[BUG] MONAI Deploy App Stuck Due To Downstream Operator Not Receiving Data*
BUG: MONAI Deploy App Stuck Due to Downstream Operator Not Receiving Data
In this article, we will explore a common issue encountered when deploying a MONAI pipeline using the MONAI Deploy App SDK. The problem arises when the downstream operator fails to receive data, resulting in a deadlock error. We will delve into the root cause of this issue and provide a step-by-step solution to resolve it.
The MONAI Deploy App SDK is a powerful tool for deploying machine learning models on various platforms. However, when working with complex pipelines, issues can arise due to incorrect operator connections or missing output definitions. In this case, the application starts successfully, but execution is halted due to the "No receiver connected to transmitter" error. The execution graph fails to tick, leading to a deadlock.
The application should:
- Load a DICOM Study from the specified input path.
- Select the correct DICOM series using the
DICOMSeriesSelectorOperator. - Convert the series into a volume using
DICOMSeriesToVolumeOperator. - Run inference on the extracted image using
DICOMBoneAgeOperator. - Output the predicted bone age in months.
-
The pipeline initializes, and the operators are properly connected.
-
However, during execution, the error message appears:
[error] [entity_executor.cpp:309] [E00025] No receiver connected to transmitter of DownstreamReceptiveSchedulingTerm 30 of entity "unnamed_operator_1". The entity will never tick. -
The scheduler stops because the pipeline does not progress past the data loading stage:
2025-03-12 08:01:17.596 INFO gxf/std/greedy_scheduler.cpp@372: Scheduler stopped: Some entities are waiting for execution, but there are no periodic or async entities to get out of the deadlock. -
The DICOM loader outputs the correct ports, and the pipeline seems correctly connected, but no data flows through the pipeline.
-
Operating System: Ubuntu 22.04
-
Python Version: 3.8
-
MONAI Deploy SDK Version: Latest
-
Installed Dependencies:
pip install gdcm pylibjpeg pylibjpeg-libjpeg pylibjpeg-openjpeg pylibjpeg-rle
๐ Starting Bone Age Prediction App...
[info] [fragment.cpp:586] Loading extensions from configs...
[2025-03-12 08:01:17,396] [INFO] (root) - Parsed args: Namespace(argv=['app.py', '--input', '/home/omar/bone_age_deploy/monai_deploy_app/input', '--output', '/home/omar/bone_age_deploy/monai_deploy_app/output'], input=PosixPath('/home/omar/bone_age_deploy/monai_deploy_app/input'), log_level=None, model=None, output=PosixPath('/home/omar/bone_age_deploy/monai_deploy_app/output'), workdir=None)
[2025-03-12 08:01:17,397] [INFO] (BoneAgePredictionApp) - โ
Output ports of dicom_loader: ['dicom_study_list']
[2025-03-12 08:01:17,397] [INFO] (BoneAgePredictionApp) - โ
Output ports of series_selector: ['study_selected_series_list']
[2025-03-12 08:01:17,397] [INFO] (BoneAgePredictionApp) - โ
Output ports of series_to_volume: ['image']
[2025-03-12 08:01:17,397] [INFO] (BoneAgePredictionApp) - โ
Input ports of dicom_processor: ['image']
[2025-03-12 08:01:17,397] [INFO] (BoneAgePredictionApp) - โ
Operators successfully connected.
[info] [gxf_executor.cpp:252] Creating context
[info] [gxf_executor.cpp:1974] Activating Graph...
[error] [entity_executor.cpp:309] [E00025] No receiver connected to transmitter of DownstreamReceptiveSchedulingTerm 30 of entity "unnamed_operator_1". The entity will never tick.
import logging
from pathlib import Path
import sys
import os
# โ
Ensure the inference model is accessible
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
import torch
import numpy as np
import pydicom
from monai.deploy.conditions import CountCondition
from monai.deploy.core import Application, Operator
from monai.deploy.operators import (
DICOMDataLoaderOperator,
DICOMSeriesSelectorOperator,
DICOMSeriesToVolumeOperator
)
from inference.inference import BoneAgeInference # Import your inference model
# ------------------------------------------------------------------------------------------------------
# โ
Configuration for the model
# ------------------------------------------------------------------------------------------------------
CONFIG = {
"model_name": "microsoft/swin-large-patch4-window7-224",
"feature_dim": 1024,
"dropout": 0.3,
"use_cuda": torch.cuda.is_available()
}
MODEL_WEIGHTS_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "model", "best_model.pth"))
# Initialize the inference model
inference_model = BoneAgeInference(MODEL_WEIGHTS_PATH, CONFIG)
# ------------------------------------------------------------------------------------------------------
# โ
Define Bone Age Inference Operator
# ------------------------------------------------------------------------------------------------------
class DICOMBoneAgeOperator(Operator):
"""Operator to process DICOM images and predict bone age."""
def __init__(self, fragment):
super().__init__(fragment)
self.logger = logging.getLogger(type(self).__name__)
def setup(self, spec):
"""Define input and output ports for MONAI Deploy."""
spec.input("image") # โ
Ensuring correct input name
spec.output("predicted_age") # โ
Output port for predicted bone age
def compute(self, input_context, output_context, execution_context):
"""Process DICOM input and predict bone age."""
self.logger.info("๐น Receiving image for inference...")
dicom_image = input_context.receive("image") # โ
Correct input
if dicom_image is None:
raise ValueError("โ No image received for processing.")
