[WIP] 30 mins timeout for nightly CI - nodes PR:298

docs/nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/a1-[autogen]/docstring.txt

@@ -1,20 +1,29 @@
-Hugging Face Pipeline for Image Classification.
+The HUGGING_FACE_PIPELINE node uses a classification pipeline to process and classify an image.
+
+    For more information about Vision Transformers,
+    see: https://huggingface.co/google/vit-base-patch16-224
+
+    For a complete list of models, see:
+    https://huggingface.co/models?pipeline_tag=image-classification
+
+    For examples of how revision parameters (such as 'main') is used,
+    see: https://huggingface.co/google/vit-base-patch16-224/commits/main
 
     Parameters
     ----------
-    default: Image
-        The input image to be classified. The image must be a PIL.Image object wrapped in a flojoy Image object.
-    model: str
+    default : Image
+        The input image to be classified.
+        The image must be a PIL.Image object, wrapped in a Flojoy Image object.
+    model : str
         The model to be used for classification.
-        If not specified, Vision Transformers (i.e. `google/vit-base-patch16-224`) are used.
-        For more information about Vision Transformers, see: https://huggingface.co/google/vit-base-patch16-224
-        For a complete list of models see: https://huggingface.co/models?pipeline_tag=image-classification
-    revision: str
+        If not specified, Vision Transformers (i.e. 'google/vit-base-patch16-224') are used.
+    revision : str
         The revision of the model to be used for classification.
-        If not specified, main is `used`. For instance see: https://huggingface.co/google/vit-base-patch16-224/commits/main
+        If not specified, 'main' is used.
 
     Returns
     -------
     DataFrame:
-        A DataFrame containing as columns the `label` classification label and `score`, its confidence score.
-        All scores are between 0 and 1 and sum to 1.
+        A DataFrame containing the columns 'label' (as classification label)
+        and 'score' (as the confidence score).
+        All scores are between 0 and 1, and sum to 1.

docs/nodes/AI_ML/LOAD_MODEL/ONNX_MODEL/ONNX_MODEL.md

@@ -0,0 +1,57 @@
+
+[//]: # (Custom component imports)
+
+import DocString from '@site/src/components/DocString';
+import PythonCode from '@site/src/components/PythonCode';
+import AppDisplay from '@site/src/components/AppDisplay';
+import SectionBreak from '@site/src/components/SectionBreak';
+import AppendixSection from '@site/src/components/AppendixSection';
+
+[//]: # (Docstring)
+
+import DocstringSource from '!!raw-loader!./a1-[autogen]/docstring.txt';
+import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
+
+<DocString>{DocstringSource}</DocString>
+<PythonCode GLink='AI_ML/LOAD_MODEL/ONNX_MODEL/ONNX_MODEL.py'>{PythonSource}</PythonCode>
+
+<SectionBreak />
+
+
+
+[//]: # (Examples)
+
+## Examples
+
+import Example1 from './examples/EX1/example.md';
+import App1 from '!!raw-loader!./examples/EX1/app.json';
+
+
+
+<AppDisplay 
+    nodeLabel='ONNX_MODEL'
+    appImg={''}
+    outputImg={''}
+    >
+    {App1}
+</AppDisplay>
+
+<Example1 />
+
+<SectionBreak />
+
+
+
+[//]: # (Appendix)
+
+import Notes from './appendix/notes.md';
+import Hardware from './appendix/hardware.md';
+import Media from './appendix/media.md';
+
+## Appendix
+
+<AppendixSection index={0} folderPath='nodes/AI_ML/LOAD_MODEL/ONNX_MODEL/appendix/'><Notes /></AppendixSection>
+<AppendixSection index={1} folderPath='nodes/AI_ML/LOAD_MODEL/ONNX_MODEL/appendix/'><Hardware /></AppendixSection>
+<AppendixSection index={2} folderPath='nodes/AI_ML/LOAD_MODEL/ONNX_MODEL/appendix/'><Media /></AppendixSection>
+
+

docs/nodes/AI_ML/LOAD_MODEL/ONNX_MODEL/a1-[autogen]/docstring.txt

@@ -0,0 +1,41 @@
+ONNX_MODEL loads a serialized ONNX model and uses it to make predictions using ONNX Runtime.
+
+    This allows supporting a wide range of deep learning frameworks and hardware platforms.
+
+    Notes
+    -----
+
+    On the one hand, ONNX is an open format to represent deep learning models.
+    ONNX defines a common set of operators - the building blocks of machine learning
+    and deep learning models - and a common file format to enable AI developers
+    to use models with a variety of frameworks, tools, runtimes, and compilers.
+
+    See: https://onnx.ai/
+
+    On the other hand, ONNX Runtime is a high-performance inference engine for machine
+    learning models in the ONNX format. ONNX Runtime has proved to considerably increase
+    performance in inferencing for a broad range of ML models and hardware platforms.
+
+    See: https://onnxruntime.ai/docs/
+
+    Moreover, the ONNX Model Zoo is a collection of pre-trained models for common
+    machine learning tasks. The models are stored in ONNX format and are ready to use
+    in different inference scenarios.
+
+    See: https://github.com/onnx/models
+
+    Parameters
+    ----------
+    file_path : str
+        Path to a ONNX model to load and use for prediction.
+
+    default : Vector
+        The input tensor to use for prediction.
+        For now, only a single input tensor is supported.
+        Note that the input tensor shape is not checked against the model's input shape.
+
+    Returns
+    -------
+    Vector:
+        The predictions made by the ONNX model.
+        For now, only a single output tensor is supported.

