The Triton backend for Python. The goal of Python backend is to let you serve models written in Python by Triton Inference Server without having to write any C++ code.
- Python Backend
- Business Logic Scripting
- Interoperability and GPU Support
- Frameworks
- Custom Metrics
- Examples
- Running with Inferentia
- Logging
- Reporting problems, asking questions
- Run the Triton Inference Server container.
docker run --shm-size=1g --ulimit memlock=-1 -p 8000:8000 -p 8001:8001 -p 8002:8002 --ulimit stack=67108864 -ti nvcr.io/nvidia/tritonserver:<xx.yy>-py3
Replace <xx.yy> with the Triton version (e.g. 21.05).
- Inside the container, clone the Python backend repository.
git clone https://github.com/triton-inference-server/python_backend -b r<xx.yy>
- Install example model.
cd python_backend
mkdir -p models/add_sub/1/
cp examples/add_sub/model.py models/add_sub/1/model.py
cp examples/add_sub/config.pbtxt models/add_sub/config.pbtxt
- Start the Triton server.
tritonserver --model-repository `pwd`/models
- In the host machine, start the client container.
docker run -ti --net host nvcr.io/nvidia/tritonserver:<xx.yy>-py3-sdk /bin/bash
- In the client container, clone the Python backend repository.
git clone https://github.com/triton-inference-server/python_backend -b r<xx.yy>
- Run the example client.
python3 python_backend/examples/add_sub/client.py
- Requirements
- cmake >= 3.17
- numpy
- rapidjson-dev
- libarchive-dev
- zlib1g-dev
pip3 install numpy
On Ubuntu or Debian you can use the command below to install rapidjson, libarchive, and zlib:
sudo apt-get install rapidjson-dev libarchive-dev zlib1g-dev
- Build Python backend. Replace <GIT_BRANCH_NAME> with the GitHub branch that you want to compile. For release branches it should be r<xx.yy> (e.g. r21.06).
mkdir build
cd build
cmake -DTRITON_ENABLE_GPU=ON -DTRITON_BACKEND_REPO_TAG=<GIT_BRANCH_NAME> -DTRITON_COMMON_REPO_TAG=<GIT_BRANCH_NAME> -DTRITON_CORE_REPO_TAG=<GIT_BRANCH_NAME> -DCMAKE_INSTALL_PREFIX:PATH=`pwd`/install ..
make install
The following required Triton repositories will be pulled and used in the build. If the CMake variables below are not specified, "main" branch of those repositories will be used. <GIT_BRANCH_NAME> should be the same as the Python backend repository branch that you are trying to compile.
- triton-inference-server/backend:
-DTRITON_BACKEND_REPO_TAG=<GIT_BRANCH_NAME> - triton-inference-server/common:
-DTRITON_COMMON_REPO_TAG=<GIT_BRANCH_NAME> - triton-inference-server/core:
-DTRITON_CORE_REPO_TAG=<GIT_BRANCH_NAME>
Set -DCMAKE_INSTALL_PREFIX to the location where the Triton Server is
installed. In the released containers, this location is /opt/tritonserver.
- Copy example model and configuration
mkdir -p models/add_sub/1/
cp examples/add_sub/model.py models/add_sub/1/model.py
cp examples/add_sub/config.pbtxt models/add_sub/config.pbtxt
- Start the Triton Server
/opt/tritonserver/bin/tritonserver --model-repository=`pwd`/models
- Use the client app to perform inference
python3 examples/add_sub/client.py
In order to use the Python backend, you need to create a Python file that has a structure similar to below:
import triton_python_backend_utils as pb_utils
class TritonPythonModel:
"""Your Python model must use the same class name. Every Python model
that is created must have "TritonPythonModel" as the class name.
"""
@staticmethod
def auto_complete_config(auto_complete_model_config):
"""`auto_complete_config` is called only once when loading the model
assuming the server was not started with
`--disable-auto-complete-config`. Implementing this function is
optional. No implementation of `auto_complete_config` will do nothing.
This function can be used to set `max_batch_size`, `input` and `output`
properties of the model using `set_max_batch_size`, `add_input`, and
`add_output`. These properties will allow Triton to load the model with
minimal model configuration in absence of a configuration file. This
function returns the `pb_utils.ModelConfig` object with these
properties. You can use the `as_dict` function to gain read-only access
to the `pb_utils.ModelConfig` object. The `pb_utils.ModelConfig` object
being returned from here will be used as the final configuration for
the model.
Note: The Python interpreter used to invoke this function will be
destroyed upon returning from this function and as a result none of the
objects created here will be available in the `initialize`, `execute`,
or `finalize` functions.
Parameters
----------
auto_complete_model_config : pb_utils.ModelConfig
An object containing the existing model configuration. You can build
upon the configuration given by this object when setting the
properties for this model.
