From d41c5836fe4a27dec8802d6a4c281569bb04a466 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Fri, 5 May 2023 09:15:42 +0000 Subject: automatic import of python-pyotritonclient --- python-pyotritonclient.spec | 430 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 430 insertions(+) create mode 100644 python-pyotritonclient.spec (limited to 'python-pyotritonclient.spec') diff --git a/python-pyotritonclient.spec b/python-pyotritonclient.spec new file mode 100644 index 0000000..565af12 --- /dev/null +++ b/python-pyotritonclient.spec @@ -0,0 +1,430 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pyotritonclient +Version: 0.2.5 +Release: 1 +Summary: A lightweight http client library for communicating with Nvidia Triton Inference Server (with Pyodide support in the browser) +License: BSD +URL: https://github.com/oeway/pyotritonclient +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d7/08/e36fa0510c40f278bb068b3348f4d1ff0b3e0a2eedf081d1f33bd9e05220/pyotritonclient-0.2.5.tar.gz +BuildArch: noarch + +Requires: python3-six +Requires: python3-numpy +Requires: python3-imjoy-rpc +Requires: python3-msgpack +Requires: python3-requests +Requires: python3-rapidjson +Requires: python3-imjoy-rpc + +%description +# Triton HTTP Client for Pyodide + +A Pyodide python http client library and utilities for communicating with Triton Inference Server (based on tritonclient from NVIDIA). + + +This is a simplified implemetation of the triton client from NVIDIA, it works both in the browser with Pyodide Python or the native Python. +It only implement the http client, and most of the API remains the similar but changed into async and with additional utility functions. + +## Installation + +To use it in native CPython, you can install the package by running: +``` +pip install pyotritonclient +``` + +For Pyodide-based Python environment, for example: [JupyterLite](https://jupyterlite.readthedocs.io/en/latest/_static/lab/index.html) or [Pyodide console](https://pyodide-cdn2.iodide.io/dev/full/console.html), you can install the client by running the following python code: +```python +import micropip +micropip.install("pyotritonclient") +``` +## Usage + +### Basic example +To execute the model, we provide utility functions to make it much easier: +```python +import numpy as np +from pyotritonclient import execute + +# create fake input tensors +input0 = np.zeros([2, 349, 467], dtype='float32') +# run inference +results = await execute(inputs=[input0, {"diameter": 30}], server_url='https://ai.imjoy.io/triton', model_name='cellpose-python') +``` + +The above example assumes you are running the code in a jupyter notebook or an environment supports top-level await, if you are trying the example code in a normal python script, please wrap the code into an async function and run with asyncio as follows: +```python +import asyncio +import numpy as np +from pyotritonclient import execute + +async def run(): + results = await execute(inputs=[np.zeros([2, 349, 467], dtype='float32'), {"diameter": 30}], server_url='https://ai.imjoy.io/triton', model_name='cellpose-python') + print(results) + +loop = asyncio.get_event_loop() +loop.run_until_complete(run()) +``` + +You can access the lower level api, see the [test example](./tests/test_client.py). + +You can also find the official [client examples](https://github.com/triton-inference-server/client/tree/main/src/python/examples) demonstrate how to use the +package to issue request to [triton inference server](https://github.com/triton-inference-server/server). However, please notice that you will need to +change the http client code into async style. For example, instead of doing `client.infer(...)`, you need to do `await client.infer(...)`. + +The http client code is forked from [triton client git repo](https://github.com/triton-inference-server/client) since commit [b3005f9db154247a4c792633e54f25f35ccadff0](https://github.com/triton-inference-server/client/tree/b3005f9db154247a4c792633e54f25f35ccadff0). + + +### Using the sequence executor with stateful models +To simplify the manipulation on stateful models with sequence, we also provide the `SequenceExecutor` to make it easier to run models in a sequence. +```python +from pyotritonclient import SequenceExcutor + + +seq = SequenceExcutor( + server_url='https://ai.imjoy.io/triton', + model_name='cellpose-train', + sequence_id=100 +) +inputs = [ + image.astype('float32'), + labels.astype('float32'), + {"steps": 1, "resume": True} +] +for (image, labels, info) in train_samples: + result = await seq.step(inputs) + +result = await seq.end(inputs) +``` + +Note that above example called `seq.end()` by sending the last inputs again to end the sequence. If you want to specify the inputs for the execution, you can run `result = await se.end(inputs)`. + +For a small batch of data, you can also run it like this: +```python +from pyotritonclient import SequenceExcutor + +seq = SequenceExcutor( + server_url='https://ai.imjoy.io/triton', + model_name='cellpose-train', + sequence_id=100 +) + +# a list of inputs +inputs_batch = [[ + image.astype('float32'), + labels.astype('float32'), + {"steps": 1, "resume": True} +] for (image, labels, _) in train_samples] + +def on_step(i, result): + """Function called on every step""" + print(i) + +results = await seq(inputs_batch, on_step=on_step) +``` + + + +## Server setup +Since we access the server from the browser environment which typically has more security restrictions, it is important that