Class InferContext¶

Defined in File request.h

Nested Relationships¶

Nested Types¶

Inheritance Relationships¶

Derived Types¶

public nvidia::inferenceserver::client::InferGrpcContext (Class InferGrpcContext)
public nvidia::inferenceserver::client::InferHttpContext (Class InferHttpContext)

Class Documentation¶

class InferContext¶

An InferContext object is used to run inference on an inference server for a specific model.

Once created an InferContext object can be used repeatedly to perform inference using the model. Options that control how inference is performed can be changed in between inference runs.

A InferContext object can use either HTTP protocol or GRPC protocol depending on the Create function (InferHttpContext::Create or InferGrpcContext::Create). For example:

std::unique_ptr<InferContext> ctx;
InferHttpContext::Create(&ctx, "localhost:8000", "mnist");
...
std::unique_ptr<Options> options0;
Options::Create(&options0);
options->SetBatchSize(b);
options->AddClassResult(output, topk);
ctx->SetRunOptions(*options0);
...
ctx->Run(&results0);  // run using options0
ctx->Run(&results1);  // run using options0
...
std::unique_ptr<Options> options1;
Options::Create(&options1);
options->AddRawResult(output);
ctx->SetRunOptions(*options);
...
ctx->Run(&results2);  // run using options1
ctx->Run(&results3);  // run using options1
...

Note: InferContext::Create methods are thread-safe. All other InferContext methods, and nested class methods are not thread-safe.
: The Run() calls are not thread-safe but a new Run() can be invoked as soon as the previous completes. The returned result objects are owned by the caller and may be retained and accessed even after the InferContext object is destroyed.
: AsyncRun() and GetAsyncRunStatus() calls are not thread-safe. What’s more, calling one method while the other one is running will result in undefined behavior given that they will modify the shared data internally.
: For more parallelism multiple InferContext objects can access the same inference server with no serialization requirements across those objects.

Subclassed by nvidia::inferenceserver::client::InferGrpcContext, nvidia::inferenceserver::client::InferHttpContext

Public Types

using ResultMap = std::map<std::string, std::unique_ptr<Result>>¶

Public Functions

virtual ~InferContext()¶: Destroy the inference context.

const std::string &ModelName() const¶

Return: The name of the model being used for this context.

int64_t ModelVersion() const¶

Return: The version of the model being used for this context. -1 indicates that the latest (i.e. highest version number) version of that model is being used.

uint64_t MaxBatchSize() const¶

Return: The maximum batch size supported by the context. A maximum batch size indicates that the context does not support batching and so only a single inference at a time can be performed.

CorrelationID CorrelationId() const¶

Return: The correlation ID associated with the context.

const std::vector<std::shared_ptr<Input>> &Inputs() const¶

Return: The inputs of the model.

const std::vector<std::shared_ptr<Output>> &Outputs() const¶

Return: The outputs of the model.

Error GetInput(const std::string &name, std::shared_ptr<Input> *input) const¶

Get a named input.

Return

Error object indicating success or failure.

Parameters

name: The name of the input.
input: Returns the Input object for ‘name’.

Error GetOutput(const std::string &name, std::shared_ptr<Output> *output) const¶

Get a named output.

Return

Error object indicating success or failure.

Parameters

name: The name of the output.
output: Returns the Output object for ‘name’.

Error SetRunOptions(const Options &options)¶

Set the options to use for all subsequent Run() invocations.

Return

Error object indicating success or failure.

Parameters

options: The options.

Error GetStat(Stat *stat)¶

Get the current statistics of the InferContext.

Return

Error object indicating success or failure.

Parameters

stat: Returns the Stat object holding the statistics.

virtual Error Run(ResultMap *results) = 0¶

Send a synchronous request to the inference server to perform an inference to produce results for the outputs specified in the most recent call to SetRunOptions().

Return

Error object indicating success or failure.

Parameters

results: Returns Result objects holding inference results as a map from output name to Result object.

virtual Error AsyncRun(std::shared_ptr<Request> *async_request) = 0¶

Send an asynchronous request to the inference server to perform an inference to produce results for the outputs specified in the most recent call to SetRunOptions().

Return

Error object indicating success or failure.

Parameters

async_request: Returns a Request object that can be used to retrieve the inference results for the request.

virtual Error GetAsyncRunResults(ResultMap *results, bool *is_ready, const std::shared_ptr<Request> &async_request, bool wait) = 0¶

Get the results of the asynchronous request referenced by ‘async_request’.

