Media Summary: Julia User Group Munich Fall in love with Julia: Julia User Group Munich Fall in love with julia Statistics & co with JuliaStats & StatsKit.jl ... In this video, I show you how to use Amazon SageMaker to train a Transformer model with AWS Trainium and compile it for AWS ...

Stephan Sahm Accelerate Python With - Detailed Analysis & Overview

Julia User Group Munich Fall in love with Julia: Julia User Group Munich Fall in love with julia Statistics & co with JuliaStats & StatsKit.jl ... In this video, I show you how to use Amazon SageMaker to train a Transformer model with AWS Trainium and compile it for AWS ... Sylvain shows how to make a script work on any kind of distributed setup with the While Julia is great, there are still a lot of existing useful differentiable From routing a 200000-token prompt across GPUs to having GLM-5.2 profile, rewrite, and optimize the kernels serving itself, ...

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Stephan Sahm:  Accelerate Python with Julia
Accelerating Python 101 | Stephan Sahm | Julia User Group Munich | Fall in love with julia
JuliaStats 101 | Stephan Sahm | Julia User Group Munich | Fall in love with julia
JuMP & DisjunctiveProgramming.jl | Stephan Sahm | Julia User Group Munich | Fall in love with julia
Real-time ML: Accelerating Python for inference (below 10ms) at scale - Feature Store Summit 2025
Stefan Behnel - Cython to speed up your Python code
Accelerate Transformers on Amazon SageMaker with AWS Trainium and AWS Inferentia
Supercharge your PyTorch training loop with 🤗 Accelerate
PyCallChainRules.jl: Reusing Differentiable Python Code in Julia | Jayesh K. Gupta | JuliaCon 2022
How I Would Learn Python FAST (if I could start over)
AWS SAM Accelerate Tutorial (With A Real Demo Inside)
Use THIS Language to Speed Up Your Python Code
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Stephan Sahm:  Accelerate Python with Julia

Stephan Sahm: Accelerate Python with Julia

Speeding up

Accelerating Python 101 | Stephan Sahm | Julia User Group Munich | Fall in love with julia

Accelerating Python 101 | Stephan Sahm | Julia User Group Munich | Fall in love with julia

Julia User Group Munich | Fall in love with Julia:

JuliaStats 101 | Stephan Sahm | Julia User Group Munich | Fall in love with julia

JuliaStats 101 | Stephan Sahm | Julia User Group Munich | Fall in love with julia

Julia User Group Munich | Fall in love with julia Statistics & co with JuliaStats & StatsKit.jl ...

JuMP & DisjunctiveProgramming.jl | Stephan Sahm | Julia User Group Munich | Fall in love with julia

JuMP & DisjunctiveProgramming.jl | Stephan Sahm | Julia User Group Munich | Fall in love with julia

Julia User Group Munich | Fall in love with Julia:

Real-time ML: Accelerating Python for inference (below 10ms) at scale - Feature Store Summit 2025

Real-time ML: Accelerating Python for inference (below 10ms) at scale - Feature Store Summit 2025

Real-time ML:

Stefan Behnel - Cython to speed up your Python code

Stefan Behnel - Cython to speed up your Python code

Cython to

Accelerate Transformers on Amazon SageMaker with AWS Trainium and AWS Inferentia

Accelerate Transformers on Amazon SageMaker with AWS Trainium and AWS Inferentia

In this video, I show you how to use Amazon SageMaker to train a Transformer model with AWS Trainium and compile it for AWS ...

Supercharge your PyTorch training loop with 🤗 Accelerate

Supercharge your PyTorch training loop with 🤗 Accelerate

Sylvain shows how to make a script work on any kind of distributed setup with the

PyCallChainRules.jl: Reusing Differentiable Python Code in Julia | Jayesh K. Gupta | JuliaCon 2022

PyCallChainRules.jl: Reusing Differentiable Python Code in Julia | Jayesh K. Gupta | JuliaCon 2022

While Julia is great, there are still a lot of existing useful differentiable

How I Would Learn Python FAST (if I could start over)

How I Would Learn Python FAST (if I could start over)

If I had to learn

AWS SAM Accelerate Tutorial (With A Real Demo Inside)

AWS SAM Accelerate Tutorial (With A Real Demo Inside)

AWS SAM

Use THIS Language to Speed Up Your Python Code

Use THIS Language to Speed Up Your Python Code

Python's

Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten

Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten

From routing a 200000-token prompt across GPUs to having GLM-5.2 profile, rewrite, and optimize the kernels serving itself, ...