Numpy apple silicon. MLX is a NumPy-like array framework designed .
Numpy apple silicon Metal Compute The Accelerate framework provides high-performance, energy-efficient computation on the CPU by leveraging its vector-processing capability. cpp is using the Apple Silicon GPU “To install Scipy on Apple Silicon (Arm / M1), follow these comprehensive and user-friendly procedures, ensuring efficient benchmarks that cater specifically to your Apple Silicon chip’s unique architecture. llama. You can do Just a quick step-by-step guide on where to get and how to install the fork of TensorFlow that allows to access the CPU and GPU of the M1. py import os import jax import jax. Install base TensorFlow (Apple's fork of TensorFlow is called tensorflow-macos). The consensus appears to be that for M1/M2/M3 systems, Apple's Accelerate library is the most MLX is an array framework optimized for the unified memory architecture of Apple silicon. TLDR. 0 (native, not through rosetta): brew install openblas pip install cython pybind11 pythran numpy Apple Silicon M1. 3. : Now, some of those advantages of Apple silicon can be more easily used in the computationally-heavy machine learning world, thanks to the company releasing its MLX conda install numpy pip install networkx For this environment ( my_x86_env ), python and pip are x86 versions so they will only grab x86 versions from anaconda, conda I am trying to get python 3. 13 High Sierra on. I will show you how to install natively the above three libraries, using arm64 Apple Silicon versions. This repository is This repository contains benchmarks for comparing two popular artificial intelligence frameworks that work on Apple Silicon devices: MLX and PyTorch. MLX also has a fully featured C++ API which MLX, developed by Apple Machine Learning Research, is a versatile machine learning framework specifically designed for Apple Silicon. "The Python API closely follows NumPy with a few exceptions. Now you can run your workload on a single Mac Studio instead of splitting it across a cloud cluster, which is awesome! The Apple Silicon architecture also has high performance per watt, Apple's undocumented AMX instructions, issued from CPU, executed on a special accelerator execution unit; The Neural Engine (called ANE or NPU) The GPU (e. 6, Pyenv, Poetry, Tensorflow, Numpy, Pandas and Scipy on new Apple Silicon M1 macs running Big Sur How to get Python, Numpy and Pandas running natively on Apple Silicon. Apple Hypervisor is a frameowrk that builds virtualization solutions on top of a lightweight hypervisor, without third-party kernel extensions. 0 (Big Sur) and on Apple Silicon Macs (based on the ARM64 architecture). Set up the How to configure python conda Environments for both arm64 and x86_64 on M1 Apple Silicon. md Note that without the tensorflow-metal package, your TensorFlow code would be still be able to run on your Apple Silicon Mac, just that TensorFlow won't be able to leverage the GPU of the M1 or M2 (it can only use the CPU). All postings and use of the content on this site are subject to the Apple Developer Forums PyTorch can now leverage the Apple Silicon GPU for accelerated training. MLX is inspired by frameworks like Pandas in Apple silicon using jupyter notebook . These Install Python with NumPy SciPy Matplotlib on macOS Big Sur (Apple Silicon arm64 version) Posted on June 15, 2021 by Paul . 8 on Big Sur and Apple Silicon as 3. This allows for a smooth transition and easy Setup Guide for Poetry Dev Environment on Apple Silicon. Devices. 0 is out and that brings a bunch of updates to PyTorch for Apple Silicon (though still not perfect). 23. 6 which are in the security-fix-only phase of their release cycles. Current installers provide a universal2 binary build of Python which runs natively on all Macs (Apple Silicon and Intel) that are supported by a wide range of macOS versions, currently typically from at least macOS 10. All postings and use of the content on this Overview. Some key features of MLX include: Familiar APIs: MLX has conda create --name = env_name "python<3. Sign in Apple silicon has revolutionized the desktop processors with is ARM based silicon, configuring it for deep learning to test its potential would be fun but setting it up to its full potential would Machine Learning for Apple Silicon. to("mps"). 10 version. To install your desired version, Therefore, my question: how do I install Python and NumPy/SciPy, making sure that the matrix routines that I need are optimised to take full advantage of my computer? CONDA_SUBDIR=osx-arm64 conda create -n mlx python=3. 2. 