Release 1.5.0

.readthedocs.yml

@@ -1,12 +1,15 @@
 # .readthedocs.yml
+version: 2
 
 build:
-  image: latest
-
+  os: "ubuntu-20.04"
+  tools:
+    python: "3.10"
 formats: []
 
 python:
-  pip_install: true
-  version: 3.6
-  extra_requirements:
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
         - docs

README.md

@@ -60,8 +60,8 @@ Please leave us [your feedback](https://forms.gle/i64fowQmiVhMMC7f9) on how we c
 
 ## Installation
 
-0. We support `Linux` platform, `Python 3.6`, `3.7`, `3.8`, `3.9` and `3.10`
-    * **`Python 3.5` is not supported!**
+0. DeepPavlov supports `Linux`, `Windows 10+` (through WSL/WSL2), `MacOS` (Big Sur+) platforms, `Python 3.6`, `3.7`, `3.8`, `3.9` and `3.10`.
+    Depending on the model used, you may need from 4 to 16 GB RAM.
 
 1. Create and activate a virtual environment:
     * `Linux`
@@ -99,6 +99,7 @@ By default, DeepPavlov installs models requirements from PyPI. PyTorch from PyPI
 capability. To run supported DeepPavlov models on GPU you should have [CUDA](https://developer.nvidia.com/cuda-toolkit)
 compatible with used GPU and [PyTorch version](deeppavlov/requirements/pytorch.txt) required by DeepPavlov models.
 See [docs](https://docs.deeppavlov.ai/en/master/intro/quick_start.html#using-gpu) for details.
+GPU with Pascal or newer architecture and 4+ GB VRAM is recommended.
 
 ### Command line interface (CLI)
 

deeppavlov/_meta.py

@@ -1,4 +1,4 @@
-__version__ = '1.4.0'
+__version__ = '1.5.0'
 __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/commands/train.py

@@ -92,7 +92,9 @@ def train_evaluate_model_from_config(config: Union[str, Path, dict],
     if iterator is None:
         try:
             data = read_data_by_config(config)
-            if config.get('train', {}).get('val_every_n_epochs') and not data.get('valid'):
+            # TODO: check class objects, not strings
+            is_mtl = config['dataset_reader']['class_name'] == 'multitask_reader'
+            if config.get('train', {}).get('val_every_n_epochs') and not data.get('valid') and not is_mtl:
                 error_message = 'The value "val_every_n_epochs" is set in the config but no validation data is provided'
                 raise AttributeError(error_message)
         except ConfigError as e:

docs/features/models/NER.ipynb

@@ -80,14 +80,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!python -m deeppavlov install ner_ontonotes_bert_torch"
+    "!python -m deeppavlov install ner_ontonotes_bert"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`ner_ontonotes_bert_torch` is the name of the model's *config_file*. [What is a Config File?](http://docs.deeppavlov.ai/en/master/intro/configuration.html) \n",
+    "`ner_ontonotes_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 NER models with their config names can be found in the [table](#3.-Models-list).\n",
@@ -126,7 +126,7 @@
    "source": [
     "from deeppavlov import build_model\n",
     "\n",
-    "ner_model = build_model('ner_ontonotes_bert_torch', download=True, install=True)"
+    "ner_model = build_model('ner_ontonotes_bert', download=True, install=True)"
    ]
   },
   {
@@ -180,7 +180,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! python -m deeppavlov interact ner_ontonotes_bert_torch -d"
+    "! python -m deeppavlov interact ner_ontonotes_bert -d"
    ]
   },
   {
@@ -198,7 +198,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! python -m deeppavlov predict ner_ontonotes_bert_torch -f <file-name>"
+    "! python -m deeppavlov predict ner_ontonotes_bert -f <file-name>"
    ]
   },
   {
@@ -224,7 +224,7 @@
    "source": [
     "from deeppavlov import evaluate_model\n",
     "\n",
-    "model = evaluate_model('ner_ontonotes_bert_torch', download=True)"
+    "model = evaluate_model('ner_ontonotes_bert', download=True)"
    ]
   },
   {
@@ -240,7 +240,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! python -m deeppavlov evaluate ner_ontonotes_bert_torch"
+    "! python -m deeppavlov evaluate ner_ontonotes_bert"
    ]
   },
   {
@@ -275,7 +275,7 @@
     "from deeppavlov import train_model\n",
     "from deeppavlov.core.commands.utils import parse_config\n",
     "\n",
-    "model_config = parse_config('ner_ontonotes_bert_torch')\n",
+    "model_config = parse_config('ner_ontonotes_bert')\n",
     "\n",
     "# dataset that the model was trained on\n",
     "print(model_config['dataset_reader']['data_path'])"
@@ -401,7 +401,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "! python -m deeppavlov train ner_ontonotes_bert_torch"
+    "! python -m deeppavlov train ner_ontonotes_bert"
    ]
   },
   {

