CallableOptionsOrBuilder

public interface CallableOptionsOrBuilder

Known Indirect Subclasses CallableOptions, CallableOptions.Builder CallableOptions Defines a subgraph in another `GraphDef` as a set of feed points and nodes to be fetched or executed.  CallableOptions.Builder Defines a subgraph in another `GraphDef` as a set of feed points and nodes to be fetched or executed.

Public Methods

abstract boolean	containsFeedDevices(String key) The Tensor objects fed in the callable and fetched from the callable are expected to be backed by host (CPU) memory by default.
abstract boolean	containsFetchDevices(String key) map<string, string> fetch_devices = 7;
abstract String	getFeed(int index) Tensors to be fed in the callable.
abstract com.google.protobuf.ByteString	getFeedBytes(int index) Tensors to be fed in the callable.
abstract int	getFeedCount() Tensors to be fed in the callable.
abstract Map<String, String>	getFeedDevices() Use getFeedDevicesMap() instead.
abstract int	getFeedDevicesCount() The Tensor objects fed in the callable and fetched from the callable are expected to be backed by host (CPU) memory by default.
abstract Map<String, String>	getFeedDevicesMap() The Tensor objects fed in the callable and fetched from the callable are expected to be backed by host (CPU) memory by default.
abstract String	getFeedDevicesOrDefault(String key, String defaultValue) The Tensor objects fed in the callable and fetched from the callable are expected to be backed by host (CPU) memory by default.
abstract String	getFeedDevicesOrThrow(String key) The Tensor objects fed in the callable and fetched from the callable are expected to be backed by host (CPU) memory by default.
abstract List<String>	getFeedList() Tensors to be fed in the callable.
abstract String	getFetch(int index) Fetches.
abstract com.google.protobuf.ByteString	getFetchBytes(int index) Fetches.
abstract int	getFetchCount() Fetches.
abstract Map<String, String>	getFetchDevices() Use getFetchDevicesMap() instead.
abstract int	getFetchDevicesCount() map<string, string> fetch_devices = 7;
abstract Map<String, String>	getFetchDevicesMap() map<string, string> fetch_devices = 7;
abstract String	getFetchDevicesOrDefault(String key, String defaultValue) map<string, string> fetch_devices = 7;
abstract String	getFetchDevicesOrThrow(String key) map<string, string> fetch_devices = 7;
abstract List<String>	getFetchList() Fetches.
abstract boolean	getFetchSkipSync() By default, RunCallable() will synchronize the GPU stream before returning fetched tensors on a GPU device, to ensure that the values in those tensors have been produced.
abstract RunOptions	getRunOptions() Options that will be applied to each run.
abstract RunOptionsOrBuilder	getRunOptionsOrBuilder() Options that will be applied to each run.
abstract String	getTarget(int index) Target Nodes.
abstract com.google.protobuf.ByteString	getTargetBytes(int index) Target Nodes.
abstract int	getTargetCount() Target Nodes.
abstract List<String>	getTargetList() Target Nodes.
abstract TensorConnection	getTensorConnection(int index) Tensors to be connected in the callable.
abstract int	getTensorConnectionCount() Tensors to be connected in the callable.
abstract List<TensorConnection>	getTensorConnectionList() Tensors to be connected in the callable.
abstract TensorConnectionOrBuilder	getTensorConnectionOrBuilder(int index) Tensors to be connected in the callable.
abstract List<? extends TensorConnectionOrBuilder>	getTensorConnectionOrBuilderList() Tensors to be connected in the callable.
abstract boolean	hasRunOptions() Options that will be applied to each run.