Returns
-------
pb_utils.ModelConfig
An object containing the auto-completed model configuration
"""
inputs = [{
'name': 'INPUT0',
'data_type': 'TYPE_FP32',
'dims': [4]
}, {
'name': 'INPUT1',
'data_type': 'TYPE_FP32',
'dims': [4]
}]
outputs = [{
'name': 'OUTPUT0',
'data_type': 'TYPE_FP32',
'dims': [4]
}, {
'name': 'OUTPUT1',
'data_type': 'TYPE_FP32',
'dims': [4]
}]
# Demonstrate the usage of `as_dict`, `add_input`, `add_output`,
# `set_max_batch_size`, and `set_dynamic_batching` functions.
# Store the model configuration as a dictionary.
config = auto_complete_model_config.as_dict()
input_names = []
output_names = []
for input in config['input']:
input_names.append(input['name'])
for output in config['output']:
output_names.append(output['name'])
for input in inputs:
# The name checking here is only for demonstrating the usage of
# `as_dict` function. `add_input` will check for conflicts and
# raise errors if an input with the same name already exists in
# the configuration but has different data_type or dims property.
if input['name'] not in input_names:
auto_complete_model_config.add_input(input)
for output in outputs:
# The name checking here is only for demonstrating the usage of
# `as_dict` function. `add_output` will check for conflicts and
# raise errors if an output with the same name already exists in
# the configuration but has different data_type or dims property.
if output['name'] not in output_names:
auto_complete_model_config.add_output(output)
auto_complete_model_config.set_max_batch_size(0)
# To enable a dynamic batcher with default settings, you can use
# auto_complete_model_config set_dynamic_batching() function. It is
# commented in this example because the max_batch_size is zero.
#
# auto_complete_model_config.set_dynamic_batching()
return auto_complete_model_config
def initialize(self, args):
"""`initialize` is called only once when the model is being loaded.
Implementing `initialize` function is optional. This function allows
the model to initialize any state associated with this model.
Parameters
----------
args : dict
Both keys and values are strings. The dictionary keys and values are:
* model_config: A JSON string containing the model configuration
* model_instance_kind: A string containing model instance kind
* model_instance_device_id: A string containing model instance device
ID
* model_repository: Model repository path
* model_version: Model version
* model_name: Model name
"""
print('Initialized...')
def execute(self, requests):
"""`execute` must be implemented in every Python model. `execute`
function receives a list of pb_utils.InferenceRequest as the only
argument. This function is called when an inference is requested
for this model.
Parameters
----------
requests : list
A list of pb_utils.InferenceRequest
Returns
-------
list
A list of pb_utils.InferenceResponse. The length of this list must
be the same as `requests`
"""
responses = []
# Every Python backend must iterate through list of requests and create
# an instance of pb_utils.InferenceResponse class for each of them. You
# should avoid storing any of the input Tensors in the class attributes
# as they will be overridden in subsequent inference requests. You can
# make a copy of the underlying NumPy array and store it if it is
# required.
for request in requests:
# Perform inference on the request and append it to responses
# list...
# You must return a list of pb_utils.InferenceResponse. Length
# of this list must match the length of `requests` list.
return responses
def finalize(self):
"""`finalize` is called only once when the model is being unloaded.
Implementing `finalize` function is optional. This function allows
the model to perform any necessary clean ups before exit.
"""
print('Cleaning up...')Every Python backend can implement four main functions:
auto_complete_config is called only once when loading the model assuming
the server was not started with
--disable-auto-complete-config.
Implementing this function is optional. No implementation of
auto_complete_config will do nothing. This function can be used to set
max_batch_size,
dynamic_batching,
input
and
output
properties of the model using set_max_batch_size, set_dynamic_batching,
add_input, and add_output. These properties will allow Triton to load the
model with
minimal model configuration
in absence of a configuration file. This function returns the
pb_utils.ModelConfig object with these properties. You can use the as_dict
function to gain read-only access to the pb_utils.ModelConfig object.
The pb_utils.ModelConfig object being returned from here will be used as the
final configuration for the model.
Note: The Python interpreter used to invoke this function will be destroyed
upon returning from this function and as a result none of the objects
created here will be available in the initialize, execute, or finalize
functions.
initialize is called once the model is being loaded. Implementing
initialize is optional. initialize allows you to do any necessary
initializations before execution. In the initialize function, you are given
an args variable. args is a Python dictionary. Both keys and
values for this Python dictionary are strings. You can find the available
keys in the args dictionary along with their description in the table
below:
| key | description |
|---|---|
| model_config | A JSON string containing the model configuration |
| model_instance_kind | A string containing model instance kind |
| model_instance_device_id | A string containing model instance device ID |
| model_repository | Model repository path |
| model_version | Model version |
| model_name | Model name |
execute function is called whenever an inference request is made. Every
Python model must implement execute function. In the execute function you
are given a list of InferenceRequest objects. There are two modes of
implementing this function. The mode you choose should depend on your use case.
That is whether or not you want to return decoupled responses from this model
or not.
This is the most generic way you would like to implement your model and
requires the execute function to return exactly one response per request.
This entails that in this mode, your execute function must return a list of
InferenceResponse objects that has the same length as requests. The work
flow in this mode is:
-
executefunction receives a batch of pb_utils.InferenceRequest as a length N array. -
Perform inference on the pb_utils.InferenceRequest and append the corresponding pb_utils.InferenceResponse to a response list.
-
Return back the response list.
-
The length of response list being returned must be N.
-
Each element in the list should be the response for the corresponding element in the request array.
-
Each element must contain a response (a response can be either output tensors or an error); an element cannot be None.
-
Triton checks to ensure that these requirements on response list are satisfied and if not returns an error response for all inference requests. Upon return from the execute function all tensor data associated with the InferenceRequest objects passed to the function are deleted, and so InferenceRequest objects should not be retained by the Python model.
