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diff --git a/python-jina.spec b/python-jina.spec new file mode 100644 index 0000000..8a007a6 --- /dev/null +++ b/python-jina.spec @@ -0,0 +1,603 @@ +%global _empty_manifest_terminate_build 0 +Name: python-jina +Version: 3.14.1 +Release: 1 +Summary: Build multimodal AI services via cloud native technologies · Neural Search · Generative AI · MLOps +License: Apache 2.0 +URL: https://github.com/jina-ai/jina/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d6/93/909b20eeddce3941d76a06c357e1d9d7386159e9420f04750d023116ff48/jina-3.14.1.tar.gz +BuildArch: noarch + + +%description +<p align="center"> +<a href="https://docs.jina.ai"><img src="https://github.com/jina-ai/jina/blob/master/.github/readme/streamline-banner.png?raw=true" alt="Jina: Streamline AI & ML Product Delivery" width="100%"></a> +</p> +### Build AI & ML Services +<!-- start build-ai-services --> +[](https://colab.research.google.com/github/jina-ai/jina/blob/master/.github/getting-started/notebook.ipynb) +Let's build a fast, reliable and scalable gRPC-based AI service. In Jina we call this an **[Executor](https://docs.jina.ai/concepts/executor/)**. Our simple Executor will use Facebook's mBART-50 model to translate French to English. We'll then use a **Deployment** to serve it. +> **Note** +> A Deployment serves just one Executor. To combine multiple Executors into a pipeline and serve that, use a [Flow](#build-a-pipeline). +> **Note** +> Run the [code in Colab](https://colab.research.google.com/assets/colab-badge.svg) to install all dependencies. +Let's implement the service's logic: +<table> +<tr> +<th><code>translate_executor.py</code> </th> +<tr> +<td> +```python +from docarray import DocumentArray +from jina import Executor, requests +from transformers import AutoTokenizer, AutoModelForSeq2SeqLM +class Translator(Executor): + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.tokenizer = AutoTokenizer.from_pretrained( + "facebook/mbart-large-50-many-to-many-mmt", src_lang="fr_XX" + ) + self.model = AutoModelForSeq2SeqLM.from_pretrained( + "facebook/mbart-large-50-many-to-many-mmt" + ) + @requests + def translate(self, docs: DocumentArray, **kwargs): + for doc in docs: + doc.text = self._translate(doc.text) + def _translate(self, text): + encoded_en = self.tokenizer(text, return_tensors="pt") + generated_tokens = self.model.generate( + **encoded_en, forced_bos_token_id=self.tokenizer.lang_code_to_id["en_XX"] + ) + return self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[ + 0 + ] +``` +</td> +</tr> +</table> +Then we deploy it with either the Python API or YAML: +<div class="table-wrapper"> +<table> +<tr> +<th> Python API: <code>deployment.py</code> </th> +<th> YAML: <code>deployment.yml</code> </th> +</tr> +<tr> +<td> +```python +from jina import Deployment +with Deployment(uses=Translator, timeout_ready=-1) as dep: + dep.block() +``` +</td> +<td> +```yaml +jtype: Deployment +with: + uses: Translator + py_modules: + - translate_executor.py # name of the module containing Translator + timeout_ready: -1 +``` +And run the YAML Deployment with the CLI: `jina deployment --uses deployment.yml` +</td> +</tr> +</table> +</div> +```text +──────────────────────────────────────── 🎉 Deployment is ready to serve! ───────────────────────────────────────── +╭────────────── 🔗 Endpoint ───────────────╮ +│ ⛓ Protocol GRPC │ +│ 🏠 Local 0.0.0.0:12345 │ +│ 🔒 Private 172.28.0.12:12345 │ +│ 🌍 Public 35.230.97.208:12345 │ +╰──────────────────────────────────────────╯ +``` +Use [Jina Client](https://docs.jina.ai/concepts/client/) to make requests to the service: +```python +from docarray import Document +from jina import Client +french_text = Document( + text='un astronaut est en train de faire une promenade dans un parc' +) +client = Client(port=12345) # use port from output above +response = client.post(on='/', inputs=[french_text]) +print(response[0].text) +``` +```text +an astronaut is walking in a park +``` +<!-- end build-ai-services --> +### Build a pipeline +<!