[WIP] Upload app.jpeg pictures and modify sort_vector - nodes PR:305

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

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+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

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+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

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+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

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+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/IO/INSTRUMENTS/FUNCTION_GENERATORS/KEYSIGHT/33XXX/ADVANCED/BURST_MODE_33510B/a1-[autogen]/docstring.txt

@@ -4,19 +4,16 @@ The BURST_MODE_33510B node is used to turn the Burst mode on or off.
     The burst mode is way to have signals come in "bursts" that are triggered
     externally or with a timer for instance.
 
-    If the "VISA_address" parameter is not specified the VISA_index will be
-    used to find the address. The LIST_VISA node can be used to show the
-    indicies of all available VISA instruments.
+    Requires a CONNECTION_33510B node at the start of the app to connect with
+    the instrument. The VISA address will then be listed under 'connection'.
 
     This node should also work with compatible Keysight 33XXX wavefunction
     generators (although they are untested).
 
     Parameters
     ----------
-    VISA_address: str
-        The VISA address to query.
-    VISA_index: int
-        The address will be found from LIST_VISA node list with this index.
+    connection: VisaConnection
+        The VISA address (requires the CONNECTION_MDO3XXX node).
     on_off: str
         Turn the burst mode on or off.
     channel: str

docs/nodes/IO/INSTRUMENTS/FUNCTION_GENERATORS/KEYSIGHT/33XXX/ADVANCED/BURST_MODE_33510B/a1-[autogen]/python_code.txt

@@ -1,22 +1,10 @@
-from flojoy import flojoy, DataContainer, TextBlob
-import pyvisa
+from flojoy import flojoy, DataContainer, TextBlob, VisaConnection
 from typing import Optional, Literal
-from qcodes.instrument_drivers.Keysight import Keysight33512B
-from usb.core import USBError
 
 
-@flojoy(
-    deps={
-        "pyvisa": "1.13.0",
-        "pyusb": "1.2.1",
-        "zeroconf": "0.102.0",
-        "pyvisa_py": "0.7.0",
-        "qcodes": "0.39.1",
-    }
-)
+@flojoy(inject_connection=True)
 def BURST_MODE_33510B(
-    VISA_address: Optional[str],
-    VISA_index: Optional[int] = 0,
+    connection: VisaConnection,
     on_off: Literal["ON", "OFF"] = "OFF",
     channel: Literal["ch1", "ch2"] = "ch1",
     trigger_source: Literal["EXT", "IMM", "TIM"] = "TIM",
@@ -28,25 +16,10 @@ def BURST_MODE_33510B(
     burst_phase: float = 0,
     burst_polarity: Literal["NORM", "INV"] = "NORM",
     default: Optional[DataContainer] = None,
-) -> Optional[DataContainer]:
+) -> TextBlob:
 
 
-    rm = pyvisa.ResourceManager("@py")
-    if VISA_address == "":
-        VISA_addresses = rm.list_resources()
-        VISA_address = VISA_addresses[int(VISA_index)]
-
-    try:
-        ks = Keysight33512B(
-            "ks",
-            VISA_address,
-            visalib="@py",
-            device_clear=False,
-        )
-    except USBError as err:
-        raise Exception(
-            "USB port error. Trying unplugging+replugging the port."
-        ) from err
+    ks = connection.get_handle()
 
     channel_str = channel
     channel = getattr(ks, channel)