[REVIEW]Upgrade the plugin to JupyterLab 3

README.md

@@ -1,91 +1,66 @@
-# gQuant - GPU Accelerated Graph Computation for Quantitative Analyst Tasks
+# gQuant - Graph Computation Tool
 
 **NOTE:** For the latest stable [README.md](https://github.com/rapidsai/gquant/blob/main/README.md) ensure you are on the `main` branch.
 
-## What is gQuant?
-gQuant is a collection of open-source GPU accelerated Python tools and examples  for quantitative analyst tasks,  built on top of the [RAPIDS AI](https://rapids.ai/) project, [Numba](https://numba.pydata.org/), and [Dask](https://dask.org/).
 
-The examples range from simple accelerated calculation of technical trading indicators through defining workflows for interactively developing trading strategies and automating many typical tasks.
+## What's Inside This Repo
 
-The extensibility of the system is highlighted by examples showing how to create a dataframe flow graph, which allows for easy re-use and composability of higher level workflows.
+There are a few projects inside this repo:
 
-The examples also show how to easily convert a single-threaded solution into a Dask distributed one. 
+1. [gquant](gquant) -  A graph computation toolkit that helps you to organize the workflows in graph computation.
+2. [gquantlab](gquantlab) - A JupyterLab plugin that provides the UI interface for `gquant`.
+3. [plugins](plugins) - A few gquant plugins with example notebooks. 
+    1. [simple_example](plugins/simple_example) - A simple external plugin example for gQuant.
+    2. [rapids_plugin](plugins/rapids_plugin) - An external plugin with a set of nodes for quantitative analyst tasks, built on top of the [RAPIDS AI](https://rapids.ai/) project, [Numba](https://numba.pydata.org/), and [Dask](https://dask.org/).
+    3. [nemo_plugin](plugins/nemo_plugin) - An external plugin with a set of nodes that wraps the [NeMo library](https://github.com/NVIDIA/NeMo) . 
 
-These examples can be used as-is or, as they are open source, can be extended to suit your environments.
+These projects are all released as independent Python projects with their own `setup.py` files. 
 
-## gQuant jupyterlab extension
+## Screenshots
 ![Tuturial](tutorial.gif "Tutorial")
 ![Quick Demo](gquantlab_demo.gif "Demo")
-The gQuant Juyterlab extension provides the user interface to build the dataframe flow TaskGraph easily. It takes advantage of the open sources projects like [jupyterlab](https://github.com/jupyterlab/jupyterlab), [ipywidget](https://github.com/jupyter-widgets/ipywidgets), [React](https://reactjs.org/) and [D3](https://d3js.org/). It features:
-1. Takes full advantage of the JupyterLab project that the extension adds commands to Jupyterlab context menu, command palette and bind them with keyboard shortcuts to speed up the productivity.  
-2. Define a new TaskGraph file format `.gq.yaml` that can be edited in the Jupyterlab. 
-3. Visually presents the TaskGraph as a DAG graph. Users can zoom in and out, freely move the nodes around, and make connections between nodes.
-4. Use the special `Ouput Collector` to gather the results and organize them in a tab widget. The IPython [rich display](https://ipython.readthedocs.io/en/stable/config/integrating.html#rich-display) is fully supported.
-5. Visually shows the progress of graph evaluation and computation dependence.
-6. Automatically generate the UI elements to edit and validate the Node configuration given the configuration JSON schema. It exposes the function API in a user-friendly way. User can change the configuration and re-run the computation to test out the hyperparameters easily.
-7. Dynamically compute the input-output ports compatibility, dataframe columns names and types, ports types to prevent connection errors. 
-8. Nodes can have multiple output ports that can be used to generate different output types. E.g. some data loader Node provides both `cudf` and `dask_cudf` output ports. The multiple GPUs distributed computation computation is automatically enabled by switching to the `dask_cudf` output port. 
-9. Provides the standard API to extend your computation Nodes.
-10. The composite node can encapsulate the TaskGraph into a single node for easy reuse. The composite node can be exported as a regular gQuant node without any coding.
-
-### Binary pip installation
-
-To install the gQuant graph computation library, first install the dependence libraries:
-```bash
-pip install dask[dataframe] distributed networkx
-conda install python-graphviz ruamel.yaml numpy pandas
-```
-Then install gquant lib:
+
+
+## Binary installation
+
+### Install the gGuant 
+To install the gQuant graph computation library, run:
 ```bash
 pip install gquant
 ```
-
-To install JupyterLab plugin, install the following dependence libraries:
+Or install `gquant` at the gQuant directory:
 ```bash
-conda install nodejs ipywidgets
+pip install .
 ```
-Then install the gquantlab lib:
+
+### Install the gQuantLab JupyterLab plugin
+To install `gquantlab` JupyterLab plugin, make sure `nodejs` of version [12^14^15] is installed. E.g.:
 ```bash
-pip install gquantlab==0.1.2
+conda install -c conda-forge nodejs=12.4.0
 ```
-Build the ipywidgets Jupyterlab plugin 
+Then install the `gquantlab`:
 ```bash
-jupyter labextension install @jupyter-widgets/jupyterlab-manager@2.0
+pip install gquantlab
 ```
-If you launch the JupyterLab, it will prompt to build the new plugin. You can
-explicitly build it by: 
+Or install `gquantlab` at the gquantlab directory:
 ```bash
-jupyter lab build
+pip install .
 ```
 