self.logger.info(f"โ
Received Image UID: {dicom_image.SOPInstanceUID}")
# Extract gender from metadata
gender = getattr(dicom_image, "PatientSex", "M") # Default: Male
self.logger.info(f"๐น Extracted gender: {gender}")
# Convert DICOM image to NumPy format
pixel_array = dicom_image.pixel_array.astype(np.float32)
image_np = np.uint8(255 * (pixel_array / np.max(pixel_array))) # Normalize
# Run inference
self.logger.info("๐น Running inference model...")
predicted_age = inference_model.predict_from_numpy(image_np, gender)
# โ
Store prediction
output_context.write("predicted_age", predicted_age)
self.logger.info(f"โ
Predicted Bone Age: {predicted_age:.2f} months for Gender: {gender}")
# ------------------------------------------------------------------------------------------------------
# โ
Define the MONAI Deploy Application
# ------------------------------------------------------------------------------------------------------
class BoneAgePredictionApp(Application):
"""MONAI Deploy App for bone age prediction from DICOM images."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.logger = logging.getLogger(type(self).__name__)
def run(self, *args, **kwargs):
"""Run the application with logging."""
self.logger.info("๐ Starting Bone Age Prediction App...")
super().run(*args, **kwargs)
self.logger.info("โ
App execution completed.")
def compose(self):
"""Define MONAI Deploy pipeline."""
# โ
Initialize MONAI Deploy App Context
app_context = Application.init_app_context(self.argv)
# โ
Set input/output paths
app_input_path = Path(app_context.input_path)
app_output_path = Path(app_context.output_path)
self.logger.info(f"๐ Input Folder: {app_input_path}")
self.logger.info(f"๐ Output Folder: {app_output_path}")
# โ
Define MONAI Deploy Operators
dicom_loader = DICOMDataLoaderOperator(
self, CountCondition(self, 1), input_folder=Path(app_input_path), force=True, name="dicom_loader_op"
)
series_selector = DICOMSeriesSelectorOperator(self, name="dicom_series_selector_op")
series_to_volume = DICOMSeriesToVolumeOperator(self, name="dicom_series_to_volume_op")
dicom_processor = DICOMBoneAgeOperator(self)
# โ
Add Operators
self.add_operator(dicom_loader)
self.add_operator(series_selector)
self.add_operator(series_to_volume)
self.add_operator(dicom_processor)
# ๐น DEBUGGING: Check if each operator is passing data correctly
self.logger.info(f"โ
Output ports of dicom_loader: {list(dicom_loader.spec.outputs.keys())}")
self.logger.info(f"โ
Output ports of series_selector: {list(series_selector.spec.outputs.keys())}")
self.logger.info(f"โ
Output ports of series_to_volume: {list(series_to_volume.spec.outputs.keys())}")
self.logger.info(f"โ
Input ports<br/>
**Q&A: Resolving the Deadlock Error in MONAI Deploy App**
**Q: What is the root cause of the deadlock error in MONAI Deploy App?**
A: The deadlock error in MONAI Deploy App is caused by the downstream operator failing to receive data from the upstream operator. This can be due to incorrect operator connections, missing output definitions, or other configuration issues.
**Q: How can I identify the issue causing the deadlock error?**
A: To identify the issue causing the deadlock error, you can:
1. Check the logs for any error messages or warnings related to the deadlock.
2. Verify that the upstream operator is producing data and that the downstream operator is correctly connected to receive the data.
3. Check the output definitions of the operators to ensure that they are correctly defined and connected.
**Q: What are some common causes of the deadlock error in MONAI Deploy App?**
A: Some common causes of the deadlock error in MONAI Deploy App include:
1. Incorrect operator connections: Make sure that the upstream and downstream operators are correctly connected.
2. Missing output definitions: Ensure that the output definitions of the operators are correctly defined and connected.
3. Configuration issues: Check the configuration of the operators and the pipeline to ensure that they are correctly set up.
**Q: How can I resolve the deadlock error in MONAI Deploy App?**
A: To resolve the deadlock error in MONAI Deploy App, you can:
1. Check the logs for any error messages or warnings related to the deadlock.
2. Verify that the upstream operator is producing data and that the downstream operator is correctly connected to receive the data.
3. Check the output definitions of the operators to ensure that they are correctly defined and connected.
4. Adjust the configuration of the operators and the pipeline as needed to resolve the issue.
**Q: What are some best practices for preventing deadlock errors in MONAI Deploy App?**
A: Some best practices for preventing deadlock errors in MONAI Deploy App include:
1. Thoroughly testing the pipeline before deploying it to production.
2. Verifying that the upstream and downstream operators are correctly connected.
3. Ensuring that the output definitions of the operators are correctly defined and connected.
4. Regularly monitoring the logs and pipeline performance to identify and resolve any issues that may arise.
**Q: Can I use MONAI Deploy App with other machine learning frameworks?**
A: Yes, MONAI Deploy App can be used with other machine learning frameworks. MONAI Deploy App is designed to be flexible and can be used with a variety of machine learning frameworks, including TensorFlow, PyTorch, and scikit-learn.
**Q: How can I get support for MONAI Deploy App?**
A: You can get support for MONAI Deploy App through various channels, including:
1. The MONAI Deploy App documentation and tutorials.
2. The MONAI Deploy App community forum.
3. The MONAI Deploy App GitHub repository.
4. The MONAI Deploy App support team.
By following these best practices and troubleshooting tips, you can resolve deadlock errors in MONAI Deploy App and ensure that your machine learning pipelines run smoothly and efficiently.