feat: python 3.11 support

Jenkinsfile

@@ -19,7 +19,7 @@ node('cuda-module') {
                     docker-compose -f utils/Docker/docker-compose.yml -p $BUILD_TAG ps | grep Exit | grep -v 'Exit 0' && exit 1
                     docker-compose -f utils/Docker/docker-compose.yml -p $BUILD_TAG up py38 py39
                     docker-compose -f utils/Docker/docker-compose.yml -p $BUILD_TAG ps | grep Exit | grep -v 'Exit 0' && exit 1
-                    docker-compose -f utils/Docker/docker-compose.yml -p $BUILD_TAG up py310
+                    docker-compose -f utils/Docker/docker-compose.yml -p $BUILD_TAG up py310 py311
                     docker-compose -f utils/Docker/docker-compose.yml -p $BUILD_TAG ps | grep Exit | grep -v 'Exit 0' && exit 1 || exit 0
                 """
                 currentBuild.result = 'SUCCESS'

README.md

@@ -1,5 +1,5 @@
 [![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE)
-![Python 3.6, 3.7, 3.8, 3.9, 3.10](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-green.svg)
+![Python 3.6, 3.7, 3.8, 3.9, 3.10, 3.11](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-green.svg)
 [![Downloads](https://pepy.tech/badge/deeppavlov)](https://pepy.tech/project/deeppavlov)
 <img align="right" height="27%" width="27%" src="docs/_static/deeppavlov_logo.png"/>
 

deeppavlov/_meta.py

@@ -1,4 +1,4 @@
-__version__ = '1.5.0'
+__version__ = '1.6.0'
 __author__ = 'Neural Networks and Deep Learning lab, MIPT'
 __description__ = 'An open source library for building end-to-end dialog systems and training chatbots.'
 __keywords__ = ['NLP', 'NER', 'SQUAD', 'Intents', 'Chatbot']

deeppavlov/core/data/utils.py

@@ -78,7 +78,7 @@ def s3_download(url: str, destination: str) -> None:
         file_object.download_file(destination, Callback=pbar.update)
 
 
-def simple_download(url: str, destination: Union[Path, str], headers: Optional[dict] = None) -> None:
+def simple_download(url: str, destination: Union[Path, str], headers: Optional[dict] = None, n_tries: int = 3) -> None:
     """Download a file from URL to target location.
 
     Displays a progress bar to the terminal during the download process.
@@ -87,58 +87,66 @@ def simple_download(url: str, destination: Union[Path, str], headers: Optional[d
         url: The source URL.
         destination: Path to the file destination (including file name).
         headers: Headers for file server.
+        n_tries: Number of retries if download fails.
 
