict(optimizer_state)
 
     data_dir = os.path.abspath("./data")
     trainset, testset = load_data(data_dir)
@@ -147,14 +149,16 @@ def train_cifar(config, checkpoint_dir=None):
                 val_steps += 1
 
         # Here we save a checkpoint. It is automatically registered with
-        # Ray Tune and will potentially be passed as the `checkpoint_dir`
-        # parameter in future iterations.
-        with tune.checkpoint_dir(step=epoch) as checkpoint_dir:
-            path = os.path.join(checkpoint_dir, "checkpoint")
-            torch.save(
-                (net.state_dict(), optimizer.state_dict()), path)
-
-        tune.report(loss=(val_loss / val_steps), accuracy=correct / total)
+        # Ray Tune and will potentially be accessed through in ``session.get_checkpoint()``
+        # in future iterations.
+        # Note to save a file like checkpoint, you still need to put it under a directory
+        # to construct an AIR checkpoint.
+        os.makedirs("my_model", exist_ok=True)  # ok to overwrite the previous one.
+        path = os.path.join("my_model", "checkpoint.pt")
+        torch.save(
+            (net.state_dict(), optimizer.state_dict()), path)
+        checkpoint = Checkpoint.from_directory("my_model")
+        session.report({"loss": (val_loss / val_steps), "accuracy": correct / total}, checkpoint=checkpoint)
     print("Finished Training")
 # __train_end__
 
@@ -165,7 +169,7 @@ def test_best_model(best_trial):
     device = "cuda:0" if torch.cuda.is_available() else "cpu"
     best_trained_model.to(device)
 
-    checkpoint_path = os.path.join(best_trial.checkpoint.dir_or_data, "checkpoint")
+    checkpoint_path = os.path.join(best_trial.checkpoint.dir_or_data, "checkpoint.pt")
 
     model_state, optimizer_state = torch.load(checkpoint_path)
     best_trained_model.load_state_dict(model_state)
diff --git a/python/ray/tune/examples/custom_func_checkpointing.py b/python/ray/tune/examples/custom_func_checkpointing.py
index 92853be4204a..638e5fcbb013 100644
--- a/python/ray/tune/examples/custom_func_checkpointing.py
+++ b/python/ray/tune/examples/custom_func_checkpointing.py
@@ -1,13 +1,14 @@
 # If want to use checkpointing with a custom training function (not a Ray
-# integration like PyTorch or Tensorflow), you must expose a
-# ``checkpoint_dir`` argument in the function signature, and call
-# ``tune.checkpoint_dir``:
+# integration like PyTorch or Tensorflow), your function can read/write
+# checkpoint through ``ray.air.session`` APIs.
 import os
 import time
 import json
 import argparse
 
 from ray import tune
+from ray.air import session
+from ray.air.checkpoint import Checkpoint
 
 
 def evaluation_fn(step, width, height):
@@ -15,25 +16,20 @@ def evaluation_fn(step, width, height):
     return (0.1 + width * step / 100) ** (-1) + height * 0.1
 
 
-def train_func(config, checkpoint_dir=None):
-    start = 0
+def train_func(config):
+    step = 0
     width, height = config["width"], config["height"]
 
-    if checkpoint_dir:
-        with open(os.path.join(checkpoint_dir, "checkpoint")) as f:
-            state = json.loads(f.read())
-            start = state["step"] + 1
+    if session.get_checkpoint():
+        loaded_checkpoint = session.get_checkpoint()
+        step = loaded_checkpoint.to_dict()["step"] + 1
 
-    for step in range(start, 100):
+    for step in range(step, 100):
         intermediate_score = evaluation_fn(step, width, height)
-
-        # Obtain a checkpoint directory
-        with tune.checkpoint_dir(step=step) as checkpoint_dir:
-            path = os.path.join(checkpoint_dir, "checkpoint")
-            with open(path, "w") as f:
-                f.write(json.dumps({"step": step}))
-
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        checkpoint = Checkpoint.from_dict({"step": step})
+        session.report(
+            {"iterations": step, "mean_loss": intermediate_score}, checkpoint=checkpoint
+        )
 
 
 # You can restore a single trial checkpoint by using
diff --git a/python/ray/tune/examples/dragonfly_example.py b/python/ray/tune/examples/dragonfly_example.py
index 3a2ff6cc3067..9de61245ef1d 100644
--- a/python/ray/tune/examples/dragonfly_example.py
+++ b/python/ray/tune/examples/dragonfly_example.py
@@ -12,6 +12,7 @@
 import time
 
 from ray import tune
+from ray.air import session
 from ray.tune.suggest import ConcurrencyLimiter
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.dragonfly import DragonflySearch
@@ -27,7 +28,7 @@ def objective(config):
         conductivity = vol1 + 0.1 * (vol2 + vol3) ** 2 + 2.3 * vol4 * (vol1 ** 1.5)
         # Add Gaussian noise to simulate experimental noise
         conductivity += np.random.normal() * 0.01
-        tune.report(timesteps_total=i, objective=conductivity)
+        session.report({"timesteps_total": i, "objective": conductivity})
         time.sleep(0.02)
 
 
diff --git a/python/ray/tune/examples/genetic_example.py b/python/ray/tune/examples/genetic_example.py
index 995afe003db3..7bbe3562f8eb 100644
--- a/python/ray/tune/examples/genetic_example.py
+++ b/python/ray/tune/examples/genetic_example.py
@@ -3,6 +3,7 @@
 It also checks that it is usable with a separate scheduler.
 """
 from ray import tune
+from ray.air import session
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.automl import GeneticSearch
 from ray.tune.automl import ContinuousSpace, DiscreteSpace, SearchSpace
@@ -19,7 +20,7 @@ def michalewicz_function(config, reporter):
     y = np.dot(sin_x, sin_z)
 
     # Negate y since we want to minimize y value
-    tune.report(timesteps_total=1, neg_mean_loss=-y)
+    session.report({"timesteps_total": 1, "neg_mean_loss": -y})
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/examples/hebo_example.py b/python/ray/tune/examples/hebo_example.py
index 18ccfe8fcdf2..6722f8ee3fb8 100644
--- a/python/ray/tune/examples/hebo_example.py
+++ b/python/ray/tune/examples/hebo_example.py
@@ -7,6 +7,7 @@
 import time
 
 from ray import tune
+from ray.air import session
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.hebo import HEBOSearch
 
@@ -24,7 +25,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/examples/hyperband_function_example.py b/python/ray/tune/examples/hyperband_function_example.py
index 481f2f441fec..937965092550 100644
--- a/python/ray/tune/examples/hyperband_function_example.py
+++ b/python/ray/tune/examples/hyperband_function_example.py
@@ -7,6 +7,8 @@
 import numpy as np
 
 import ray
+from ray.air import session
+from ray.air.checkpoint import Checkpoint
 from ray import tune
 from ray.tune.schedulers import HyperBandScheduler
 
@@ -23,15 +25,13 @@ def train(config, checkpoint_dir=None):
 
         # Checkpoint the state of the training every 3 steps
         # Note that this is only required for certain schedulers
+        checkpoint = None
         if timestep % 3 == 0:
-            with tune.checkpoint_dir(step=timestep) as checkpoint_dir:
-                path = os.path.join(checkpoint_dir, "checkpoint")
-                with open(path, "w") as f:
-                    f.write(json.dumps({"timestep": timestep}))
+            checkpoint = Checkpoint.from_dict({"timestep": timestep})
 
         # Here we use `episode_reward_mean`, but you can also report other
         # objectives such as loss or accuracy.
-        tune.report(episode_reward_mean=v)
+        session.report({"episode_reward_mean": v}, checkpoint=checkpoint)
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/examples/hyperopt_conditional_search_space_example.py b/python/ray/tune/examples/hyperopt_conditional_search_space_example.py
index f3c4a1dd60e0..7b9325b4e168 100644
--- a/python/ray/tune/examples/hyperopt_conditional_search_space_example.py
+++ b/python/ray/tune/examples/hyperopt_conditional_search_space_example.py
@@ -11,6 +11,7 @@
 
 import ray
 from ray import tune
+from ray.air import session
 from ray.tune.suggest import ConcurrencyLimiter
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.hyperopt import HyperOptSearch
@@ -58,7 +59,7 @@ def easy_objective(config_in):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height, mult)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
         time.sleep(0.1)
 
 
diff --git a/python/ray/tune/examples/logging_example.py b/python/ray/tune/examples/logging_example.py
index 828ecf024d41..562adcee1480 100755
--- a/python/ray/tune/examples/logging_example.py
+++ b/python/ray/tune/examples/logging_example.py
@@ -4,6 +4,7 @@
 import time
 
 from ray import tune
+from ray.air import session
 from ray.tune.logger import LoggerCallback
 
 
@@ -29,7 +30,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/examples/mlflow_example.py b/python/ray/tune/examples/mlflow_example.py
index 5c58c5516c0c..c18889cb2556 100644
--- a/python/ray/tune/examples/mlflow_example.py
+++ b/python/ray/tune/examples/mlflow_example.py
@@ -8,6 +8,7 @@
 import mlflow
 
 from ray import tune
+from ray.air import session
 from ray.tune.integration.mlflow import MLflowLoggerCallback, mlflow_mixin
 
 
@@ -23,7 +24,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
         time.sleep(0.1)
 
 
@@ -58,7 +59,7 @@ def decorated_easy_objective(config):
         # Log the metrics to mlflow
         mlflow.log_metrics(dict(mean_loss=intermediate_score), step=step)
         # Feed the score back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
         time.sleep(0.1)
 
 
diff --git a/python/ray/tune/examples/mnist_pytorch.py b/python/ray/tune/examples/mnist_pytorch.py
index abfe64f8502c..1da411e11231 100644
--- a/python/ray/tune/examples/mnist_pytorch.py
+++ b/python/ray/tune/examples/mnist_pytorch.py
@@ -11,6 +11,7 @@
 
 import ray
 from ray import tune
+from ray.air import session
 from ray.tune.schedulers import AsyncHyperBandScheduler
 