In case one of the requests has an error, you can use the TritonError object
to set the error message for that specific request. Below is an example of
setting errors for an InferenceResponse object:
import triton_python_backend_utils as pb_utils
class TritonPythonModel:
...
def execute(self, requests):
responses = []
for request in requests:
if an_error_occurred:
# If there is an error, there is no need to pass the
# "output_tensors" to the InferenceResponse. The "output_tensors"
# that are passed in this case will be ignored.
responses.append(pb_utils.InferenceResponse(
error=pb_utils.TritonError("An Error Occurred")))
return responsesThis mode allows user to send multiple responses for a request or not send any responses for a request. A model may also send responses out-of-order relative to the order that the request batches are executed. Such models are called decoupled models. In order to use this mode, the transaction policy in the model configuration must be set to decoupled.
In decoupled mode, model must use InferenceResponseSender object per
request to keep creating and sending any number of responses for the
request. The workflow in this mode may look like:
-
executefunction receives a batch of pb_utils.InferenceRequest as a length N array. -
Iterate through each pb_utils.InferenceRequest and perform for the following steps for each pb_utils.InferenceRequest object:
-
Get
InferenceResponseSenderobject for the InferenceRequest using InferenceRequest.get_response_sender(). -
Create and populate pb_utils.InferenceResponse to be sent back.
-
Use InferenceResponseSender.send() to send the above response. If this is the last request then pass pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL as a flag with InferenceResponseSender.send(). Otherwise continue with Step 1 for sending next request.
-
-
The return value for
executefunction in this mode should be None.
Similar to above, in case one of the requests has an error, you can use
the TritonError object to set the error message for that specific
request. After setting errors for an pb_utils.InferenceResponse
object, use InferenceResponseSender.send() to send response with the
error back to the user.
The decoupled mode is powerful and supports various other use cases:
-
If the model should not send any response for the request, then call InferenceResponseSender.send() with no response but flag parameter set to pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL.
-
The model can also send responses out-of-order in which it received requests.
-
The request data and
InferenceResponseSenderobject can be passed to a separate thread in the model. This means main caller thread can exit fromexecutefunction and the model can still continue generating responses as long as it holdsInferenceResponseSenderobject.
The decoupled examples demonstrate full power of what can be acheived from decoupled API. Read Decoupled Backends and Models for more details on how to host a decoupled model.
- Currently, async BLS requests are not supported in decoupled mode.
Implementing finalize is optional. This function allows you to do any clean
ups necessary before the model is unloaded from Triton server.
You can look at the add_sub example which contains
a complete example of implementing all these functions for a Python model
that adds and subtracts the inputs given to it. After implementing all the
necessary functions, you should save this file as model.py.
Every Python Triton model must provide a config.pbtxt file describing
the model configuration. In order to use this backend you must set the
backend field of your model config.pbtxt file to python. You
shouldn't set platform field of the configuration.
Your models directory should look like below:
models
└── add_sub
├── 1
│ └── model.py
└── config.pbtxt
You can retrieve the parameters associated with an inference request
using the inference_request.parameters() function. This function
returns a JSON string where the keys are the keys of the parameters
object and the values are the values for the parameters field. Note that
you need to parse this string using json.loads to convert it to a dictionary.
You can read more about the inference request parameters in the parameters extension documentation.
Python backend shipped in the NVIDIA GPU Cloud containers uses Python 3.10. Python backend is able to use the libaries that exist in the current Python environment. These libraries can be installed in a virtualenv, conda environment, or the global system Python. These libraries will only be used if the Python version matches the Python version of the Python backend's stub executable. For example, if you install a set of libraries in a Python 3.9 environment and your Python backend stub is compiled with Python 3.10 these libraries will NOT be available in your Python model served using Triton. You would need to compile the stub executble with Python 3.9 using the instructions in Building Custom Python Backend Stub section.
Important Note: You only need to compile a custom Python backend stub if the Python version is different from Python 3.10 which is shipped by default in the Triton containers.
Python backend uses a stub process to connect your model.py file to the
Triton C++ core. This stub process has an embedded Python interpreter with
a fixed Python version. If you intend to use a Python interpreter with
different version from the default Python backend stub, you need to compile
your own Python backend stub by following the steps below:
- Install the software packages below:
- cmake
- rapidjson and libarchive (instructions for installing these packages in Ubuntu or Debian are included in Building from Source Section)
- Make sure that the expected Python version is available in your environment.
If you are using conda, you should make sure to activate the environment by
conda activate <conda-env-name>. Note that you don't have to use conda and
can install Python however you wish. Python backend relies on
pybind11 to find the correct Python
version. If you noticed that the correct Python version is not picked up, you
can read more on how
pybind11 decides which Python to use.
- Clone the Python backend repository and compile the Python backend stub (replace <GIT_BRANCH_NAME> with the branch name that you want to use, for release branches it should be r<xx.yy>):
git clone https://github.com/triton-inference-server/python_backend -b
<GIT_BRANCH_NAME>
cd python_backend
mkdir build && cd build
cmake -DTRITON_ENABLE_GPU=ON -DTRITON_BACKEND_REPO_TAG=<GIT_BRANCH_NAME> -DTRITON_COMMON_REPO_TAG=<GIT_BRANCH_NAME> -DTRITON_CORE_REPO_TAG=<GIT_BRANCH_NAME> -DCMAKE_INSTALL_PREFIX:PATH=`pwd`/install ..
make triton-python-backend-stubNow, you have access to a Python backend stub with your Python version. You can verify
that using ldd:
ldd triton_python_backend_stub
...
libpython3.6m.so.1.0 => /home/ubuntu/envs/miniconda3/envs/python-3-6/lib/libpython3.6m.so.1.0 (0x00007fbb69cf3000)
...
