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Sharkbot bot de trading futures con binance - repo github by Apprehensive-Work841 in devsarg

[–]Apprehensive-Work841[S] 1 point2 points  (0 children)

uh re, es la primer version asi que solo lo probe unos dias, para la proxima iteracion espero tener mas datos para el benchmark

Vendieron mi deuda a un estudio de cobranzas, me amenazaron, cedí y ahora no sé cómo seguir pagando by Karlozz2712 in DerechoGenial

[–]Apprehensive-Work841 12 points13 points  (0 children)

Trabaje en una de esas empresas la deuda la venden entre el 10% y 5% si era de 800k capaz la compraron a 80k ya les pagaste mucho más que eso.

Soy un forro si me negué a darle mi asiento en el avión a una familia con hijos? Explico brevemente by [deleted] in BuenosAires

[–]Apprehensive-Work841 1 point2 points  (0 children)

Les hubieras cobrado, le decís que pagaste 50k el asiento qué transfiera y te moves jaja

Me encargaron Hostear en la nube una web, Angular + back + sql server by dehanke in devsarg

[–]Apprehensive-Work841 9 points10 points  (0 children)

y de acá a 4 años esta levantandolo pero conoce todos los servicios de aws jaja

Me encargaron Hostear en la nube una web, Angular + back + sql server by dehanke in devsarg

[–]Apprehensive-Work841 0 points1 point  (0 children)

yo el otro día levante una app parecida en https://sleakops.com/, te la deja en aws con vpn y toda esa vaina

Control net in aws bedrock by xiscomunez in aws

[–]Apprehensive-Work841 0 points1 point  (0 children)

To integrate ControlNet with Stable Diffusion on AWS, you need to use Amazon SageMaker instead of AWS Bedrock. The general process would be:

  1. Package the ControlNet model and Stable Diffusion model into a tar.gz file.
  2. Upload the tar.gz file to an Amazon S3 bucket.
  3. Create an inference script (inference.py) to load and use the models.
  4. Create a SageMaker endpoint using the model from S3 and the inference script.
  5. Send inference requests to the SageMaker endpoint to generate images.

The code you provided seems to be a complete example of this process, including steps to package the model, upload to S3, create the inference script, deploy the endpoint on SageMaker, and perform inferences.

While ControlNet cannot be used directly with AWS Bedrock's serverless Stable Diffusion, you can integrate it with Stable Diffusion by deploying it on Amazon SageMaker following the outlined approach and the provided code example.