 # Change these values if you want the training to run quicker or slower.
@@ -103,7 +104,7 @@ def train_mnist(config):
         train(model, optimizer, train_loader, device)
         acc = test(model, test_loader, device)
         # Set this to run Tune.
-        tune.report(mean_accuracy=acc)
+        session.report({"mean_accuracy": acc})
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/examples/mnist_pytorch_lightning.py b/python/ray/tune/examples/mnist_pytorch_lightning.py
index e5fae529eb3c..4974e190455c 100644
--- a/python/ray/tune/examples/mnist_pytorch_lightning.py
+++ b/python/ray/tune/examples/mnist_pytorch_lightning.py
@@ -147,7 +147,7 @@ def train_mnist_tune(config, num_epochs=10, num_gpus=0, data_dir="~/data"):
         # If fractional GPUs passed in, convert to int.
         gpus=math.ceil(num_gpus),
         logger=TensorBoardLogger(
-            save_dir=tune.get_trial_dir(), name="", version="."),
+            save_dir=os.getcwd(), name="", version="."),
         enable_progress_bar=False,
         callbacks=[
             TuneReportCallback(
@@ -173,7 +173,7 @@ def train_mnist_tune_checkpoint(config,
         # If fractional GPUs passed in, convert to int.
         "gpus": math.ceil(num_gpus),
         "logger": TensorBoardLogger(
-            save_dir=tune.get_trial_dir(), name="", version="."),
+            save_dir=os.getcwd(), name="", version="."),
         "enable_progress_bar": False,
         "callbacks": [
             TuneReportCheckpointCallback(
diff --git a/python/ray/tune/examples/nevergrad_example.py b/python/ray/tune/examples/nevergrad_example.py
index 2c97a5a06284..f0c05126b2fd 100644
--- a/python/ray/tune/examples/nevergrad_example.py
+++ b/python/ray/tune/examples/nevergrad_example.py
@@ -7,6 +7,7 @@
 import time
 
 from ray import tune
+from ray.air import session
 from ray.tune.suggest import ConcurrencyLimiter
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.nevergrad import NevergradSearch
@@ -24,7 +25,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
         time.sleep(0.1)
 
 
diff --git a/python/ray/tune/examples/optuna_define_by_run_example.py b/python/ray/tune/examples/optuna_define_by_run_example.py
index db5698a9362b..91d0d512cd46 100644
--- a/python/ray/tune/examples/optuna_define_by_run_example.py
+++ b/python/ray/tune/examples/optuna_define_by_run_example.py
@@ -12,6 +12,7 @@
 
 import ray
 from ray import tune
+from ray.air import session
 from ray.tune.suggest import ConcurrencyLimiter
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.optuna import OptunaSearch
@@ -30,7 +31,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height, mult)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
         time.sleep(0.1)
 
 
diff --git a/python/ray/tune/examples/optuna_example.py b/python/ray/tune/examples/optuna_example.py
index a6cac1d1e619..3a7d932b13e0 100644
--- a/python/ray/tune/examples/optuna_example.py
+++ b/python/ray/tune/examples/optuna_example.py
@@ -11,6 +11,7 @@
 
 import ray
 from ray import tune
+from ray.air import session
 from ray.tune.suggest import ConcurrencyLimiter
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.optuna import OptunaSearch
@@ -28,7 +29,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
         time.sleep(0.1)
 
 
diff --git a/python/ray/tune/examples/optuna_multiobjective_example.py b/python/ray/tune/examples/optuna_multiobjective_example.py
index 439c1c93cbde..fe882fe942f9 100644
--- a/python/ray/tune/examples/optuna_multiobjective_example.py
+++ b/python/ray/tune/examples/optuna_multiobjective_example.py
@@ -10,6 +10,7 @@
 
 import ray
 from ray import tune
+from ray.air import session
 from ray.tune.suggest import ConcurrencyLimiter
 from ray.tune.suggest.optuna import OptunaSearch
 
@@ -26,8 +27,12 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(
-            iterations=step, loss=intermediate_score, gain=intermediate_score * width
+        session.report(
+            {
+                "iterations": step,
+                "loss": intermediate_score,
+                "gain": intermediate_score * width,
+            }
         )
         time.sleep(0.1)
 
diff --git a/python/ray/tune/examples/pbt_convnet_function_example.py b/python/ray/tune/examples/pbt_convnet_function_example.py
index 2403ce77302f..31bebcbd1a6a 100644
--- a/python/ray/tune/examples/pbt_convnet_function_example.py
+++ b/python/ray/tune/examples/pbt_convnet_function_example.py
@@ -9,6 +9,8 @@
 from ray.tune.examples.mnist_pytorch import train, test, ConvNet, get_data_loaders
 
 from ray import tune
+from ray.air import session
+from ray.air.checkpoint import Checkpoint
 from ray.tune.schedulers import PopulationBasedTraining
 from ray.tune.trial import ExportFormat
 
@@ -16,7 +18,7 @@
 
 
 # __train_begin__
-def train_convnet(config, checkpoint_dir=None):
+def train_convnet(config):
     # Create our data loaders, model, and optmizer.
     step = 0
     train_loader, test_loader = get_data_loaders()
@@ -27,36 +29,38 @@ def train_convnet(config, checkpoint_dir=None):
         momentum=config.get("momentum", 0.9),
     )
 
-    # If checkpoint_dir is not None, then we are resuming from a checkpoint.
+    # If `session.get_checkpoint()` is not None, then we are resuming from a checkpoint.
     # Load model state and iteration step from checkpoint.
-    if checkpoint_dir:
+    if session.get_checkpoint():
         print("Loading from checkpoint.")
-        path = os.path.join(checkpoint_dir, "checkpoint")
-        checkpoint = torch.load(path)
-        model.load_state_dict(checkpoint["model_state_dict"])
-        step = checkpoint["step"]
+        loaded_checkpoint = session.get_checkpoint()
+        with loaded_checkpoint.as_directory() as loaded_checkpoint_dir:
+            path = os.path.join(loaded_checkpoint_dir, "checkpoint.pt")
+            checkpoint = torch.load(path)
+            model.load_state_dict(checkpoint["model_state_dict"])
+            step = checkpoint["step"]
 
     while True:
         train(model, optimizer, train_loader)
         acc = test(model, test_loader)
+        checkpoint = None
         if step % 5 == 0:
             # Every 5 steps, checkpoint our current state.
             # First get the checkpoint directory from tune.
-            with tune.checkpoint_dir(step=step) as checkpoint_dir:
-                # Then create a checkpoint file in this directory.
-                path = os.path.join(checkpoint_dir, "checkpoint")
-                # Save state to checkpoint file.
-                # No need to save optimizer for SGD.
-                torch.save(
-                    {
-                        "step": step,
-                        "model_state_dict": model.state_dict(),
-                        "mean_accuracy": acc,
-                    },
-                    path,
-                )
+            # Need to create a directory under current working directory
+            # to construct an AIR Checkpoint object from.
+            os.makedirs("my_model", exist_ok=True)
+            torch.save(
+                {
+                    "step": step,
+                    "model_state_dict": model.state_dict(),
+                },
+                "my_model/checkpoint.pt",
+            )
+            checkpoint = Checkpoint.from_directory("my_model")
+
         step += 1
-        tune.report(mean_accuracy=acc)
+        session.report({"mean_accuracy": acc}, checkpoint=checkpoint)
 
 
 # __train_end__
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 c9c0a5e840dd..cb33c96c9a67 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
@@ -4,6 +4,8 @@
 """
 import ray
 from ray import tune
+from ray.air import session
+from ray.air.checkpoint import Checkpoint
 from ray.tune.schedulers import PopulationBasedTraining
 
 import argparse
@@ -22,7 +24,7 @@
 
 
 # __Train_begin__
-def dcgan_train(config, checkpoint_dir=None):
+def dcgan_train(config):
     step = 0
     use_cuda = config.get("use_gpu") and torch.cuda.is_available()
     device = torch.device("cuda" if use_cuda else "cpu")
@@ -40,14 +42,16 @@ def dcgan_train(config, checkpoint_dir=None):
     with FileLock(os.path.expanduser("~/.data.lock")):
         dataloader = get_data_loader()
 
-    if checkpoint_dir is not None:
-        path = os.path.join(checkpoint_dir, "checkpoint")
-        checkpoint = torch.load(path)
-        netD.load_state_dict(checkpoint["netDmodel"])
-        netG.load_state_dict(checkpoint["netGmodel"])
-        optimizerD.load_state_dict(checkpoint["optimD"])
-        optimizerG.load_state_dict(checkpoint["optimG"])
-        step = checkpoint["step"]
+    if session.get_checkpoint():
+        loaded_checkpoint = session.get_checkpoint()
+        with loaded_checkpoint.as_directory() as loaded_checkpoint_dir:
+            path = os.path.join(loaded_checkpoint_dir, "checkpoint.pt")
+            checkpoint = torch.load(path)
+            netD.load_state_dict(checkpoint["netDmodel"])
+            netG.load_state_dict(checkpoint["netGmodel"])
+            optimizerD.load_state_dict(checkpoint["optimD"])
+            optimizerG.load_state_dict(checkpoint["optimG"])
+            step = checkpoint["step"]
 
         if "netD_lr" in config:
             for param_group in optimizerD.param_groups:
@@ -69,19 +73,22 @@ def dcgan_train(config, checkpoint_dir=None):
             config["mnist_model_ref"],
         )
         step += 1
-        with tune.checkpoint_dir(step=step) as checkpoint_dir:
-            path = os.path.join(checkpoint_dir, "checkpoint")
-            torch.save(
-                {
-                    "netDmodel": netD.state_dict(),
-                    "netGmodel": netG.state_dict(),
-                    "optimD": optimizerD.state_dict(),
-                    "optimG": optimizerG.state_dict(),
-                    "step": step,
-                },
-                path,
-            )
-        tune.report(lossg=lossG, lossd=lossD, is_score=is_score)
+        os.makedirs("my_model", exist_ok=True)
+        torch.save(
+            {
+                "netDmodel": netD.state_dict(),
+                "netGmodel": netG.state_dict(),
+                "optimD": optimizerD.state_dict(),
+                "optimG": optimizerG.state_dict(),
+                "step": step,
+            },
+            "my_model/checkpoint.pt",
+        )
+
+        session.report(
+            {"lossg": lossG, "lossd": lossD, "is_score": is_score},
+            checkpoint=Checkpoint.from_directory("my_model"),
+        )
 