There are many other shared libraries printed in addition to the library posted
above. However, it is important to see libpython<major>.<minor>m.so.1.0 in the
list of linked shared libraries. If you use a different Python version, you
should see that version instead. You need to copy the
triton_python_backend_stub to the model directory of the models that want to
use the custom Python backend
stub. For example, if you have model_a in your
model repository,
the folder structure should look like below:
models
|-- model_a
|-- 1
| |-- model.py
|-- config.pbtxt
`-- triton_python_backend_stub
Note the location of triton_python_backend_stub in the directory structure
above.
If you want to create a tar file that contains all your Python dependencies or
you want to use different Python environments for each Python model you need to
create a Custom Execution Environment in Python backend.
Currently, Python backend only supports
conda-pack for this purpose.
conda-pack ensures that your conda
environment is portable. You can create a tar file for your conda environment
using conda-pack command:
conda-pack
Collecting packages...
Packing environment at '/home/iman/miniconda3/envs/python-3-6' to 'python-3-6.tar.gz'
[########################################] | 100% Completed | 4.5s
Important Note: Before installing the packages in your conda environment,
make sure that you have exported
PYTHONNOUSERSITE
environment variable:
export PYTHONNOUSERSITE=True
If this variable is not exported and similar packages are installed outside your conda environment, your tar file may not contain all the dependencies required for an isolated Python environment.
After creating the tar file from the conda environment, you need to tell Python
backend to use that environment for your model. You can do this by adding the
lines below to the config.pbtxt file:
name: "model_a"
backend: "python"
...
parameters: {
key: "EXECUTION_ENV_PATH",
value: {string_value: "/home/iman/miniconda3/envs/python-3-6/python3.6.tar.gz"}
}
It is also possible to provide the execution environment path relative to the model folder in model repository:
name: "model_a"
backend: "python"
...
parameters: {
key: "EXECUTION_ENV_PATH",
value: {string_value: "$$TRITON_MODEL_DIRECTORY/python3.6.tar.gz"}
}
In this case, python3.tar.gz should be placed in the model folder and the
model repository should look like below:
models
|-- model_a
| |-- 1
| | `-- model.py
| |-- config.pbtxt
| |-- python3.6.tar.gz
| `-- triton_python_backend_stub
In the example above, $$TRITON_MODEL_DIRECTORY is resolved to
$pwd/models/model_a.
This is useful if you want to use S3, GCS, or Azure and you do not have access to the absolute path of the execution env that is stored in the cloud object storage service.
-
The version of the Python interpreter in the execution environment must match the version of triton_python_backend_stub.
-
If you don't want to use a different Python interpreter, you can skip Building Custom Python Backend Stub. In this case you only need to pack your environment using
conda-packand provide the path to tar file in the model config. However, the previous note still applies here and the version of the Python interpreter inside the conda environment must match the Python version of stub used by Python backend. The default version of the stub is Python 3.10. -
You can share a single execution environment across multiple models. You need to provide the path to the tar file in the
EXECUTION_ENV_PATHin theconfig.pbtxtof all the models that want to use the execution environment. -
If
$$TRITON_MODEL_DIRECTORYis used in theEXECUTION_ENV_PATH, the finalEXECUTION_ENV_PATHmust not escape from the$$TRITON_MODEL_DIRECTORY, as the behavior of accessing anywhere outside the$$TRITON_MODEL_DIRECTORYis undefined. -
If a non-
$$TRITON_MODEL_DIRECTORYEXECUTION_ENV_PATHis used, only local file system paths are currently supported. The behavior of using cloud paths is undefined. -
If you need to compile the Python backend stub, it is recommended that you compile it in the official Triton NGC containers. Otherwise, your compiled stub may use dependencies that are not available in the Triton container that you are using for deployment. For example, compiling the Python backend stub on an OS other than Ubuntu 22.04 can lead to unexpected errors.
If there is an error that affects the initialize, execute, or finalize
function of the Python model you can use TritonInferenceException.
Example below shows how you can do error handling in finalize:
import triton_python_backend_utils as pb_utils
class TritonPythonModel:
...
def finalize(self):
if error_during_finalize:
raise pb_utils.TritonModelException(
"An error occurred during finalize.")Starting from 21.04 release, Python backend uses shared memory to connect user's code to Triton. Note that this change is completely transparent and does not require any change to the existing user's model code.
Python backend, by default, allocates 64 MBs for each model instance. Then,
it will grow the shared memory region by 64 MBs whenever an increase is
required. You can configure the default shared memory used by each model
instance using the shm-default-byte-size flag. The amount of shared memory
growth can be configured using the shm-growth-byte-size.
You can also configure the timeout used for connecting Triton main process
to the Python backend stubs using the stub-timeout-seconds. The default
value is 30 seconds.
The config values described above can be passed to Triton using
--backend-config flag:
/opt/tritonserver/bin/tritonserver --model-repository=`pwd`/models --backend-config=python,<config-key>=<config-value>
Also, if you are running Triton inside a Docker container you need to
properly set the --shm-size flag depending on the size of your inputs and
outputs. The default value for docker run command is 64MB which is very
small.