     """
-    destination = Path(destination)
-    destination.parent.mkdir(parents=True, exist_ok=True)
-
-    log.info('Downloading from {} to {}'.format(url, destination))
-
-    if url.startswith('s3://'):
-        return s3_download(url, str(destination))
-
-    chunk_size = 32 * 1024
-    temporary = destination.with_suffix(destination.suffix + '.part')
-
-    r = requests.get(url, stream=True, headers=headers)
-    if r.status_code != 200:
-        raise RuntimeError(f'Got status code {r.status_code} when trying to download {url}')
-    total_length = int(r.headers.get('content-length', 0))
-
-    if temporary.exists() and temporary.stat().st_size > total_length:
-        temporary.write_bytes(b'')  # clearing temporary file when total_length is inconsistent
-
-    with temporary.open('ab') as f:
-        downloaded = f.tell()
-        if downloaded != 0:
-            log.warning(f'Found a partial download {temporary}')
-        with tqdm(initial=downloaded, total=total_length, unit='B', unit_scale=True) as pbar:
-            while True:
-                if downloaded != 0:
-                    log.warning(f'Download stopped abruptly, trying to resume from {downloaded} '
-                                f'to reach {total_length}')
-                    headers['Range'] = f'bytes={downloaded}-'
-                    r = requests.get(url, headers=headers, stream=True)
-                    if 'content-length' not in r.headers or \
-                            total_length - downloaded != int(r.headers['content-length']):
-                        raise RuntimeError('It looks like the server does not support resuming downloads.')
-
-                try:
-                    for chunk in r.iter_content(chunk_size=chunk_size):
-                        if chunk:  # filter out keep-alive new chunks
-                            downloaded += len(chunk)
-                            pbar.update(len(chunk))
-                            f.write(chunk)
-                except requests.exceptions.ChunkedEncodingError:
-                    if downloaded == 0:
-                        r = requests.get(url, stream=True, headers=headers)
-
-                if downloaded >= total_length:
-                    # Note that total_length is 0 if the server didn't return the content length,
-                    # in this case we perform just one iteration and assume that we are done.
-                    break
-
-    temporary.rename(destination)
+    try:
+        destination = Path(destination)
+        destination.parent.mkdir(parents=True, exist_ok=True)
+
+        log.info('Downloading from {} to {}'.format(url, destination))
+
+        if url.startswith('s3://'):
+            return s3_download(url, str(destination))
+
+        chunk_size = 32 * 1024
+        temporary = destination.with_suffix(destination.suffix + '.part')
+
+        r = requests.get(url, stream=True, headers=headers)
+        if r.status_code != 200:
+            raise RuntimeError(f'Got status code {r.status_code} when trying to download {url}')
+        total_length = int(r.headers.get('content-length', 0))
+
+        if temporary.exists() and temporary.stat().st_size > total_length:
+            temporary.write_bytes(b'')  # clearing temporary file when total_length is inconsistent
+
+        with temporary.open('ab') as f:
+            downloaded = f.tell()
+            if downloaded != 0:
+                log.warning(f'Found a partial download {temporary}')
+            with tqdm(initial=downloaded, total=total_length, unit='B', unit_scale=True) as pbar:
+                while True:
+                    if downloaded != 0:
+                        log.warning(f'Download stopped abruptly, trying to resume from {downloaded} '
+                                    f'to reach {total_length}')
+                        headers['Range'] = f'bytes={downloaded}-'
+                        r = requests.get(url, headers=headers, stream=True)
+                        if 'content-length' not in r.headers or \
+                                total_length - downloaded != int(r.headers['content-length']):
+                            raise RuntimeError('It looks like the server does not support resuming downloads.')
+
+                    try:
+                        for chunk in r.iter_content(chunk_size=chunk_size):
+                            if chunk:  # filter out keep-alive new chunks
+                                downloaded += len(chunk)
+                                pbar.update(len(chunk))
+                                f.write(chunk)
+                    except requests.exceptions.ChunkedEncodingError:
+                        if downloaded == 0:
+                            r = requests.get(url, stream=True, headers=headers)
+
+                    if downloaded >= total_length:
+                        # Note that total_length is 0 if the server didn't return the content length,
+                        # in this case we perform just one iteration and assume that we are done.
+                        break
+
+        temporary.rename(destination)
+    except Exception as e:
+        if n_tries > 0:
+            log.warning(f'Download failed: {e}, retrying')
+            simple_download(url, destination, headers, n_tries - 1)
+        else:
+            raise e
 
 
 def download(dest_file_path: [List[Union[str, Path]]], source_url: str, force_download: bool = True,

deeppavlov/requirements/datasets.txt

@@ -1 +1,2 @@
-datasets>=1.16.0,<2.5.0
+datasets>=1.16.0,<2.5.0;python_version<="3.10"
+datasets==2.2.*;python_version=="3.11.*"

deeppavlov/requirements/faiss.txt

@@ -1 +1,2 @@
-faiss-cpu==1.7.2
+faiss-cpu==1.7.2;python_version<="3.10"
+faiss-cpu==1.7.4;python_version=="3.11.*"

deeppavlov/requirements/kenlm.txt

@@ -1 +1,2 @@
-pypi-kenlm==0.1.20220713
+pypi-kenlm==0.1.20220713;python_version<="3.10"
+kenlm==0.2.*;python_version=="3.11.*"

docs/features/models/KBQA.ipynb

@@ -22,13 +22,13 @@
     "    \n",
     "    4.2. [Predict using CLI](#4.2-Predict-using-CLI)\n",
     "\n",
-    "    4.3. [Using entity linking and Wiki parser as standalone services for KBQA](#4.3-Using-entity-linking-and-Wiki-parser-as-standalone-services-for-KBQA)\n",
+    "    4.3. [Using entity linking and Wiki parser as standalone services for KBQA](#4.3-Using-entity-linking-and-Wiki-parser-as-standalone-tools-for-KBQA)\n",
     "     \n",
     "5. [Customize the model](#5.-Customize-the-model)\n",
     "    \n",
-    "    5.1. [Train your model from Python](#5.1-Train-your-model-from-Python)\n",
+    "    5.1. [Description of config parameters](#5.1-Description-of-config-parameters)\n",
     "    \n",
-    "    5.2. [Train your model from CLI](#5.2-Train-your-model-from-CLI)\n",
+    "    5.2. [Train KBQA components](#5.2-Train-KBQA-components)\n",
     "\n",
     "# 1. Introduction to the task\n",
     "\n",