Return

Error object indicating success or failure.

Parameters

results: Returns Result objects holding inference results as a map from output name to Result object.
is_ready: Returns boolean that indicates if the results are ready, the results are valid only if is_ready returns true.
async_request: Request handle to retrieve results.
wait: If true, block until the request completes. Otherwise, return immediately.

Error GetReadyAsyncRequest(std::shared_ptr<Request> *async_request, bool *is_ready, bool wait)¶

Get any one completed asynchronous request.

Return

Error object indicating success or failure.

Parameters

async_request: Returns the Request object holding the completed request.
is_ready: Returns boolean that indicates if the request is ready, the async_request returned is valid only if is_ready returns true.
wait: If true, block until the request completes. Otherwise, return immediately.

Protected Types

using AsyncReqMap = std::map<uintptr_t, std::shared_ptr<Request>>¶

Protected Functions

InferContext(const std::string&, int64_t, CorrelationID, bool)¶

virtual void AsyncTransfer() = 0¶

virtual Error PreRunProcessing(std::shared_ptr<Request> &request) = 0¶

Error IsRequestReady(const std::shared_ptr<Request> &async_request, bool *is_ready, bool wait)¶

Error UpdateStat(const RequestTimers &timer)¶

Protected Attributes

AsyncReqMap ongoing_async_requests_¶

const std::string model_name_¶

const int64_t model_version_¶

const CorrelationID correlation_id_¶

const bool verbose_¶

uint64_t max_batch_size_¶

uint64_t batch_size_¶

uint64_t async_request_id_¶

std::vector<std::shared_ptr<Input>> inputs_¶

std::vector<std::shared_ptr<Output>> outputs_¶

InferRequestHeader infer_request_¶

std::shared_ptr<Request> sync_request_¶

Stat context_stat_¶

std::thread worker_¶

std::mutex mutex_¶

std::condition_variable cv_¶

bool exiting_¶

class Input¶

An input to the model.

Public Functions

virtual ~Input()¶: Destroy the input.

virtual const std::string &Name() const = 0¶

Return: The name of the input.

virtual int64_t ByteSize() const = 0¶

Return: The size in bytes of this input. This is the size for one instance of the input, not the entire size of a batched input. When the byte-size is not known, for example for non-fixed-sized types like TYPE_STRING or for inputs with variable-size dimensions, this will return -1.

virtual size_t TotalByteSize() const = 0¶

Return: The size in bytes of entire batch of this input. For fixed-sized types this is just ByteSize() * batch-size, but for non-fixed-sized types like TYPE_STRING it is the only way to get the entire input size.

virtual DataType DType() const = 0¶

Return: The data-type of the input.

virtual ModelInput::Format Format() const = 0¶

Return: The format of the input.

virtual const DimsList &Dims() const = 0¶

Return: The dimensions/shape of the input specified in the model configuration. Variable-size dimensions are reported as -1.

virtual Error Reset() = 0¶

Prepare this input to receive new tensor values.

Forget any existing values that were set by previous calls to SetRaw().

Return: Error object indicating success or failure.

virtual const std::vector<int64_t> &Shape() const = 0¶

Get the shape for this input that was most recently set by SetShape.

Return: The shape, or empty vector if SetShape has not been called.

virtual Error SetShape(const std::vector<int64_t> &dims) = 0¶

Set the shape for this input.

The shape must be set for inputs that have variable-size dimensions and is optional for other inputs. The shape must be set before calling SetRaw or SetFromString.

Return

Error object indicating success or failure.

Parameters

dims: The dimensions of the shape.

virtual Error SetRaw(const uint8_t *input, size_t input_byte_size) = 0¶

Set tensor values for this input from a byte array.

The array is not copied and so it must not be modified or destroyed until this input is no longer needed (that is until the Run() call(s) that use the input have completed). For batched inputs this function must be called batch-size times to provide all tensor values for a batch of this input.

Return

Error object indicating success or failure.

Parameters

input: The pointer to the array holding the tensor value.
input_byte_size: The size of the array in bytes, must match the size expected by the input.

virtual Error SetRaw(const std::vector<uint8_t> &input) = 0¶

Set tensor values for this input from a byte vector.