Python interpreter uses a global lock known as GIL. Because of GIL, it is not possible have multiple threads running in the same Python interpreter simultaneously as each thread requires to acquire the GIL when accessing Python objects which will serialize all the operations. In order to work around this issue, Python backend spawns a separate process for each model instance. This is in contrast with how other Triton backends such as ONNXRuntime, TensorFlow, and PyTorch handle multiple instances. Increasing the instance count for these backends will create additional threads instead of spawning separate processes.
Python backend uses shared memory to transfer requests to the stub process.
When running multiple instances of Triton Server on the same machine that use
Python models, there would be shared memory region name conflicts that can
result in segmentation faults or hangs. In order to avoid this issue, you need
to specify different shm-region-prefix-name using the --backend-config flag.
# Triton instance 1
tritonserver --model-repository=/models --backend-config=python,shm-region-prefix-name=prefix1
# Triton instance 2
tritonserver --model-repository=/models --backend-config=python,shm-region-prefix-name=prefix2
Note that the hangs would only occur if the /dev/shm is shared between
the two instances of the server. If you run the servers in different containers that
don't share this location, you don't need to specify shm-region-prefix-name.
Triton's ensemble feature supports many use cases where multiple models are composed into a pipeline (or more generally a DAG, directed acyclic graph). However, there are many other use cases that are not supported because as part of the model pipeline they require loops, conditionals (if-then-else), data-dependent control-flow and other custom logic to be intermixed with model execution. We call this combination of custom logic and model executions Business Logic Scripting (BLS).
Starting from 21.08, you can implement BLS in your Python model. A new set of
utility functions allows you to execute inference requests on other models
being served by Triton as a part of executing your Python model. Note that BLS
should only be used inside the execute function and is not supported
in the initialize or finalize methods. Example below shows how to use this
feature:
import triton_python_backend_utils as pb_utils
class TritonPythonModel:
...
def execute(self, requests):
...
# Create an InferenceRequest object. `model_name`,
# `requested_output_names`, and `inputs` are the required arguments and
# must be provided when constructing an InferenceRequest object. Make
# sure to replace `inputs` argument with a list of `pb_utils.Tensor`
# objects.
inference_request = pb_utils.InferenceRequest(
model_name='model_name',
requested_output_names=['REQUESTED_OUTPUT_1', 'REQUESTED_OUTPUT_2'],
inputs=[<pb_utils.Tensor object>])
# `pb_utils.InferenceRequest` supports request_id, correlation_id,
# model version, timeout and preferred_memory in addition to the
# arguments described above.
# These arguments are optional. An example containing all the arguments:
# inference_request = pb_utils.InferenceRequest(model_name='model_name',
# requested_output_names=['REQUESTED_OUTPUT_1', 'REQUESTED_OUTPUT_2'],
# inputs=[<list of pb_utils.Tensor objects>],
# request_id="1", correlation_id=4, model_version=1, flags=0, timeout=5,
# preferred_memory=pb_utils.PreferredMemory(
# pb_utils.TRITONSERVER_MEMORY_GPU, # or pb_utils.TRITONSERVER_MEMORY_CPU
# 0))
# Execute the inference_request and wait for the response
inference_response = inference_request.exec()
# Check if the inference response has an error
if inference_response.has_error():
raise pb_utils.TritonModelException(
inference_response.error().message())
else:
# Extract the output tensors from the inference response.
output1 = pb_utils.get_output_tensor_by_name(
inference_response, 'REQUESTED_OUTPUT_1')
output2 = pb_utils.get_output_tensor_by_name(
inference_response, 'REQUESTED_OUTPUT_2')
# Decide the next steps for model execution based on the received
# output tensors. It is possible to use the same output tensors
# to for the final inference response too.In addition to the inference_request.exec function that allows you to
execute blocking inference requests, inference_request.async_exec allows
you to perform async inference requests. This can be useful when you do not
need the result of the inference immediately. Using async_exec function, it
is possible to have multiple inflight inference requests and wait for the
responses only when needed. Example below shows how to use async_exec:
import triton_python_backend_utils as pb_utils
import asyncio
class TritonPythonModel:
...
# You must add the Python 'async' keyword to the beginning of `execute`
# function if you want to use `async_exec` function.
async def execute(self, requests):
...
# Create an InferenceRequest object. `model_name`,
# `requested_output_names`, and `inputs` are the required arguments and
# must be provided when constructing an InferenceRequest object. Make
# sure to replace `inputs` argument with a list of `pb_utils.Tensor`
# objects.
inference_request = pb_utils.InferenceRequest(
model_name='model_name',
requested_output_names=['REQUESTED_OUTPUT_1', 'REQUESTED_OUTPUT_2'],
inputs=[<pb_utils.Tensor object>])
infer_response_awaits = []
for i in range(4):
# async_exec function returns an
# [Awaitable](https://docs.python.org/3/library/asyncio-task.html#awaitables)
# object.
infer_response_awaits.append(inference_request.async_exec())
# Wait for all of the inference requests to complete.
infer_responses = await asyncio.gather(*infer_response_awaits)
for infer_response in infer_responses:
# Check if the inference response has an error
if inference_response.has_error():
raise pb_utils.TritonModelException(
inference_response.error().message())
else:
# Extract the output tensors from the inference response.
output1 = pb_utils.get_output_tensor_by_name(
inference_response, 'REQUESTED_OUTPUT_1')
output2 = pb_utils.get_output_tensor_by_name(
inference_response, 'REQUESTED_OUTPUT_2')
