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Articles related to "server"


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


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


Microsoft Plans to Reuse More Server Parts with the Help of AI

  • The company is planning to set up dedicated Circular Centers at its new data center campuses around the world, where AI algorithms will help sort through parts from decommissioned servers or other hardware and figure out which ones can be reused without leaving the campus.
  • To support its already huge and rapidly growing cloud services business, Microsoft now operates more than 160 data centers, housing more than 3 million servers and “related hardware.” A Microsoft server’s average lifespan is about five years, the company said.
  • Based on Circular Center pilots, Microsoft expects to increase its reuse of server parts by 90 percent by 2025.
  • The first Microsoft Circular Centers will be built at the new major data center campuses or regions it builds, the company said.

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AMD Gains Server Chip Market Share at Intel’s Expense

  • Ian King (Bloomberg) -- Advanced Micro Devices Inc. is gaining share in the lucrative market for server chips, the latest sign it’s benefiting from close ties to a major Taiwanese factory partner to win orders from larger rival Intel Corp.
  • Strides in server chips will help propel third-quarter revenue to about $2.55 billion, Santa Clara, California-based AMD said Tuesday, topping analysts’ average prediction for $2.3 billion.
  • After decades of lagging behind Intel, AMD has been catching up in recent years, helped by advances at Taiwan Semiconductor Manufacturing Co., which makes chips on its behalf.
  • AMD Chief Executive Officer Lisa Su said her bullish outlook is based on expectations that her company will keep adding share as new products gain wider adoption at computer makers.
  • She said AMD has passed 10% share of the profitable server chip market and that while supply of leading-edge chips is “tight,” the company is confident it can meet increasing demand.

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