the server is configured to enable browser access. + +Please make sure you configured following aspects: + * The server must provide HTTPS endpoints instead of HTTP + * The server should send the following headers: + - `Access-Control-Allow-Headers: Inference-Header-Content-Length,Accept-Encoding,Content-Encoding,Access-Control-Allow-Headers` + - `Access-Control-Expose-Headers: Inference-Header-Content-Length,Range,Origin,Content-Type` + - `Access-Control-Allow-Methods: GET,HEAD,OPTIONS,PUT,POST` + - `Access-Control-Allow-Origin: *` (This is optional depending on whether you want to support CORS) + + +%package -n python3-pyotritonclient +Summary: A lightweight http client library for communicating with Nvidia Triton Inference Server (with Pyodide support in the browser) +Provides: python-pyotritonclient +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pyotritonclient +# Triton HTTP Client for Pyodide + +A Pyodide python http client library and utilities for communicating with Triton Inference Server (based on tritonclient from NVIDIA). + + +This is a simplified implemetation of the triton client from NVIDIA, it works both in the browser with Pyodide Python or the native Python. +It only implement the http client, and most of the API remains the similar but changed into async and with additional utility functions. + +## Installation + +To use it in native CPython, you can install the package by running: +``` +pip install pyotritonclient +``` + +For Pyodide-based Python environment, for example: [JupyterLite](https://jupyterlite.readthedocs.io/en/latest/_static/lab/index.html) or [Pyodide console](https://pyodide-cdn2.iodide.io/dev/full/console.html), you can install the client by running the following python code: +```python +import micropip +micropip.install("pyotritonclient") +``` +## Usage + +### Basic example +To execute the model, we provide utility functions to make it much easier: +```python +import numpy as np +from pyotritonclient import execute + +# create fake input tensors +input0 = np.zeros([2, 349, 467], dtype='float32') +# run inference +results = await execute(inputs=[input0, {"diameter": 30}], server_url='https://ai.imjoy.io/triton', model_name='cellpose-python') +``` + +The above example assumes you are running the code in a jupyter notebook or an environment supports top-level await, if you are trying the example code in a normal python script, please wrap the code into an async function and run with asyncio as follows: +```python +import asyncio +import numpy as np +from pyotritonclient import execute + +async def run(): + results = await execute(inputs=[np.zeros([2, 349, 467], dtype='float32'), {"diameter": 30}], server_url='https://ai.imjoy.io/triton', model_name='cellpose-python') + print(results) + +loop = asyncio.get_event_loop() +loop.run_until_complete(run()) +``` + +You can access the lower level api, see the [test example](./tests/test_client.py). + +You can also find the official [client examples](https://github.com/triton-inference-server/client/tree/main/src/python/examples) demonstrate how to use the +package to issue request to [triton inference server](https://github.com/triton-inference-server/server). However, please notice that you will need to +change the http client code into async style. For example, instead of doing `client.infer(...)`, you need to do `await client.infer(...)`. + +The http client code is forked from [triton client git repo](https://github.com/triton-inference-server/client) since commit [b3005f9db154247a4c792633e54f25f35ccadff0](https://github.com/triton-inference-server/client/tree/b3005f9db154247a4c792633e54f25f35ccadff0). + + +### Using the sequence executor with stateful models +To simplify the manipulation on stateful models with sequence, we also provide the `SequenceExecutor` to make it easier to run models in a sequence. +```python +from pyotritonclient import SequenceExcutor + + +seq = SequenceExcutor( + server_url='https://ai.imjoy.io/triton', + model_name='cellpose-train', + sequence_id=100 +) +inputs = [ + image.astype('float32'), + labels.astype('float32'), + {"steps": 1, "resume": True} +] +for (image, labels, info) in train_samples: + result = await seq.step(inputs) + +result = await seq.end(inputs) +``` + +Note that above example called `seq.end()` by sending the last inputs again to end the sequence. If you want to specify the inputs for the execution, you can run `result = await se.end(inputs)`. + +For a small batch of data, you can also run it like this: +```python +from pyotritonclient import SequenceExcutor + +seq = SequenceExcutor( + server_url='https://ai.imjoy.io/triton', + model_name='cellpose-train', + sequence_id=100 +) + +# a list of inputs +inputs_batch = [[ + image.astype('float32'), + labels.astype('float32'), + {"steps": 1, "resume": True} +] for (image, labels, _) in train_samples] + +def on_step(i, result): + """Function called on every step""" + print(i) + +results = await seq(inputs_batch, on_step=on_step) +``` + + + +## Server setup +Since we access the server from the browser environment which typically has more security restrictions, it is important that the server is configured to enable browser access. + +Please make sure you configured following aspects: + * The server must provide HTTPS endpoints instead of HTTP + * The server should send the following headers: + - `Access-Control-Allow-Headers: Inference-Header-Content-Length,Accept-Encoding,Content-Encoding,Access-Control-Allow-Headers` + - `Access-Control-Expose-Headers: