Here's how DOCS.JAX.DEV makes money* and how much!

*Please read our disclaimer before using our estimates.
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DOCS . JAX . DEV {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Docs.jax.dev Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. External Links
  10. Libraries
  11. Hosting Providers

We are analyzing https://docs.jax.dev/en/latest/faq.html.

Title:
Frequently asked questions (FAQ) — JAX documentation
Description:
No description found...
Website Age:
4 years and 8 months (reg. 2020-11-01).

Matching Content Categories {📚}

  • Technology & Computing
  • Dating & Relationships
  • Mobile Technology & AI

Content Management System {📝}

What CMS is docs.jax.dev built with?

Custom-built

No common CMS systems were detected on Docs.jax.dev, but we identified it was custom coded using Bootstrap (CSS).

Traffic Estimate {📈}

What is the average monthly size of docs.jax.dev audience?

🚀 Good Traffic: 50k - 100k visitors per month


Based on our best estimate, this website will receive around 50,153 visitors per month in the current month.

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How Does Docs.jax.dev Make Money? {💸}

We're unsure how the site profits.

The purpose of some websites isn't monetary gain; they're meant to inform, educate, or foster collaboration. Everyone has unique reasons for building websites. This could be an example. Docs.jax.dev has a secret sauce for making money, but we can't detect it yet.

Keywords {🔍}

jax, function, jit, return, def, values, device, numpy, data, code, tracer, tracers, result, computation, functions, customclass, buffer, output, class, array, import, case, abstract, change, python, mul, selfx, selfmul, default, arguments, regular, concrete, gradient, object, type, gpu, runtime, buffers, donated, jaxjit, time, compilation, arrays, shape, donation, inside, operations, input, call, method,

Topics {✒️}

higher-order control-flow primitives performing matrix-matrix multiplication nvidia--cu12 pip packages memory-efficient functional update complicated jit-compiled sequences conditional control-flow based common rank-based functions frequently asked questions usual search path jit-compiled helper function cuda library initialization python control flow accelerators takes time floating point arithmetic numeric jax primitives cuda libraries fail cuda runtime libraries helper function external accessing intermediate values mutable python containers environment variable jax_platforms absl flag --jax_platforms jitted functions behave autodiff systems tend reduced pre-allocation resolving dynamic libraries generally expect jax jax sets rpath generally expect numpy concern decorating stand put significant effort internal compilation cache jax import numpy introduce abstract tracers transformations introduce abstract setting xla_python_client_preallocate=false converting tracers back running full applications jax-relative location jit decorated function np import jax remain regular values side-effect due requires concrete values key-word arguments introduce concrete tracers correctly jit-compiling enable 64-bit dtypes executed multiple times supports jit compilation

Questions {❓}

  • @jit # <---- How to do this correctly?
  • Did that actually speed things up?
  • How can I convert a JAX Tracer to a NumPy array?
  • How to use jit with methods?
  • Is JAX faster than NumPy?
  • Is this a fair comparison?
  • The fact that the gradient is everywhere zero may be confusing at first glance: after all, the output does change in response to the input, so how can the gradient be zero?
  • Why are gradients zero for functions based on sort order?
  • Why do some CUDA libraries fail to load/initialize?
  • Why is this?

Libraries {📚}

  • Bootstrap
  • Clipboard.js
  • Typed.js

Emails and Hosting {✉️}

Mail Servers:

  • aspmx.l.google.com
  • alt1.aspmx.l.google.com
  • alt2.aspmx.l.google.com
  • aspmx2.googlemail.com
  • aspmx3.googlemail.com

Name Servers:

  • ivan.ns.cloudflare.com
  • tegan.ns.cloudflare.com
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