 
 # __Train_end__
diff --git a/python/ray/tune/examples/pbt_function.py b/python/ray/tune/examples/pbt_function.py
index 2b5eab4510bc..437d0938928f 100644
--- a/python/ray/tune/examples/pbt_function.py
+++ b/python/ray/tune/examples/pbt_function.py
@@ -2,16 +2,16 @@
 
 import numpy as np
 import argparse
-import json
-import os
 import random
 
 import ray
 from ray import tune
+from ray.air import session
+from ray.air.checkpoint import Checkpoint
 from ray.tune.schedulers import PopulationBasedTraining
 
 
-def pbt_function(config, checkpoint_dir=None):
+def pbt_function(config):
     """Toy PBT problem for benchmarking adaptive learning rate.
 
     The goal is to optimize this trainable's accuracy. The accuracy increases
@@ -35,11 +35,10 @@ def pbt_function(config, checkpoint_dir=None):
     lr = config["lr"]
     accuracy = 0.0  # end = 1000
     start = 0
-    if checkpoint_dir:
-        with open(os.path.join(checkpoint_dir, "checkpoint")) as f:
-            state = json.loads(f.read())
-            accuracy = state["acc"]
-            start = state["step"]
+    if session.get_checkpoint():
+        state = session.get_checkpoint().to_dict()
+        accuracy = state["acc"]
+        start = state["step"]
 
     midpoint = 100  # lr starts decreasing after acc > midpoint
     q_tolerance = 3  # penalize exceeding lr by more than this multiple
@@ -64,18 +63,19 @@ def pbt_function(config, checkpoint_dir=None):
         accuracy += noise_level * np.random.normal()
         accuracy = max(0, accuracy)
 
+        checkpoint = None
         if step % 3 == 0:
-            with tune.checkpoint_dir(step=step) as checkpoint_dir:
-                path = os.path.join(checkpoint_dir, "checkpoint")
-                with open(path, "w") as f:
-                    f.write(json.dumps({"acc": accuracy, "step": start}))
-
-        tune.report(
-            mean_accuracy=accuracy,
-            cur_lr=lr,
-            optimal_lr=optimal_lr,  # for debugging
-            q_err=q_err,  # for debugging
-            done=accuracy > midpoint * 2,  # this stops the training process
+            checkpoint = Checkpoint.from_dict({"acc": accuracy, "step": start})
+
+        session.report(
+            {
+                "mean_accuracy": accuracy,
+                "cur_lr": lr,
+                "optimal_lr": optimal_lr,  # for debugging
+                "q_err": q_err,  # for debugging
+                "done": accuracy > midpoint * 2,  # this stops the training process
+            },
+            checkpoint=checkpoint,
         )
 
 
diff --git a/python/ray/tune/examples/sigopt_example.py b/python/ray/tune/examples/sigopt_example.py
index 5332fd823c6b..427511ad8079 100644
--- a/python/ray/tune/examples/sigopt_example.py
+++ b/python/ray/tune/examples/sigopt_example.py
@@ -8,6 +8,7 @@
 import time
 
 from ray import tune
+from ray.air import session
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.sigopt import SigOptSearch
 
@@ -24,7 +25,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluate(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
         time.sleep(0.1)
 
 
diff --git a/python/ray/tune/examples/sigopt_multi_objective_example.py b/python/ray/tune/examples/sigopt_multi_objective_example.py
index 688949f746fd..2c57520809e9 100644
--- a/python/ray/tune/examples/sigopt_multi_objective_example.py
+++ b/python/ray/tune/examples/sigopt_multi_objective_example.py
@@ -8,6 +8,7 @@
 
 import numpy as np
 from ray import tune
+from ray.air import session
 from ray.tune.suggest.sigopt import SigOptSearch
 
 np.random.seed(0)
@@ -26,7 +27,7 @@ def easy_objective(config):
     w2 = config["total_weight"] - w1
 
     average, std = evaluate(w1, w2)
-    tune.report(average=average, std=std, sharpe=average / std)
+    session.report({"average": average, "std": std, "sharpe": average / std})
     time.sleep(0.1)
 
 
diff --git a/python/ray/tune/examples/sigopt_prior_beliefs_example.py b/python/ray/tune/examples/sigopt_prior_beliefs_example.py
index 82554e0fc471..7b54a6c99d46 100644
--- a/python/ray/tune/examples/sigopt_prior_beliefs_example.py
+++ b/python/ray/tune/examples/sigopt_prior_beliefs_example.py
@@ -7,6 +7,7 @@
 
 import numpy as np
 from ray import tune
+from ray.air import session
 
 from ray.tune.suggest.sigopt import SigOptSearch
 
@@ -34,7 +35,7 @@ def easy_objective(config):
         w3 = 1 - total
 
     average, std = evaluate(w1, w2, w3)
-    tune.report(average=average, std=std)
+    session.report({"average": average, "std": std})
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/examples/skopt_example.py b/python/ray/tune/examples/skopt_example.py
index fe5f783e39bf..0ab41c5b1433 100644
--- a/python/ray/tune/examples/skopt_example.py
+++ b/python/ray/tune/examples/skopt_example.py
@@ -7,6 +7,7 @@
 import time
 
 from ray import tune
+from ray.air import session
 from ray.tune.suggest import ConcurrencyLimiter
 from ray.tune.schedulers import AsyncHyperBandScheduler
 from ray.tune.suggest.skopt import SkOptSearch
@@ -25,7 +26,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/examples/tune_basic_example.py b/python/ray/tune/examples/tune_basic_example.py
index dc3745ba3db2..491f1dbeeda8 100644
--- a/python/ray/tune/examples/tune_basic_example.py
+++ b/python/ray/tune/examples/tune_basic_example.py
@@ -2,6 +2,7 @@
 import time
 
 import ray
+from ray.air import session
 from ray import tune
 
 
@@ -18,7 +19,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/examples/tune_cifar10_gluon.py b/python/ray/tune/examples/tune_cifar10_gluon.py
index 46a132f5edc7..f022d56c916d 100644
--- a/python/ray/tune/examples/tune_cifar10_gluon.py
+++ b/python/ray/tune/examples/tune_cifar10_gluon.py
@@ -15,6 +15,7 @@
 
 from ray.tune.schedulers import create_scheduler
 from ray import tune
+from ray.air import session
 
 # Training settings
 parser = argparse.ArgumentParser(description="CIFAR-10 Example")
@@ -189,7 +190,7 @@ def test():
     for epoch in range(1, args.epochs + 1):
         train(epoch)
         test_loss, test_acc = test()
-        tune.report(mean_loss=test_loss, mean_accuracy=test_acc)
+        session.report({"mean_loss": test_loss, "mean_accuracy": test_acc})
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/examples/wandb_example.py b/python/ray/tune/examples/wandb_example.py
index d046df374e70..c6961b3a1799 100644
--- a/python/ray/tune/examples/wandb_example.py
+++ b/python/ray/tune/examples/wandb_example.py
@@ -6,6 +6,7 @@
 import wandb
 
 from ray import tune
+from ray.air import session
 from ray.tune import Trainable
 from ray.tune.integration.wandb import (
     WandbLoggerCallback,
@@ -17,7 +18,7 @@
 def train_function(config, checkpoint_dir=None):
     for i in range(30):
         loss = config["mean"] + config["sd"] * np.random.randn()
-        tune.report(loss=loss)
+        session.report({"loss": loss})
 
 
 def tune_function(api_key_file):
@@ -41,7 +42,7 @@ def tune_function(api_key_file):
 def decorated_train_function(config, checkpoint_dir=None):
     for i in range(30):
         loss = config["mean"] + config["sd"] * np.random.randn()
-        tune.report(loss=loss)
+        session.report({"loss": loss})
         wandb.log(dict(loss=loss))
 
 
diff --git a/python/ray/tune/examples/xgboost_dynamic_resources_example.py b/python/ray/tune/examples/xgboost_dynamic_resources_example.py
index 079333127f62..de261f9727c8 100644
--- a/python/ray/tune/examples/xgboost_dynamic_resources_example.py
+++ b/python/ray/tune/examples/xgboost_dynamic_resources_example.py
@@ -9,6 +9,7 @@
 
 import ray
 from ray import tune
+from ray.air import session
 from ray.tune.schedulers import ResourceChangingScheduler, ASHAScheduler
 from ray.tune import Trainable
 from ray.tune.resources import Resources
@@ -57,8 +58,8 @@ def train_breast_cancer(config: dict, checkpoint_dir=None):
         xgb_model.load_model(os.path.join(checkpoint_dir, CHECKPOINT_FILENAME))
 
