[api] Initial API deprecations for Ray 1.0

BUILD.bazel

@@ -1783,11 +1783,6 @@ filegroup(
         "python/ray/experimental/*.py",
         "python/ray/util/*.py",
         "python/ray/internal/*.py",
-        "python/ray/projects/*.py",
-        "python/ray/projects/schema.json",
-        "python/ray/projects/templates/cluster_template.yaml",
-        "python/ray/projects/templates/project_template.yaml",
-        "python/ray/projects/templates/requirements.txt",
         "python/ray/workers/default_worker.py",
     ]),
 )

ci/long_running_distributed_tests/workloads/pytorch_pbt_failure.py

@@ -93,7 +93,7 @@ def optimizer_creator(model, config):
         momentum=config.get("momentum", 0.9))
 
 
-ray.init(address="auto" if not args.smoke_test else None, log_to_driver=True)
+ray.init(address="auto" if not args.smoke_test else None, _log_to_driver=True)
 num_training_workers = 1 if args.smoke_test else 3
 
 executor = FailureInjectorExecutor(queue_trials=True)

ci/long_running_tests/workloads/serve.py

@@ -38,7 +38,8 @@
 @serve.accept_batch
 def echo(_):
     time.sleep(0.01)  # Sleep for 10ms
-    ray.show_in_webui(str(serve.context.batch_size), key="Current batch size")
+    ray.show_in_dashboard(
+        str(serve.context.batch_size), key="Current batch size")
     return ["hi {}".format(i) for i in range(serve.context.batch_size)]
 
 

ci/long_running_tests/workloads/serve_failure.py

@@ -26,7 +26,7 @@
         dashboard_host="0.0.0.0")
 
 ray.init(
-    address=cluster.address, dashboard_host="0.0.0.0", log_to_driver=False)
+    address=cluster.address, dashboard_host="0.0.0.0", _log_to_driver=False)
 serve.init()
 
 

ci/travis/ci.sh

@@ -153,7 +153,6 @@ test_python() {
       -python/ray/tests:test_multiprocessing  # test_connect_to_ray() fails to connect to raylet
       -python/ray/tests:test_node_manager
       -python/ray/tests:test_object_manager
-      -python/ray/tests:test_projects
       -python/ray/tests:test_ray_init  # test_redis_port() seems to fail here, but pass in isolation
       -python/ray/tests:test_resource_demand_scheduler
       -python/ray/tests:test_stress  # timeout
@@ -279,12 +278,12 @@ build_wheels() {
       # caused timeouts in the past. See the "cache: false" line below.
       local MOUNT_BAZEL_CACHE=(
         -v "${HOME}/ray-bazel-cache":/root/ray-bazel-cache
-        -e TRAVIS=true
-        -e TRAVIS_PULL_REQUEST="${TRAVIS_PULL_REQUEST:-false}"
-        -e encrypted_1c30b31fe1ee_key="${encrypted_1c30b31fe1ee_key-}"
-        -e encrypted_1c30b31fe1ee_iv="${encrypted_1c30b31fe1ee_iv-}"
-        -e TRAVIS_COMMIT="${TRAVIS_COMMIT}"
-        -e CI="${CI}"
+        -e "TRAVIS=true"
+        -e "TRAVIS_PULL_REQUEST=${TRAVIS_PULL_REQUEST:-false}"
+        -e "encrypted_1c30b31fe1ee_key=${encrypted_1c30b31fe1ee_key-}"
+        -e "encrypted_1c30b31fe1ee_iv=${encrypted_1c30b31fe1ee_iv-}"
+        -e "TRAVIS_COMMIT=${TRAVIS_COMMIT}"
+        -e "CI=${CI}"
       )
 
       # This command should be kept in sync with ray/python/README-building-wheels.md,

doc/source/actors.rst

@@ -72,7 +72,7 @@ Resources with Actors
 You can specify that an actor requires CPUs or GPUs in the decorator. While Ray has built-in support for CPUs and GPUs, Ray can also handle custom resources.
 
 When using GPUs, Ray will automatically set the environment variable ``CUDA_VISIBLE_DEVICES`` for the actor after instantiated. The actor will have access to a list of the IDs of the GPUs
-that it is allowed to use via ``ray.get_gpu_ids(as_str=True)``. This is a list of strings,
+that it is allowed to use via ``ray.get_gpu_ids()``. This is a list of strings,
 like ``[]``, or ``['1']``, or ``['2', '5', '6']``. Under some circumstances, the IDs of GPUs could be given as UUID strings instead of indices (see the `CUDA programming guide <https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars>`__).
 