# Decide the next steps for model execution based on the received
# output tensors.A complete example for sync and async BLS in Python backend is included in the Examples section.
Starting from 23.03 release, you can execute inference requests on decoupled
models in both default mode and
decoupled mode. By setting the decoupled parameter to
True, the exec and async_exec function will return an
iterator of
inference responses returned by a decoupled model. If the decoupled parameter
is set to False, the exec and async_exec function will return a single
response as shown in the example above. Besides, you can set the timeout via
the parameter 'timeout' in microseconds within the constructor of
InferenceRequest. If the request times out, the request will respond with an
error. The default of 'timeout' is 0 which indicates that the request has no
timeout.
Additionally, starting from the 23.04 release, you have the flexibility to
select a specific device to receive output tensors from BLS calls. This
can be achieved by setting the optional preferred_memory parameter within the
InferenceRequest constructor. To do this, you can create a PreferredMemory
object and specify the preferred_memory_type as either
TRITONSERVER_MEMORY_GPU or TRITONSERVER_MEMORY_CPU, as well as the
preferred_device_id as an integer to indicate the memory type and device ID
on which you wish to receive output tensors. If you do not specify the
preferred_memory parameter, the output tensors will be allocated on the
same device where the output tensors were received from the model to which the
BLS call is made.
Example below shows how to use this feature:
import triton_python_backend_utils as pb_utils
class TritonPythonModel:
...
def execute(self, requests):
...
# Create an InferenceRequest object. `model_name`,
# `requested_output_names`, and `inputs` are the required arguments and
# must be provided when constructing an InferenceRequest object. Make
# sure to replace `inputs` argument with a list of `pb_utils.Tensor`
# objects.
inference_request = pb_utils.InferenceRequest(
model_name='model_name',
requested_output_names=['REQUESTED_OUTPUT_1', 'REQUESTED_OUTPUT_2'],
inputs=[<pb_utils.Tensor object>])
# `pb_utils.InferenceRequest` supports request_id, correlation_id,
# model version, timeout and preferred_memory in addition to the
# arguments described above.
# These arguments are optional. An example containing all the arguments:
# inference_request = pb_utils.InferenceRequest(model_name='model_name',
# requested_output_names=['REQUESTED_OUTPUT_1', 'REQUESTED_OUTPUT_2'],
# inputs=[<list of pb_utils.Tensor objects>],
# request_id="1", correlation_id=4, model_version=1, flags=0, timeout=5,
# preferred_memory=pb_utils.PreferredMemory(
# pb_utils.TRITONSERVER_MEMORY_GPU, # or pb_utils.TRITONSERVER_MEMORY_CPU
# 0))
# Execute the inference_request and wait for the response. Here we are
# running a BLS request on a decoupled model, hence setting the parameter
# 'decoupled' to 'True'.
inference_responses = inference_request.exec(decoupled=True)
for inference_response in inference_responses:
# Check if the inference response has an error
if inference_response.has_error():
raise pb_utils.TritonModelException(
inference_response.error().message())
# For some models, it is possible that the last response is empty
if len(infer_response.output_tensors()) > 0:
# Extract the output tensors from the inference response.
output1 = pb_utils.get_output_tensor_by_name(
inference_response, 'REQUESTED_OUTPUT_1')
output2 = pb_utils.get_output_tensor_by_name(
inference_response, 'REQUESTED_OUTPUT_2')
# Decide the next steps for model execution based on the received
# output tensors. It is possible to use the same output tensors to
# for the final inference response too.In addition to the inference_request.exec(decoupled=True) function that
allows you to execute blocking inference requests on decoupled models,
inference_request.async_exec(decoupled=True) allows you to perform async
inference requests. This can be useful when you do not need the result of the
inference immediately. Using async_exec function, it is possible to have
multiple inflight inference requests and wait for the responses only when
needed. Example below shows how to use async_exec:
import triton_python_backend_utils as pb_utils
import asyncio
class TritonPythonModel:
...
# You must add the Python 'async' keyword to the beginning of `execute`
# function if you want to use `async_exec` function.
async def execute(self, requests):
...
# Create an InferenceRequest object. `model_name`,
# `requested_output_names`, and `inputs` are the required arguments and
# must be provided when constructing an InferenceRequest object. Make
# sure to replace `inputs` argument with a list of `pb_utils.Tensor`
# objects.
inference_request = pb_utils.InferenceRequest(
model_name='model_name',
requested_output_names=['REQUESTED_OUTPUT_1', 'REQUESTED_OUTPUT_2'],
inputs=[<pb_utils.Tensor object>])
infer_response_awaits = []
for i in range(4):
# async_exec function returns an
# [Awaitable](https://docs.python.org/3/library/asyncio-task.html#awaitables)
# object.
infer_response_awaits.append(
inference_request.async_exec(decoupled=True))
# Wait for all of the inference requests to complete.
async_responses = await asyncio.gather(*infer_response_awaits)
for infer_responses in async_responses:
for infer_response in infer_responses:
# Check if the inference response has an error
if inference_response.has_error():
raise pb_utils.TritonModelException(
inference_response.error().message())
# For some models, it is possible that the last response is empty
if len(infer_response.output_tensors()) > 0:
# Extract the output tensors from the inference response.
output1 = pb_utils.get_output_tensor_by_name(
inference_response, 'REQUESTED_OUTPUT_1')
output2 = pb_utils.get_output_tensor_by_name(
inference_response, 'REQUESTED_OUTPUT_2')
# Decide the next steps for model execution based on the received
# output tensors.A complete example for sync and async BLS for decoupled models is included in the Examples section.
Starting from the 22.04 release, the lifetime of the BLS output tensors have been improved such that if a tensor is no longer needed in your Python model it will be automatically deallocated. This can increase the number of BLS requests that you can execute in your model without running into the out of GPU or shared memory error.