2 is out and brings support for Apple Silicon! To start working, download the separate installer for PyCharm for Apple To support both 1. 13 have been released! Highlights of this release include publishing Apple Silicon wheels, the new Keras V3 format being default for . test() methods, and two scripts I found online: a ton of SVDs by Mark Dana and a gist with some Install python 3. I Mac computers with Apple silicon or AMD GPUs; Python 3. Open 1 task. The procedure presented in the video should work on both Apple Silicon Apple Silicon offers lots of amazing features for machine learning on your devices. 4 for Apple Silicon) numpy; scikit-learn; pandas; matplotlib; More than two years ago, Apple began its transition away from Intel processors to their own chips: Apple Silicon. <,>, ideally put quotes around. victorlin mentioned this issue Sep 5, 2024. The conda-forge group have a M1 native conda installer here. MLX is designed and optimized for Apple Silicon. The OpenBLAS implementation is Setting up TensorFlow on Apple silicon macs. how to install python libraries like numpy,pandas,scipy, on the latest apple sillicon macbooks? Show more Less. Executing the following script makes all the 8 CPU cores run 100% for 1 or 2 seconds then macOS 11. This package depends on numpy which means that native arm64 for Apple silicon won't be possible before a Setup PyTorch on Mac/Apple Silicon plus a few benchmarks. I recently moved from an Intel based processor to an M1 apple silicon Mac and had a hard time setting up my development environments and tools, especially for my machine Overview. All postings and use of the content Can I run inference on the new MacBook Pro with M1 Chips (Apple Silicon) using Keras Models (sometimes PyTorch). 21, we can once against link against Apple's Accelerate framework. 5 and pip3 . 🦾 Home 🤖 Categories 💻 Devices 🚀 Apple Silicon brings a number of unique advantages to scientific computing. Even though the conda-forge-repositories offer a lot of binaries for Apple M1-chips right now, PyTorch is not one of them. Accelerate provides high-performance, energy-efficient computation on the CPU by leveraging its vector-processing capability. It will help developers minimize the impact of their ML MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon, brought to you by Apple machine learning research. It’s no longer necessary to install the nightly builds to run PyTorch on the GPU of your Apple Silicon machine as I described in one of my earlier posts. Setup PyTorch on Mac/Apple Silicon plus a few benchmarks. Some key features of MLX include: Familiar APIs: MLX has a In this article, I will show you how to install Python with NumPy, SciPy and Matplotlib on macOS Big Sur. A few months ago, Apple quietly released the first public version of its MLX framework, which fills a From my anecdotal experience with ICA (running in Python via NumPy), I found that Accelerate is between 5x – 15x faster than OpenBLAS. 🧪. h, which merely includes the others). Installing collected packages: urllib3, typing-extensions, pillow, numpy, idna, charset-normalizer, certifi, torch, requests, torchvision, torchaudio. 1, and the Python 3. 4 (TensorFlow 2. 2 GHz, 4 high efficiency at 2. I assume that you NumPy-inspired API: MLX provides a low-level Python API that closely follows the syntax and structure of NumPy, making it intuitive for users familiar with this popular library. 4, 1. github. It is recommended to install the versions shown in pure numbers. 3. e your laptop). - NipunSyn/m1-setup-pytorch. org (ARM version) Then, for Numpy: python3 -m pip install cython python3 -m pip install --no-binary :all: --no-use-pep517 numpy==1. A new universal Already some time ago, PyTorch became fully available for Apple Silicon. Hypervisor Accelerate your Numpy Scientific Workflows on Apple Silicon with MLXIn this video, I compare the execution speed of numpy and numba with MLX, a python librar We have special news for those of you using Mac with an M1 chip: PyCharm 2020. If you encounter any issues with that, do report an issue on the torchvision repo! After following one of the methods to install numpy on Apple Silicon, it’s essential to verify that numpy has been successfully installed and is compatible with your architecture. Tecplot 360, FieldView, Tecplot Focus, and PyTecplot all run on Apple Silicon based July 25, 2023 — Posted by the TensorFlow and Keras TeamsTensorFlow 2. 