tensorflow_macos
TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework. (by apple)
flamegraph
Easy flamegraphs for Rust projects and everything else, without Perl or pipes <3 (by flamegraph-rs)
Our great sponsors
| tensorflow_macos | flamegraph | |
|---|---|---|
| 33 | 47 | |
| 2,887 | 4,262 | |
| - | 2.8% | |
| 3.4 | 7.4 | |
| almost 3 years ago | 9 days ago | |
| Shell | Rust | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tensorflow_macos
Posts with mentions or reviews of tensorflow_macos.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-17.
-
Updated Apple Silicon Guide for M2 Pro and M2 Max Chips
https://github.com/apple/tensorflow_macos is no longer needed
-
The hunt for the M1’s neural engine
Tensorflow has a CoreML enabled version which run on ANE.
https://github.com/apple/tensorflow_macos
-
M1 Mac users
Apple released a guide on how to use the M1's integrated Neural Chip in TensorFlow. Have a look at this Apple documentation page (and maybe also this GitHub that talks about TensorFlow together with Apple's own ML Compute platform).
-
MacBook Air or Wait for new potential MacBook Air with M2
Tensorflow does work on Apple Silicon
- Kernels dying when using tensorflow in Jupyter Notebooks.
-
Main PyTorch maintainer confirms that work is being done to support Apple Silicon GPU acceleration for the popular machine learning framework.
Apple did some work to optimize tensorflow for M1, can be found here https://github.com/apple/tensorflow_macos It's alpha, but works fine, I tried it
-
The M1 Max is the fastest GPU we have ever measured in Affinity Photo benchmark
https://github.com/apple/tensorflow_macos/issues/25
https://forums.macrumors.com/threads/apple-silicon-deep-lear...
It is expected that the M1 Max should have similar performance to a RTX-2080 or Titan X.
-
MacBook Pro M1 Pro benchmark
In case anyone is interested, in ran a fairly simple MNIST benchmark (proposed here : https://github.com/apple/tensorflow_macos/issues/25) on my recently acquired M1 Pro MBP (16-core GPU, 16GB RAM).
-
Error while installing tensorflow on Mac M1
The only method I know of to download tensorflow on M1 macs is the one documented here: https://github.com/apple/tensorflow_macos
- How exactly does the Neural Engine benefit the consumer?
flamegraph
Posts with mentions or reviews of flamegraph.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-02-15.
-
Rust Tooling: 8 tools that will increase your productivity
You can install cargo-flamegraph with cargo install flamegraph. There are some underlying requirements to be able to use cargo-flamegraph; you will want to take a look at the repo here to make sure you have the right dependencies.
-
Need help making sense of these benchmark results
I tried to diagnose the issue with flamegraph, but unfortunately the flamegraph didn't show anything beyond the next call for some reason
-
Why is my code so slow ? advent of code 2022, day 16 (basic graph stuff)
having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go)
-
why is my code so slow ? advent of code 2023, day 16 (basic graph stuff)
I'm currently implementing a solution for the first part of the day 16. It work but it is really slow... I'd like to : - understand why - having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go) - eventually have some clue/solution/idea - have general feedback on what in my "coding style" is not appropriate for rust (I come from java/kotlin/ts even if I've already coded a bit in c/c++) : for example I love iterator & sequence but i feel they are not really suited to overuse in rust (mostly because of async & result).
-
how expensive is an operation?
Use a profiler. Flamegraph is a good way to visualise profiler output. This lets you identify which functions are taking up a large amount of time - and hence helps you identify where to focus your optimisation efforts.
-
Slow Rust Redis
You tried trying to see what takes the most time under load via flames? https://github.com/flamegraph-rs/flamegraph
- making a virtual machine in rust
-
Need help with rust performance
Well, in cases like that the answer is straight forward, use a profiler like https://github.com/flamegraph-rs/flamegraph
-
superdiff - a way to find similar code blocks in projects (comments appreciated)
I don't see any obvious problems with your algorithm. I've had luck using cargo-flamegraph to identify the slow parts of my code. That's going to show you which parts to focus on improving the performance of!
-
Data-driven performance optimization with Rust and Miri
From the readme of cargo flamegraph:
What are some alternatives?
When comparing tensorflow_macos and flamegraph you can also consider the following projects:
miniforge - A conda-forge distribution.
cargo-flamegraph - Easy flamegraphs for Rust projects and everything else, without Perl or pipes <3
Pointnet_Pointnet2_pytorch - PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
tracing - Application level tracing for Rust.
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
hashbrown - Rust port of Google's SwissTable hash map
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
heaptrack - A heap memory profiler for Linux
Python-docker - Docker Official Image packaging for Python
snmalloc-rs - rust bindings of snmalloc
coremltools - Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
bcc - BCC - Tools for BPF-based Linux IO analysis, networking, monitoring, and more
tensorflow_macos vs miniforge
flamegraph vs cargo-flamegraph
tensorflow_macos vs Pointnet_Pointnet2_pytorch
flamegraph vs tracing
tensorflow_macos vs tinygrad
flamegraph vs hashbrown
tensorflow_macos vs ROCm
flamegraph vs heaptrack
tensorflow_macos vs Python-docker
flamegraph vs snmalloc-rs
tensorflow_macos vs coremltools
flamegraph vs bcc