 .. code-block:: python

doc/source/configure.rst

@@ -159,7 +159,7 @@ To add authentication via the Python API, start Ray using:
 
 .. code-block:: python
 
-  ray.init(redis_password="password")
+  ray.init(_redis_password="password")
 
 To add authentication via the CLI or to connect to an existing Ray instance with
 password-protected Redis ports:
@@ -182,48 +182,4 @@ to localhost when the ray is started using ``ray.init``.
 See the `Redis security documentation <https://redis.io/topics/security>`__
 for more information.
 
-
-Using the Object Store with Huge Pages
---------------------------------------
-
-Plasma is a high-performance shared memory object store originally developed in
-Ray and now being developed in `Apache Arrow`_. See the `relevant
-documentation`_.
-
-On Linux, it is possible to increase the write throughput of the Plasma object
-store by using huge pages. You first need to create a file system and activate
-huge pages as follows.
-
-.. code-block:: shell
-
-  sudo mkdir -p /mnt/hugepages
-  gid=`id -g`
-  uid=`id -u`
-  sudo mount -t hugetlbfs -o uid=$uid -o gid=$gid none /mnt/hugepages
-  sudo bash -c "echo $gid > /proc/sys/vm/hugetlb_shm_group"
-  # This typically corresponds to 20000 2MB pages (about 40GB), but this
-  # depends on the platform.
-  sudo bash -c "echo 20000 > /proc/sys/vm/nr_hugepages"
-
-**Note:** Once you create the huge pages, they will take up memory which will
-never be freed unless you remove the huge pages. If you run into memory issues,
-that may be the issue.
-
-You need root access to create the file system, but not for running the object
-store.
-
-You can then start Ray with huge pages on a single machine as follows.
-
-.. code-block:: python
-
-  ray.init(huge_pages=True, plasma_directory="/mnt/hugepages")
-
-In the cluster case, you can do it by passing ``--huge-pages`` and
-``--plasma-directory=/mnt/hugepages`` into ``ray start`` on any machines where
-huge pages should be enabled.
-
-See the relevant `Arrow documentation for huge pages`_.
-
 .. _`Apache Arrow`: https://arrow.apache.org/
-.. _`relevant documentation`: https://arrow.apache.org/docs/python/plasma.html#the-plasma-in-memory-object-store
-.. _`Arrow documentation for huge pages`: https://arrow.apache.org/docs/python/plasma.html#using-plasma-with-huge-pages

doc/source/index.rst

@@ -206,7 +206,6 @@ Academic Papers
    joblib.rst
    iter.rst
    pandas_on_ray.rst
-   projects.rst
 
 .. toctree::
    :hidden:

doc/source/package-ref.rst

@@ -74,12 +74,12 @@ ray.get_resource_ids
 
 .. autofunction:: ray.get_resource_ids
 
-.. _ray-get_webui_url-ref:
+.. _ray-get_dashboard_url-ref:
 
-ray.get_webui_url
-~~~~~~~~~~~~~~~~~
+ray.get_dashboard_url
+~~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: ray.get_webui_url
+.. autofunction:: ray.get_dashboard_url
 
 .. _ray-shutdown-ref:
 
@@ -88,21 +88,6 @@ ray.shutdown
 
 .. autofunction:: ray.shutdown
 
-
-.. _ray-register_custom_serializer-ref:
-
-ray.register_custom_serializer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: ray.register_custom_serializer
-
-.. _ray-profile-ref:
-
-ray.profile
-~~~~~~~~~~~
-
-.. autofunction:: ray.profile
-
 .. _ray-method-ref:
 
 ray.method
@@ -123,27 +108,13 @@ ray.nodes
 
 .. autofunction:: ray.nodes
 
-.. _ray-objects-ref:
-
-ray.objects
-~~~~~~~~~~~
-
-.. autofunction:: ray.objects
-
 .. _ray-timeline-ref:
 
 ray.timeline
 ~~~~~~~~~~~~
 
 .. autofunction:: ray.timeline
 
-.. _ray-object_transfer_timeline-ref:
-
-ray.object_transfer_timeline
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: ray.object_transfer_timeline
-
 .. _ray-cluster_resources-ref:
 
 ray.cluster_resources
@@ -221,24 +192,12 @@ The Ray Command Line API
    :prog: ray stack
    :show-nested:
 
-.. _ray-stat-doc:
-
-.. click:: ray.scripts.scripts:statistics
-   :prog: ray statistics
-   :show-nested:
-
 .. _ray-memory-doc:
 
 .. click:: ray.scripts.scripts:memory
    :prog: ray memory
    :show-nested:
 
-.. _ray-globalgc-doc:
-
-.. click:: ray.scripts.scripts:globalgc
-   :prog: ray globalgc
-   :show-nested:
-
 .. _ray-timeline-doc:
 
 .. click:: ray.scripts.scripts:timeline