Flex-casual format gains popularity



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Flexible service formats, which have been around for years, are drawing new attention, as restaurant operators seek to offer their guests more convenience.

While fast casual, with its counter-ordering model, has gotten most of the attention in the past decade, such concepts as Russo’s New York Pizzeria, Mama Fu’s Asian and Wolfgang Puck Bistro have found that a “flex-casual” model works well for their customers.

The flex-casual model offers counter service by day and full service by night. Newer concepts, such as Flat Out Crazy Restaurant Group’s SC Asian at the Macy’s store in San Francisco, adapts a bit of flex casual as well.

Wolfgang Puck Bistro at Universal CityWalk in Los Angeles debuted a flex-casual format in April 2009.

“This setting provides a fast lunch for the business diner who doesn’t have time to wait, and at the same time allows for a more formal, destination location for diners who want to come for a nice dinner or special occasion,” said Alyssa Gioscia Roberts, operations coordinator for Wolfgang Puck Worldwide Inc.

Randy Murphy, whose Murphy Restaurant Group of Austin, Texas, acquired the Mama Fu’s concept in March 2008, added that the flex-casual model works for his restaurant. As a franchisee of Mama Fu’s before the acquisition, he said he could never get comfortable with relying mostly on lunch for revenue.

So his Austin Mama Fu’s restaurant began offering counter service during the day and full service at night. The switchover from fast casual between 4 p.m. and 5 p.m. is fairly seamless, Murphy said, as long as you have a host or server watching the front to capture the customers as they come in.

The flex-casual format has also shifted more dollars to the dinner daypart, Murphy added.


Flex-casual format gains popularity - Recipes

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

The GitHub repository contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.

Transfer Learning from Sparse Models

This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order. Currently supported ML Frameworks are the following: torch>=1.1.0,<=1.8.0 , tensorflow>=1.8.0,<=2.0.0 , tensorflow.keras >= 2.2.0 .

More information on installation such as optional dependencies and requirements can be found here.

To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe. The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.

ScheduledModifierManager classes can be created from recipes in all supported ML frameworks. The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe. Managers can apply recipes in one shot or training aware ways. One shot is invoked by calling .apply(. ) on the manager while training aware requires calls into initialize(. ) (optional), modify(. ) , and finalize(. ) .

For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines. For example, the following applies a recipe in a training aware manner:

Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:

More information on the codebase and contained processes can be found in the SparseML docs:


Flex-casual format gains popularity - Recipes

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

The GitHub repository contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.

Transfer Learning from Sparse Models

This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order. Currently supported ML Frameworks are the following: torch>=1.1.0,<=1.8.0 , tensorflow>=1.8.0,<=2.0.0 , tensorflow.keras >= 2.2.0 .

More information on installation such as optional dependencies and requirements can be found here.

To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe. The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.

ScheduledModifierManager classes can be created from recipes in all supported ML frameworks. The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe. Managers can apply recipes in one shot or training aware ways. One shot is invoked by calling .apply(. ) on the manager while training aware requires calls into initialize(. ) (optional), modify(. ) , and finalize(. ) .

For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines. For example, the following applies a recipe in a training aware manner:

Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:

More information on the codebase and contained processes can be found in the SparseML docs:


Flex-casual format gains popularity - Recipes

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

The GitHub repository contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.

Transfer Learning from Sparse Models

This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order. Currently supported ML Frameworks are the following: torch>=1.1.0,<=1.8.0 , tensorflow>=1.8.0,<=2.0.0 , tensorflow.keras >= 2.2.0 .

More information on installation such as optional dependencies and requirements can be found here.

To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe. The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.

ScheduledModifierManager classes can be created from recipes in all supported ML frameworks. The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe. Managers can apply recipes in one shot or training aware ways. One shot is invoked by calling .apply(. ) on the manager while training aware requires calls into initialize(. ) (optional), modify(. ) , and finalize(. ) .

For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines. For example, the following applies a recipe in a training aware manner:

Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:

More information on the codebase and contained processes can be found in the SparseML docs:


Flex-casual format gains popularity - Recipes

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

The GitHub repository contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.

Transfer Learning from Sparse Models

This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order. Currently supported ML Frameworks are the following: torch>=1.1.0,<=1.8.0 , tensorflow>=1.8.0,<=2.0.0 , tensorflow.keras >= 2.2.0 .

More information on installation such as optional dependencies and requirements can be found here.

To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe. The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.

ScheduledModifierManager classes can be created from recipes in all supported ML frameworks. The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe. Managers can apply recipes in one shot or training aware ways. One shot is invoked by calling .apply(. ) on the manager while training aware requires calls into initialize(. ) (optional), modify(. ) , and finalize(. ) .

For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines. For example, the following applies a recipe in a training aware manner:

Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:

More information on the codebase and contained processes can be found in the SparseML docs:


Flex-casual format gains popularity - Recipes

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

The GitHub repository contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.