     # we can obtain current trial resources through
-    # tune.get_trial_resources()
-    config["nthread"] = int(tune.get_trial_resources().head_cpus)
+    # `session.get_trial_resources()`
+    config["nthread"] = int(session.get_trial_resources().head_cpus)
     print(f"nthreads: {config['nthread']} xgb_model: {xgb_model}")
     # Train the classifier, using the Tune callback
     xgb.train(
diff --git a/python/ray/tune/examples/zoopt_example.py b/python/ray/tune/examples/zoopt_example.py
index 2795c46ddf03..b79523ee814c 100644
--- a/python/ray/tune/examples/zoopt_example.py
+++ b/python/ray/tune/examples/zoopt_example.py
@@ -7,6 +7,7 @@
 import time
 
 from ray import tune
+from ray.air import session
 from ray.tune.suggest.zoopt import ZOOptSearch
 from ray.tune.schedulers import AsyncHyperBandScheduler
 
@@ -24,7 +25,7 @@ def easy_objective(config):
         # Iterative training function - can be any arbitrary training procedure
         intermediate_score = evaluation_fn(step, width, height)
         # Feed the score back back to Tune.
-        tune.report(iterations=step, mean_loss=intermediate_score)
+        session.report({"iterations": step, "mean_loss": intermediate_score})
 
 
 if __name__ == "__main__":
diff --git a/python/ray/tune/session.py b/python/ray/tune/session.py
index f58e21a633f9..d79564de1e34 100644
--- a/python/ray/tune/session.py
+++ b/python/ray/tune/session.py
@@ -2,6 +2,7 @@
 import logging
 import os
 import traceback
+import warnings
 from contextlib import contextmanager
 from typing import Dict, Optional, Set
 
@@ -24,6 +25,13 @@
 # V2 Session API.
 _session_v2: Optional["_TuneSessionImpl"] = None
 
+_deprecation_msg = (
+    "`tune.report` and `tune.checkpoint_dir` APIs are deprecated in Ray "
+    "2.0, and is replaced by `ray.air.session`. This will provide an easy-"
+    "to-use API across Tune session and Data parallel worker sessions."
+    "The old APIs will be removed in the future. "
+)
+
 
 class _TuneSessionImpl(Session):
     """Session client that function trainable can interact with."""
@@ -214,6 +222,10 @@ def run_me(config):
         **kwargs: Any key value pair to be logged by Tune. Any of these
             metrics can be used for early stopping or optimization.
     """
+    warnings.warn(
+        _deprecation_msg,
+        DeprecationWarning,
+    )
     _session = get_session()
     if _session:
         if _session._iter:
@@ -274,6 +286,11 @@ def func(config, checkpoint_dir=None):
 
     .. versionadded:: 0.8.7
     """
+    warnings.warn(
+        _deprecation_msg,
+        DeprecationWarning,
+    )
+
     _session = get_session()
 
     if step is None:
@@ -301,6 +318,10 @@ def get_trial_dir():
 
     For function API use only.
     """
+    warnings.warn(
+        _deprecation_msg,
+        DeprecationWarning,
+    )
     _session = get_session()
     if _session:
         return _session.logdir
@@ -312,6 +333,10 @@ def get_trial_name():
 
     For function API use only.
     """
+    warnings.warn(
+        _deprecation_msg,
+        DeprecationWarning,
+    )
     _session = get_session()
     if _session:
         return _session.trial_name
@@ -323,6 +348,10 @@ def get_trial_id():
 
     For function API use only.
     """
+    warnings.warn(
+        _deprecation_msg,
+        DeprecationWarning,
+    )
     _session = get_session()
     if _session:
         return _session.trial_id
@@ -337,6 +366,10 @@ def get_trial_resources():
 
     For function API use only.
     """
+    warnings.warn(
+        _deprecation_msg,
+        DeprecationWarning,
+    )
     _session = get_session()
     if _session:
         return _session.trial_resources
diff --git a/python/ray/tune/utils/trainable.py b/python/ray/tune/utils/trainable.py
index 587940a6e8a8..ce690a434e2e 100644
--- a/python/ray/tune/utils/trainable.py
+++ b/python/ray/tune/utils/trainable.py
@@ -320,11 +320,12 @@ def with_parameters(trainable, **kwargs):
     .. code-block:: python
 
         from ray import tune
+        from ray.air import session
 
         def train(config, data=None):
             for sample in data:
                 loss = update_model(sample)
-                tune.report(loss=loss)
+                session.report({"loss": loss})
 
         data = HugeDataset(download=True)
 

From 8a21ead29b36afa0d3886aaddfab64c54d7e6013 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Thu, 23 Jun 2022 15:48:53 -0700
Subject: [PATCH 02/28] typo

---
 doc/source/tune/tutorials/tune-metrics.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/source/tune/tutorials/tune-metrics.rst b/doc/source/tune/tutorials/tune-metrics.rst
index 8a8be276df87..fd2145c07a61 100644
--- a/doc/source/tune/tutorials/tune-metrics.rst
+++ b/doc/source/tune/tutorials/tune-metrics.rst
@@ -25,7 +25,7 @@ This simple callback just prints a metric each time a result is received:
 
     def train(config):
         for i in range(10):
-            session.report(metric=i)
+            session.report({"metric": i})
 
 
     tune.run(
@@ -47,7 +47,7 @@ You can log arbitrary values and metrics in both Function and Class training API
     def trainable(config):
         for i in range(num_epochs):
             ...
-            session.report(acc=accuracy, metric_foo=random_metric_1, bar=metric_2)
+            session.report({"acc": accuracy, "metric_foo": random_metric_1, "bar": metric_2})
 
     class Trainable(tune.Trainable):
         def step(self):

From d7640c4afd9271501b1308105e0d53075c22427d Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Sun, 26 Jun 2022 16:44:10 -0700
Subject: [PATCH 03/28] fix tests.

---
 doc/source/tune/api_docs/schedulers.rst              |  2 +-
 doc/source/tune/api_docs/trainable.rst               |  2 +-
 doc/source/tune/tutorials/tune-lifecycle.rst         |  2 +-
 python/ray/train/data_parallel_trainer.py            |  2 +-
 .../ray/tune/examples/custom_func_checkpointing.py   | 12 +++++++-----
 .../examples/xgboost_dynamic_resources_example.py    |  5 ++---
 6 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/doc/source/tune/api_docs/schedulers.rst b/doc/source/tune/api_docs/schedulers.rst
index 8abd694209ec..07e4405097db 100644
--- a/doc/source/tune/api_docs/schedulers.rst
+++ b/doc/source/tune/api_docs/schedulers.rst
@@ -245,7 +245,7 @@ It wraps around another scheduler and uses its decisions.
     by calling ``Trainable.trial_resources``.
 
 * If you are using the functional API for tuning, the current trial resources can be
-    obtained by calling `session.get_trial_resources()` inside the training function.
+    obtained by calling `tune.get_trial_resources()` inside the training function.
     The function should be able to :ref:`load and save checkpoints <tune-checkpoint-syncing>`
     (the latter preferably every iteration).
 
diff --git a/doc/source/tune/api_docs/trainable.rst b/doc/source/tune/api_docs/trainable.rst
index 26733583fd39..7de1f595fc79 100644
--- a/doc/source/tune/api_docs/trainable.rst
+++ b/doc/source/tune/api_docs/trainable.rst
@@ -5,7 +5,7 @@
 .. TODO: Reusing actors and advanced resources allocation seem ill-placed.
 
 Training (tune.Trainable, session.report)
-======================================
+==========================================
 
 Training can be done with either a **Class API** (``tune.Trainable``) or **function API** (``session.report``).
 
diff --git a/doc/source/tune/tutorials/tune-lifecycle.rst b/doc/source/tune/tutorials/tune-lifecycle.rst
index 413646b77f98..b0457313b276 100644
--- a/doc/source/tune/tutorials/tune-lifecycle.rst
+++ b/doc/source/tune/tutorials/tune-lifecycle.rst
@@ -182,7 +182,7 @@ and also are given the ability to reorder/prioritize incoming trials.
 
