Input Data#
Use the --help option to see complete documentation for all
input data options. By default Perf Analyzer sends random data to all the inputs
of your model. You can select a different input data mode with the
--input-data option:
random: (default) Send random data for each input. Note: Perf Analyzer only generates random data once per input and reuses that for all inferences
zero: Send zeros for each input.
directory path: A path to a directory containing a binary file for each input, named the same as the input. Each binary file must contain the data required for that input for a batch-1 request. Each file should contain the raw binary representation of the input in row-major order.
file path: A path to a JSON file containing data to be used with every inference request. See the “Real Input Data” section for further details.
--input-datacan be provided multiple times with different file paths to specific multiple JSON files.
For tensors with STRING/BYTES datatype, the
--string-length and
--string-data options may be used in some cases
(see --help for full documentation).
For models that support batching you can use the -b option to
indicate the batch size of the requests that Perf Analyzer should send. For
models with variable-sized inputs you must provide the
--shape argument so that Perf Analyzer knows what
shape tensors to use. For example, for a model that has an input called
IMAGE that has shape [3, N, M], where N and M are variable-size
dimensions, to tell Perf Analyzer to send batch size 4 requests of shape
[3, 224, 224]:
$ perf_analyzer -m mymodel -b 4 --shape IMAGE:3,224,224
Real Input Data#
The performance of some models is highly dependent on the data used. For such
cases you can provide data to be used with every inference request made by Perf
Analyzer in a JSON file. Perf Analyzer will use the provided data in a
round-robin order when sending inference requests. For sequence models, if a
sequence length is specified via
--sequence-length, Perf Analyzer will also loop
through the provided data in a round-robin order up to the specified sequence
length (with a percentage variation customizable via
--sequence-length-variation).
Otherwise, the sequence length will be the number of inputs specified in
user-provided input data.
Each entry in the "data" array must specify all input tensors with the exact
size expected by the model for a single batch. The following example describes
data for a model with inputs named, INPUT0 and INPUT1, shape [4, 4] and
data type INT32:
{
"data":
[
{
"INPUT0": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"INPUT1": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
},
{
"INPUT0": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"INPUT1": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
},
{
"INPUT0": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"INPUT1": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
},
{
"INPUT0": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"INPUT1": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
}
]
}
Note that the [4, 4] tensor has been flattened in a row-major format for the
inputs. In addition to specifying explicit tensors, you can also provide Base64
encoded binary data for the tensors. Each data object must list its data in a
row-major order. Binary data must be in little-endian byte order. The following
example highlights how this can be achieved:
{
"data":
[
{
"INPUT0": {"b64": "YmFzZTY0IGRlY29kZXI="},
"INPUT1": {"b64": "YmFzZTY0IGRlY29kZXI="}
},
{
"INPUT0": {"b64": "YmFzZTY0IGRlY29kZXI="},
"INPUT1": {"b64": "YmFzZTY0IGRlY29kZXI="}
},
{
"INPUT0": {"b64": "YmFzZTY0IGRlY29kZXI="},
"INPUT1": {"b64": "YmFzZTY0IGRlY29kZXI="}
}
]
}
In case of sequence models, multiple data streams can be specified in the JSON
file. Each sequence will get a data stream of its own and Perf Analyzer will
ensure the data from each stream is played back to the same correlation ID. The
below example highlights how to specify data for multiple streams for a sequence
model with a single input named INPUT, shape [1] and data type STRING:
{
"data":
[
[
{
"INPUT": ["1"]
},
{
"INPUT": ["2"]
},
{
"INPUT": ["3"]
},
{
"INPUT": ["4"]
}
],
[
{
"INPUT": ["1"]
},
{
"INPUT": ["1"]
},
{
"INPUT": ["1"]
}
],
[
{
"INPUT": ["1"]
},
{
"INPUT": ["1"]
}
]
]
}
The above example describes three data streams with lengths 4, 3 and 2 respectively. Perf Analyzer will hence produce sequences of length 4, 3 and 2 in this case.
You can also provide an optional "shape" field to the tensors. This is
especially useful while profiling the models with variable-sized tensors as
input. Additionally note that when providing the "shape" field, tensor
contents must be provided separately in a “content” field in row-major order.
The specified shape values will override default input shapes provided as a
command line option (see --shape) for variable-sized
inputs. In the absence of a "shape" field, the provided defaults will be used.
There is no need to specify shape as a command line option if all the input data
provide shape values for variable tensors. Below is an example JSON file for a
model with a single input INPUT, shape [-1, -1] and data type INT32:
{
"data":
[
{
"INPUT":
{
"content": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"shape": [2,8]
}
},
{
"INPUT":
{
"content": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"shape": [8,2]
}
},
{
"INPUT":
{
"content": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
}
},
{
"INPUT":
{
"content": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"shape": [4,4]
}
}
]
}
The following is the example to provide contents as base64 string with explicit shapes:
{
"data":
[
{
"INPUT":
{
"content": {"b64": "/9j/4AAQSkZ(...)"},
"shape": [7964]
}
},
{
"INPUT":
{
"content": {"b64": "/9j/4AAQSkZ(...)"},
"shape": [7964]
}
}
]
}
Note that for STRING type, an element is represented by a 4-byte unsigned
integer giving the length followed by the actual bytes. The byte array to be
encoded using base64 must include the 4-byte unsigned integers.
Output Validation#
When real input data is provided, it is optional to request Perf Analyzer to validate the inference output for the input data.
Validation output can be specified in the "validation_data" field have the
same format as the "data" field for real input. Note that the entries in
"validation_data" must align with "data" for proper mapping. The following
example describes validation data for a model with inputs named INPUT0 and
INPUT1, outputs named OUTPUT0 and OUTPUT1, all tensors have shape [4, 4]
and data type INT32:
{
"data":
[
{
"INPUT0": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"INPUT1": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
}
],
"validation_data":
[
{
"OUTPUT0": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"OUTPUT1": [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
}
]
}
Besides the above example, the validation outputs can be specified in the same variations described in the real input data section.