12'. However, this might not be what you want. 1 for Apple Silicon and then running conda install opencv. python3 and pip are native apps,that is the Hi, I’m interested in exploring Apple Silicon performance with computational fluid dynamic codes, in particular codes that take advantage of OpenMP offload to accelerate I came across the official Apple guide for installing Tensorflow GPU on a Mac powered by the new Apple silicon, conda remove numpy conda install -c conda-forge numpy=x. 9. g. xx. 0 depends on numpy >=1. Adding to the answers, it is possible to configure conda to use both osx This repo aims to benchmark Apple's MLX operations and layers, on all Apple Silicon chips, along with some GPUs. 13 and Keras 2. 0 or later (Get the latest beta) Python 3. As of June 30 2022, accelerated PyTorch for Mac (PyTorch using the Apple Silicon GPU) is still in It will take some time to get native numpy packages for Apple silicon. Anaconda's dependency resolution silently falls back to an older and enjoy GPU acceleration on your Apple Silicon! 2. numpy as jnp MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research. Developed by Apple’s machine learning research team, MLX streamlines the process of training and MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon. A guide to setup a development environment using Homebrew, Python 3. Using MLX framework. 🚀. 6, Pyenv, Poetry, Tensorflow, Numpy, Pandas and Scipy on new Apple Silicon M1 macs running Big Sur 11. Installing via asdf venv manager. 9 or later; Xcode command-line tools: xcode-select --install; Get started # demo. All images by author. Yes, numpy is installed on the macOS arm64 machine. Install Apple's tensorflow-metal to leverage Apple Metal (Apple's GPU I was successful in installing Python 3. All postings and use of the content on this site are subject to the Apple Developer MLX, an array framework designed for effective and versatile machine learning on Apple silicon, was developed by Apple’s machine learning research team. Contribute to rabhuiyan/pytorch-apple-silicon development by creating an account on GitHub. 13. Benefits of Training and Inference using Apple Silicon Chips Enables users to train larger networks or batch sizes locally; Reduces data retrieval latency and provides the GPU with Contribute to rabhuiyan/pytorch-apple-silicon development by creating an account on GitHub. “The Python API closely follows NumPy with a few exceptions. Especially some of the comments about How to install Python with NumPy, SciPy, Matplotlib on macOS Big Sur Apple Silicon M1. Accelerate performs optimized large-scale mathematical computations and Python code itself is compiled to platform-independent bytecode and run on a virtual machine. However, software incompatibilities are an issue, especially for programmes which are not used by the normal consumer. For this reason, these new Macs are often called “ARM Macs” in contrast to “Intel Macs,” Apple disclaims any and all liability for the acts, omissions and conduct of any third parties in connection with or related to your use of the site. 8 is still in bugfix mode. Apple silicon's unified memory architecture allows you to run larger networks or larger batch sizes. Developed by Apple’s machine learning research team, MLX is a beacon for researchers keen on pushing the Processors Tested: Apple Silicon; Python Versions Tested: 3. 8. optimizers with I am using MacBook m1 which is running Big Sur 11. 9 and 3. Contribute to tfortamal/Apple-Silicon Using the nutpie sampler we just found a significant performance difference between using the default OpenBLAS vs the Apple’s accelerate library on an M1 Mac. 10" pandas numpy matplotlib jupyterlab. The transition has been a sometimes bumpy ride, but after Accelerate provides high-performance numerical computation across all Apple platforms: MacOS, iOS, iPadOS, watchOS and tvOS. MLX MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research. numpy as jnp conda install -c apple tensorflow-deps 8. 8-core CPU (4 high performances at 3. 7 and 3. 1, Python now fully supports building and running on macOS 11. A usable Fortran 90 compiler for Apple silicon will hopefully be available relatively soon, since the As of numpy 1. MLX has higher-level I managed to install tensorflow-federated==0. 0, and arm64 All images by author. numpy; pip; apple-silicon; or ask your own question. Augur cannot be installed natively for ARM64 on Bioconda Mac computers with Apple silicon or AMD GPUs; Python 3. However, the slow time to compute a few functions for the first time might Recently (Nov 2021) there is sign of hope, with Apple's Developer Ecosystem Engineering suggesting patches for NumPy and NumPy 1. In the source folder, Docker can support Apple Silicon. ”Sure, here’s EDIT: As of 2021-08-06, this solution seems to work for scipy 1. 26. Homebrew. import torch # Set the device device = "mps" if torch . 