 Trainables
 ~~~~~~~~~~
-[`source code <https://github.com/ray-project/ray/blob/master/python/ray/tune/trainable.py>`__]
+[`source code <https://github.com/ray-project/ray/blob/master/python/ray/tune/trainable/trainable.py>`__]
 These are user-provided objects that are used for
 the training process. If a class is provided, it is expected to conform to the
 Trainable interface. If a function is provided. it is wrapped into a
diff --git a/python/ray/train/data_parallel_trainer.py b/python/ray/train/data_parallel_trainer.py
index 3e448dfed553..e1749b725d62 100644
--- a/python/ray/train/data_parallel_trainer.py
+++ b/python/ray/train/data_parallel_trainer.py
@@ -361,7 +361,7 @@ def training_loop(self) -> None:
             # TODO(ml-team): add ability to report results from multiple workers.
             first_worker_results = results[0]
 
-            session.report(**first_worker_results)
+            session.report(first_worker_results)
 
         # Shutdown workers.
         backend_executor.shutdown()
diff --git a/python/ray/tune/examples/custom_func_checkpointing.py b/python/ray/tune/examples/custom_func_checkpointing.py
index 638e5fcbb013..f9de9a9b4183 100644
--- a/python/ray/tune/examples/custom_func_checkpointing.py
+++ b/python/ray/tune/examples/custom_func_checkpointing.py
@@ -3,12 +3,12 @@
 # checkpoint through ``ray.air.session`` APIs.
 import os
 import time
-import json
 import argparse
 
 from ray import tune
 from ray.air import session
-from ray.air.checkpoint import Checkpoint
+from ray.air.checkpoint import Checkpoint, _DICT_CHECKPOINT_FILE_NAME
+from ray import cloudpickle as pickle
 
 
 def evaluation_fn(step, width, height):
@@ -69,6 +69,8 @@ def train_func(config):
         },
     )
     print("Best hyperparameters: ", analysis.best_config)
-    print("Best checkpoint directory: ", analysis.best_checkpoint)
-    with open(os.path.join(analysis.best_checkpoint, "checkpoint"), "r") as f:
-        print("Best checkpoint: ", json.load(f))
+    best_checkpoint = analysis.best_checkpoint
+    with best_checkpoint.as_directory() as best_ckpt_dir:
+        print("Best checkpoint directory: ", best_ckpt_dir)
+        with open(os.path.join(best_ckpt_dir, _DICT_CHECKPOINT_FILE_NAME), "rb") as f:
+            print("Best checkpoint: ", pickle.load(f))
diff --git a/python/ray/tune/examples/xgboost_dynamic_resources_example.py b/python/ray/tune/examples/xgboost_dynamic_resources_example.py
index f2269a2129ac..35a6e9905646 100644
--- a/python/ray/tune/examples/xgboost_dynamic_resources_example.py
+++ b/python/ray/tune/examples/xgboost_dynamic_resources_example.py
@@ -9,7 +9,6 @@
 
 import ray
 from ray import tune
-from ray.air import session
 from ray.tune.schedulers import ResourceChangingScheduler, ASHAScheduler
 from ray.tune import Trainable
 from ray.tune.resources import Resources
@@ -58,8 +57,8 @@ def train_breast_cancer(config: dict, checkpoint_dir=None):
         xgb_model.load_model(os.path.join(checkpoint_dir, CHECKPOINT_FILENAME))
 
     # we can obtain current trial resources through
-    # `session.get_trial_resources()`
-    config["nthread"] = int(session.get_trial_resources().head_cpus)
+    # `tune.get_trial_resources()`
+    config["nthread"] = int(tune.get_trial_resources().head_cpus)
     print(f"nthreads: {config['nthread']} xgb_model: {xgb_model}")
     # Train the classifier, using the Tune callback
     xgb.train(

From c1d48fec8634e48b68c992ab72cc3ca912c0b89c Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Sun, 26 Jun 2022 19:39:29 -0700
Subject: [PATCH 04/28] fix test

---
 python/ray/air/session.py                                | 2 +-
 python/ray/train/data_parallel_trainer.py                | 3 ++-
 python/ray/tune/examples/pbt_convnet_function_example.py | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/python/ray/air/session.py b/python/ray/air/session.py
index 057f43137e75..aacddd187772 100644
--- a/python/ray/air/session.py
+++ b/python/ray/air/session.py
@@ -64,7 +64,7 @@ def get_checkpoint() -> Optional[Checkpoint]:
 
     Returns:
         Checkpoint object if the session is currently being resumed.
-        Otherwise, return None.
+            Otherwise, return None.
 
     Example:
         .. code-block: python
diff --git a/python/ray/train/data_parallel_trainer.py b/python/ray/train/data_parallel_trainer.py
index e1749b725d62..59ed5cb0be4c 100644
--- a/python/ray/train/data_parallel_trainer.py
+++ b/python/ray/train/data_parallel_trainer.py
@@ -5,6 +5,7 @@
 from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Type, Union
 
 import ray
+from ray import tune
 from ray.air import session
 from ray.air.checkpoint import Checkpoint
 from ray.air.config import DatasetConfig, RunConfig, ScalingConfig
@@ -361,7 +362,7 @@ def training_loop(self) -> None:
             # TODO(ml-team): add ability to report results from multiple workers.
             first_worker_results = results[0]
 
-            session.report(first_worker_results)
+            tune.report(**first_worker_results)
 
         # Shutdown workers.
         backend_executor.shutdown()
diff --git a/python/ray/tune/examples/pbt_convnet_function_example.py b/python/ray/tune/examples/pbt_convnet_function_example.py
index 1387ef5603e1..a3a2affe317a 100644
--- a/python/ray/tune/examples/pbt_convnet_function_example.py
+++ b/python/ray/tune/examples/pbt_convnet_function_example.py
@@ -70,7 +70,7 @@ def test_best_model(analysis):
     """Test the best model given output of tune.run"""
     best_checkpoint_path = analysis.best_checkpoint
     best_model = ConvNet()
-    best_checkpoint = torch.load(os.path.join(best_checkpoint_path, "checkpoint"))
+    best_checkpoint = torch.load(os.path.join(best_checkpoint_path, "checkpoint.pt"))
     best_model.load_state_dict(best_checkpoint["model_state_dict"])
     # Note that test only runs on a small random set of the test data, thus the
     # accuracy may be different from metrics shown in tuning process.

From 1e74e4c05fc5a7d35e7822044a2064d7ef46fcf3 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Sun, 26 Jun 2022 21:44:13 -0700
Subject: [PATCH 05/28] fix lint

---
 python/ray/air/session.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/python/ray/air/session.py b/python/ray/air/session.py
index aacddd187772..b920b0fab52d 100644
--- a/python/ray/air/session.py
+++ b/python/ray/air/session.py
@@ -74,8 +74,7 @@ def get_checkpoint() -> Optional[Checkpoint]:
         from ray.air.checkpoint import Checkpoint
         def train_func():
             if session.get_checkpoint():
-                with session.get_checkpoint().as_directory() as
-                        loaded_checkpoint_dir:
+                with session.get_checkpoint().as_directory() as loaded_checkpoint_dir:
                     import tensorflow as tf
                     model = tf.keras.models.load_model(loaded_checkpoint_dir)
             else:
@@ -83,8 +82,7 @@ def train_func():
 
             model.save("my_model", overwrite=True)
             session.report(
-                metrics={"iter": 1},
-                checkpoint=Checkpoint.from_directory("my_model")
+                metrics={"iter": 1}, checkpoint=Checkpoint.from_directory("my_model")
             )
 
         scaling_config = {"num_workers": 2}

From 429db43522634c1462748129cdada0ee2d4b4524 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Mon, 27 Jun 2022 07:28:50 -0700
Subject: [PATCH 06/28] lint

---
 python/ray/air/session.py | 59 ++++++++++++++++++++-------------------
 1 file changed, 31 insertions(+), 28 deletions(-)

diff --git a/python/ray/air/session.py b/python/ray/air/session.py
index b920b0fab52d..ba945a0782e6 100644
--- a/python/ray/air/session.py
+++ b/python/ray/air/session.py
@@ -69,37 +69,40 @@ def get_checkpoint() -> Optional[Checkpoint]:
     Example:
         .. code-block: python
 
-        ######## Using it in the *per worker* train loop (TrainSession) ######
-        from ray.air import session
-        from ray.air.checkpoint import Checkpoint
-        def train_func():
-            if session.get_checkpoint():
-                with session.get_checkpoint().as_directory() as loaded_checkpoint_dir:
-                    import tensorflow as tf
-                    model = tf.keras.models.load_model(loaded_checkpoint_dir)
-            else:
-                model = build_model()
+            ######## Using it in the *per worker* train loop (TrainSession) ######
+            from ray.air import session
+            from ray.air.checkpoint import Checkpoint
+            def train_func():
+                ckpt = session.get_checkpoint()
+                if ckpt:
+                    with ckpt.as_directory() as loaded_checkpoint_dir:
+                        import tensorflow as tf
+                        
+                        model = tf.keras.models.load_model(loaded_checkpoint_dir)
+                else:
+                    model = build_model()
+
+                model.save("my_model", overwrite=True)
+                session.report(
+                    metrics={"iter": 1},
+                    checkpoint=Checkpoint.from_directory("my_model")
+                )
 
-            model.save("my_model", overwrite=True)
-            session.report(
-                metrics={"iter": 1}, checkpoint=Checkpoint.from_directory("my_model")
+            scaling_config = {"num_workers": 2}
+            trainer = TensorflowTrainer(
+                train_loop_per_worker=train_func, scaling_config=scaling_config
             )
+            result = trainer.fit()
 
-        scaling_config = {"num_workers": 2}
-        trainer = TensorflowTrainer(
-            train_loop_per_worker=train_func, scaling_config=scaling_config
-        )
-        result = trainer.fit()
-
-        # trainer2 will pick up from the checkpoint saved by trainer1.
-        trainer2 = TensorflowTrainer(
-            train_loop_per_worker=train_func,
-            scaling_config=scaling_config,
-            # this is ultimately what is accessed through
-            # ``Session.get_checkpoint()``