Public Methods

public abstract boolean containsFeedDevices (String key)

 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
 The options below allow changing that - feeding tensors backed by
 device memory, or returning tensors that are backed by device memory.
 The maps below map the name of a feed/fetch tensor (which appears in
 'feed' or 'fetch' fields above), to the fully qualified name of the device
 owning the memory backing the contents of the tensor.
 For example, creating a callable with the following options:
 CallableOptions {
   feed: "a:0"
   feed: "b:0"
   fetch: "x:0"
   fetch: "y:0"
   feed_devices: {
     "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
   }
   fetch_devices: {
     "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
  }
 }
 means that the Callable expects:
 - The first argument ("a:0") is a Tensor backed by GPU memory.
 - The second argument ("b:0") is a Tensor backed by host memory.
 and of its return values:
 - The first output ("x:0") will be backed by host memory.
 - The second output ("y:0") will be backed by GPU memory.
 FEEDS:
 It is the responsibility of the caller to ensure that the memory of the fed
 tensors will be correctly initialized and synchronized before it is
 accessed by operations executed during the call to Session::RunCallable().
 This is typically ensured by using the TensorFlow memory allocators
 (Device::GetAllocator()) to create the Tensor to be fed.
 Alternatively, for CUDA-enabled GPU devices, this typically means that the
 operation that produced the contents of the tensor has completed, i.e., the
 CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
 cuStreamSynchronize()).
 

map<string, string> feed_devices = 6;

public abstract boolean containsFetchDevices (String key)

map<string, string> fetch_devices = 7;

public abstract String getFeed (int index)

 Tensors to be fed in the callable. Each feed is the name of a tensor.
 

repeated string feed = 1;

public abstract com.google.protobuf.ByteString getFeedBytes (int index)

 Tensors to be fed in the callable. Each feed is the name of a tensor.
 

repeated string feed = 1;

public abstract int getFeedCount ()

 Tensors to be fed in the callable. Each feed is the name of a tensor.
 

repeated string feed = 1;

public abstract Map<String, String> getFeedDevices ()

Use getFeedDevicesMap() instead.

public abstract int getFeedDevicesCount ()

 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
 The options below allow changing that - feeding tensors backed by
 device memory, or returning tensors that are backed by device memory.
 The maps below map the name of a feed/fetch tensor (which appears in
 'feed' or 'fetch' fields above), to the fully qualified name of the device
 owning the memory backing the contents of the tensor.
 For example, creating a callable with the following options:
 CallableOptions {
   feed: "a:0"
   feed: "b:0"
   fetch: "x:0"
   fetch: "y:0"
   feed_devices: {
     "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
   }
   fetch_devices: {
     "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
  }
 }
 means that the Callable expects:
 - The first argument ("a:0") is a Tensor backed by GPU memory.
 - The second argument ("b:0") is a Tensor backed by host memory.
 and of its return values:
 - The first output ("x:0") will be backed by host memory.
 - The second output ("y:0") will be backed by GPU memory.
 FEEDS:
 It is the responsibility of the caller to ensure that the memory of the fed
 tensors will be correctly initialized and synchronized before it is
 accessed by operations executed during the call to Session::RunCallable().
 This is typically ensured by using the TensorFlow memory allocators
 (Device::GetAllocator()) to create the Tensor to be fed.
 Alternatively, for CUDA-enabled GPU devices, this typically means that the
 operation that produced the contents of the tensor has completed, i.e., the
 CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
 cuStreamSynchronize()).
 

map<string, string> feed_devices = 6;

public abstract Map<String, String> getFeedDevicesMap ()

 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
 The options below allow changing that - feeding tensors backed by
 device memory, or returning tensors that are backed by device memory.
 The maps below map the name of a feed/fetch tensor (which appears in
 'feed' or 'fetch' fields above), to the fully qualified name of the device
 owning the memory backing the contents of the tensor.
 For example, creating a callable with the following options:
 CallableOptions {
   feed: "a:0"
   feed: "b:0"
   fetch: "x:0"
   fetch: "y:0"
   feed_devices: {
     "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
   }
   fetch_devices: {
     "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
  }
 }
 means that the Callable expects:
 - The first argument ("a:0") is a Tensor backed by GPU memory.
 - The second argument ("b:0") is a Tensor backed by host memory.
 and of its return values:
 - The first output ("x:0") will be backed by host memory.
 - The second output ("y:0") will be backed by GPU memory.
 FEEDS:
 It is the responsibility of the caller to ensure that the memory of the fed
 tensors will be correctly initialized and synchronized before it is
 accessed by operations executed during the call to Session::RunCallable().
 This is typically ensured by using the TensorFlow memory allocators
 (Device::GetAllocator()) to create the Tensor to be fed.
 Alternatively, for CUDA-enabled GPU devices, this typically means that the
 operation that produced the contents of the tensor has completed, i.e., the
 CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
 cuStreamSynchronize()).
 

