In Spark: MatrixFactorizationModel.scala “recommendProductsForUsers” function takes very long time to complete
I have 9 nodes cluster and each node has the following configurations,
I’m trying to generate recommendations for all the users in MatrixFactorizationModel using 'recommendProductsForUsers' function. Looks like it takes very long time to complete (eg: For 1 month of data it takes approximately around 34 hours). Is it due to the iteration for multiple times over the matrix?
How can I reduce the execution time?
These are my spark-submit configuration:
spark-submit --jars $JAR_LOC --class com.collaborativefiltering.CustomerCollaborativeJob --driver-memory 5G --num-executors 7 --executor-cores 2 --executor-memory 20G --master yarn-client cust_rec/cust-rec.jar --period 1month --out /PATH --rank 50 --numIterations 2 --lambda 0.25 --alpha 300 --topK 20
Thank you very much in advance.
I found in MatrixFactorizationModel the recommendProductsForUsers runs through multiple iteration so the computational time is high. Once I started to run my jobs in cloud, I tested the job by increasing the nodes and spark-executors. It actually worked! I was able to run and complete the job within 4 hours.
- The “Forward/Backward Passage Size” is too large for the pytorch model (Yolov3)
- How many images(minimum) should be there in each classes for training YOLO?
- Why do neural networks work so well?
- Will larger batch size make computation time less in machine learning?
- Why KL divergence is negative in Pytorch?
- Creating a voice identification system using machine learning
- Forward pass with all samples
- fit method in sklearn
- Calibrating Probabilities in lightgbm or XGBoost
- Implementation of F1-score, IOU and Dice Score
- Is it ok to have the training history very similar to the validation history?
- How to understand Shapley value for binary classification problem?
- Stochastic Gradient Descent for Logistic Regression always returns a cost of Inf and weight vector never gets any closer
- Data format for Libsvm SVR training in Matlab
- Why shouldn't we use multiple activation functions in the same layer?
- How to implement a butterworth filter
- weka java api stringtovector exception
- Predict training data in sklearn
- Text classification with weka
- How can i apply feature reduction methods in Weka?
- 'super' object has no attribute '__sklearn_tags__'
- lightgbm.cv: cvbooster.best_iteration always returns -1
- Wrong detection from yolov5 model
- torchrl: Using SyncDataCollector with a custom pytorch dqn
- OCR Preprocessing for Oman License Plates - Issues with Alphabet Recognition
- Training difference between LightGBM API and Sklearn API
- Why can't I wrap LGBM?
- Why RAG is slower than LLM?
- Azure AutoML Image Classification Job
- What's the best way to use a sklearn feature selector in a grid search, to evaluate the usefulness of all features?