docs/nodes/AI_ML/LOAD_MODEL/ONNX_MODEL/a1-[autogen]/python_code.txt

@@ -0,0 +1,62 @@
+from flojoy import flojoy, run_in_venv, Vector
+from flojoy.utils import FLOJOY_CACHE_DIR
+
+
+@flojoy
+@run_in_venv(
+    pip_dependencies=[
+        "onnxruntime",
+        "numpy",
+        "onnx",
+    ]
+)
+def ONNX_MODEL(
+    file_path: str,
+    default: Vector,
+) -> Vector:
+
+
+    import os
+    import onnx
+    import urllib.request
+    import numpy as np
+    import onnxruntime as rt
+
+    model_name = os.path.basename(file_path)
+
+    if file_path.startswith("http://") or file_path.startswith("https://"):
+        # Downloading the ONNX model from a URL to FLOJOY_CACHE_DIR.
+        onnx_model_zoo_cache = os.path.join(
+            FLOJOY_CACHE_DIR, "cache", "onnx", "model_zoo"
+        )
+
+        os.makedirs(onnx_model_zoo_cache, exist_ok=True)
+
+        filename = os.path.join(onnx_model_zoo_cache, model_name)
+
+        urllib.request.urlretrieve(
+            url=file_path,
+            filename=filename,
+        )
+
+        # Using the downloaded file.
+        file_path = filename
+
+    # Pre-loading the serialized model to validate whether is well-formed or not.
+    model = onnx.load(file_path)
+    onnx.checker.check_model(model)
+
+    # Using ONNX runtime for the ONNX model to make predictions.
+    sess = rt.InferenceSession(file_path, providers=["CPUExecutionProvider"])
+
+    # TODO(jjerphan): Assuming a single input and a single output for now.
+    input_name = sess.get_inputs()[0].name
+    label_name = sess.get_outputs()[0].name
+
+    # TODO(jjerphan): For now NumPy is assumed to be the main backend for Flojoy.
+    # We might adapt it in the future so that we can use other backends
+    # for tensor libraries for application using Deep Learning libraries.
+    input_tensor = np.asarray(default.v, dtype=np.float32)
+    predictions = sess.run([label_name], {input_name: input_tensor})[0]
+
+    return Vector(v=predictions)

docs/nodes/AI_ML/LOAD_MODEL/ONNX_MODEL/appendix/hardware.md

@@ -0,0 +1 @@
+This node does not require any peripheral hardware to operate. Please see INSTRUMENTS for nodes that interact with the physical world through connected hardware.

docs/nodes/AI_ML/LOAD_MODEL/ONNX_MODEL/appendix/media.md

@@ -0,0 +1 @@
+No supporting screenshots, photos, or videos have been added to the media.md file for this node.

docs/nodes/AI_ML/LOAD_MODEL/ONNX_MODEL/appendix/notes.md

@@ -0,0 +1 @@
+No theory or technical notes have been contributed for this node yet.

docs/nodes/AI_ML/NLP/COUNT_VECTORIZER/a1-[autogen]/docstring.txt

@@ -1,10 +1,8 @@
+The COUNT_VECTORIZER node receives a collection (matrix, vector or dataframe) of text documents and converts it to a matrix of token counts.
 
-The COUNT_VECTORIZER node receives a collection (matrix, vector or dataframe) of
-text documents to a matrix of token counts.
-
-Returns
--------
-tokens: DataFrame
-    holds all the unique tokens observed from the input.
-word_count_vector: Vector
-    contains the occurences of these tokens from each sentence.
+    Returns
+    -------
+    tokens: DataFrame
+        Holds all the unique tokens observed from the input.
+    word_count_vector: Vector
+        Contains the occurences of these tokens from each sentence.

docs/nodes/AI_ML/PREDICT_TIME_SERIES/PROPHET_PREDICT/a1-[autogen]/docstring.txt

@@ -1,44 +1,44 @@
+The PROPHET_PREDICT node runs a Prophet model on the incoming dataframe.
 
-The PROPHET_PREDICT node rains a Prophet model on the incoming dataframe.
+    The DataContainer input type must be a dataframe, and the first column (or index) of the dataframe must be of a datetime type.
 