-- start build-pipelines --> +[](https://colab.research.google.com/github/jina-ai/jina/blob/docs-readme-changes/.github/getting-started/notebook.ipynb) +Sometimes you want to chain microservices together into a pipeline. That's where a [Flow](https://docs.jina.ai/concepts/flow/) comes in. +A Flow is a [DAG](https://de.wikipedia.org/wiki/DAG) pipeline, composed of a set of steps, It orchestrates a set of [Executors](https://docs.jina.ai/concepts/executor/) and a [Gateway](https://docs.jina.ai/concepts/gateway/) to offer an end-to-end service. +> **Note** +> If you just want to serve a single Executor, you can use a [Deployment](#build-ai--ml-services). +For instance, let's combine [our French translation service](#build-ai--ml-services) with a Stable Diffusion image generation service from Jina AI's [Executor Hub](https://cloud.jina.ai/executors). Chaining these services together into a [Flow](https://docs.jina.ai/concepts/flow/) will give us a multilingual image generation service. +Build the Flow with either Python or YAML: +<div class="table-wrapper"> +<table> +<tr> +<th> Python API: <code>flow.py</code> </th> +<th> YAML: <code>flow.yml</code> </th> +</tr> +<tr> +<td> +```python +from jina import Flow +flow = ( + Flow() + .add(uses=Translator, timeout_ready=-1) + .add( + uses='jinaai://jina-ai/TextToImage', + timeout_ready=-1, + install_requirements=True, + ) +) # use the Executor from Executor hub +with flow: + flow.block() +``` +</td> +<td> +```yaml +jtype: Flow +executors: + - uses: Translator + timeout_ready: -1 + py_modules: + - translate_executor.py + - uses: jinaai://jina-ai/TextToImage + timeout_ready: -1 + install_requirements: true +``` +Then run the YAML Flow with the CLI: `jina flow --uses flow.yml` +</td> +</tr> +</table> +</div> +```text +─────────────────────────────────────────── 🎉 Flow is ready to serve! ──────────────────────────────────────────── +╭────────────── 🔗 Endpoint ───────────────╮ +│ ⛓ Protocol GRPC │ +│ 🏠 Local 0.0.0.0:12345 │ +│ 🔒 Private 172.28.0.12:12345 │ +│ 🌍 Public 35.240.201.66:12345 │ +╰──────────────────────────────────────────╯ +``` +Then, use [Jina Client](https://docs.jina.ai/concepts/client/) to make requests to the Flow: +```python +from jina import Client, Document +client = Client(port=12345) # use port from output above +french_text = Document( + text='un astronaut est en train de faire une promenade dans un parc' +) +response = client.post(on='/', inputs=[french_text]) +response[0].display() +``` + +You can also deploy a Flow to JCloud. +First, turn the `flow.yml` file into a [JCloud-compatible YAML](https://docs.jina.ai/concepts/jcloud/yaml-spec/) by specifying resource requirements and using containerized Hub Executors. +Then, use `jina cloud deploy` command to deploy to the cloud: +```shell +wget https://raw.githubusercontent.com/jina-ai/jina/master/.github/getting-started/jcloud-flow.yml +jina cloud deploy jcloud-flow.yml +``` +⚠️ **Caution: Make sure to delete/clean up the Flow once you are done with this tutorial to save resources and credits.** +Read more about [deploying Flows to JCloud](https://docs.jina.ai/concepts/jcloud/#deploy). +<!-- end build-pipelines --> + +%package -n python3-jina +Summary: Build multimodal AI services via cloud native technologies · Neural Search · Generative AI · MLOps +Provides: python-jina +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-jina +<p align="center"> +<a href="https://docs.jina.ai"><img src="https://github.com/jina-ai/jina/blob/master/.github/readme/streamline-banner.png?raw=true" alt="Jina: Streamline AI & ML Product Delivery" width="100%"></a> +</p> +### Build AI & ML Services +<!-- start build-ai-services --> +[](https://colab.research.google.com/github/jina-ai/jina/blob/master/.github/getting-started/notebook.ipynb) +Let's build a fast, reliable and scalable gRPC-based AI service. In Jina we call this an **[Executor](https://docs.jina.ai/concepts/executor/)**. Our simple Executor will use Facebook's mBART-50 model to translate French to English. We'll then use a **Deployment** to serve it. +> **Note** +> A Deployment serves just one Executor. To combine multiple Executors into a pipeline and serve that, use a [Flow](#build-a-pipeline). +> **Note** +> Run the [code in Colab](https://colab.research.google.com/assets/colab-badge.svg) to