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Minor Ray API doc refinements (#8060)
* Added small section on installation when using Anaconda. Also fixed an obsolete link to Anaconda. * Delete more temporary directories when running the doc "make clean". * Fine-tuning the core Ray API documentation * Fix doc lines that were too long Co-authored-by: Dean Wampler <dean@concurrentthought.com>
This commit is contained in:
Dean Wampler
+60
-45
@@ -545,10 +545,11 @@ def init(address=None,
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use_pickle=True,
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_internal_config=None,
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lru_evict=False):
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"""Connect to an existing Ray cluster or start one and connect to it.
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"""
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Connect to an existing Ray cluster or start one and connect to it.
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This method handles two cases. Either a Ray cluster already exists and we
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just attach this driver to it, or we start all of the processes associated
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This method handles two cases; either a Ray cluster already exists and we
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just attach this driver to it or we start all of the processes associated
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with a Ray cluster and attach to the newly started cluster.
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To start Ray and all of the relevant processes, use this as follows:
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@@ -566,30 +567,33 @@ def init(address=None,
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Args:
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address (str): The address of the Ray cluster to connect to. If
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this address is not provided, then this command will start Redis, a
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raylet, a plasma store, a plasma manager, and some workers.
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It will also kill these processes when Python exits.
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this address is not provided, then this command will start Redis,
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a raylet, a plasma store, a plasma manager, and some workers.
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It will also kill these processes when Python exits. If the driver
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is running on a node in a Ray cluster, using `auto` as the value
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tells the driver to detect the the cluster, removing the need to
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specify a specific node address.
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redis_address (str): Deprecated; same as address.
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redis_port (int): The port that the primary Redis shard should listen
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to. If None, then a random port will be chosen.
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num_cpus (int): Number of cpus the user wishes all raylets to
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be configured with.
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num_gpus (int): Number of gpus the user wishes all raylets to
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be configured with.
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resources: A dictionary mapping the name of a resource to the quantity
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of that resource available.
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num_cpus (int): Number of CPUs the user wishes to assign to each
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raylet.
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num_gpus (int): Number of GPUs the user wishes to assign to each
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raylet.
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resources: A dictionary mapping the names of custom resources to the
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quantities for them available.
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memory: The amount of memory (in bytes) that is available for use by
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workers requesting memory resources. By default, this is autoset
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based on available system memory.
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workers requesting memory resources. By default, this is
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automatically set based on available system memory.
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object_store_memory: The amount of memory (in bytes) to start the
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object store with. By default, this is autoset based on available
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system memory, subject to a 20GB cap.
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object store with. By default, this is automatically set based on
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available system memory, subject to a 20GB cap.
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redis_max_memory: The max amount of memory (in bytes) to allow each
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redis shard to use. Once the limit is exceeded, redis will start
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LRU eviction of entries. This only applies to the sharded redis
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tables (task, object, and profile tables). By default, this is
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autoset based on available system memory, subject to a 10GB cap.
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log_to_driver (bool): If true, then output from all of the worker
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log_to_driver (bool): If true, the output from all of the worker
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processes on all nodes will be directed to the driver.
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node_ip_address (str): The IP address of the node that we are on.
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object_id_seed (int): Used to seed the deterministic generation of
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@@ -597,55 +601,61 @@ def init(address=None,
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same driver in order to generate the object IDs in a consistent
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manner. However, the same ID should not be used for different
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drivers.
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local_mode (bool): True if the code should be executed serially. This
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local_mode (bool): If true, the code will be executed serially. This
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is useful for debugging.
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driver_object_store_memory (int): Limit the amount of memory the driver
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can use in the object store for creating objects. By default, this
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is autoset based on available system memory, subject to a 20GB cap.
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ignore_reinit_error: True if we should suppress errors from calling
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ray.init() a second time.
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ignore_reinit_error: If true, Ray suppresses errors from calling
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ray.init() a second time. Ray won't be restarted.
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num_redis_shards: The number of Redis shards to start in addition to
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the primary Redis shard.
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redis_max_clients: If provided, attempt to configure Redis with this
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maxclients number.
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redis_password (str): Prevents external clients without the password
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from connecting to Redis if provided.
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plasma_directory: A directory where the Plasma memory mapped files will
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be created.