The vector is not copied and so it must not be modified or destroyed until this input is no longer needed (that is until the Run() call(s) that use the input have completed). For batched inputs this function must be called batch-size times to provide all tensor values for a batch of this input.

Return

Error object indicating success or failure.

Parameters

input: The vector holding tensor values.

virtual Error SetFromString(const std::vector<std::string> &input) = 0¶

Set tensor values for this input from a vector or strings.

This method can only be used for tensors with STRING data-type. The strings are assigned in row-major order to the elements of the tensor. The strings are copied and so the ‘input’ does not need to be preserved as with SetRaw(). For batched inputs this function must be called batch-size times to provide all tensor values for a batch of this input.

Return

Error object indicating success or failure.

Parameters

input: The vector holding tensor string values.

class Options¶

Run options to be applied to all subsequent Run() invocations.

Public Functions

virtual ~Options()¶

virtual bool Flag(InferRequestHeader::Flag flag) const = 0¶

Get the value of a request flag being used for all subsequent inferences.

Cannot be used with FLAG_NONE.

Return

The true/false value currently set for the flag. If ‘flag’ is FLAG_NONE then return false.

Parameters

flag: The flag to get the value for.

virtual void SetFlag(InferRequestHeader::Flag flag, bool value) = 0¶

Set a request flag to be used for all subsequent inferences.

Parameters

flag: The flag to set. Cannot be used with FLAG_NONE.
value: The true/false value to set for the flag. If ‘flag’ is FLAG_NONE then do nothing.

virtual uint32_t Flags() const = 0¶

Get the value of all request flags being used for all subsequent inferences.

Return: The bitwise-or of flag values as a single uint32_t value.

virtual void SetFlags(uint32_t flags) = 0¶

Set all request flags to be used for all subsequent inferences.

Parameters

flags: The bitwise-or of flag values to set.

virtual size_t BatchSize() const = 0¶

Return: The batch size to use for all subsequent inferences.

virtual void SetBatchSize(size_t batch_size) = 0¶

Set the batch size to use for all subsequent inferences.

Parameters

batch_size: The batch size.

virtual Error AddRawResult(const std::shared_ptr<InferContext::Output> &output) = 0¶

Add ‘output’ to the list of requested RAW results.

Run() will return the output’s full tensor as a result.

Return

Error object indicating success or failure.

Parameters

output: The output.

virtual Error AddClassResult(const std::shared_ptr<InferContext::Output> &output, uint64_t k) = 0¶

Add ‘output’ to the list of requested CLASS results.

Run() will return the highest ‘k’ values of ‘output’ as a result.

Return

Error object indicating success or failure.

Parameters

output: The output.
k: Set how many class results to return for the output.

Public Static Functions

static Error Create(std::unique_ptr<Options> *options)¶

Create a new Options object with default values.

Return: Error object indicating success or failure.

class Output¶

An output from the model.

Public Functions

virtual ~Output()¶: Destroy the output.

virtual const std::string &Name() const = 0¶

Return: The name of the output.

virtual DataType DType() const = 0¶

Return: The data-type of the output.

virtual const DimsList &Dims() const = 0¶

Return: The dimensions/shape of the output specified in the model configuration. Variable-size dimensions are reported as -1.

class Request¶

Handle to a inference request.

The request handle is used to get request results if the request is sent by AsyncRun().

Public Functions

virtual ~Request()¶: Destroy the request handle.

virtual uint64_t Id() const = 0¶

Return: The unique identifier of the request.

class RequestTimers¶

Timer to record the timestamp for different stages of request handling.

Public Types

enum Kind¶

The kind of the timer.

Values:

REQUEST_START¶: The start of request handling.

REQUEST_END¶: The end of request handling.

SEND_START¶: The start of sending request bytes to the server (i.e. first byte).

SEND_END¶: The end of sending request bytes to the server (i.e. last byte).

RECEIVE_START¶

The start of receiving response bytes from the server (i.e.

first byte).

RECEIVE_END¶

The end of receiving response bytes from the server (i.e.

last byte).

Public Functions

RequestTimers()¶: Construct a timer with zero-ed timestamps.

Error Reset()¶

Reset all timestamp values to zero.

Must be called before re-using the timer.

Return: Error object indicating success or failure.

Error Record(Kind kind)¶

Record the current timestamp for a request stage.

Return

Error object indicating success or failure.

