Mia  Marquardt

Mia Marquardt


Training with Multiple Workers using TensorFlow Quantum

 Training large machine learning models is a core ability for TensorFlow. Over the years, scale has become an important feature in many modern machine learning systems for NLP, image recognition, drug discovery etc. Making use of multiple machines to boost computational power and throughput has led to great advances in the field. Similarly in quantum computing and quantum machine learning, the availability of more machine resources speeds up the simulation of larger quantum states and more complex systems. In this tutorial you will walk through how to use TensorFlow and TensorFlow quantum to conduct large scale and distributed QML simulations. Running larger simulations with greater FLOP/s counts unlocks new possibilities for research that otherwise wouldn’t be possible at smaller scales. In the figure below we have outlined approximate scaling capabilities for several different hardware settings for quantum simulation.

Kubernetes to simplify this process. [Kubernetes](https://kubernetes.io/)  is an open source container orchestration system, and it is a proven platform to effectively manage large-scale workloads. While it is possible to have a multi-worker setup with a cluster of physical or virtual machines, Kubernetes offers many advantages, including:

*   Service discovery - workers can easily identify each other using well-known DNS names, rather than manually configuring IP destinations.
*   Automatic bin-packing - your workloads are automatically scheduled on different machines based on resource demand and current consumption.
*   Automated rollouts and rollbacks - the number of worker replicas can be changed by changing a configuration, and Kubernetes automatically adds/removes workers in response and schedules in machines where resources are available.

This tutorial guides you through a TensorFlow Quantum multi-worker setup using [Google Cloud](https://cloud.google.com/)  products, including [Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine) , a managed Kubernetes platform. You will have the chance to take the single-worker [Quantum Convolutional Neural Network (QCNN) tutorial](https://www.tensorflow.org/quantum/tutorials/qcnn)  in TensorFlow Quantum and augment it for multi-worker training.

From our experiments in the multi-worker setting, training a 23-qubit QCNN with 1,000 training examples, which corresponds to roughly 3,000 circuits simulated using full state vector simulation takes 5 minutes per epoch on a 32 node (512 vCPU) cluster, which costs a few US dollars. By comparison, the same training job on a single-worker would take roughly 4 hours per epoch. Pushing things a little bit farther, [hundreds of thousands of 30-qubit circuits could be run in a few hours using more than 10,000 virtual CPUs](https://blog.tensorflow.org/2020/11/characterizing-quantum-advantage-in.html) which could have taken weeks to run in a single-worker setting. The actual performance and cost may vary depending on your cloud setup, such as VM machine type, total cluster running time, etc. Before performing larger experiments, we recommend starting with a small cluster first, like the one used in this tutorial.

The source code for this tutorial is available in the [TensorFlow Quantum](https://github.com/tensorflow/quantum/tree/research/qcnn_multiworker) GitHub repository. `README.md` contains the quickest way to get this tutorial up and running. This tutorial will instead focus on walk through each step in detail, to help you understand the underlying concepts and integrate them with your own projects. Let’s get started!

### **1\. Setting up Infrastructure in Google Cloud**

### **2\. Preparing Your Kubernetes Cluster**

### **3\. Training with MultiWorkerMirroredStrategy**

### **4\. Understanding Training Performance Using TensorBoard**

### **5\. Running Inference**

### **6\. Cleaning Up**


What is GEEK

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Training with Multiple Workers using TensorFlow Quantum
Chloe  Butler

Chloe Butler


PyTorch Unsupervised Sentiment Discovery


This repo has been deprecated. Please visit Megatron-LM for our up to date Large-scale unsupervised pretraining and finetuning code.

If you would still like to use this codebase, see our tagged releases and install required software/dependencies that was available publicly at that date.

PyTorch Unsupervised Sentiment Discovery

This codebase contains pretrained binary sentiment and multimodel emotion classification models as well as code to reproduce results from our series of large scale pretraining + transfer NLP papers: Large Scale Language Modeling: Converging on 40GB of Text in Four Hours and Practical Text Classification With Large Pre-Trained Language Models. This effort was born out of a desire to reproduce, analyze, and scale the Generating Reviews and Discovering Sentiment paper from OpenAI.