-            resume_from_checkpoint=result.checkpoint,
-        )
-        result2 = trainer2.fit()
+            # trainer2 will pick up from the checkpoint saved by trainer1.
+            trainer2 = TensorflowTrainer(
+                train_loop_per_worker=train_func,
+                scaling_config=scaling_config,
+                # this is ultimately what is accessed through
+                # ``Session.get_checkpoint()``
+                resume_from_checkpoint=result.checkpoint,
+            )
+            result2 = trainer2.fit()
     """
 
     return _get_session().loaded_checkpoint

From f6b6eba92b27ae0f37d0061dd393cd98f9475896 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Mon, 27 Jun 2022 09:21:46 -0700
Subject: [PATCH 07/28] fix lint again...

---
 python/ray/air/session.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/ray/air/session.py b/python/ray/air/session.py
index ba945a0782e6..8f2d1b0eca4d 100644
--- a/python/ray/air/session.py
+++ b/python/ray/air/session.py
@@ -77,7 +77,7 @@ def train_func():
                 if ckpt:
                     with ckpt.as_directory() as loaded_checkpoint_dir:
                         import tensorflow as tf
-                        
+
                         model = tf.keras.models.load_model(loaded_checkpoint_dir)
                 else:
                     model = build_model()

From 3d3421dc06706b5600f3f70980f2355747fc95ca Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Mon, 27 Jun 2022 10:13:37 -0700
Subject: [PATCH 08/28] fix lint...

---
 doc/source/tune/examples/optuna_example.ipynb | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/source/tune/examples/optuna_example.ipynb b/doc/source/tune/examples/optuna_example.ipynb
index 4b1a22fb4871..3a0f4228e59f 100644
--- a/doc/source/tune/examples/optuna_example.ipynb
+++ b/doc/source/tune/examples/optuna_example.ipynb
@@ -123,6 +123,7 @@
     "    for step in range(config[\"steps\"]):\n",
     "        score = evaluate(step, config[\"width\"], config[\"height\"], config[\"activation\"])\n",
     "        session.report({\"iterations\": step, \"mean_loss\": score})\n",
+    "                  "
    ]
   },
   {

From 5e156de500c2c32e9a293998e42081989dbb8b44 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Mon, 27 Jun 2022 11:16:52 -0700
Subject: [PATCH 09/28] Add session to Ray Air doc.

---
 doc/source/ray-air/key-concepts.rst | 20 ++++++++++++++++++++
 doc/source/ray-air/package-ref.rst  |  9 +++++++++
 2 files changed, 29 insertions(+)

diff --git a/doc/source/ray-air/key-concepts.rst b/doc/source/ray-air/key-concepts.rst
index 83edeee93463..60aba4ef4ea1 100644
--- a/doc/source/ray-air/key-concepts.rst
+++ b/doc/source/ray-air/key-concepts.rst
@@ -47,6 +47,26 @@ Trainer objects will produce a :ref:`Results <air-results-ref>` object after cal
     :end-before: __air_trainer_output_end__
 
 
+Session
+-------
+
+AIR exposes a functional API for users to define training behavior, or for developers to create their own trainers.
+In both cases, there is a need for the following interactions:
+1. To disseminate information downstream, including trial_name, trial_id, trial_resources and rank information etc.
+2. To report information to upstream, including metrics and checkpoint.
+
+To facilitate such interactions, we introduce :ref:`Session <air-session-ref>`. The following figure shows how Session
+looks like in a DataParallel training scenario and the conceptual image of Session at different levels (what we call
+as Tune Session and Train Session).
+
+.. image:: images/session.svg
+   :width: 650px
+   :align: center
+
+..
+  https://docs.google.com/drawings/d/1g0pv8gqgG29aPEPTcd4BC0LaRNbW1sAkv3H6W1TCp0c/edit
+
+
 Tuner
 -----
 
diff --git a/doc/source/ray-air/package-ref.rst b/doc/source/ray-air/package-ref.rst
index 3b57ecfe6dc5..9b3261983711 100644
--- a/doc/source/ray-air/package-ref.rst
+++ b/doc/source/ray-air/package-ref.rst
@@ -124,3 +124,12 @@ Configs
 .. automodule:: ray.air.config
     :members:
 
+
+.. _air-session-ref:
+
+Session
+##########
+
+.. automodule:: ray.air.session
+    :members:
+

From f215cd95a30ebab15371b25dfca44eef8695f8b2 Mon Sep 17 00:00:00 2001
From: xwjiang2010 <87673679+xwjiang2010@users.noreply.github.com>
Date: Mon, 27 Jun 2022 11:33:58 -0700
Subject: [PATCH 10/28] Update doc/source/ray-air/key-concepts.rst

Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
---
 doc/source/ray-air/key-concepts.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/ray-air/key-concepts.rst b/doc/source/ray-air/key-concepts.rst
index 60aba4ef4ea1..b4a7b704cd72 100644
--- a/doc/source/ray-air/key-concepts.rst
+++ b/doc/source/ray-air/key-concepts.rst
@@ -50,7 +50,7 @@ Trainer objects will produce a :ref:`Results <air-results-ref>` object after cal
 Session
 -------
 
-AIR exposes a functional API for users to define training behavior, or for developers to create their own trainers.
+Ray AIR exposes a functional API for users to define training behavior, or for developers to create their own ``Trainer``\s.
 In both cases, there is a need for the following interactions:
 1. To disseminate information downstream, including trial_name, trial_id, trial_resources and rank information etc.
 2. To report information to upstream, including metrics and checkpoint.

From 6a01a1cd2a739e82077bbe9c99d488fa6fbd6435 Mon Sep 17 00:00:00 2001
From: xwjiang2010 <87673679+xwjiang2010@users.noreply.github.com>
Date: Mon, 27 Jun 2022 11:34:13 -0700
Subject: [PATCH 11/28] Update doc/source/ray-air/key-concepts.rst

Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
---
 doc/source/ray-air/key-concepts.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/ray-air/key-concepts.rst b/doc/source/ray-air/key-concepts.rst
index b4a7b704cd72..a69349352382 100644
--- a/doc/source/ray-air/key-concepts.rst
+++ b/doc/source/ray-air/key-concepts.rst
@@ -52,7 +52,7 @@ Session
 
 Ray AIR exposes a functional API for users to define training behavior, or for developers to create their own ``Trainer``\s.
 In both cases, there is a need for the following interactions:
-1. To disseminate information downstream, including trial_name, trial_id, trial_resources and rank information etc.
+1. To disseminate information downstream, including ``trial_name``, ``trial_id``, ``trial_resources`, rank information etc.
 2. To report information to upstream, including metrics and checkpoint.
 
 To facilitate such interactions, we introduce :ref:`Session <air-session-ref>`. The following figure shows how Session

From be6ad70330d55069dc01b5499335047f8fed8ae5 Mon Sep 17 00:00:00 2001
From: xwjiang2010 <87673679+xwjiang2010@users.noreply.github.com>
Date: Mon, 27 Jun 2022 11:34:17 -0700
Subject: [PATCH 12/28] Update doc/source/ray-air/key-concepts.rst

Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
---
 doc/source/ray-air/key-concepts.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/ray-air/key-concepts.rst b/doc/source/ray-air/key-concepts.rst
index a69349352382..1650e68a9fec 100644
--- a/doc/source/ray-air/key-concepts.rst
+++ b/doc/source/ray-air/key-concepts.rst
@@ -56,7 +56,7 @@ In both cases, there is a need for the following interactions:
 2. To report information to upstream, including metrics and checkpoint.
 
 To facilitate such interactions, we introduce :ref:`Session <air-session-ref>`. The following figure shows how Session
-looks like in a DataParallel training scenario and the conceptual image of Session at different levels (what we call
+looks like in a Data Parallel training scenario and the conceptual image of Session at different levels (what we call
 as Tune Session and Train Session).
 
 .. image:: images/session.svg

From bd62bf644b632a6c7b1490633f9fab6870915692 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Mon, 27 Jun 2022 13:22:17 -0700
Subject: [PATCH 13/28] fix lint

---
 doc/source/ray-air/key-concepts.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/ray-air/key-concepts.rst b/doc/source/ray-air/key-concepts.rst
index 1650e68a9fec..64ca2c3a268a 100644
--- a/doc/source/ray-air/key-concepts.rst
+++ b/doc/source/ray-air/key-concepts.rst
@@ -52,7 +52,7 @@ Session
 
 Ray AIR exposes a functional API for users to define training behavior, or for developers to create their own ``Trainer``\s.
 In both cases, there is a need for the following interactions:
-1. To disseminate information downstream, including ``trial_name``, ``trial_id``, ``trial_resources`, rank information etc.
+1. To disseminate information downstream, including ``trial_name``, ``trial_id``, ``trial_resources``, rank information etc.
 2. To report information to upstream, including metrics and checkpoint.
 
 To facilitate such interactions, we introduce :ref:`Session <air-session-ref>`. The following figure shows how Session

From a3ca89c7a94d8eb0c6020d66726f63e6a84a0197 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Mon, 27 Jun 2022 15:44:47 -0700
Subject: [PATCH 14/28] add svg

---
 doc/source/images/session.svg | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 doc/source/images/session.svg

diff --git a/doc/source/images/session.svg b/doc/source/images/session.svg
new file mode 100644
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+++ b/doc/source/images/session.svg
@@ -0,0 +1 @@
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\ No newline at end of file

From e5ba09b9ab911e9dfdbb1f12c2b7d6b8c1de9417 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Mon, 27 Jun 2022 17:13:57 -0700
Subject: [PATCH 15/28] add image

---
 doc/source/{ => ray-air}/images/session.svg | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename doc/source/{ => ray-air}/images/session.svg (100%)

diff --git a/doc/source/images/session.svg b/doc/source/ray-air/images/session.svg
similarity index 100%
rename from doc/source/images/session.svg
rename to doc/source/ray-air/images/session.svg

From b575d985ae11df30ea8331ae15e8df485c30b1b5 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Tue, 28 Jun 2022 07:24:41 -0700
Subject: [PATCH 16/28] fix lint

---