map<string, string> feed_devices = 6;

public abstract String getFeedDevicesOrDefault (String key, String defaultValue)

 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
 The options below allow changing that - feeding tensors backed by
 device memory, or returning tensors that are backed by device memory.
 The maps below map the name of a feed/fetch tensor (which appears in
 'feed' or 'fetch' fields above), to the fully qualified name of the device
 owning the memory backing the contents of the tensor.
 For example, creating a callable with the following options:
 CallableOptions {
   feed: "a:0"
   feed: "b:0"
   fetch: "x:0"
   fetch: "y:0"
   feed_devices: {
     "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
   }
   fetch_devices: {
     "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
  }
 }
 means that the Callable expects:
 - The first argument ("a:0") is a Tensor backed by GPU memory.
 - The second argument ("b:0") is a Tensor backed by host memory.
 and of its return values:
 - The first output ("x:0") will be backed by host memory.
 - The second output ("y:0") will be backed by GPU memory.
 FEEDS:
 It is the responsibility of the caller to ensure that the memory of the fed
 tensors will be correctly initialized and synchronized before it is
 accessed by operations executed during the call to Session::RunCallable().
 This is typically ensured by using the TensorFlow memory allocators
 (Device::GetAllocator()) to create the Tensor to be fed.
 Alternatively, for CUDA-enabled GPU devices, this typically means that the
 operation that produced the contents of the tensor has completed, i.e., the
 CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
 cuStreamSynchronize()).
 

map<string, string> feed_devices = 6;

public abstract String getFeedDevicesOrThrow (String key)

 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
 The options below allow changing that - feeding tensors backed by
 device memory, or returning tensors that are backed by device memory.
 The maps below map the name of a feed/fetch tensor (which appears in
 'feed' or 'fetch' fields above), to the fully qualified name of the device
 owning the memory backing the contents of the tensor.
 For example, creating a callable with the following options:
 CallableOptions {
   feed: "a:0"
   feed: "b:0"
   fetch: "x:0"
   fetch: "y:0"
   feed_devices: {
     "a:0": "/job:localhost/replica:0/task:0/device:GPU:0"
   }
   fetch_devices: {
     "y:0": "/job:localhost/replica:0/task:0/device:GPU:0"
  }
 }
 means that the Callable expects:
 - The first argument ("a:0") is a Tensor backed by GPU memory.
 - The second argument ("b:0") is a Tensor backed by host memory.
 and of its return values:
 - The first output ("x:0") will be backed by host memory.
 - The second output ("y:0") will be backed by GPU memory.
 FEEDS:
 It is the responsibility of the caller to ensure that the memory of the fed
 tensors will be correctly initialized and synchronized before it is
 accessed by operations executed during the call to Session::RunCallable().
 This is typically ensured by using the TensorFlow memory allocators
 (Device::GetAllocator()) to create the Tensor to be fed.
 Alternatively, for CUDA-enabled GPU devices, this typically means that the
 operation that produced the contents of the tensor has completed, i.e., the
 CUDA stream has been synchronized (e.g., via cuCtxSynchronize() or
 cuStreamSynchronize()).
 

map<string, string> feed_devices = 6;

public abstract List<String> getFeedList ()

 Tensors to be fed in the callable. Each feed is the name of a tensor.
 

repeated string feed = 1;

public abstract String getFetch (int index)

 Fetches. A list of tensor names. The caller of the callable expects a
 tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
 order of specified fetches does not change the execution order.
 

repeated string fetch = 2;

public abstract com.google.protobuf.ByteString getFetchBytes (int index)

 Fetches. A list of tensor names. The caller of the callable expects a
 tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
 order of specified fetches does not change the execution order.
 

repeated string fetch = 2;

public abstract int getFetchCount ()