The techniques used in this repository are general purpose and our easy to use command line interface can be used to train state of the art classification models on your own difficult classification datasets.

This codebase supports mixed precision training as well as distributed, multi-gpu, multi-node training for language models (support is provided based on the NVIDIA APEx project). In addition to training language models, this codebase can be used to easily transfer and finetune trained models on custom text classification datasets.

For example, a Transformer language model for unsupervised modeling of large text datasets, such as the amazon-review dataset, is implemented in PyTorch. We also support other tokenization methods, such as character or sentencepiece tokenization, and language models using various recurrent architectures.

The learned language model can be transferred to other natural language processing (NLP) tasks where it is used to featurize text samples. The featurizations provide a strong initialization point for discriminative language tasks, and allow for competitive task performance given only a few labeled samples. For example, we consider finetuning our models on the difficult task of multimodal emotion classification based on a subset of the plutchik wheel of emotions.

plutchik fig

Created by Robert Plutchik, this wheel is used to illustrate different emotions in a compelling and nuanced way. He suggested that there are 8 primary bipolar emotions (joy versus sadness, anger versus fear, trust versus disgust, and surprise versus anticipation) with different levels of emotional intensity. For our classification task we utilize tweets from the SemEval2018 Task 1E-c emotion classification dataset to perform multilabel classification of anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. This is a difficult task that suffers from real world classification problems such as class imbalance and labeler disagreement.

semeval results

On the full SemEval emotion classification dataset we find that finetuning our model on the data achieves competitive state of the art performance with no additional domain-specific feature engineering.

semeval leaderboard



Install the sentiment_discovery package with python3 setup.py install in order to run the modules/scripts within this repo.

Python Requirements

At this time we only support python3.

  • numpy
  • pytorch (>= 0.4.1)
  • pandas
  • scikit-learn
  • matplotlib
  • unidecode
  • sentencepiece
  • seaborn
  • emoji

Pretrained models

We've included our sentencepiece tokenizer model and vocab as a zip file:

We've included a transformer language model base as well as a 4096-d mlstm language model base. For examples on how to use these models please see our finetuning and transfer sections. Even though these models were trained with FP16 they can be used in FP32 training/inference.

We've also included classifiers trained on a subset of SemEval emotions corresponding to the 8 plutchik emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust):

Lastly, we've also included already trained classification models for SST and IMDB binary sentiment classification:

To use classification models that reproduce results from our original large batch language modeling paper please use the following commit hash and set of models.

We did not include pretrained models leveraging ELMo. To reproduce our papers' results with ELMo, please see our available resources.

Each file has a dictionary containing a PyTorch state_dict consisting of a language model (lm_encoder keys) trained on Amazon reviews and a classifier (classifier key) as well as accompanying args necessary to run a model with that state_dict.

Data Downloads

In the ./data folder we've provided processed copies of the Binary Stanford Sentiment Treebank (Binary SST), IMDB Movie Review, and the SemEval2018 Tweet Emotion datasets as part of this repository. In order to train on the amazon dataset please download the "aggressively deduplicated data" version from Julian McAuley's original site. Access requests to the dataset should be approved instantly. While using the dataset make sure to load it with the --loose-json flag.


In addition to providing easily reusable code of the core functionalities (models, distributed, fp16, etc.) of this work, we also provide scripts to perform the high-level functionalities of the original paper:

  • sentiment classification of input text
  • unsupervised reconstruction/language modeling of a corpus of text (+ script for launching distributed workers)
  • transfer of learned language model to perform sentiment analysis on a specified corpus
  • sampling from language model to generate text (possibly of fixed sentiment) + heatmap visualization of sentiment in text

Classifying text

Classify an input csv/json using one of our pretrained models or your own. Performs classification on Binary SST by default. Output classification probabilities are saved to a .npy file

python3 run_classifier.py --load_model ama_sst.pt                               # classify Binary SST
python3 run_classifier.py --load_model ama_sst_16.pt --fp16                     # run classification in fp16
python3 run_classifier.py --load_model ama_sst.pt --text-key <text-column> --data <path.csv>     # classify your own dataset

See here for more documentation.