 doc/source/tune/api_docs/trainable.rst | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/doc/source/tune/api_docs/trainable.rst b/doc/source/tune/api_docs/trainable.rst
index 7de1f595fc79..61f5d0007bea 100644
--- a/doc/source/tune/api_docs/trainable.rst
+++ b/doc/source/tune/api_docs/trainable.rst
@@ -344,14 +344,19 @@ session (Function API)
 --------------------------------------------
 
 .. autofunction:: ray.air.session.report
+    :noindex:
 
 .. autofunction:: ray.air.session.get_checkpoint
+    :noindex:
 
 .. autofunction:: ray.air.session.get_trial_name
+    :noindex:
 
 .. autofunction:: ray.air.session.get_trial_id
+    :noindex:
 
 .. autofunction:: ray.air.session.get_trial_resources
+    :noindex:
 
 tune.Trainable (Class API)
 --------------------------

From 11c069d86baf39e935867c6092bed7fe49487545 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Tue, 28 Jun 2022 09:34:39 -0700
Subject: [PATCH 17/28] address comments

---
 doc/source/ray-air/images/session.svg         |  2 +-
 doc/source/ray-air/key-concepts.rst           |  9 ++++--
 doc/source/tune/api_docs/trainable.rst        | 32 +------------------
 doc/source/tune/tutorials/tune-output.rst     |  1 +
 python/ray/tune/tests/test_function_api.py    | 14 ++++++++
 .../ray/tune/trainable/function_trainable.py  | 10 ++++++
 6 files changed, 33 insertions(+), 35 deletions(-)

diff --git a/doc/source/ray-air/images/session.svg b/doc/source/ray-air/images/session.svg
index bf8c92354490..083bc1d980c6 100644
--- a/doc/source/ray-air/images/session.svg
+++ b/doc/source/ray-air/images/session.svg
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/doc/source/ray-air/key-concepts.rst b/doc/source/ray-air/key-concepts.rst
index 64ca2c3a268a..aa3c99870c26 100644
--- a/doc/source/ray-air/key-concepts.rst
+++ b/doc/source/ray-air/key-concepts.rst
@@ -52,12 +52,15 @@ Session
 
 Ray AIR exposes a functional API for users to define training behavior, or for developers to create their own ``Trainer``\s.
 In both cases, there is a need for the following interactions:
+
 1. To disseminate information downstream, including ``trial_name``, ``trial_id``, ``trial_resources``, rank information etc.
 2. To report information to upstream, including metrics and checkpoint.
 
-To facilitate such interactions, we introduce :ref:`Session <air-session-ref>`. The following figure shows how Session
-looks like in a Data Parallel training scenario and the conceptual image of Session at different levels (what we call
-as Tune Session and Train Session).
+To facilitate such interactions, we introduce the :ref:`Session <air-session-ref>` concept.
+
+The session concept exists on several levels: The execution layer (called `Tune Session`) and the Data Parallel training layer
+(called `Train Session`).
+The following figure shows how these two sessions look like in a Data Parallel training scenario.
 
 .. image:: images/session.svg
    :width: 650px
diff --git a/doc/source/tune/api_docs/trainable.rst b/doc/source/tune/api_docs/trainable.rst
index 61f5d0007bea..19597054ced8 100644
--- a/doc/source/tune/api_docs/trainable.rst
+++ b/doc/source/tune/api_docs/trainable.rst
@@ -47,36 +47,6 @@ Tune will run this function on a separate thread in a Ray actor process.
 You'll notice that Ray Tune will output extra values in addition to the user reported metrics,
 such as ``iterations_since_restore``. See :ref:`tune-autofilled-metrics` for an explanation/glossary of these values.
 
-Function API return and yield values
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Instead of using ``session.report()``, you can also use Python's ``yield``
-statement to report metrics to Ray Tune:
-
-
-.. code-block:: python
-
-    def trainable(config):
-        # config (dict): A dict of hyperparameters.
-
-        for x in range(20):
-            intermediate_score = objective(x, config["a"], config["b"])
-
-            yield {"score": intermediate_score}  # This sends the score to Tune.
-
-    analysis = tune.run(
-        trainable,
-        config={"a": 2, "b": 4}
-    )
-
-    print("best config: ", analysis.get_best_config(metric="score", mode="max"))
-
-If you yield a dictionary object, this will work just as ``session.report()``.
-If you yield a number, if will be reported to Ray Tune with the key ``_metric``, i.e.
-as if you had called ``session.report({"_metric": value})``.
-
-Ray Tune supports the same functionality for return values if you only
-report metrics at the end of each run:
-
 .. code-block:: python
 
     def trainable(config):
@@ -341,7 +311,7 @@ It is also possible to specify memory (``"memory"``, in bytes) and custom resour
 .. _tune-function-docstring:
 
 session (Function API)
---------------------------------------------
+----------------------
 
 .. autofunction:: ray.air.session.report
     :noindex:
diff --git a/doc/source/tune/tutorials/tune-output.rst b/doc/source/tune/tutorials/tune-output.rst
index e85198e45123..ea1ad31e877b 100644
--- a/doc/source/tune/tutorials/tune-output.rst
+++ b/doc/source/tune/tutorials/tune-output.rst
@@ -183,6 +183,7 @@ You can do this in the trainable, as shown below:
     ``library`` refers to whatever 3rd party logging library you are using.
 
     .. code-block:: python
+
         from ray.air import session
 
         def trainable(config):
diff --git a/python/ray/tune/tests/test_function_api.py b/python/ray/tune/tests/test_function_api.py
index 1697cdcbae50..e75e62fa6f69 100644
--- a/python/ray/tune/tests/test_function_api.py
+++ b/python/ray/tune/tests/test_function_api.py
@@ -1,9 +1,11 @@
 import json
+import logging
 import os
 import sys
 import shutil
 import tempfile
 import unittest
+import warnings
 
 import ray
 from ray.rllib import _register_all
@@ -593,6 +595,18 @@ def train(config):
         self.assertEqual(trial_2.last_result["m"], 8 + 9)
 
 
+def test_checkpoint_dir_deprecation():
+    def train(config, checkpoint_dir=None):
+        for i in range(10):
+            tune.report({"foo": "bar"})
+
+    with warnings.catch_warnings(record=True) as w:
+        tune.run(train, num_samples=1)
+        assert len(w) == 1
+        assert issubclass(w[-1].category, DeprecationWarning)
+        assert "To save and load checkpoint in tune function" in str(w[-1].message)
+
+
 if __name__ == "__main__":
     import pytest
 
diff --git a/python/ray/tune/trainable/function_trainable.py b/python/ray/tune/trainable/function_trainable.py
index f5aa63c279c3..fc34823684d1 100644
--- a/python/ray/tune/trainable/function_trainable.py
+++ b/python/ray/tune/trainable/function_trainable.py
@@ -6,6 +6,7 @@
 import threading
 import time
 import uuid
+import warnings
 from functools import partial
 from numbers import Number
 from typing import Any, Callable, Dict, Optional
@@ -618,6 +619,15 @@ def wrap_function(
                 "arguments to be `func(config, checkpoint_dir=None)`."
             )
 
+    if use_checkpoint:
+        if log_once("tune_checkpoint_dir_deprecation") and warn:
+            warnings.warn(
+                "`checkpoint_dir` in `func(config, checkpoint_dir)` is being deprecated."
+                "To save and load checkpoint in tune function, "
+                "please use `ray.air.session` API.",
+                DeprecationWarning,
+            )
+
     class ImplicitFunc(*inherit_from):
         _name = name or (
             train_func.__name__ if hasattr(train_func, "__name__") else "func"

From f6f24c8c1ae16471c3385f8c6efd407b9c1cdc20 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Tue, 28 Jun 2022 10:16:32 -0700
Subject: [PATCH 18/28] lint

---
 python/ray/tune/tests/test_function_api.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/python/ray/tune/tests/test_function_api.py b/python/ray/tune/tests/test_function_api.py
index e75e62fa6f69..f24b8436ffae 100644
--- a/python/ray/tune/tests/test_function_api.py
+++ b/python/ray/tune/tests/test_function_api.py
@@ -1,5 +1,4 @@
 import json
-import logging
 import os
 import sys
 import shutil

From 29a88c2b9007d37cb7d68336b652b3c6b37c0962 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Tue, 28 Jun 2022 11:09:46 -0700
Subject: [PATCH 19/28] lint

---
 python/ray/tune/trainable/function_trainable.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/ray/tune/trainable/function_trainable.py b/python/ray/tune/trainable/function_trainable.py
index fc34823684d1..f5697dc1b92a 100644
--- a/python/ray/tune/trainable/function_trainable.py
+++ b/python/ray/tune/trainable/function_trainable.py
@@ -622,8 +622,8 @@ def wrap_function(
     if use_checkpoint:
         if log_once("tune_checkpoint_dir_deprecation") and warn:
             warnings.warn(
-                "`checkpoint_dir` in `func(config, checkpoint_dir)` is being deprecated."
-                "To save and load checkpoint in tune function, "
+                "`checkpoint_dir` in `func(config, checkpoint_dir)` is being "
+                "deprecated. To save and load checkpoint in tune function, "
                 "please use `ray.air.session` API.",
                 DeprecationWarning,
             )

From 3f2f739d44d079ad352b96fb73df4cb5b13bbdbd Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Tue, 28 Jun 2022 14:48:19 -0700
Subject: [PATCH 20/28] warnings.filterwarnings("always")

---
 python/ray/tune/tests/test_function_api.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/python/ray/tune/tests/test_function_api.py b/python/ray/tune/tests/test_function_api.py
index f24b8436ffae..492cdda03374 100644
--- a/python/ray/tune/tests/test_function_api.py
+++ b/python/ray/tune/tests/test_function_api.py
@@ -595,6 +595,8 @@ def train(config):
 
 
 def test_checkpoint_dir_deprecation():
+    warnings.filterwarnings("always")
+
     def train(config, checkpoint_dir=None):
         for i in range(10):
             tune.report({"foo": "bar"})

From 0cfa8e49a13c8c7c666b9a521826a55844a41596 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Wed, 29 Jun 2022 07:24:29 -0700
Subject: [PATCH 21/28] move test to a separate file

---
 python/ray/tune/BUILD                         |  7 +++++
 .../tune/tests/test_function_api_legacy.py    | 29 +++++++++++++++++++