Note: Async BLS is not supported on Python 3.6 or lower due to the async
keyword and asyncio.run being introduced in Python 3.7.
Stateful models
require setting additional flags in the inference request to indicate the
start and end of a sequence. The flags argument in the pb_utils.InferenceRequest
object can be used to indicate whether the request is the first or last request
in the sequence. An example indicating that the request is starting the
sequence:
inference_request = pb_utils.InferenceRequest(model_name='model_name',
requested_output_names=['REQUESTED_OUTPUT_1', 'REQUESTED_OUTPUT_2'],
inputs=[<list of pb_utils.Tensor objects>],
request_id="1", correlation_id=4,
flags=pb_utils.TRITONSERVER_REQUEST_FLAG_SEQUENCE_START)For indicating the ending of the sequence you can use the
pb_utils.TRITONSERVER_REQUEST_FLAG_SEQUENCE_END flag. If the request is both
starting and ending a sequence at the same time (i.e. the sequence has only a
single request), you can use the bitwise OR operator to enable both of the
flags:
flags = pb_utils.TRITONSERVER_REQUEST_FLAG_SEQUENCE_START | pb_utils.TRITONSERVER_REQUEST_FLAG_SEQUENCE_END
-
You need to make sure that the inference requests performed as a part of your model do not create a circular dependency. For example, if model A performs an inference request on itself and there are no more model instances ready to execute the inference request, the model will block on the inference execution forever.
-
Async BLS is not supported when running a Python model in decoupled mode.
Starting from 21.09 release, Python backend supports
DLPack for zero-copy transfer of Python
backend tensors to other frameworks. The methods below are added to the
pb_utils.Tensor object to facilitate the same:
This method can be called on existing instantiated tensors to convert a Tensor to DLPack. The code snippet below shows how this works with PyTorch:
from torch.utils.dlpack import from_dlpack
import triton_python_backend_utils as pb_utils
class TritonPythonModel:
def execute(self, requests):
...
input0 = pb_utils.get_input_tensor_by_name(request, "INPUT0")
# We have converted a Python backend tensor to a PyTorch tensor without
# making any copies.
pytorch_tensor = from_dlpack(input0.to_dlpack())This static method can be used for creating a Tensor object from the DLPack
encoding of the tensor. For example:
from torch.utils.dlpack import to_dlpack
import torch
import triton_python_backend_utils as pb_utils
class TritonPythonModel:
def execute(self, requests):
...
pytorch_tensor = torch.tensor([1, 2, 3], device='cuda')
# Create a Python backend tensor from the DLPack encoding of a PyTorch
# tensor.
input0 = pb_utils.Tensor.from_dlpack("INPUT0", to_dlpack(pytorch_tensor))Python backend allows tensors implementing
__dlpack__
and __dlpack_device__
interface
to be converted to Python backend tensors. For instance:
input0 = pb_utils.Tensor.from_dlpack("INPUT0", pytorch_tensor)This method only supports contiguous Tensors that are in C-order. If the tensor is not C-order contiguous an exception will be raised.
This function can be used to check whether a tensor is placed in CPU or not.
By default, the Python backend moves all input tensors to CPU before providing
them to the Python model. Starting from 21.09, you can change this default
behavior. By setting FORCE_CPU_ONLY_INPUT_TENSORS to "no", Triton will not
move input tensors to CPU for the Python model. Instead, Triton will provide the
input tensors to the Python model in either CPU or GPU memory, depending on how
those tensors were last used. You cannot predict which memory will be used for
each input tensor so your Python model must be able to handle tensors in both
CPU and GPU memory. To enable this setting, you need to add this setting to the
parameters section of model configuration:
parameters: { key: "FORCE_CPU_ONLY_INPUT_TENSORS" value: {string_value:"no"}}
Since Python Backend models can support most python packages, it is a common
workflow for users to use Deep Learning Frameworks like PyTorch in their
model.py implementation. This section will document some notes and FAQ about
this workflow.
Note
Using a deep learning framework/package in a Python Backend model is not necessarily the same as using the corresponding Triton Backend implementation. For example, the PyTorch Backend is different from using a Python Backend model that uses
import torch. If you are seeing significantly different results from a model executed by the framework (ex: PyTorch) compared to the Python Backend model running the same framework, some of the first things you should check is that the framework versions being used and the input/output preparation are the same.
For a simple example of using PyTorch in a Python Backend model, see the AddSubNet PyTorch example.
When running PyTorch code, you may notice slight differences in output values across runs or across servers depending on hardware, system load, driver, or even batch size. These differences are generally related to the selection of CUDA kernels used to execute the operations, based on the factors mentioned.