Training Language Models (+ Distributed/FP16 Training)

Train a language model on a csv/json corpus. By default we train a weight-normalized, 4096-d mLSTM, with a 64-d character embedding. This is the first step of a 2-step process to training your own sentiment classifier. Saves model to lang_model.pt by default.

python3 pretrain.py                                                               #train a large model on imdb
python3 pretrain.py --model LSTM --nhid 512                                       #train a small LSTM instead
python3 pretrain.py --fp16 --dynamic-loss-scale                                   #train a model with fp16
python3 -m multiproc pretrain.py                                                  #distributed model training
python3 pretrain.py --data ./data/amazon/reviews.json --lazy --loose-json \       #train a model on amazon data
  --text-key reviewText --label-key overall --optim Adam --split 1000,1,1 
python3 pretrain.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \  #train a model with our sentencepiece tokenization
  --tokenizer-type bpe --tokenizer-path ama_32k_tokenizer.model 
python3 pretrain.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \  #train a transformer model with our sentencepiece tokenization
  --tokenizer-type bpe --tokenizer-path ama_32k_tokenizer.model --model transformer \
  --decoder-layers 12 --decoder-embed-dim 768 --decoder-ffn-embed-dim 3072 \
  --decoder-learned-pos --decoder-attention-heads 8
bash ./experiments/train_mlstm_singlenode.sh                                      #run our mLSTM training script on 1 DGX-1V
bash ./experiments/train_transformer_singlenode.sh                                #run our transformer training script on 1 DGX-1V 

For more documentation of our language modeling functionality look here

In order to learn about our language modeling experiments and reproduce results see the training reproduction section in analysis.

For information about how we achieve numerical stability with FP16 training see our fp16 training analysis.

Sentiment Transfer

Given a trained language model, this script will featurize text from train, val, and test csv/json's. It then uses sklearn logistic regression to fit a classifier to predict sentiment from these features. Lastly it performs feature selection to try and fit a regression model to the top n most relevant neurons (features). By default only one neuron is used for this second regression.

python3 transfer.py --load mlstm.pt                                 #performs transfer to SST, saves results to `<model>_transfer/` directory
python3 transfer.py --load mlstm.pt --neurons 5                     #use 5 neurons for the second regression
python3 transfer.py --load mlstm.pt --fp16                          #run model in fp16 for featurization step
bash ./experiments/run_sk_sst.sh                                    #run transfer learning with mlstm on imdb dataset
bash ./experiments/run_sk_imdb.sh                                   #run transfer learning with mlstm on sst dataset

Additional documentation of the command line arguments available for transfer can be found here

Classifier Finetuning

Given a trained language model and classification dataset, this script will build a classifier that leverages the trained language model as a text feature encoder. The difference between this script and transfer.py is that the model training is performed end to end: the loss from the classifier is backpropagated into the language model encoder as well. This script allows one to build more complex classification models, metrics, and loss functions than transfer.py. This script supports building arbitrary multilable, multilayer, and multihead perceptron classifiers. Additionally it allows using language modeling as an auxiliary task loss during training and multihead variance as an auxiliary loss during training. Lastly this script supports automatically selecting classification thresholds from validation performance. To measure validation performance this script includes more complex metrics including: f1-score, mathew correlation coefficient, jaccard index, recall, precision, and accuracy.

python3 finetune_classifier.py --load mlstm.pt --lr 2e-5 --aux-lm-loss --aux-lm-loss-weight .02   #finetune mLSTM model on sst (default dataset) with auxiliary loss
python3 finetune_classifier.py --load mlstm.pt --automatic-thresholding --threshold-metric f1     #finetune mLSTM model on sst and automatically select classification thresholds based on the validation f1 score
python3 finetune_classifier.py --tokenizer-type SentencePieceTokenizer --vocab-size 32000 \       #finetune transformer with sentencepiece on SST
  --tokenizer-type bpe tokenizer-path ama_32k_tokenizer.model --model transformer --lr 2e-5 \
  --decoder-layers 12 --decoder-embed-dim 768 --decoder-ffn-embed-dim 3072 \
  --decoder-learned-pos --decoder-attention-heads 8 --load transformer.pt --use-final-embed
python3 finetune_classifier.py --automatic-thresholding --non-binary-cols l1 l2 l3 --lr 2e-5\     #finetune multilayer classifier with 3 classes and 4 heads per class on some custom dataset and automatically select classfication thresholds
  --classifier-hidden-layers 2048 1024 3 --heads-per-class 4 --aux-head-variance-loss-weight 1.   #`aux-head-variance-loss-weight` is an auxiliary loss to increase the variance between each of the 4 head's weights
  --data <custom_train>.csv --val <custom_val>.csv --test <custom_test>.csv --load mlstm.pt
bash ./experiments/se_transformer_multihead.sh                                                    #finetune a multihead transformer on 8 semeval categories

See how to reproduce our finetuning experiments in the finetuning reproduction section of analysis.