-The DataContainer input type must be a dataframe, and the first column (or index) of dataframe must be of a datetime type.
+    This node always returns a DataContainer of a dataframe type. It will also always return an 'extra' field with a key 'prophet' of which the value is the JSONified Prophet model.
+    This model can be loaded as follows:
 
-This node always returns a DataContainer of a dataframe type. It will also always return an "extra" field with a key "prophet" of which the value is the JSONified Prophet model.
-This model can be loaded as follows:
-    ```python
-    from prophet.serialize import model_from_json
+        ```python
+        from prophet.serialize import model_from_json
 
-    model = model_from_json(dc_inputs.extra["prophet"])
-    ```
+        model = model_from_json(dc_inputs.extra["prophet"])
+        ```
 
-Parameters
-----------
-run_forecast : bool
-    If True (default case), the dataframe of the returning DataContainer
-    ("m" parameter of the DataContainer) will be the forecasted dataframe.
-    It will also have an "extra" field with the key "original", which is
-    the original dataframe passed in.
+    Parameters
+    ----------
+    run_forecast : bool
+        If True (default case), the dataframe of the returning DataContainer
+        ('m' parameter of the DataContainer) will be the forecasted dataframe.
+        It will also have an 'extra' field with the key 'original', which is
+        the original dataframe passed in.
 
-    If False, the returning dataframe will be the original data.
+        If False, the returning dataframe will be the original data.
 
-    This node will also always have an "extra" field, run_forecast, which
-    matches that of the parameters passed in. This is for future nodes
-    to know if a forecast has already been run.
+        This node will also always have an 'extra' field, run_forecast, which
+        matches that of the parameters passed in. This is for future nodes
+        to know if a forecast has already been run.
 
-    Default = True
+        Default = True
 
-periods : int
-    The number of periods to predict out. Only used if run_forecast is True.
-    Default = 365
+    periods : int
+        The number of periods to predict out. Only used if run_forecast is True.
+        Default = 365
 
-Returns
--------
-DataFrame
-    With parameter as df.
-    Indicates either the original df passed in, or the forecasted df
-    (depending on if run_forecast is True).
+    Returns
+    -------
+    DataFrame
+        With parameter as df.
+        Indicates either the original df passed in, or the forecasted df
+        (depending on if run_forecast is True).
 
-DataContainer
-    With parameter as "extra".
-    Contains keys run_forecast which correspond to the input parameter,
-    and potentially "original" in the event that run_forecast is True.
+    DataContainer
+        With parameter as 'extra'.
+        Contains keys run_forecast which correspond to the input parameter,
+        and potentially 'original' in the event that run_forecast is True.

docs/nodes/AI_ML/REGRESSION/LEAST_SQUARES/a1-[autogen]/docstring.txt

@@ -1,10 +1,9 @@
+The LEAST_SQUARE node computes the coefficients that minimize the distance between the inputs 'Matrix' or 'OrderedPair' class and the regression.
 
-The LEAST_SQUARE node computes the coefficients that minimizes the distance between the inputs 'Matrix' or 'OrderedPair' class and the regression.
-
-Returns
--------
-OrderedPair
-    x: input matrix (data points)
-    y: fitted line computed with returned regression weights
-Matrix
-    m : fitted matrix computed with returned regression weights
+    Returns
+    -------
+    OrderedPair
+        x: input matrix (data points)
+        y: fitted line computed with returned regression weights
+    Matrix
+        m: fitted matrix computed with returned regression weights

docs/nodes/AI_ML/SEGMENTATION/DEEPLAB_V3/a1-[autogen]/docstring.txt

@@ -1,10 +1,9 @@
-
 The DEEPLAB_V3 node returns a segmentation mask from an input image in a dataframe.
 
-The input image is expected to be a DataContainer of an "image" type.
+    The input image is expected to be a DataContainer of an 'image' type.
 
-The output is a DataContainer of an "image" type with the same dimensions as the input image, but with the red, green, and blue channels replaced with the segmentation mask.
+    The output is a DataContainer of an 'image' type with the same dimensions as the input image, but with the red, green, and blue channels replaced with the segmentation mask.
 
-Returns
--------
-Image
+    Returns
+    -------
+    Image

docs/nodes/EXTRACTORS/FILE/READ_S3/a1-[autogen]/docstring.txt

@@ -7,9 +7,9 @@ The READ_S3 node takes a S3_key name, S3 bucket name, and file name as input, an
     Parameters
     ----------
     s3_name : str
-        name of the key that the user used to save access and secret access key
+        name of the key that the user used to save the access and secret access keys
     bucket_name : str
-        AWS S3 bucket name that they are trying to access
+        Amazon S3 bucket name that they are trying to access
     file_name : str
         name of the file that they want to extract
 

docs/nodes/GENERATORS/SAMPLE_DATASETS/R_DATASET/a1-[autogen]/docstring.txt

@@ -1,11 +1,10 @@
+The R_DATASET node retrieves a pandas DataFrame from 'rdatasets', using the provided dataset_key parameter, and returns it wrapped in a DataContainer.
 
-The R_DATASET node retrieves a pandas DataFrame from rdatasets using the provided dataset_key parameter and returns it wrapped in a DataContainer.
+    Parameters
+    ----------
+    dataset_key : str
 
-Parameters
-----------
-dataset_key : str
-
-Returns
--------
-DataFrame
-    A DataContainer object containing the retrieved pandas DataFrame.
+    Returns
+    -------
+    DataFrame
+        A DataContainer object containing the retrieved pandas DataFrame.