install all dependencies. +Let's implement the service's logic: +<table> +<tr> +<th><code>translate_executor.py</code> </th> +<tr> +<td> +```python +from docarray import DocumentArray +from jina import Executor, requests +from transformers import AutoTokenizer, AutoModelForSeq2SeqLM +class Translator(Executor): + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.tokenizer = AutoTokenizer.from_pretrained( + "facebook/mbart-large-50-many-to-many-mmt", src_lang="fr_XX" + ) + self.model = AutoModelForSeq2SeqLM.from_pretrained( + "facebook/mbart-large-50-many-to-many-mmt" + ) + @requests + def translate(self, docs: DocumentArray, **kwargs): + for doc in docs: + doc.text = self._translate(doc.text) + def _translate(self, text): + encoded_en = self.tokenizer(text, return_tensors="pt") + generated_tokens = self.model.generate( + **encoded_en, forced_bos_token_id=self.tokenizer.lang_code_to_id["en_XX"] + ) + return self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[ + 0 + ] +``` +</td> +</tr> +</table> +Then we deploy it with either the Python API or YAML: +<div class="table-wrapper"> +<table> +<tr> +<th> Python API: <code>deployment.py</code> </th> +<th> YAML: <code>deployment.yml</code> </th> +</tr> +<tr> +<td> +```python +from jina import Deployment +with Deployment(uses=Translator, timeout_ready=-1) as dep: + dep.block() +``` +</td> +<td> +```yaml +jtype: Deployment +with: + uses: Translator + py_modules: + - translate_executor.py # name of the module containing Translator + timeout_ready: -1 +``` +And run the YAML Deployment with the CLI: `jina deployment --uses deployment.yml` +</td> +</tr> +</table> +</div> +```text +──────────────────────────────────────── 🎉 Deployment is ready to serve! ───────────────────────────────────────── +╭────────────── 🔗 Endpoint ───────────────╮ +│ ⛓ Protocol GRPC │ +│ 🏠 Local 0.0.0.0:12345 │ +│ 🔒 Private 172.28.0.12:12345 │ +│ 🌍 Public 35.230.97.208:12345 │ +╰──────────────────────────────────────────╯ +``` +Use [Jina Client](https://docs.jina.ai/concepts/client/) to make requests to the service: +```python +from docarray import Document +from jina import Client +french_text = Document( + text='un astronaut est en train de faire une promenade dans un parc' +) +client = Client(port=12345) # use port from output above +response = client.post(on='/', inputs=[french_text]) +print(response[0].text) +``` +```text +an astronaut is walking in a park +``` +<!-- end build-ai-services --> +### Build a pipeline +<!-- start build-pipelines --> +[](https://colab.research.google.com/github/jina-ai/jina/blob/docs-readme-changes/.github/getting-started/notebook.ipynb) +Sometimes you want to chain microservices together into a pipeline. That's where a [Flow](https://docs.jina.ai/concepts/flow/) comes in. +A Flow is a [DAG](https://de.wikipedia.org/wiki/DAG) pipeline, composed of a set of steps, It orchestrates a set of [Executors](https://docs.jina.ai/concepts/executor/) and a [Gateway](https://docs.jina.ai/concepts/gateway/) to offer an end-to-end service. +> **Note** +> If you just want to serve a single Executor, you can use a [Deployment](#build-ai--ml-services). +For instance, let's combine [our French translation service](#build-ai--ml-services) with a Stable Diffusion image generation service from Jina AI's [Executor Hub](https://cloud.jina.ai/executors). Chaining these services together into a [Flow](https://docs.jina.ai/concepts/flow/) will give us a multilingual image generation service. +Build the Flow with either Python or YAML: +<div class="table-wrapper"> +<table> +<tr> +<th> Python API: <code>flow.py</code> </th> +<th> YAML: <code>flow.yml</code> </th> +</tr> +<tr> +<td> +```python +from jina import Flow +flow = ( + Flow() + .add(uses=Translator, timeout_ready=-1) + .add( + uses='jinaai://jina-ai/TextToImage', + timeout_ready=-1, + install_requirements=True, + ) +) # use the Executor from Executor hub +with flow: + flow.block() +``` +</td> +<td> +```yaml +jtype: Flow +executors: + - uses: Translator + timeout_ready: -1 + py_modules: + - translate_executor.py + - uses: jinaai://jina-ai/TextToImage + timeout_ready: -1 + install_requirements: true +``` +Then run the YAML Flow