For most intents and purposes, these differences aren't large enough to affect a model's final prediction. However, to understand where these differences come from, see this doc.
On Ampere devices and later, there is an optimization related to FP32 operations called TensorFloat32 (TF32). Typically this optimization will improve overall performance at the cost of minor precision loss, but similarly this precision loss is acceptable for most model predictions. For more info on TF32 in PyTorch and how to enable/disable it as needed, see here.
Similar to the PyTorch determinism section above, TensorFlow can have slight differences in outputs based on various factors like hardware, system configurations, or batch sizes due to the library's internal CUDA kernel selection process. For more information on improving the determinism of outputs in TensorFlow, see here.
Starting from 23.05, you can utlize Custom Metrics API to register and collect
custom metrics in the initialize, execute, and finalize functions of your
Python model. The Custom Metrics API is the Python equivalent of the
TRITON C API custom metrics
support. You will need to take the ownership of the custom metrics created
through the APIs and must manage their lifetime. Note that a MetricFamily
object should be deleted only after all the Metric objects under it are
deleted if you'd like to explicitly delete the custom metrics objects.
Example below shows how to use this feature:
import triton_python_backend_utils as pb_utils
class TritonPythonModel:
def initialize(self, args):
# Create a MetricFamily object to report the latency of the model
# execution. The 'kind' parameter must be either 'COUNTER' or
# 'GAUGE'.
self.metric_family = pb_utils.MetricFamily(
name="preprocess_latency_ns",
description="Cumulative time spent pre-processing requests",
kind=pb_utils.MetricFamily.COUNTER # or pb_utils.MetricFamily.GAUGE
)
# Create a Metric object under the MetricFamily object. The 'labels'
# is a dictionary of key-value pairs.
self.metric = self.metric_family.Metric(
labels={"model" : "model_name", "version" : "1"}
)
def execute(self, requests):
responses = []
for request in requests:
# Pre-processing - time it to capture latency
start_ns = time.time_ns()
self.preprocess(request)
end_ns = time.time_ns()
# Update metric to track cumulative pre-processing latency
self.metric.increment(end_ns - start_ns)
...
print("Cumulative pre-processing latency:", self.metric.value())
return responsesYou can look at the custom_metrics example which contains a complete example of demonstrating the Custom Metrics API for a Python model.
For using the Triton Python client in these examples you need to install
the
Triton Python Client Library.
The Python client for each of the examples is in the client.py file.
There is no dependencies required for the AddSub NumPy example. Instructions on how to use this model is explained in the quick start section. You can find the files in examples/add_sub.
In order to use this model, you need to install PyTorch. We recommend using
pip method mentioned in the
PyTorch website.
Make sure that PyTorch is available in the same Python environment as other
dependencies. Alternatively, you can create a
Python Execution Environment.
You can find the files for this example in examples/pytorch.
The JAX example shows how to serve JAX in Triton using Python Backend. You can find the complete example instructions in examples/jax.
The BLS example needs the dependencies required for both of the above examples. You can find the complete example instructions in examples/bls and examples/bls_decoupled.
The Preprocessing example shows how to use Python Backend to do model preprocessing. You can find the complete example instructions in examples/preprocessing.
The examples of decoupled models shows how to develop and serve decoupled models in Triton using Python backend. You can find the complete example instructions in examples/decoupled.
Triton model configuration allows users to provide kind to instance group settings. A python backend model can be written to respect the kind setting to control the execution of a model instance either on CPU or GPU.
In the model instance kind example we demonstrate how this can be achieved for your python model.
The auto-complete config example demonstrates how to use the
auto_complete_config function to define
minimal model configuration
when a configuration file is not available. You can find the complete example
instructions in examples/auto_complete.
The example shows how to use custom metrics API in Python Backend. You can find the complete example instructions in examples/custom_metrics.
Please see the README.md located in the python_backend/inferentia sub folder.
Starting from 22.09 release, your Python model can log information using the following methods:
import triton_python_backend_utils as pb_utils
class TritonPythonModel:
def execute(self, requests):
...
logger = pb_utils.Logger
logger.log_info("Info Msg!")
logger.log_warn("Warning Msg!")
logger.log_error("Error Msg!")
logger.log_verbose("Verbose Msg!")Note: The logger can be defined and used in following class methods:
- initialize
- execute
- finalize
Log messages can also be sent with their log-level explcitiy specified:
# log-level options: INFO, WARNING, ERROR, VERBOSE
logger.log("Specific Msg!", logger.INFO)If no log-level is specified, this method will log INFO level messages.
Note that the Triton server's settings determine which log messages appear within the server log. For example, if a model attempts to log a verbose-level message, but Triton is not set to log verbose-level messages, it will not appear in the server log. For more information on Triton's log settings and how to adjust them dynamically, please see Triton's logging extension documentation.
We appreciate any feedback, questions or bug reporting regarding this project. When help with code is needed, follow the process outlined in the Stack Overflow (https://stackoverflow.com/help/mcve) document. Ensure posted examples are:
-
minimal – use as little code as possible that still produces the same problem
-
complete – provide all parts needed to reproduce the problem. Check if you can strip external dependency and still show the problem. The less time we spend on reproducing problems the more time we have to fix it
-
verifiable – test the code you're about to provide to make sure it reproduces the problem. Remove all other problems that are not related to your request/question.