Parameters

kind: The Kind of the timestamp.

class Result¶

An inference result corresponding to an output.

Public Types

enum ResultFormat¶

Format in which result is returned.

Values:

RAW = 0¶: RAW format is the entire result tensor of values.

CLASS = 1¶: CLASS format is the top-k highest probability values of the result and the associated class label (if provided by the model).

Public Functions

virtual ~Result()¶: Destroy the result.

virtual const std::string &ModelName() const = 0¶

Return: The name of the model that produced this result.

virtual int64_t ModelVersion() const = 0¶

Return: The version of the model that produced this result.

virtual const std::shared_ptr<Output> GetOutput() const = 0¶

Return: The Output object corresponding to this result.

virtual Error GetRawShape(std::vector<int64_t> *shape) const = 0¶

Get the shape of a raw result.

The shape does not include the batch dimension.

Return

Error object indicating success or failure.

Parameters

shape: Returns the shape.

virtual Error GetRaw(size_t batch_idx, const std::vector<uint8_t> **buf) const = 0¶

Get a reference to entire raw result data for a specific batch entry.

Returns error if this result is not RAW format.

Return

Error object indicating success or failure.

Parameters

batch_idx: Returns the results for this entry of the batch.
buf: Returns the vector of result bytes.

virtual Error GetRawAtCursor(size_t batch_idx, const uint8_t **buf, size_t adv_byte_size) = 0¶

Get a reference to raw result data for a specific batch entry at the current “cursor” and advance the cursor by the specified number of bytes.

More typically use GetRawAtCursor<T>() method to return the data as a specific type T. Use ResetCursor() to reset the cursor to the beginning of the result. Returns error if this result is not RAW format.

Return

Error object indicating success or failure.

Parameters

batch_idx: Returns results for this entry of the batch.
buf: Returns pointer to ‘adv_byte_size’ bytes of data.
adv_byte_size: The number of bytes of data to get a reference to.

template<typename T> Error GetRawAtCursor(size_t batch_idx, T *out)¶

Read a value for a specific batch entry at the current “cursor” from the result tensor as the specified type T and advance the cursor.

Use ResetCursor() to reset the cursor to the beginning of the result. Returns error if this result is not RAW format.

Return

Error object indicating success or failure.

Parameters

batch_idx: Returns results for this entry of the batch.
out: Returns the value at the cursor.

virtual Error GetClassCount(size_t batch_idx, size_t *cnt) const = 0¶

Get the number of class results for a batch.

Returns error if this result is not CLASS format.

Return

Error object indicating success or failure.

Parameters

batch_idx: The index in the batch.
cnt: Returns the number of ClassResult entries for the batch entry.

virtual Error GetClassAtCursor(size_t batch_idx, ClassResult *result) = 0¶

Get the ClassResult result for a specific batch entry at the current cursor.

Use ResetCursor() to reset the cursor to the beginning of the result. Returns error if this result is not CLASS format.

Return

Error object indicating success or failure.

Parameters

batch_idx: The index in the batch.
result: Returns the ClassResult value for the batch at the cursor.

virtual Error ResetCursors() = 0¶

Reset cursor to beginning of result for all batch entries.

Return: Error object indicating success or failure.

virtual Error ResetCursor(size_t batch_idx) = 0¶

Reset cursor to beginning of result for specified batch entry.

Return

Error object indicating success or failure.

Parameters

batch_idx: The index in the batch.

template<> Error GetRawAtCursor(size_t batch_idx, std::string *out)¶

struct ClassResult¶

The result value for CLASS format results.

Public Members

size_t idx¶: The index of the class in the result vector.

float value¶: The value of the class.

std::string label¶: The label for the class, if provided by the model.

struct Stat¶

Cumulative statistic of the InferContext.

Note: For GRPC protocol, ‘cumulative_send_time_ns’ represents the time for marshaling infer request. ‘cumulative_receive_time_ns’ represents the time for unmarshaling infer response.

Public Functions

Stat()¶: Create a new Stat object with zero-ed statistics.

Public Members

size_t completed_request_count¶: Total number of requests completed.

uint64_t cumulative_total_request_time_ns¶: Time from the request start until the response is completely received.

uint64_t cumulative_send_time_ns¶: Time from the request start until the last byte is sent.

uint64_t cumulative_receive_time_ns¶: Time from receiving first byte of the response until the response is completely received.