CallableOptionsOrBuilder

interfaz pública CallableOptionsOrBuilder
Subclases indirectas conocidas

Métodos públicos

booleano abstracto
contieneFeedDevices (clave de cadena) 
 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
booleano abstracto
contieneFetchDevices (clave de cadena)
map<string, string> fetch_devices = 7;
cadena abstracta
getFeed (índice int) 
 Tensors to be fed in the callable.
resumen com.google.protobuf.ByteString
getFeedBytes (índice int) 
 Tensors to be fed in the callable.
resumen entero
obtenerFeedCount () 
 Tensors to be fed in the callable.
Mapa abstracto<Cadena, Cadena>
getFeedDevices ()
Utilice getFeedDevicesMap() en su lugar.
resumen entero
getFeedDevicesCount () 
 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
Mapa abstracto<Cadena, Cadena>
getFeedDevicesMap () 
 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
cadena abstracta
getFeedDevicesOrDefault (clave de cadena, valor predeterminado de cadena) 
 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
cadena abstracta
getFeedDevicesOrThrow (clave de cadena) 
 The Tensor objects fed in the callable and fetched from the callable
 are expected to be backed by host (CPU) memory by default.
Lista abstracta<Cadena>
obtener lista de alimentación () 
 Tensors to be fed in the callable.
cadena abstracta
getFetch (índice int) 
 Fetches.
resumen com.google.protobuf.ByteString
getFetchBytes (índice int) 
 Fetches.
resumen entero
getFetchCount () 
 Fetches.
Mapa abstracto<Cadena, Cadena>
getFetchDevices ()
Utilice getFetchDevicesMap() en su lugar.
resumen entero
getFetchDevicesCount ()
map<string, string> fetch_devices = 7;
Mapa abstracto<Cadena, Cadena>
getFetchDevicesMap ()
map<string, string> fetch_devices = 7;
cadena abstracta
getFetchDevicesOrDefault (clave de cadena, valor predeterminado de cadena)
map<string, string> fetch_devices = 7;
cadena abstracta
getFetchDevicesOrThrow (clave de cadena)
map<string, string> fetch_devices = 7;
Lista abstracta<Cadena>
obtenerFetchList () 
 Fetches.
booleano abstracto
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.
Opciones de ejecución abstractas
getRunOptions () 
 Options that will be applied to each run.
resumen RunOptionsOrBuilder
getRunOptionsOrBuilder () 
 Options that will be applied to each run.
cadena abstracta
getTarget (índice int) 
 Target Nodes.
resumen com.google.protobuf.ByteString
getTargetBytes (índice int) 
 Target Nodes.
resumen entero
getTargetCount () 
 Target Nodes.
Lista abstracta<Cadena>
obtener lista de objetivos () 
 Target Nodes.
conexión tensor abstracta
getTensorConnection (índice int) 
 Tensors to be connected in the callable.
resumen entero
getTensorConnectionCount () 
 Tensors to be connected in the callable.
Lista abstracta <TensorConnection>
getTensorConnectionList () 
 Tensors to be connected in the callable.
TensorConnectionOrBuilder abstracto
getTensorConnectionOrBuilder (índice int) 
 Tensors to be connected in the callable.
Lista abstracta<? extiende TensorConnectionOrBuilder >
getTensorConnectionOrBuilderList () 
 Tensors to be connected in the callable.
booleano abstracto
tiene opciones de ejecución () 
 Options that will be applied to each run.

Métodos públicos

booleano abstracto público contieneFeedDevices (clave de cadena) 

 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;

booleano abstracto público contieneFetchDevices (clave de cadena)

map<string, string> fetch_devices = 7;

Cadena abstracta pública getFeed (índice int) 

 Tensors to be fed in the callable. Each feed is the name of a tensor.
repeated string feed = 1;

resumen público com.google.protobuf.ByteString getFeedBytes (índice int) 

 Tensors to be fed in the callable. Each feed is the name of a tensor.
repeated string feed = 1;

resumen público int getFeedCount () 

 Tensors to be fed in the callable. Each feed is the name of a tensor.
repeated string feed = 1;

Mapa abstracto público<Cadena, Cadena> getFeedDevices ()

Utilice getFeedDevicesMap() en su lugar.

resumen público 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;

Mapa abstracto público<Cadena, Cadena> 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;

cadena abstracta pública getFeedDevicesOrDefault (clave de cadena, valor predeterminado de cadena) 

 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;

Cadena abstracta pública getFeedDevicesOrThrow (clave de cadena) 

 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;

Lista abstracta pública<Cadena> getFeedList () 

 Tensors to be fed in the callable. Each feed is the name of a tensor.
repeated string feed = 1;

Cadena abstracta pública getFetch (índice int) 

 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;

resumen público com.google.protobuf.ByteString getFetchBytes (índice int) 

 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;

resumen público 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;

Mapa abstracto público<Cadena, Cadena> getFetchDevices ()

Utilice getFetchDevicesMap() en su lugar.

resumen público int getFetchDevicesCount ()

map<string, string> fetch_devices = 7;

Mapa abstracto público<Cadena, Cadena> getFetchDevicesMap ()

map<string, string> fetch_devices = 7;

cadena abstracta pública getFetchDevicesOrDefault (clave de cadena, valor predeterminado de cadena)

map<string, string> fetch_devices = 7;

Cadena abstracta pública getFetchDevicesOrThrow (clave de cadena)

map<string, string> fetch_devices = 7;

Lista abstracta pública<Cadena> 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;

getFetchSkipSync booleano abstracto público () 

 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;

resumen público RunOptions getRunOptions () 

 Options that will be applied to each run.
.tensorflow.RunOptions run_options = 4;

resumen público RunOptionsOrBuilder getRunOptionsOrBuilder () 

 Options that will be applied to each run.
.tensorflow.RunOptions run_options = 4;

Cadena abstracta pública getTarget (índice int) 

 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;

resumen público com.google.protobuf.ByteString getTargetBytes (índice int) 

 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;

resumen público 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;

Lista abstracta pública<Cadena> 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;

resumen público TensorConnection getTensorConnection (índice int) 

 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;

resumen público 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;

Lista abstracta pública < 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;

resumen público TensorConnectionOrBuilder getTensorConnectionOrBuilder (índice int) 

 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;

Lista de resúmenes públicos <? extiende 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;

hasRunOptions booleano abstracto público () 

 Options that will be applied to each run.
.tensorflow.RunOptions run_options = 4;