 Fetches. A list of tensor names. The caller of the callable expects a
 tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
 order of specified fetches does not change the execution order.
 

repeated string fetch = 2;

public abstract Map<String, String> getFetchDevices ()

Use getFetchDevicesMap() instead.

public abstract int getFetchDevicesCount ()

map<string, string> fetch_devices = 7;

public abstract Map<String, String> getFetchDevicesMap ()

map<string, string> fetch_devices = 7;

public abstract String getFetchDevicesOrDefault (String key, String defaultValue)

map<string, string> fetch_devices = 7;

public abstract String getFetchDevicesOrThrow (String key)

map<string, string> fetch_devices = 7;

public abstract List<String> getFetchList ()

 Fetches. A list of tensor names. The caller of the callable expects a
 tensor to be returned for each fetch[i] (see RunStepResponse.tensor). The
 order of specified fetches does not change the execution order.
 

repeated string fetch = 2;

public abstract boolean getFetchSkipSync ()

 By default, RunCallable() will synchronize the GPU stream before returning
 fetched tensors on a GPU device, to ensure that the values in those tensors
 have been produced. This simplifies interacting with the tensors, but
 potentially incurs a performance hit.
 If this options is set to true, the caller is responsible for ensuring
 that the values in the fetched tensors have been produced before they are
 used. The caller can do this by invoking `Device::Sync()` on the underlying
 device(s), or by feeding the tensors back to the same Session using
 `feed_devices` with the same corresponding device name.
 

bool fetch_skip_sync = 8;

public abstract RunOptions getRunOptions ()

 Options that will be applied to each run.
 

.tensorflow.RunOptions run_options = 4;

public abstract RunOptionsOrBuilder getRunOptionsOrBuilder ()

 Options that will be applied to each run.
 

.tensorflow.RunOptions run_options = 4;

public abstract String getTarget (int index)

 Target Nodes. A list of node names. The named nodes will be run by the
 callable but their outputs will not be returned.
 

repeated string target = 3;

public abstract com.google.protobuf.ByteString getTargetBytes (int index)

 Target Nodes. A list of node names. The named nodes will be run by the
 callable but their outputs will not be returned.
 

repeated string target = 3;

public abstract int getTargetCount ()

 Target Nodes. A list of node names. The named nodes will be run by the
 callable but their outputs will not be returned.
 

repeated string target = 3;

public abstract List<String> getTargetList ()

 Target Nodes. A list of node names. The named nodes will be run by the
 callable but their outputs will not be returned.
 

repeated string target = 3;

public abstract TensorConnection getTensorConnection (int index)

 Tensors to be connected in the callable. Each TensorConnection denotes
 a pair of tensors in the graph, between which an edge will be created
 in the callable.
 

repeated .tensorflow.TensorConnection tensor_connection = 5;

public abstract int getTensorConnectionCount ()

 Tensors to be connected in the callable. Each TensorConnection denotes
 a pair of tensors in the graph, between which an edge will be created
 in the callable.
 

repeated .tensorflow.TensorConnection tensor_connection = 5;

public abstract List<TensorConnection> getTensorConnectionList ()

 Tensors to be connected in the callable. Each TensorConnection denotes
 a pair of tensors in the graph, between which an edge will be created
 in the callable.
 

repeated .tensorflow.TensorConnection tensor_connection = 5;

public abstract TensorConnectionOrBuilder getTensorConnectionOrBuilder (int index)

 Tensors to be connected in the callable. Each TensorConnection denotes
 a pair of tensors in the graph, between which an edge will be created
 in the callable.
 

repeated .tensorflow.TensorConnection tensor_connection = 5;

public abstract List<? extends TensorConnectionOrBuilder> getTensorConnectionOrBuilderList ()

 Tensors to be connected in the callable. Each TensorConnection denotes
 a pair of tensors in the graph, between which an edge will be created
 in the callable.
 

repeated .tensorflow.TensorConnection tensor_connection = 5;

public abstract boolean hasRunOptions ()

 Options that will be applied to each run.
 

.tensorflow.RunOptions run_options = 4;