-Note, the gQuant node plugins are defined in the `gquantrc` file. Check the `System environment` for details
-
+### Install the gQuant plugins
 
-### Prerequisites
-- NVIDIA Pascal™ GPU architecture or better.
-- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) with driver v396.37+ or [CUDA 10.0](https://developer.nvidia.com/cuda-10.0-download-archive) with driver v410.48+.
-- Ubuntu 16.04 or 18.04.
-- [NVIDIA-docker v2+](https://github.com/nvidia/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-20-if-im-not-using-the-latest-docker-version).
-
-
-### Download data files
-
-Run the following command at the project root diretory 
+Under the plugin root directory, install the plugin as normal python packages.
 ```bash
-bash download_data.sh
-
+pip install .
 ```
 
-### Install
+Note, gQuant node plugins can be registered in two ways: 
 
-gQuant source code can be downloaded from [GitHub](https://github.com/rapidsai/gquant).
+  1. (Recommended)Write a external plugin using 'entry point' to register it. Check the `plugins` directory for details
+  2. Register the plugin in `gquantrc` file. Check the `System environment` for details
 
-- Git clone source code:
-
-```bash
-$ git clone https://github.com/rapidsai/gQuant.git
-```
 
+## Docker Install
 
 - Build and run the container:
 
@@ -99,12 +74,12 @@ In the production mode, you can launch the container by following command and st
 $ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 gquant/gquant:[tag from the build]
 ```
 
-### Example notebooks
+## Example notebooks
 
-Example notebooks, tutorial showcasing, can be found in __notebooks__ folder.
+Example notebooks, tutorial showcasing, can be found in __notebooks__ folder in the plugin directory.
 
 
-### System environment 
+## System environment 
 
 There are a few system environment that the user can overwrite. 
 

docker/build.sh

@@ -153,12 +153,12 @@ RUN wget \
 RUN conda install -y -c rapidsai -c nvidia -c conda-forge \
     -c defaults rapids=$RAPIDS_VERSION cudatoolkit=$CUDA_STR python=3.7
 
-RUN conda install -y -c conda-forge jupyterlab'<3.0.0'
+RUN conda install -y -c conda-forge jupyterlab
 
 RUN conda install -y -c conda-forge python-graphviz bqplot nodejs ipywidgets \
     pytables mkl numexpr pydot flask pylint flake8 autopep8
 
-RUN jupyter labextension install @jupyter-widgets/jupyterlab-manager@2.0 --no-build  
+RUN jupyter labextension install @jupyter-widgets/jupyterlab-manager --no-build  
 RUN jupyter labextension install bqplot --no-build  
 #RUN jupyter labextension install jupyterlab-nvdashboard --no-build  
 RUN jupyter lab build && jupyter lab clean
@@ -169,7 +169,7 @@ RUN pip install jupyterlab-nvdashboard
 RUN jupyter labextension install jupyterlab-nvdashboard
 
 ## install the dask extension
-RUN pip install "dask_labextension<5.0.0"
+RUN pip install dask_labextension
 RUN jupyter labextension install dask-labextension
 RUN jupyter serverextension enable dask_labextension
 

gquant/LICENSE

@@ -0,0 +1,201 @@
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