with the CLI: `jina flow --uses flow.yml` +</td> +</tr> +</table> +</div> +```text +─────────────────────────────────────────── 🎉 Flow is ready to serve! ──────────────────────────────────────────── +╭────────────── 🔗 Endpoint ───────────────╮ +│ ⛓ Protocol GRPC │ +│ 🏠 Local 0.0.0.0:12345 │ +│ 🔒 Private 172.28.0.12:12345 │ +│ 🌍 Public 35.240.201.66:12345 │ +╰──────────────────────────────────────────╯ +``` +Then, use [Jina Client](https://docs.jina.ai/concepts/client/) to make requests to the Flow: +```python +from jina import Client, Document +client = Client(port=12345) # use port from output above +french_text = Document( + text='un astronaut est en train de faire une promenade dans un parc' +) +response = client.post(on='/', inputs=[french_text]) +response[0].display() +``` + +You can also deploy a Flow to JCloud. +First, turn the `flow.yml` file into a [JCloud-compatible YAML](https://docs.jina.ai/concepts/jcloud/yaml-spec/) by specifying resource requirements and using containerized Hub Executors. +Then, use `jina cloud deploy` command to deploy to the cloud: +```shell +wget https://raw.githubusercontent.com/jina-ai/jina/master/.github/getting-started/jcloud-flow.yml +jina cloud deploy jcloud-flow.yml +``` +⚠️ **Caution: Make sure to delete/clean up the Flow once you are done with this tutorial to save resources and credits.** +Read more about [deploying Flows to JCloud](https://docs.jina.ai/concepts/jcloud/#deploy). +<!-- end build-pipelines --> + +%package help +Summary: Development documents and examples for jina +Provides: python3-jina-doc +%description help +<p align="center"> +<a href="https://docs.jina.ai"><img src="https://github.com/jina-ai/jina/blob/master/.github/readme/streamline-banner.png?raw=true" alt="Jina: Streamline AI & ML Product Delivery" width="100%"></a> +</p> +### Build AI & ML Services +<!-- start build-ai-services --> +[](https://colab.research.google.com/github/jina-ai/jina/blob/master/.github/getting-started/notebook.ipynb) +Let's build a fast, reliable and scalable gRPC-based AI service. In Jina we call this an **[Executor](https://docs.jina.ai/concepts/executor/)**. Our simple Executor will use Facebook's mBART-50 model to translate French to English. We'll then use a **Deployment** to serve it. +> **Note** +> A Deployment serves just one Executor. To combine multiple Executors into a pipeline and serve that, use a [Flow](#build-a-pipeline). +> **Note** +> Run the [code in Colab](https://colab.research.google.com/assets/colab-badge.svg) to install all dependencies. +Let's implement the service's logic: +<table> +<tr> +<th><code>translate_executor.py</code> </th> +<tr> +<td> +```python +from docarray import DocumentArray +from jina import Executor, requests +from transformers import AutoTokenizer, AutoModelForSeq2SeqLM +class Translator(Executor): + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.tokenizer = AutoTokenizer.from_pretrained( + "facebook/mbart-large-50-many-to-many-mmt", src_lang="fr_XX" + ) + self.model = AutoModelForSeq2SeqLM.from_pretrained( + "facebook/mbart-large-50-many-to-many-mmt" + ) + @requests + def translate(self, docs: DocumentArray, **kwargs): + for doc in docs: + doc.text = self._translate(doc.text) + def _translate(self, text): + encoded_en = self.tokenizer(text, return_tensors="pt") + generated_tokens = self.model.generate( + **encoded_en, forced_bos_token_id=self.tokenizer.lang_code_to_id["en_XX"] + ) + return self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[ + 0 + ] +``` +</td> +</tr> +</table> +Then we deploy it with either the Python API or YAML: +<div class="table-wrapper"> +<table> +<tr> +<th> Python API: <code>deployment.py</code> </th> +<th> YAML: <code>deployment.yml</code> </th> +</tr> +<tr> +<td> +```python +from jina import Deployment +with Deployment(uses=Translator, timeout_ready=-1) as dep: + dep.block() +``` +</td> +<td> +```yaml +jtype: Deployment +with: + uses: Translator + py_modules: + - translate_executor.py # name of the module containing Translator + timeout_ready: -1 +``` +And run the YAML Deployment with the CLI: `jina deployment --uses deployment.yml` +</td> +</tr> +</table> +</div> +```text +──────────────────────────────────────── 🎉 Deployment is ready to serve! ───────────────────────────────────────── +╭────────────── 🔗 Endpoint ───────────────╮ +│ ⛓ Protocol GRPC │ +│ 🏠 Local 0.0.0.0:12345 │ +│ 🔒 Private 172.28.0.12:12345 │ +│ 🌍 Public 35.230.97.208:12345 │ +╰──────────────────────────────────────────╯ +``` +Use [Jina Client](https://docs.jina.ai/concepts/client/) to make requests to the service: +```python +from docarray import Document +from jina import Client +french_text = Document( + text='un astronaut est en train de faire une promenade dans un parc' +) +client = Client(port=12345) # use port from output above +response = client.post(on='/', inputs=[french_text]) +print(response[0].text) +``` +```text +an astronaut is walking in a park +``` +<!-- end build-ai-services --> +### Build a pipeline +<!-- start build-pipelines --> +[](https://colab.research.google.com/github/jina-ai/jina/blob/docs-readme-changes/.github/getting-started/notebook.ipynb) +Sometimes you want to chain microservices together into a pipeline. That's where a [Flow](https://docs.jina.ai/concepts/flow/) comes in. +A Flow is a [DAG](https://de.wikipedia.org/wiki/DAG) pipeline, composed of a set of steps, It orchestrates a set of [Executors](https://docs.jina.ai/concepts/executor/) and a [Gateway](https://docs.jina.ai/concepts/gateway/) to offer an end-to-end service. +> **Note** +> If you just want to serve a single Executor, you can use a [Deployment](#build-ai--ml-services). +For instance, let's combine [our French translation service](#build-ai--ml-services) with a Stable Diffusion image generation service from Jina AI's [Executor Hub](https://cloud.jina.ai/executors). Chaining these services together into a [Flow](https://docs.jina.ai/concepts/flow/) will give us a multilingual image generation service. +Build the Flow with either Python or YAML: +<div class="table-wrapper"> +<table> +<tr> +<th> Python API: <code>flow.py</code> </th> +<th> YAML: <code>flow.yml</code> </th> +</tr> +<tr> +<td> +```python +from jina import Flow +flow = ( + Flow() + .add(uses=Translator, timeout_ready=-1) + .add( + uses='jinaai://jina-ai/TextToImage', + timeout_ready=-1, + install_requirements=True, + ) +) # use the Executor from Executor hub +with flow: + flow.block() +``` +</td> +<td> +```yaml +jtype: Flow +executors: + - uses: Translator + timeout_ready: -1 + py_modules: + - translate_executor.py + - uses: jinaai://jina-ai/TextToImage + timeout_ready: -1 + install_requirements: true +``` +Then run the YAML Flow with the CLI: `jina flow --uses flow.yml` +</td> +</tr> +</table> +</div> +```text +─────────────────────────────────────────── 🎉 Flow is ready to serve! ──────────────────────────────────────────── +╭────────────── 🔗 Endpoint ───────────────╮ +│ ⛓ Protocol GRPC │ +│ 🏠 Local 0.0.0.0:12345 │ +│ 🔒 Private 172.28.0.12:12345 │ +│ 🌍 Public 35.240.201.66:12345 │ +╰──────────────────────────────────────────╯ +``` +Then, use [Jina Client](https://docs.jina.ai/concepts/client/) to make requests to the Flow: +```python +from jina import Client, Document +client = Client(port=12345) # use port from output above +french_text = Document( + text='un astronaut est en train de faire une promenade dans un parc' +) +response = client.post(on='/', inputs=[french_text]) +response[0].display() +``` + +You can also deploy a Flow to JCloud. +First, turn the `flow.yml` file into a [JCloud-compatible YAML](https://docs.jina.ai/concepts/jcloud/yaml-spec/) by specifying resource requirements and using containerized Hub Executors. +Then, use `jina cloud deploy` command to deploy to the cloud: +```shell +wget https://raw.githubusercontent.com/jina-ai/jina/master/.github/getting-started/jcloud-flow.yml +jina cloud deploy jcloud-flow.yml +``` +⚠️ **Caution: Make sure to delete/clean up the Flow once you are done with this tutorial to save resources and credits.** +Read more about [deploying Flows to JCloud](https://docs.jina.ai/concepts/jcloud/#deploy). +<!-- end build-pipelines --> + +%prep +%autosetup -n jina-3.14.1 + +%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-jina -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 3.14.1-1 +- Package Spec generated |