docs/features/models/NER.ipynb

@@ -22,7 +22,7 @@
     "    \n",
     "    4.2. [Predict using CLI](#4.2-Predict-using-CLI)\n",
     "    \n",
-    "5. [Evaluate](#6.-Evaluate)\n",
+    "5. [Evaluate](#5.-Evaluate)\n",
     "    \n",
     "    5.1. [Evaluate from Python](#5.1-Evaluate-from-Python)\n",
     "    \n",

docs/features/models/SQuAD.ipynb

@@ -105,7 +105,7 @@
     "`squad_bert` is the name of the model's *config_file*. [What is a Config File?](http://docs.deeppavlov.ai/en/master/intro/configuration.html) \n",
     "\n",
     "Configuration file defines the model and describes its hyperparameters. To use another model, change the name of the *config_file* here and further.\n",
-    "The full list of the models with their config names can be found in the [table](#6.-Models-list).\n",
+    "The full list of the models with their config names can be found in the [table](#3.-Models-list).\n",
     "\n",
     "# 3. Models list\n",
     "\n",

docs/features/models/classification.ipynb

@@ -162,7 +162,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3.2 Predict using CLI\n",
+    "## 4.2 Predict using CLI\n",
     "\n",
     "You can also get predictions in an interactive mode through CLI (Command Line Interface)."
    ]
@@ -198,9 +198,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 4. Evaluation\n",
+    "# 5. Evaluation\n",
     "\n",
-    "## 4.1 Evaluate from Python"
+    "## 5.1 Evaluate from Python"
    ]
   },
   {
@@ -218,7 +218,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4.2 Evaluate from CLI"
+    "## 5.2 Evaluate from CLI"
    ]
   },
   {
@@ -234,9 +234,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 5. Customize the model\n",
+    "# 6. Train the model on your data\n",
     "\n",
-    "## 5.1 Train your model from Python\n",
+    "## 6.1 Train your model from Python\n",
     "\n",
     "### Provide your data path\n",
     "\n",
@@ -346,7 +346,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 5.2 Train your model from CLI\n",
+    "## 6.2 Train your model from CLI\n",
     "\n",
     "To train the model on your data, create a copy of a config file and change the *data_path* variable in it. After that, train the model using your new *config_file*. You can also change any of the hyperparameters of the model."
    ]

docs/features/models/few_shot_classification.ipynb

@@ -119,7 +119,7 @@
     "\n",
     "## 4.2 Predict using Python\n",
     "\n",
-    "After [installing](#4.-Get-started-with-the-model) the model, build it from the config and predict."
+    "After [installing](#2.-Get-started-with-the-model) the model, build it from the config and predict."
    ]
   },
   {
@@ -192,7 +192,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4.2 Predict using CLI\n",
+    "## 4.3 Predict using CLI\n",
     "\n",
     "You can also get predictions in an interactive mode through CLI (Сommand Line Interface)."
    ]

docs/features/models/morpho_tagger.ipynb

@@ -22,7 +22,7 @@
     "\n",
     "    4.2. [Predict using CLI](#4.2-Predict-using-CLI)\n",
     "\n",
-    "5. [Customize the model](#4.-Customize-the-model)\n",
+    "5. [Customize the model](#5.-Customize-the-model)\n",
     "\n",
     "# 1. Introduction to the task\n",
     "\n",

docs/features/models/relation_extraction.ipynb

@@ -198,7 +198,7 @@
     "|NUM    | Percents, money, quantities                                                                    |\n",
     "|MISC   | Products, including vehicles, weapons, etc. <br> Events, including elections, battles, sporting MISC events, etc. Laws, cases, languages, etc.   |\n",
     "\n",
-    "**Model Output**: one or several of the [97 relations](#5.1-Relations-used-in-English-model) found between the given entities; relation id in [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) (e.g. 'P26') and relation name ('spouse').\n",
+    "**Model Output**: one or several of the [97 relations](#6.1-Relations-used-in-English-model) found between the given entities; relation id in [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) (e.g. 'P26') and relation name ('spouse').\n",
     "\n",
     "### Russian"
    ]
@@ -244,8 +244,34 @@
     "- list of entities positions (i.e. all start and end positions of both entities' mentions)\n",
     "- list of NER tags of both entities.\n",
     "\n",
-    "**Model Output**: one or several of the [30 relations](#5.2-Relations-used-in-Russian-model) found between the given entities; a Russian relation name (e.g. \"участник\") or an English one, if Russian one is unavailable, and, if applicable, its id in [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) (e.g. 'P710').\n",
+    "**Model Output**: one or several of the [30 relations](#6.2-Relations-used-in-Russian-model) found between the given entities; a Russian relation name (e.g. \"участник\") or an English one, if Russian one is unavailable, and, if applicable, its id in [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) (e.g. 'P710').\n",
     "\n",
+    "## 4.2 Predict using CLI\n",
+    "\n",
+    "You can also get predictions in an interactive mode through CLI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! python -m deeppavlov interact re_docred [-d]\n",
+    "! python -m deeppavlov interact re_rured [-d]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`-d` is an optional download key (alternative to `download=True` in Python code). It is used to download the pre-trained model along with embeddings and all other files needed to run the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "# 5. Customize the model\n",
     "\n",
     "## 5.1 Description of config parameters\n",

