python and mongodb version update. fix datarun fetch bug

Author: dyuliu

Makefile

@@ -61,14 +61,14 @@ init-db: clean-db
 load-db: init-db  ## load the demo dataset (NASA)
 	curl -o sintel.tar.bz2 "https://sintel-db.s3.us-east-2.amazonaws.com/sintel.tar.bz2"
 	tar -xf sintel.tar.bz2 -C ./db-instance/data/ && rm sintel.tar.bz2
-	mongo sintel --eval "db.dropDatabase()"
+	mongosh sintel --eval "db.dropDatabase()"
 	mongorestore --db sintel ./db-instance/data/sintel/
 
 .PHONY: load-db-test
 load-db-test: init-db  ## load the demo testing dataset for pytest
 	curl -o sintel_test.tar.bz2 "https://d3-ai-sintel.s3.us-east-2.amazonaws.com/sintel_test.tar.bz2"
 	tar -xf sintel_test.tar.bz2 -C ./db-instance/data/ && rm sintel_test.tar.bz2
-	mongo sintel_test --eval "db.dropDatabase()"
+	mongosh sintel_test --eval "db.dropDatabase()"
 	mongorestore --db sintel_test ./db-instance/data/sintel_test/
 
 # ------------------ session: docker ------------------- #

README.md

@@ -26,9 +26,9 @@
 
 Make sure you have installed all of the following prerequisites on your development machine:
 
--   **Python (>= 3.0)** - Sintel has been developed and runs on [Python 3.6](https://www.python.org/downloads/release/python-360/). Although it is not strictly required, the usage of a [virtualenv](https://virtualenv.pypa.io/en/latest/) is highly recommended in order to avoid interfering with other software installed in the system where **MTV** is run. To this end, [Anaconda python](https://www.anaconda.com/distribution/#download-section) is suggested to maintain the virtual environments.
+-   **Python (>= 3.8)** - Sintel has been developed and runs on [Python 3.8](https://www.python.org/downloads/release/python-360/). Although it is not strictly required, the usage of a [virtualenv](https://virtualenv.pypa.io/en/latest/) is highly recommended in order to avoid interfering with other software installed in the system where **MTV** is run. To this end, [Anaconda python](https://www.anaconda.com/distribution/#download-section) is suggested to maintain the virtual environments.
 -   **Git** - [Download & Install Git](https://git-scm.com/downloads). OSX and Linux machines typically have this already installed.
--   **MongoDB (>= 3.6)** - [Download & Install MongoDB](http://www.mongodb.org/downloads), and make sure it's running on the default port (27017).
+-   **MongoDB (>= 4.4)** - [Download & Install MongoDB](http://www.mongodb.org/downloads), and make sure it's running on the default port (27017).
 
 ## Get Started
 
@@ -48,6 +48,11 @@ dependencies for code linting and testing
 $ make install-develop
 ```
 
+Download demo data and load it to the mongodb. The database name by default is `sintel`.
+```bash
+$ make load-db
+```
+
 ### Running Your Application
 
 Please activate your virtualenv, and then launch the API server:
@@ -66,17 +71,10 @@ Run the following command for the purpose of development
 $ sintel run -E development -v
 ```
 
-### Data
-
-The command `make install` or `make install-develop` has already pull the demo dataset and restore it into MongoDB. The database name by default is `sintel`.
+### Use Your Own Data
 
-##### Working with [Orion](https://github.com/D3-AI/Orion) to generate your own data
 
-You can type the following command to update the data from Orion to Sintel-supported formats. Note that you can configure the mongodb in the file `./sintel/config.yaml`.
-
-```bash
-$ sintel update db -v
-```
+Follow the tutorial `.tutorials/Anomaly Detection.ipynb` for detail.
 
 
 ## Use Docker to deploy

setup.py

@@ -19,8 +19,8 @@
 
 install_requires = [
     # Sintel
-    'orion-ml==0.5.2',
-    # 'orion-ml@git+https://git@github.com/sintel-dev/Orion.git',
+    # 'orion-ml==0.5.2',
+    'orion-ml@git+https://git@github.com/sintel-dev/Orion.git@sintel#egg=orion-ml',
     # General
     'termcolor==1.1.0',
     'PyYAML==5.1',
@@ -41,7 +41,7 @@
     'itsdangerous==2.0.1',
     'MarkupSafe==2.0.1',
     'requests==2.24.0',
-    'Werkzeug==0.15.5',
+    'Werkzeug>=0.15.5',
     'gevent>=21.12.0',
     'flasgger==0.9.5',
     'Jinja2>=2.10,<3.1',

sintel/resources/datarun.py

@@ -14,8 +14,9 @@
 
 def get_signalrun(signalrun_doc):
 
-    primitive_name = ('mlprimitives.custom.timeseries_preprocessing'
+    primitive_name = ('mlstars.custom.timeseries_preprocessing'
                       '.time_segments_aggregate#1')
+    print(primitive_name)
     pipeline = signalrun_doc.datarun.pipeline
     interval = int(pipeline.json['hyperparameters']
                    [primitive_name].get('interval', None))