Transfer Learning from Sparse Models

This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order. Currently supported ML Frameworks are the following: torch>=1.1.0,<=1.8.0 , tensorflow>=1.8.0,<=2.0.0 , tensorflow.keras >= 2.2.0 .

More information on installation such as optional dependencies and requirements can be found here.

To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe. The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.

ScheduledModifierManager classes can be created from recipes in all supported ML frameworks. The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe. Managers can apply recipes in one shot or training aware ways. One shot is invoked by calling .apply(. ) on the manager while training aware requires calls into initialize(. ) (optional), modify(. ) , and finalize(. ) .

For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines. For example, the following applies a recipe in a training aware manner:

Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:

More information on the codebase and contained processes can be found in the SparseML docs:


Flex-casual format gains popularity - Recipes

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

The GitHub repository contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.

Transfer Learning from Sparse Models

This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order. Currently supported ML Frameworks are the following: torch>=1.1.0,<=1.8.0 , tensorflow>=1.8.0,<=2.0.0 , tensorflow.keras >= 2.2.0 .

More information on installation such as optional dependencies and requirements can be found here.

To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe. The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.

ScheduledModifierManager classes can be created from recipes in all supported ML frameworks. The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe. Managers can apply recipes in one shot or training aware ways. One shot is invoked by calling .apply(. ) on the manager while training aware requires calls into initialize(. ) (optional), modify(. ) , and finalize(. ) .

For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines. For example, the following applies a recipe in a training aware manner:

Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:

More information on the codebase and contained processes can be found in the SparseML docs:


Flex-casual format gains popularity - Recipes

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

The GitHub repository contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.

Transfer Learning from Sparse Models

This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order. Currently supported ML Frameworks are the following: torch>=1.1.0,<=1.8.0 , tensorflow>=1.8.0,<=2.0.0 , tensorflow.keras >= 2.2.0 .

More information on installation such as optional dependencies and requirements can be found here.

To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe. The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.

ScheduledModifierManager classes can be created from recipes in all supported ML frameworks. The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe. Managers can apply recipes in one shot or training aware ways. One shot is invoked by calling .apply(. ) on the manager while training aware requires calls into initialize(. ) (optional), modify(. ) , and finalize(. ) .

For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines. For example, the following applies a recipe in a training aware manner:

Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:

More information on the codebase and contained processes can be found in the SparseML docs:


Flex-casual format gains popularity - Recipes

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

The GitHub repository contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.

Transfer Learning from Sparse Models

This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order. Currently supported ML Frameworks are the following: torch>=1.1.0,<=1.8.0 , tensorflow>=1.8.0,<=2.0.0 , tensorflow.keras >= 2.2.0 .

More information on installation such as optional dependencies and requirements can be found here.

To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe. The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.

ScheduledModifierManager classes can be created from recipes in all supported ML frameworks. The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe. Managers can apply recipes in one shot or training aware ways. One shot is invoked by calling .apply(. ) on the manager while training aware requires calls into initialize(. ) (optional), modify(. ) , and finalize(. ) .

For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines. For example, the following applies a recipe in a training aware manner:

Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:

More information on the codebase and contained processes can be found in the SparseML docs:


Flex-casual format gains popularity - Recipes

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

The GitHub repository contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.

Transfer Learning from Sparse Models

This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order. Currently supported ML Frameworks are the following: torch>=1.1.0,<=1.8.0 , tensorflow>=1.8.0,<=2.0.0 , tensorflow.keras >= 2.2.0 .

More information on installation such as optional dependencies and requirements can be found here.

To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe. The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.

ScheduledModifierManager classes can be created from recipes in all supported ML frameworks. The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe. Managers can apply recipes in one shot or training aware ways. One shot is invoked by calling .apply(. ) on the manager while training aware requires calls into initialize(. ) (optional), modify(. ) , and finalize(. ) .

For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines. For example, the following applies a recipe in a training aware manner:

Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:

More information on the codebase and contained processes can be found in the SparseML docs:


Flex-casual format gains popularity - Recipes

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

The GitHub repository contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.

Transfer Learning from Sparse Models

This repository is tested on Python 3.6+, and Linux/Debian systems. It is recommended to install in a virtual environment to keep your system in order. Currently supported ML Frameworks are the following: torch>=1.1.0,<=1.8.0 , tensorflow>=1.8.0,<=2.0.0 , tensorflow.keras >= 2.2.0 .

More information on installation such as optional dependencies and requirements can be found here.

To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe. The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.

ScheduledModifierManager classes can be created from recipes in all supported ML frameworks. The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe. Managers can apply recipes in one shot or training aware ways. One shot is invoked by calling .apply(. ) on the manager while training aware requires calls into initialize(. ) (optional), modify(. ) , and finalize(. ) .

For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines. For example, the following applies a recipe in a training aware manner:

Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:

More information on the codebase and contained processes can be found in the SparseML docs:


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Comments:

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  2. Howi

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