docs/features/models/spelling_correction.ipynb

@@ -22,7 +22,7 @@
     "\n",
     "    4.2. [Predict using CLI](#4.2-Predict-using-CLI)\n",
     "\n",
-    "5. [Customize the model](#4.-Customize-the-model)\n",
+    "5. [Customize the model](#5.-Customize-the-model)\n",
     "\n",
     "    5.1. [Training configuration](#5.1-Training-configuration)\n",
     "\n",

docs/features/models/syntax_parser.ipynb

@@ -22,7 +22,7 @@
     "\n",
     "    4.2. [Predict using CLI](#4.2-Predict-using-CLI)\n",
     "\n",
-    "5. [Customize the model](#4.-Customize-the-model)\n",
+    "5. [Customize the model](#5.-Customize-the-model)\n",
     "\n",
     "# 1. Introduction to the task\n",
     "\n",

docs/intro/installation.rst

@@ -1,7 +1,7 @@
 Installation
 ============
 
-DeepPavlov supports **Linux**, **Windows 10+** (through WSL/WSL2), **MacOS** (Big Sur+) platforms, **Python 3.6-3.10**.
+DeepPavlov supports **Linux**, **Windows 10+** (through WSL/WSL2), **MacOS** (Big Sur+) platforms, **Python 3.6-3.11**.
 Depending on the model used, you may need from 4 to 16 GB RAM.
 
 Install with pip

docs/intro/quick_start.rst

@@ -2,7 +2,7 @@ QuickStart
 ------------
 
 First, follow instructions on :doc:`Installation page </intro/installation>`
-to install ``deeppavlov`` package for Python 3.6/3.7/3.8/3.9/3.10.
+to install ``deeppavlov`` package for Python 3.6-3.11.
 
 DeepPavlov contains a bunch of great pre-trained NLP models. Each model is
 determined by its config file. List of models is available on

requirements.txt

@@ -7,7 +7,8 @@ prometheus-client>=0.13.0,<=1.16.0
 pydantic<2
 pybind11==2.10.3
 requests>=2.19.0,<3.0.0
-scikit-learn>=0.24,<1.1.0
+scikit-learn>=0.24,<1.1.0;python_version<="3.10"
+scikit-learn==1.4.0;python_version=="3.11.*"
 tqdm>=4.42.0,<4.65.0
 uvicorn>=0.13.0,<0.19.0
 wheel

setup.py

@@ -68,11 +68,17 @@ def readme():
                 'pexpect'
             ],
             'docs': [
-                'sphinx==3.5.4;python_version<"3.10"',
-                'sphinx==4.5.0;python_version>="3.10"',
-                'sphinx_rtd_theme==0.5.2',
-                'docutils<0.17,>=0.12',
-                'nbsphinx==0.8.4',
+                'sphinx==3.5.4;python_version<="3.7"',
+                'sphinx==5.0.0;python_version=="3.8"',
+                'sphinx==5.0.0;python_version=="3.9"',
+                'sphinx==5.0.0;python_version=="3.10"',
+                'sphinx==7.2.*;python_version=="3.11.*"',
+                'sphinx_rtd_theme==0.5.2;python_version<="3.10"',
+                'sphinx_rtd_theme==2.0.0;python_version=="3.11.*"',
+                'docutils<0.17,>=0.12;python_version<="3.10"',
+                'docutils==0.20.1;python_version=="3.11.*"',
+                'nbsphinx==0.8.4;python_version<="3.10"',
+                'nbsphinx==0.9.3;python_version=="3.11.*"',
                 'ipykernel==5.5.4',
                 'jinja2<=3.0.3',
                 'sphinx-copybutton==0.5.0',