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Articles related to "app"


Android’s AirDrop rival, Nearby Share, is finally here

  • Now when you want to share a file or image, you can simply click tap on the share icon, then select ‘Nearby.’ Your phone will then locate other devices that are open to sharing and display them on a list you can quickly choose from, no special app required.
  • You can set yourself to hidden, visible only to come contacts, or visible to all contacts.While you shouldn’t technically need a new phone to use it — it can work with any device running Android 6.0 or later — Google says the feature is rolling out to ‘select Google Pixel and Samsung devices’ today.
  • The feature will also eventually be compatible with Chromebooks; Google says Nearby Share will work on Chrome OS ‘in the coming months.’ Hopefully, Google considers bringing the feature to PCs too, perhaps via an app or a partnership with Microsoft.

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Deploy a Scikit-Learn NLP Model with Docker, GCP Cloud Run and Flask

  • Be sure to check out the README and code in our GitHub repository instructions on setting up this app locally with Docker!
  • This Docker image now accessible at the GCP container registry or GCR and can be accessed via URL with Cloud Run. Note: Replace PROJECT-ID with your GCP project ID and container-name with your containers’ name.
  • You have just deployed an application packaged in a container to Cloud Run. You only pay for the CPU, memory, and networking consumed during request handling.
  • We’ve covered setting up an app to serve a model and building docker containers locally.
  • Next, we stored our docker image in the cloud and used it to build an app on Google Cloud Run. Getting any decently good model out quickly can have significant business and tech value.

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How to not deploy Keras/TensorFlow models

  • Some of them say “production”, but they often simply use the un-optimized model and embed it into a Flask web server.
  • When your web server only serves one single request at the time, you are fine, as the model was loaded in this thread and predict is called from this thread.
  • But once you allow more than one requests at the time, your web server stops working, because you can simply not access a TensorFlow model from different threads.
  • When using docker containers to deploy deep learning models to production, the most examples do NOT utilize GPUs, they don’t even use GPU instances.
  • As you can see, loading trained model and putting it into Flask docker containers is not an elegant solution.
  • If you want deep learning in production, start from the model, then think about servers and finally about scaling instances.

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Deploy a Scikit-Learn NLP Model with Docker, GCP Cloud Run and Flask

  • Be sure to check out the README and code in our GitHub repository instructions on setting up this app locally with Docker!
  • This Docker image now accessible at the GCP container registry or GCR and can be accessed via URL with Cloud Run. Note: Replace PROJECT-ID with your GCP project ID and container-name with your containers’ name.
  • You have just deployed an application packaged in a container to Cloud Run. You only pay for the CPU, memory, and networking consumed during request handling.
  • We’ve covered setting up an app to serve a model and building docker containers locally.
  • Next, we stored our docker image in the cloud and used it to build an app on Google Cloud Run. Getting any decently good model out quickly can have significant business and tech value.

save | comments | report | share on


How to not deploy Keras/TensorFlow models

  • Some of them say “production”, but they often simply use the un-optimized model and embed it into a Flask web server.
  • When your web server only serves one single request at the time, you are fine, as the model was loaded in this thread and predict is called from this thread.
  • But once you allow more than one requests at the time, your web server stops working, because you can simply not access a TensorFlow model from different threads.
  • When using docker containers to deploy deep learning models to production, the most examples do NOT utilize GPUs, they don’t even use GPU instances.
  • As you can see, loading trained model and putting it into Flask docker containers is not an elegant solution.
  • If you want deep learning in production, start from the model, then think about servers and finally about scaling instances.

save | comments | report | share on


Beware of find-my-phone, Wi-Fi, and Bluetooth, NSA tells mobile users

  • The National Security Agency is recommending that some government workers and people generally concerned about privacy turn off find-my-phone, Wi-Fi, and Bluetooth whenever those services are not needed, as well as limit location data usage by apps.
  • The New York Times also published this sobering feature outlining services that use mobile location data to track the histories of millions of people over extended periods.
  • Both OSes require users to manually turn off ad personalization and reset advertising IDs. In iOS, people can do this in Settings > Privacy > Advertising.
  • While in the Privacy section, users should review which apps have access to location data.
  • In Android 10, users can limit ad tracking and reset advertising IDs by going to Settings > Privacy and clicking Ads. Both the Reset Advertising ID and Opt Out of Ads personalization are there.

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Deploy a Scikit-Learn NLP Model with Docker, GCP Cloud Run and Flask

  • Be sure to check out the README and code in our GitHub repository instructions on setting up this app locally with Docker!
  • This Docker image now accessible at the GCP container registry or GCR and can be accessed via URL with Cloud Run. Note: Replace PROJECT-ID with your GCP project ID and container-name with your containers’ name.
  • You have just deployed an application packaged in a container to Cloud Run. You only pay for the CPU, memory, and networking consumed during request handling.
  • We’ve covered setting up an app to serve a model and building docker containers locally.
  • Next, we stored our docker image in the cloud and used it to build an app on Google Cloud Run. Getting any decently good model out quickly can have significant business and tech value.

save | comments | report | share on


How to not deploy Keras/TensorFlow models

  • Some of them say “production”, but they often simply use the un-optimized model and embed it into a Flask web server.
  • When your web server only serves one single request at the time, you are fine, as the model was loaded in this thread and predict is called from this thread.
  • But once you allow more than one requests at the time, your web server stops working, because you can simply not access a TensorFlow model from different threads.
  • When using docker containers to deploy deep learning models to production, the most examples do NOT utilize GPUs, they don’t even use GPU instances.
  • As you can see, loading trained model and putting it into Flask docker containers is not an elegant solution.
  • If you want deep learning in production, start from the model, then think about servers and finally about scaling instances.

save | comments | report | share on


Deploy a Scikit-Learn NLP Model with Docker, GCP Cloud Run and Flask

  • Be sure to check out the README and code in our GitHub repository instructions on setting up this app locally with Docker!
  • This Docker image now accessible at the GCP container registry or GCR and can be accessed via URL with Cloud Run. Note: Replace PROJECT-ID with your GCP project ID and container-name with your containers’ name.
  • You have just deployed an application packaged in a container to Cloud Run. You only pay for the CPU, memory, and networking consumed during request handling.
  • We’ve covered setting up an app to serve a model and building docker containers locally.
  • Next, we stored our docker image in the cloud and used it to build an app on Google Cloud Run. Getting any decently good model out quickly can have significant business and tech value.

save | comments | report | share on


How to not deploy Keras/TensorFlow models

  • Some of them say “production”, but they often simply use the un-optimized model and embed it into a Flask web server.
  • When your web server only serves one single request at the time, you are fine, as the model was loaded in this thread and predict is called from this thread.
  • But once you allow more than one requests at the time, your web server stops working, because you can simply not access a TensorFlow model from different threads.
  • When using docker containers to deploy deep learning models to production, the most examples do NOT utilize GPUs, they don’t even use GPU instances.
  • As you can see, loading trained model and putting it into Flask docker containers is not an elegant solution.
  • If you want deep learning in production, start from the model, then think about servers and finally about scaling instances.

save | comments | report | share on