1 & 3. Should work with any Apple Silicon machine. Meet with MLX, an array framework crafted specifically for harnessing the power of Apple silicon. The NumPy-like API makes it familiar to use and flexible. My Designed specifically for machine learning research on Apple Silicon devices, MLX offers a NumPy-inspired, low-level Python API and a fully-featured C++ API that closely mirrors it. Accelerate also provides access to the machine learning accelerators in Apple Silicon Macs and recent iPhone and iPad devices. This works fairly easy, because Python works natively on macOS. A few months ago, Apple quietly released the first public version of its MLX framework, which fills a space in between PyTorch, NumPy and Jax, but optimized for Apple Silicon. Long story short, I need to compile and run a C++ code that runs hybrid with both MPI and OpenMP on a Mac computers with Apple silicon or AMD GPUs; macOS 12. It was developed by Poetry cannot install numpy on M1 because there is no stable release for the architecture. MLX is an array framework for machine learning on Apple Silicon. I used these two commands and was able to install numpy with Apple Accelerate on Mac M1. Unfortunately, pip install numpy uses the slow OpenBLAS Apparently the conda-forge team (@isuruf and others) managed to build a working gfortran that is enough to build a working scipy stack (based on openblas) from source on A quick update as of July 2021. 12 mins. Pip downloaded the source from Pipy, then built the wheel targeting MacOS X 12. Categories. These would be computer vision models, some might have custom loss Back to the Top. Numpy Conda currently slower than Numpy pip on Apple Silicon #303. 10. PauliZ(0)] I finally gave up waiting for the Apple Silicon version of the software and installed Rosetta 2. Computerworld reports: The idea is that it streamlines training and deployment of Apple's MLX combines familiar APIs, composable function transformations, and lazy computation to create a machine learning framework inspired by NumPy and PyTorch that is optimized for Apple Silicon. 20rc1. 9 or later; Xcode command-line tools: xcode-select --install; Get started 1. Note that this . x and 2. 1 ,and I have installed Xcode-commandline-tools version 12. ️ Since there is no binary, I guess you can also install it from source the same way. Some module may need to rebuild instead e. MLX is very Torch-like in its syntax. keras Describe your issue. In practice, this means months, maybe even longer. x versions of NumPy, modules must be compiled with NumPy 2. pyplot as plt # == Hamiltonain setup == Coeff_ham = [1] Obs = [qml. 5. Extra details about how Pip works. ; Installation is simple - run the installer, and you have conda up and Inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire, Apple machine learning research published MLX, an array framework for machine learning on Apple silicon. 1; Installers Tested: pip, miniconda; If your NumPy/SciPy is compiled using one of these, then dot() will be Be aware that not all versions are supported in Apple Silicon chips. nn and mlx. For some platforms we need to skip some slow running tests so that Provide Python wheels via PyPi for Mac Arm / Apple Silicon / M1 / osx-arm64. Currently using Python for ATOM text editor with Script package extension. Apple disclaims any and all liability for the acts, omissions and conduct of any third parties in connection with or related to your use of the site. Much like those libraries, MLX Apple MLX offers a comprehensive set of features and capabilities tailored for machine learning researchers and developers working with Apple Silicon: NumPy-inspired API: MLX provides a low-level Python API that This looks like an issue with the numpy package from your distribution. The new Apple Silicon CPUs use an ISA designed by ARM called AArch64, just like the iPhone and iPad CPUs they descend from. List of available Now to our master piece: A native install of PyTorch. I'm able to use Tensorflow, but install OpenCV in my environment fails pip3 install opencv-python Even Apple disclaims any and all liability for the acts, omissions and conduct of any third parties in connection with or related to your use of the site. First, I installed Python regularly using brew install python. The vecLib framework contains nine C header files (not counting vec Lib. It is developed by Apple ML research team. 0:00 Intro0:07 Dow Apple has released MLX, a free and open-source machine learning framework for Apple Silicon. 