 2 files changed, 36 insertions(+)
 create mode 100644 python/ray/tune/tests/test_function_api_legacy.py

diff --git a/python/ray/tune/BUILD b/python/ray/tune/BUILD
index 31b3a0dfdb08..e4a6ffa8094f 100644
--- a/python/ray/tune/BUILD
+++ b/python/ray/tune/BUILD
@@ -128,6 +128,13 @@ py_test(
     tags = ["team:ml", "exclusive", "tests_dir_F"],
 )
 
+py_test(
+    name = "test_function_api_legacy",
+    size = "medium",
+    srcs = ["tests/test_function_api_legacy.py"],
+    deps = [":tune_lib"],
+    tags = ["team:ml", "exclusive", "tests_dir_F"],
+)
 
 py_test(
     name = "test_legacy_import",
diff --git a/python/ray/tune/tests/test_function_api_legacy.py b/python/ray/tune/tests/test_function_api_legacy.py
new file mode 100644
index 000000000000..d795d511537b
--- /dev/null
+++ b/python/ray/tune/tests/test_function_api_legacy.py
@@ -0,0 +1,29 @@
+import sys
+import warnings
+
+from ray import tune
+
+
+def test_checkpoint_dir_deprecation():
+    warnings.filterwarnings("always")
+
+    def train(config, checkpoint_dir=None):
+        for i in range(10):
+            tune.report({"foo": "bar"})
+
+    with warnings.catch_warnings(record=True) as w:
+        tune.run(train, num_samples=1)
+        found_pattern = False
+        for _w in w:
+            if issubclass(
+                _w.category, DeprecationWarning
+            ) and "To save and load checkpoint in tune function" in str(_w.message):
+                found_pattern = True
+                break
+        assert found_pattern
+
+
+if __name__ == "__main__":
+    import pytest
+
+    sys.exit(pytest.main(["-v", __file__]))

From b7a0db82276e719ce4fbcb93b017a6d3b381bfe2 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Wed, 29 Jun 2022 08:30:19 -0700
Subject: [PATCH 22/28] fix lint

---
 doc/source/tune/examples/tune-wandb.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/source/tune/examples/tune-wandb.ipynb b/doc/source/tune/examples/tune-wandb.ipynb
index 62ad45efaa6e..389b6e65d5d0 100644
--- a/doc/source/tune/examples/tune-wandb.ipynb
+++ b/doc/source/tune/examples/tune-wandb.ipynb
@@ -96,7 +96,7 @@
     "    for i in range(30):\n",
     "        loss = config[\"mean\"] + config[\"sd\"] * np.random.randn()\n",
     "        session.report({\"loss\": loss})"
-   ],
+   ]
   },
   {
    "cell_type": "markdown",

From 363a4aa7496f9dc7a6d5f6d669f17b9774bcc6c2 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Wed, 29 Jun 2022 11:09:43 -0700
Subject: [PATCH 23/28] remove test that is already in the other test file.

---
 python/ray/tune/tests/test_function_api.py | 15 ---------------
 1 file changed, 15 deletions(-)

diff --git a/python/ray/tune/tests/test_function_api.py b/python/ray/tune/tests/test_function_api.py
index 492cdda03374..1697cdcbae50 100644
--- a/python/ray/tune/tests/test_function_api.py
+++ b/python/ray/tune/tests/test_function_api.py
@@ -4,7 +4,6 @@
 import shutil
 import tempfile
 import unittest
-import warnings
 
 import ray
 from ray.rllib import _register_all
@@ -594,20 +593,6 @@ def train(config):
         self.assertEqual(trial_2.last_result["m"], 8 + 9)
 
 
-def test_checkpoint_dir_deprecation():
-    warnings.filterwarnings("always")
-
-    def train(config, checkpoint_dir=None):
-        for i in range(10):
-            tune.report({"foo": "bar"})
-
-    with warnings.catch_warnings(record=True) as w:
-        tune.run(train, num_samples=1)
-        assert len(w) == 1
-        assert issubclass(w[-1].category, DeprecationWarning)
-        assert "To save and load checkpoint in tune function" in str(w[-1].message)
-
-
 if __name__ == "__main__":
     import pytest
 

From da8f0b1b1e5cd4515dc49f68ad01322fd8d84356 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Wed, 29 Jun 2022 12:18:49 -0700
Subject: [PATCH 24/28] retrigger checks


From f6e29f0c87d6dacb5ae8ce7019fdf1980451d8f4 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Wed, 29 Jun 2022 19:30:13 -0700
Subject: [PATCH 25/28] address comments

---
 .../tune/tests/test_function_api_legacy.py    |  4 +++-
 .../ray/tune/trainable/function_trainable.py  | 24 ++++++++++++++-----
 2 files changed, 21 insertions(+), 7 deletions(-)

diff --git a/python/ray/tune/tests/test_function_api_legacy.py b/python/ray/tune/tests/test_function_api_legacy.py
index d795d511537b..3d388e3d00f8 100644
--- a/python/ray/tune/tests/test_function_api_legacy.py
+++ b/python/ray/tune/tests/test_function_api_legacy.py
@@ -17,7 +17,9 @@ def train(config, checkpoint_dir=None):
         for _w in w:
             if issubclass(
                 _w.category, DeprecationWarning
-            ) and "To save and load checkpoint in tune function" in str(_w.message):
+            ) and "To save and load checkpoint in trainable function" in str(
+                _w.message
+            ):
                 found_pattern = True
                 break
         assert found_pattern
diff --git a/python/ray/tune/trainable/function_trainable.py b/python/ray/tune/trainable/function_trainable.py
index 1eb3cfa67b13..abd1e9b0e8b6 100644
--- a/python/ray/tune/trainable/function_trainable.py
+++ b/python/ray/tune/trainable/function_trainable.py
@@ -623,12 +623,24 @@ def wrap_function(
 
     if use_checkpoint:
         if log_once("tune_checkpoint_dir_deprecation") and warn:
-            warnings.warn(
-                "`checkpoint_dir` in `func(config, checkpoint_dir)` is being "
-                "deprecated. To save and load checkpoint in tune function, "
-                "please use `ray.air.session` API.",
-                DeprecationWarning,
-            )
+            with warnings.catch_warnings():
+                warnings.simplefilter("always")
+                warning_msg = (
+                    "`checkpoint_dir` in `func(config, checkpoint_dir)` is "
+                    "being deprecated. "
+                    "To save and load checkpoint in trainable functions, "
+                    "please use the `ray.air.session` API:\n\n"
+                    "from ray.air import session\n\n"
+                    "def train(config):\n"
+                    "    # ...\n"
+                    '    session.report({"metric": metric}, checkpoint=checkpoint)\n\n'
+                    "For more information please see "
+                    "https://docs.ray.io/en/master/ray-air/key-concepts.html#session\n"
+                )
+                warnings.warn(
+                    warning_msg,
+                    DeprecationWarning,
+                )
 
     class ImplicitFunc(*inherit_from):
         _name = name or (

From d3b1b8da63f4e24eb879432a08a057c9816e90f8 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Wed, 29 Jun 2022 19:31:28 -0700
Subject: [PATCH 26/28] retrigger checks


From 7ceb6c0447d6822d48d854cef2c5f3f1c73ccfd7 Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Fri, 1 Jul 2022 11:20:01 -0700
Subject: [PATCH 27/28] [air] Do not warn of `checkpoint_dir` if it's coming
 from us (base_trainer).

This will be removed once internally we migrate to use `session` API.
---
 python/ray/tune/trainable/function_trainable.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/python/ray/tune/trainable/function_trainable.py b/python/ray/tune/trainable/function_trainable.py
index abd1e9b0e8b6..0a1cd0bfaa3d 100644
--- a/python/ray/tune/trainable/function_trainable.py
+++ b/python/ray/tune/trainable/function_trainable.py
@@ -622,7 +622,17 @@ def wrap_function(
             )
 
     if use_checkpoint:
-        if log_once("tune_checkpoint_dir_deprecation") and warn:
+        import traceback
+
+        tb_list = traceback.format_list(traceback.extract_stack())
+        # Do not warn user if "base_trainer" is in the stacktrace!!
+        # TODO(xwjiang): Remove this, when our internal impl is using new API.
+        has_base_trainer = any("base_trainer" in tb for tb in tb_list)
+        if (
+            log_once("tune_checkpoint_dir_deprecation")
+            and warn
+            and not has_base_trainer
+        ):
             with warnings.catch_warnings():
                 warnings.simplefilter("always")
                 warning_msg = (

From e23deab6ba56d33aa0c60bf88fed4230586f675e Mon Sep 17 00:00:00 2001
From: Xiaowei Jiang <xwjiang2010@gmail.com>
Date: Sat, 2 Jul 2022 08:04:14 -0700
Subject: [PATCH 28/28] address comment

---
 python/ray/train/base_trainer.py                |  2 +-
 python/ray/tune/trainable/function_trainable.py | 11 +----------
 2 files changed, 2 insertions(+), 11 deletions(-)

diff --git a/python/ray/train/base_trainer.py b/python/ray/train/base_trainer.py
index 7458d9dc1b4b..c07f71b80d57 100644
--- a/python/ray/train/base_trainer.py
+++ b/python/ray/train/base_trainer.py
@@ -353,7 +353,7 @@ def train_func(config, checkpoint_dir=None):
         # stdout messages and the results directory.
         train_func.__name__ = trainer_cls.__name__
 
-        trainable_cls = wrap_function(train_func)
+        trainable_cls = wrap_function(train_func, warn=False)
 
         class TrainTrainable(trainable_cls):
             """Add default resources to the Trainable."""
diff --git a/python/ray/tune/trainable/function_trainable.py b/python/ray/tune/trainable/function_trainable.py
index 29604d4d0f6f..abd1e9b0e8b6 100644
--- a/python/ray/tune/trainable/function_trainable.py
+++ b/python/ray/tune/trainable/function_trainable.py
@@ -622,16 +622,7 @@ def wrap_function(
             )
 
     if use_checkpoint:
-        import traceback
-        tb_list = traceback.format_list(traceback.extract_stack())
-        # Do not warn user if "base_trainer" is in the stacktrace!!
-        # TODO(xwjiang): Remove this, when our internal impl is using new API.
-        has_base_trainer = any("base_trainer" in tb for tb in tb_list)
-        if (
-            log_once("tune_checkpoint_dir_deprecation")
-            and warn
-            and not has_base_trainer
-        ):
+        if log_once("tune_checkpoint_dir_deprecation") and warn:
             with warnings.catch_warnings():
                 warnings.simplefilter("always")
                 warning_msg = (