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plasma_directory: A directory where the Plasma memory mapped files
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will be created.
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huge_pages: Boolean flag indicating whether to start the Object
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Store with hugetlbfs support. Requires plasma_directory.
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include_java: Boolean flag indicating whether to enable java worker.
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include_webui: Boolean flag indicating whether to start the web
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UI, which displays the status of the Ray cluster. If this argument
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is None, then the UI will be started if the relevant dependencies
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are present.
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include_java: Boolean flag indicating whether or not to enable java
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workers.
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include_webui: Boolean flag indicating whether or not to start the web
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UI for the Ray dashboard, which displays the status of the Ray
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cluster. If this argument is None, then the UI will be started if
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the relevant dependencies are present.
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webui_host: The host to bind the web UI server to. Can either be
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localhost (127.0.0.1) or 0.0.0.0 (available from all interfaces).
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By default, this is set to localhost to prevent access from
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external machines.
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job_id: The ID of this job.
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configure_logging: True if allow the logging cofiguration here.
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Otherwise, the users may want to configure it by their own.
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logging_level: Logging level, default will be logging.INFO.
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logging_format: Logging format, default contains a timestamp,
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filename, line number, and message. See ray_constants.py.
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plasma_store_socket_name (str): If provided, it will specify the socket
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configure_logging: True (default) if configuration of logging is
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allowed here. Otherwise, the user may want to configure it
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separately.
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logging_level: Logging level, defaults to logging.INFO. Ignored unless
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"configure_logging" is true.
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logging_format: Logging format, defaults to string containing a
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timestamp, filename, line number, and message. See the source file
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ray_constants.py for details. Ignored unless "configure_logging"
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is true.
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plasma_store_socket_name (str): If provided, specifies the socket
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name used by the plasma store.
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raylet_socket_name (str): If provided, it will specify the socket path
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raylet_socket_name (str): If provided, specifies the socket path
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used by the raylet process.
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temp_dir (str): If provided, it will specify the root temporary
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directory for the Ray process.
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load_code_from_local: Whether code should be loaded from a local module
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or from the GCS.
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temp_dir (str): If provided, specifies the root temporary
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directory for the Ray process. Defaults to an OS-specific
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conventional location, e.g., "/tmp/ray".
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load_code_from_local: Whether code should be loaded from a local
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module or from the GCS.
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use_pickle: Deprecated.
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_internal_config (str): JSON configuration for overriding
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RayConfig defaults. For testing purposes ONLY.
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lru_evict (bool): If True, when an object store is full, it will evict
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objects in LRU order to make more space and when under memory
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pressure, ray.UnreconstructableError may be thrown. If False, then
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reference counting will be used to decide which objects are safe to
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evict and when under memory pressure, ray.ObjectStoreFullError may
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be thrown.
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reference counting will be used to decide which objects are safe
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to evict and when under memory pressure, ray.ObjectStoreFullError
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may be thrown.
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Returns:
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Address information about the started processes.
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@@ -1514,6 +1524,7 @@ def put(value, weakref=False):
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weakref: If set, allows the object to be evicted while a reference
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to the returned ID exists. You might want to set this if putting
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a lot of objects that you might not need in the future.
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It allows Ray to more aggressively reclaim memory.
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Returns:
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The object ID assigned to this value.
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@@ -1638,7 +1649,11 @@ def kill(actor):
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you can call ``actor.__ray_terminate__.remote()`` instead to queue a
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termination task.
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If this actor is reconstructable, it will be attempted to be reconstructed.
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In both cases, the worker is actually killed, but it will be restarted by
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Ray.
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If this actor is reconstructable, an attempt will be made to reconstruct
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it.
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Args:
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actor (ActorHandle): Handle to the actor to kill.
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@@ -1656,9 +1671,9 @@ def _mode(worker=global_worker):
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"""This is a wrapper around worker.mode.
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We use this wrapper so that in the remote decorator, we can call _mode()
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instead of worker.mode. The difference is that when we attempt to serialize
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remote functions, we don't attempt to serialize the worker object, which
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cannot be serialized.
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instead of worker.mode. The difference is that when we attempt to
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serialize remote functions, we don't attempt to serialize the worker
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object, which cannot be serialized.
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"""
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return worker.mode
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