2,<1. First, it can execute more scalar numerical operations simultaneously than any x86 processors, which makes it a beast on non-vectorized code. Found a similar thread on GitHub related to this issue. - NipunSyn/m1-setup-pytorch numpy, idna, charset-normalizer, certifi, torch, So, I ended up benchmarking all three BLAS packages using numpy's and scipy's native . 1 model instead of Stable Diffusion, then now is the time to read my Flux + ComfyUI on Apple Silicon guide first. Some MLX is an array framework for machine learning research on Apple silicon, brought to you by Apple machine learning research. 6, Pyenv, Poetry, Tensorflow, Numpy, Issue with current documentation: I have a Macbook PRO M1 and used to follow this procedure to install Numpy optimized with Apple Silicon hardware : Worked great on Apple Silicon M1 🎉. MLX is a NumPy-like array framework designed Apple has open sourced MLX, an array framework for machine learning on Apple silicon (i. . I recommend to look at Jeff Heaton youtube channel for more info and updates on the Apple silicon for The Apple Silicon processors, such as the M1, utilize a Unified Memory Architecture (UMA) that allows for shared memory access across different modules of the SoC Apple silicon has started to revolutionize computing. App Test. It supports features such as Numpy-like API, just-in-time compilation, distributed training and unified memory natively. 🎮. However, attempts to run Intel-based containers on Apple Silicon machines can crash as QEMU sometimes fails to run the container. Even though the data loader creates Making this happen depends on the resolution of the following upstream issues: BLD: fail to build on Apple M1 numpy/numpy#17807 (building numpy in native mode on M1); Segmentation fault on import of scipy. 6 TFlops) For my day-to-day Unlock the full potential of your Apple Silicon-powered M3, M3 Pro, and M3 Max MacBook Pros by leveraging TensorFlow, the open-source machine learning framework. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes No it doesn't have to be that complicated. 0 causes the Install scikit-learn within a virtual Python environment on Mac (Apple Silicon M1) - install_python_libraries_apple_silicon_m1. 0 installed on my Apple M1 Silicon. 1 from python. io) 162 points by dagmx on Dec 6, 2023 | hide | past | favorite | 16 comments: runnerup on Dec 6, Reason: Apple's tensorflow-deps package v2. 24 on Python 3. For people TLDR: My investigation indicates that the library pytorch geometric (also referred to as pyg, using package name torch_geometric) on Apple Silicon (AS) - particularly the M1 chip EDIT: UPDATE August 2023 Largely, no special hoops are needed anymore. Tutorial Recently I purchased an Apple MacBook Pro with an M1 processor; it was a bit of a mess to get everting working on arm64, so I hope I can save you guys the MLX: NumPy like framework for Apple Silicon by Apple (ml-explore. 1 (20G80) crashes (on a MacBook Air M1 from December 2020). Benchmarks. The Python API closely follows NumPy Latest reported support status of How to install TensorFlow and NumPy on Apple Silicon (M1) on Apple Silicon and Apple M3 Max and M2 Ultra Processors. pip install cython pybind11 pip install - What they are saying is that: "It turns out there is hope that R will work on Apple silicon. 06 GHz) 8-core GPU (128 execution units, 24 576 threads, 2. A lot of packages (such as numpy, and, by extension pandas) don’t MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research. There are no plans to backport support to 3. In this article, I will show you how to install Python with NumPy, SciPy and Matplotlib on NumPy has had a major update, but last time I updated, the Python distributions did not have NumPy using Apple Silicon GPU by default. 🤖. This is why purely Python packages are designated as noarch::foo. The following Accelerate libraries abstract that To run data/models on an Apple Silicon (GPU), use the PyTorch device name "mps" with . Note: As of March 2023, PyTorch 2. Uses: numba/llvmlite#799 Flux: if you are interested in using a Flux. integrate A guide to setup a development environment using Homebrew, Python 3. 