docs/features/models/multitask_bert.rst

@@ -2,7 +2,8 @@ Multi-task BERT in DeepPavlov
 =============================
 
 Multi-task BERT in DeepPavlov is an implementation of BERT training algorithm published in the paper
-`Multi-Task Deep Neural Networks for Natural Language Understanding <https://arxiv.org/abs/1901.11504>`_.
+`Knowledge Transfer Between Tasks and Languages in the Multi-task
+Encoder-agnostic Transformer-based Models <https://www.dialog-21.ru/media/5902/karpovdpluskonovalovv002.pdf>`_.
 
 The idea is to share BERT body between several tasks. This is necessary if a model pipe has several
 components using BERT and the amount of GPU memory is limited. Each task has its own 'head' part attached to the

docs/integrations/rest_api.rst

@@ -117,3 +117,26 @@ Here are POST request payload examples for some of the library models:
 |                                         | |  "question_raw":["What strained the relationship between Great Britain and its colonies?"]}                                                       |
 +-----------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------+
 
+REST API Usage Example
+======================
+
+To start server with ``squad_bert`` model run:
+
+.. code:: bash
+
+    python -m deeppavlov riseapi squad_bert -id
+
+To get response from this model on another terminal run:
+
+.. code:: bash
+
+    curl -X POST http://0.0.0.0:5000/model -H 'Content-Type: application/json' -d '{
+        "context_raw": [
+            "All work and no play makes Jack a dull boy.",
+            "I used to be an adventurer like you, then I took an arrow in the knee."
+        ],
+        "question_raw": [
+            "What makes Jack a dull boy?",
+            "Who I used to be?"
+        ]
+    }'

docs/intro/installation.rst

@@ -1,43 +1,69 @@
 Installation
 ============
 
+DeepPavlov supports **Linux**, **Windows 10+** (through WSL/WSL2), **MacOS** (Big Sur+) platforms, **Python 3.6-3.10**.
+Depending on the model used, you may need from 4 to 16 GB RAM.
 
-We support ``Linux`` platform, ``Python 3.6``, ``3.7``, ``3.8``, ``3.9`` and ``3.10``.
-
-.. note::
-
-    * ``Python 3.5`` is not supported!
+Install with pip
+~~~~~~~~~~~~~~~~
 
+You should install DeepPavlov in a `virtual environment <https://docs.python.org/3/library/venv.html>`_. If you’re
+unfamiliar with Python virtual environments, take a look at this
+`guide <https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/>`_. A virtual
+environment makes it easier to manage different projects, and avoid compatibility issues between dependencies.
 
 #. Create a virtual environment:
 
     .. code:: bash
 
         python -m venv env
 
-#. Activate the environment:
+#. Activate the virtual environment on Linux (`source` could be replaced with `.`):
 
-    * Linux
-
-        .. code:: bash
+    .. code:: bash
 
-            source ./env/bin/activate
+        source env/bin/activate
 
-#. Install the package inside this virtual environment:
+#. Install DeepPavlov inside this virtual environment:
 
     .. code:: bash
 
         pip install deeppavlov
 
+Install from source
+~~~~~~~~~~~~~~~~~~~
+
+Install DeepPavlov **dev** branch from source with the following command:
+
+    .. code:: bash
+
+        pip install git+http://github.com/deeppavlov/DeepPavlov@dev
+
+This command installs the bleeding edge dev version rather than the latest release version. The dev version is useful
+for staying up-to-date with the latest developments. For instance, if a bug has been fixed since the last release but
+a new release hasn’t been rolled out yet. However, this means the dev version may not always be stable.
+
+Editable install
+~~~~~~~~~~~~~~~~
+
+You will need an editable install if you want to make changes in the DeepPavlov source code that immediately take place
+without requiring a new installation.
+
+Clone the repository and install DeepPavlov with the following commands:
+
+    .. code:: bash
+
+        git clone http://github.com/deeppavlov/DeepPavlov.git
+        pip install -e DeepPavlov
 
 Docker Images
--------------
+~~~~~~~~~~~~~
 
 We have built several DeepPavlov based Docker images, which include:
 
     * DeepPavlov based Jupyter notebook Docker image;
     * Docker images which serve some of our models and allow to access them
-      via REST API (``riseapi`` mode).
+      via REST API (:doc:`riseapi </integrations/rest_api>` mode).
 
 Here is our `DockerHub repository <https://hub.docker.com/u/deeppavlov/>`_ with
 images and deployment instructions.