Here's another simple example of deploying a YOLOv5 object detection model using the DeepSparse Pipeline. Check out the Github Repo here: http://
github.com/neuralmagic/de
epsparse
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@sumanth_077
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Deploying YOLOv5 Object Detection with DeepSparse Pipeline
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DeepSparse: Run GPU-Speed Models on CPUs with Sparse Execution
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DeepSparse does this by sparse execution which is removing redundant information from a trained deep learning model. This allows them to run on CPUs – at GPU speeds and better Now it's available in the AWS marketplace for pennies Check it out here https://
tinyurl.com/build-with-dee
psparse
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DeepSparse: GPU-Class ML Inference Performance on CPUs
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Latency is critical when deploying machine learning models for real-time inference But running large models at low latency requires expensive hardware. DeepSparse enables the deployment of large models with GPU-class performance on CPUs Here is how DeepSparse does it:
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Congratulations to Akshay for Awesome ML Content
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Congrats Akshay . Keep that awesome ML content coming.
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Free GPU Support Benefits Machine Learning Projects
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Absolutely Darshil. Free GPU support is really handy for ML Projects.
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Daily Python, Machine Learning and Language Models Content Share
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That's a wrap. Every day, I share and simplify complex concepts around Python, Machine Learning & Language Models. Follow me → @Sumanth_077 if you haven't already to ensure you don't miss that. Like/RT the first tweet to support my work and help this reach more people
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Prevent Google Colab from Disconnecting Idle Sessions
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3. Stop Colab from Disconnecting: If we leave the notebook idle, Google Colab automatically disconnects it. You can avoid disconnecting the session by writing this simple function in the console prompt.
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Download Kaggle Datasets into Google Colab Seamlessly
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2. Kaggle Datasets into Google Colab: You can download any dataset seamlessly from Kaggle onto your Colab workspace Checkout this to learn how you can do that:
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How to Enable GPU and TPU Acceleration in Google Colab
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1. Activate GPU AND TPU: The default hardware of Google Colab is the CPU. However, you can enable GPU (and even TPU) support for more computationally demanding tasks. Click on: “Runtime” → “Change runtime type” → “Hardware accelerator”. Then select the desired hardware.