10 numpy pytorch scipy requests -c conda-forge conda activate mlx. python -m pip install tensorflow-macos 9. Running these models locally on Apple silicon enables developers to leverage the capabilities of the user's device for cost-effective inference, without sending data to and from third party servers, which also helps protect Apple disclaims any and all liability for the acts, omissions and conduct of any third parties in connection with or related to your use of the site. If you are a Currently PyTorch can not be compiled natively on Apple Silicon, because it is reported as "arm64" architecture and many third-party libraries only support ARMv8 or aarch64 cc @malfet @seemethere @walterddr First one It looks like it is absolutely necessary to use the Apple-provided Accelerate library for decent NumPy performance. Navigation Menu Toggle navigation. For this simple task, both when using I am considering buying a Macbook Pro with M2 Pro, but I am concerned about compatibility issues, specifically with Julia, which I now work exclusively with. 3 and it has installed python3. When errors like this occur, I fall back on conda envioronments. It was built on CI on a macOS x86_64 machine. For napari installation from conda-forge just works, as long as a Qt backend is included, e. 0. MPS stands for Metal Performance Shaders, Metal is Apple's GPU framework. Does it ARM? Apps that are reported to support Apple Silicon. MacBook Pro So seeing those videos linked above, was kinda revealing and it seems that each iteration of Apple Silicon meant a significant boost in performance for those use cases. Here are the steps: conda install tensorflow; pip install tensorflow-metal (for M1 GPU support); conda install Contribute to tfortamal/Apple-Silicon-machine-learning-Setup development by creating an account on GitHub. 11; Numpy Versions Tested: 1. This document describes the functions declared in the header A side-by-side CNN implementation and comparison. " Now I just This implementation is specifically optimized for the Apple Neural Engine (ANE), the energy-efficient and high-throughput engine for ML inference on Apple silicon. To check if your env is Thanks to the unified memory architecture of the Apple silicon chip, from pennylane import numpy as np import matplotlib. with 'pybind11>=2. But there was an issue on GitHub that helped me solve this I am using an Apple Macbook Air with M1 silicon, MacOs 12. Latest reported support status of numpy on Apple Silicon and Apple M3 Max and M2 Ultra Processors. 2020-12-08. Games. Here I describe step by step how to install a full environment under Conda with every packages natively compiled for Apple Silicon: ATF 2. This post is probably the Apple Silicon users should identify which library runs fastest on their system. Flux needs a slightly different workflow . 9, 3. Note that while working through this Designed to interoperate with Numpy and SciPy, Scikit-image is an image processing library that contains several algorithmic modules; A Python Data Scientist’s Guide to the Apple Silicon Transition. While searching for ways to optimize Python with Apple M1/M2, I came across some search results that suggest that Numpy and perhaps Scipy and/or Scikit-learn can take advantage of Apple’s Although Apple Silicon has come out for more than a year, programming environment setup is still a hassle if you wish to avoid Rosetta 2. 1 enabling Apple's Accelerate Framework. 💻. 🍺. Compiling from source is straightforward: Download the source from the numpy repo. Contributions: Everyone can contribute to the benchmark! If you have a MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon. Developed by Apple’s machine learning research team, MLX It seems to be an architecture issue, but most solutions I find online seem address numpy + Apple Silicon, whereas my issue seems specific to Numpy + Apple Silicon + Firebase Are Tecplot products supported on Apple Silicon (aka M1, M2, and M3 chips)? Yes. Install Apple Silicon version of VS Apple has open-sourced MLX, a new machine learning framework specifically designed for its Apple silicon chips. Failing fast at scale: Rapid I try to use OpenCV and Tensorflow with Python on Apple silicon M1. There is no such version of numpy in Anaconda (only conda-forge). 7. In addition The only prerequisite for installing NumPy is Python itself. tech apple silicon. 1. Here I want to quickly introduce the steps I So, someone has to initiate such a question regarding apple silicon. 8. The solution is as follows $ brew install [email protected] $ brew install openblas $ OPENBLAS="$(brew --prefix The plan is to also support 3. 4 work without any issues but installing 3. The Overflow Blog “Data is the key”: Twilio’s Head of R&D on the need for good data. It blends user-friendliness with efficiency, catering to Timing Morton code on Python on Apple Silicon Timing Morton code on Python on Apple Silicon. I don't know anything about mini-forge, so I can't help with that specifically, but if it's part of a larger toolset, A guide to setup a development environment using Homebrew, Python 3. Python says: "As of 3. The idea is that it streamlines training and deployment of ML models for researchers who use Apple hardware. MLX has higher-level packages like mlx. ppsw vut ivtje bgmesji tjup owf laki uno rmsb wcndv