Inference-Header-Content-Length,Range,Origin,Content-Type` + - `Access-Control-Allow-Methods: GET,HEAD,OPTIONS,PUT,POST` + - `Access-Control-Allow-Origin: *` (This is optional depending on whether you want to support CORS) + + +%package help +Summary: Development documents and examples for pyotritonclient +Provides: python3-pyotritonclient-doc +%description help +# Triton HTTP Client for Pyodide + +A Pyodide python http client library and utilities for communicating with Triton Inference Server (based on tritonclient from NVIDIA). + + +This is a simplified implemetation of the triton client from NVIDIA, it works both in the browser with Pyodide Python or the native Python. +It only implement the http client, and most of the API remains the similar but changed into async and with additional utility functions. + +## Installation + +To use it in native CPython, you can install the package by running: +``` +pip install pyotritonclient +``` + +For Pyodide-based Python environment, for example: [JupyterLite](https://jupyterlite.readthedocs.io/en/latest/_static/lab/index.html) or [Pyodide console](https://pyodide-cdn2.iodide.io/dev/full/console.html), you can install the client by running the following python code: +```python +import micropip +micropip.install("pyotritonclient") +``` +## Usage + +### Basic example +To execute the model, we provide utility functions to make it much easier: +```python +import numpy as np +from pyotritonclient import execute + +# create fake input tensors +input0 = np.zeros([2, 349, 467], dtype='float32') +# run inference +results = await execute(inputs=[input0, {"diameter": 30}], server_url='https://ai.imjoy.io/triton', model_name='cellpose-python') +``` + +The above example assumes you are running the code in a jupyter notebook or an environment supports top-level await, if you are trying the example code in a normal python script, please wrap the code into an async function and run with asyncio as follows: +```python +import asyncio +import numpy as np +from pyotritonclient import execute + +async def run(): + results = await execute(inputs=[np.zeros([2, 349, 467], dtype='float32'), {"diameter": 30}], server_url='https://ai.imjoy.io/triton', model_name='cellpose-python') + print(results) + +loop = asyncio.get_event_loop() +loop.run_until_complete(run()) +``` + +You can access the lower level api, see the [test example](./tests/test_client.py). + +You can also find the official [client examples](https://github.com/triton-inference-server/client/tree/main/src/python/examples) demonstrate how to use the +package to issue request to [triton inference server](https://github.com/triton-inference-server/server). However, please notice that you will need to +change the http client code into async style. For example, instead of doing `client.infer(...)`, you need to do `await client.infer(...)`. + +The http client code is forked from [triton client git repo](https://github.com/triton-inference-server/client) since commit [b3005f9db154247a4c792633e54f25f35ccadff0](https://github.com/triton-inference-server/client/tree/b3005f9db154247a4c792633e54f25f35ccadff0). + + +### Using the sequence executor with stateful models +To simplify the manipulation on stateful models with sequence, we also provide the `SequenceExecutor` to make it easier to run models in a sequence. +```python +from pyotritonclient import SequenceExcutor + + +seq = SequenceExcutor( + server_url='https://ai.imjoy.io/triton', + model_name='cellpose-train', + sequence_id=100 +) +inputs = [ + image.astype('float32'), + labels.astype('float32'), + {"steps": 1, "resume": True} +] +for (image, labels, info) in train_samples: + result = await seq.step(inputs) + +result = await seq.end(inputs) +``` + +Note that above example called `seq.end()` by sending the last inputs again to end the sequence. If you want to specify the inputs for the execution, you can run `result = await se.end(inputs)`. + +For a small batch of data, you can also run it like this: +```python +from pyotritonclient import SequenceExcutor + +seq = SequenceExcutor( + server_url='https://ai.imjoy.io/triton', + model_name='cellpose-train', + sequence_id=100 +) + +# a list of inputs +inputs_batch = [[ + image.astype('float32'), + labels.astype('float32'), + {"steps": 1, "resume": True} +] for (image, labels, _) in train_samples] + +def on_step(i, result): + """Function called on every step""" + print(i) + +results = await seq(inputs_batch, on_step=on_step) +``` + + + +## Server setup +Since we access the server from the browser environment which typically has more security restrictions, it is important that the server is configured to enable browser access. + +Please make sure you configured following aspects: + * The server must provide HTTPS endpoints instead of HTTP + * The server should send the following headers: + - `Access-Control-Allow-Headers: Inference-Header-Content-Length,Accept-Encoding,Content-Encoding,Access-Control-Allow-Headers` + - `Access-Control-Expose-Headers: Inference-Header-Content-Length,Range,Origin,Content-Type` + - `Access-Control-Allow-Methods: GET,HEAD,OPTIONS,PUT,POST` + - `Access-Control-Allow-Origin: *` (This is optional depending on whether you want to support CORS) + + +%prep +%autosetup -n pyotritonclient-0.2.5 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-pyotritonclient -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot - 0.2.5-1 +- Package Spec generated -- cgit v1.2.3