Additional documentation of the command line arguments available for finetune_classifier.py can be found here



A special thanks to our amazing summer intern Neel Kant for all the work he did with transformers, tokenization, and pretraining+finetuning classification models.

A special thanks to @csarofeen and @Michael Carilli for their help developing and documenting our RNN interface, Distributed Data Parallel model, and fp16 optimizer. The latest versions of these utilities can be found at the APEx github page.

Thanks to @guillitte for providing a lightweight pytorch port of openai's sentiment-neuron repo.

This project uses the amazon review dataset collected by J. McAuley


Want to help out? Open up an issue with questions/suggestions or pull requests ranging from minor fixes to new functionality.

May your learning be Deep and Unsupervised.

Download details:

Author: NVIDIA
Source: https://github.com/NVIDIA/sentiment-discovery

License: View license


Jon  Gislason

Jon Gislason


Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

A Demo Code Of Training and Testing using Tensorflow

ProbFace, arxiv

This is a demo code of training and testing [ProbFace] using Tensorflow. ProbFace is a reliable Probabilistic Face Embeddging (PFE) method. The representation of each face will be an Guassian distribution parametrized by (mu, sigma), where mu is the original embedding and sigma is the learned uncertainty. Experiments show that ProbFace could

  • improve the robustness of PFE.
  • simplify the calculation of the multal likelihood score (MLS).
  • improve the recognition performance on the risk-controlled scenarios.

#machine learning #tensorflow #testing #a demo code of training and testing using tensorflow #a demo code of training #testing using tensorflow

Seamus  Quitzon

Seamus Quitzon


Php how to delete multiple rows through checkbox using ajax in laravel

First thing, we will need a table and i am creating products table for this example. So run the following query to create table.

CREATE TABLE `products` (
 `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
 `name` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL,
 `description` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
 `created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP,
 `updated_at` datetime DEFAULT NULL,

Next, we will need to insert some dummy records in this table that will be deleted.

INSERT INTO `products` (`name`, `description`) VALUES

('Test product 1', 'Product description example1'),

('Test product 2', 'Product description example2'),

('Test product 3', 'Product description example3'),

('Test product 4', 'Product description example4'),

('Test product 5', 'Product description example5');

Now we are redy to create a model corresponding to this products table. Here we will create Product model. So let’s create a model file Product.php file under app directory and put the code below.


namespace App;

use Illuminate\Database\Eloquent\Model;

class Product extends Model
    protected $fillable = [

Step 2: Create Route

Now, in this second step we will create some routes to handle the request for this example. So opeen routes/web.php file and copy the routes as given below.


Route::get('product', 'ProductController@index');
Route::delete('product/{id}', ['as'=>'product.destroy','uses'=>'ProductController@destroy']);
Route::delete('delete-multiple-product', ['as'=>'product.multiple-delete','uses'=>'ProductController@deleteMultiple']);

#laravel #delete multiple rows in laravel using ajax #laravel ajax delete #laravel ajax multiple checkbox delete #laravel delete multiple rows #laravel delete records using ajax #laravel multiple checkbox delete rows #laravel multiple delete

Uriah  Dietrich

Uriah Dietrich


Google Announces TensorFlow Quantum 0.5.0: Expected Features & Updates

Google is celebrating the first anniversary of TensorFlow Quantum (TFQ), a library for rapid prototyping of hybrid quantum-classical ML models. TFQ is regarded as a tipping point for developments in hybrid quantum and classic machine learning models the company has been pushing for years.

#developers corner #google tensorflow quantum #tensorflow quantum #tensorflow quantum 0.5.0