Release 0.0.6b (#311)

* Add files via upload * Update README.md `Model` - > `Component` * fix: indent in 04 tutorial * docs: add info on installation of config requirements * feat: new version number * feat: download en for spacy if spacy is required * feat: download en for spacy if spacy is required * feat: version 0.0.6b
deeppavlov · Jul 10, 2018 · 3f23f1e · 3f23f1e
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diff --git a/README.md b/README.md
@@ -1,7 +1,9 @@
 [![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/deepmipt/DeepPavlov/blob/master/LICENSE)
 ![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg)
 
-_We are still in a really early Alpha release._
+_We are still in a really early Alpha release._  
+__In version 0.0.6 everything from package `deeppavlov.skills` except `deeppavlov.skills.pattern_matching_skill` was moved to `deeppavlov.models` so your imports might break__  
+
 
 DeepPavlov is an open-source conversational AI library built on [TensorFlow](https://www.tensorflow.org/) and [Keras](https://keras.io/). It is designed for
  * development of production ready chat-bots and complex conversational systems,
@@ -52,14 +54,10 @@ print(HelloBot(['Hello!', 'Boo...', 'Bye.']))
    git clone https://github.com/deepmipt/DeepPavlov.git
    cd DeepPavlov
    ```
-4. Install the requirements:
+4. Install basic requirements:
     ```
     python setup.py develop
     ```
-5. Install `spacy` dependencies:
-    ```
-    python -m spacy download en
-    ```
 
 # Demo 
 
@@ -80,16 +78,16 @@ Our goal is to enable AI-application developers and researchers with:
 ## Key Concepts
  * `Agent` is a conversational agent communicating with users in natural language (text).
  * `Skill` fulfills user’s goal in some domain. Typically, this is accomplished by presenting information or completing transaction (e.g. answer question by FAQ, booking tickets etc.). However, for some tasks a success of interaction is defined as continuous engagement (e.g. chit-chat).
- * `Model` is a reusable functional component of `Skill`.
+ * `Component` is a reusable functional component of `Skill`.
    * `Rule-based Models` cannot be trained.
    * `Machine Learning Models` can be trained only stand alone.
    * `Deep Learning Models` can be trained independently and in an end-to-end mode being joined in a chain.
  * `Skill Manager` performs selection of the `Skill` to generate response.
  * ` Chainer` builds an agent/component pipeline from heterogeneous components (rule-based/ml/dl). It allows to train and infer models in a pipeline as a whole.
 
-The smallest building block of the library is `Model`. `Model` stands for any kind of function in an NLP pipeline. It can be implemented as a neural network, a non-neural ML model or a rule-based system. Besides that, `Model` can have nested structure, i.e. a `Model` can include other `Model`'(s). 
+The smallest building block of the library is `Component`. `Component` stands for any kind of function in an NLP pipeline. It can be implemented as a neural network, a non-neural ML model or a rule-based system. Besides that, `Component` can have nested structure, i.e. a `Component` can include other `Component`'(s). 
 
-`Model`s can be joined into a `Skill`. `Skill` solves a larger NLP task compared to `Model`. However, in terms of implementation `Skill`s are not different from `Model`s. The only restriction of `Skill`s is that their input and output should both be strings. Therefore, `Skill`s are usually associated with dialogue tasks. 
+`Component`s can be joined into a `Skill`. `Skill` solves a larger NLP task compared to `Component`. However, in terms of implementation `Skill`s are not different from `Component`s. The only restriction of `Skill`s is that their input and output should both be strings. Therefore, `Skill`s are usually associated with dialogue tasks. 
 
 `Agent` is supposed to be a multi-purpose dialogue system that comprises several `Skill`s and can switch between them. It can be a dialogue system that contains a goal-oriented and chatbot skills and chooses which one to use for generating the answer depending on user input.
 
@@ -99,7 +97,13 @@ DeepPavlov is built on top of machine learning frameworks [TensorFlow](https://w
 
 # Quick start
 
-To use our pre-trained models, you should first download them:
+To use our pre-trained models, you should first install their requirements:
+```
+python -m deeppavlov install <path_to_config>
+```
+
+
+Then download the models and data for them:
 ```
 python -m deeppavlov download <path_to_config>
 ```
@@ -441,4 +445,3 @@ DeepPavlov is built and maintained by [Neural Networks and Deep Learning Lab](ht
 <p align="center">
 <img src="https://ipavlov.ai/img/ipavlov_footer.png" width="50%" height="50%"/>
 </p>
-
diff --git a/deeppavlov/package_meta.py b/deeppavlov/package_meta.py
@@ -1,2 +1,2 @@
-__version__ = '0.0.5.3'
+__version__ = '0.0.6b'
 __author__ = 'Neural Networks and Deep Learning lab, MIPT'
diff --git a/dp_agnt_diag.png b/dp_agnt_diag.png
diff --git a/examples/tutorials/00_deeppavlov_intro.ipynb b/examples/tutorials/00_deeppavlov_intro.ipynb
@@ -43,13 +43,13 @@
     "    \n",
     "    `cd DeepPavlov`\n",
     "\n",
-    "- Install the requirements:\n",
+    "- Install basic requirements:\n",
     "\n",
     "    `python setup.py develop`\n",
     "\n",
-    "- Install spacy dependencies:\n",
+    "- Install packages required for these tutorials:\n",
     "\n",
-    "    `python -m spacy download en`\n",
+    "    `python -m deeppavlov install gobot_dstc2`\n",
     "\n"
    ]
   },

diff --git a/examples/tutorials/04_deeppavlov_chitchat.ipynb b/examples/tutorials/04_deeppavlov_chitchat.ipynb
@@ -601,7 +601,7 @@
     "            else:\n",
     "                input_token_emb = tf.nn.embedding_lookup(embedding_matrix, output_token)\n",
     "\n",
-    "             \"\"\"\n",
+    "            \"\"\"\n",
     "            ATTENTION MECHANISM\n",
     "            # here you can add attention to your model\n",
     "            # you can find details about attention further in tutorial\n",

diff --git a/utils/pip_wrapper/pip_wrapper.py b/utils/pip_wrapper/pip_wrapper.py
@@ -12,6 +12,7 @@
 log = get_logger(__name__)
 
 _tf_re = re.compile(r'\s*tensorflow\s*([<=>;]|$)')
+_spacy_re = re.compile(r'\s*spacy\s*([<=>;]|$)')
 
 
 def install(*packages):
@@ -20,9 +21,12 @@ def install(*packages):
                                                              env=os.environ.copy()):
         log.warn('found tensorflow-gpu installed, so upgrading it instead of tensorflow')
         packages = [_tf_re.sub(r'tensorflow-gpu\1', package) for package in packages]
-    return subprocess.check_call([sys.executable, '-m', 'pip', 'install',
-                                  *[re.sub(r'\s', '', package) for package in packages]],
-                                 env=os.environ.copy())
+    result = subprocess.check_call([sys.executable, '-m', 'pip', 'install',
+                                   *[re.sub(r'\s', '', package) for package in packages]],
+                                   env=os.environ.copy())
+    if any(_spacy_re.match(package) for package in packages):
+        subprocess.check_call([sys.executable, '-m', 'spacy', 'download', 'en'], env=os.environ.copy())
+    return result
 
 
 def install_from_config(config: [str, Path, dict]):