Hello … i can not imagine what i do wrong … NSString *libPath = [[NSBundle mainBundle] pathForResource:@"./default" ofType:@"metallib"]; id<MTLLibrary> library = [device newLibraryWithFile:libPath error:&error]; Same dir like the binary default.metallib prog To run wit options like prog —option My prog say always if i run lib not found … Hard&Software : Xcode 13.4.1 (13F100) mbp M1 max

my Mac got unsupported architecture Xcode: 13.4.1 included headers: #include <stdio.h> #include <stdlib.h> #include <unistd.h> #include <string.h> #include <ctype.h> #include <memory.h> /*  * Architecture validation for current SDK  */ #if !defined(__sys_cdefs_arch_unknown__) && defined(__i386__) #elif !defined(__sys_cdefs_arch_unknown__) && defined(__x86_64__) #elif !defined(__sys_cdefs_arch_unknown__) && defined(__arm__) #elif !defined(__sys_cdefs_arch_unknown__) && defined(__arm64__) #else #error Unsupported architecture #endif where is arm64e ???

hello ... iv'e got a weird problem with this program on macOS Ventura 13.1 on a MacBook Pro M1 MAX and clang prog: #include <stdlib.h> #include <stdint.h> #include <stdbool.h> #include <stdarg.h> #include <errno.h> #include <sys/types.h> #include <sys/stat.h> #include <sys/time.h> #include <math.h> #include <unistd.h> #include <string.h> #include <ctype.h> #include <memory.h> #include <iconv.h> #define CA_PRIVATE_IMPLEMENTATION static const char *coin(int fex) {     bool isDuplicate = false;     //---------------------------------------------------------------------     const char charset[] = "ABCDEFGHIJKLMNOPQRSTUVWXYZ";     int max_index = (sizeof(charset) - 1);     //---------------------------------------------------------------------     const char *picked = NULL;     const char *number = NULL;     int j = 0,i = 0;          for (i = 0; i < fex; i++)     {                  do         {             int target = rand() % max_index; // Generate             strncpy((char *)number,&charset[target],1); // Pick             // Check for duplicates             for (j = 0; j < fex; j++)             {                 if (number == &picked[j])                 {                     isDuplicate = true;                     break; // Duplicate detected                 }                 else {                     memmove((void *)&charset[target], &charset[target + 1], max_index - target);                     max_index--;                     isDuplicate = false;                     break; // No Duplicate detected                 }             } // end for         } // end do         while (isDuplicate); // equivalent to while(isDuplicate == true)                  if (!isDuplicate) { // equivalent to if(isDuplicate == false)             strncpy((char *)&picked[j],number,1); // picked         }     } // end for     return picked; } int main(void) {     time_t start, stop;     start = time(NULL);     for (int i = 0; i < 6; i++){         printf("%s\n", coin(26));     }     printf("%s\n",coin(5));     stop = time(NULL);     printf("Time elapsed : %ld seconds\n",(stop - start));          return 0; }

hello I've got a problem with C pthread on macOS Ventura 13.2.1 and some troubles with libxml2 it load the full xml parameters on first try to load the first parameter it load all 4 in one line. I cannot figure out is pthreads the problem or the xml file what loaded out off memory ... like: punch_allloadthreads4 xml file &lt;?xml version="1.0" encoding="utf-8"?&gt; &lt;um-configuration version="1.0"&gt;     &lt;config&gt;         &lt;param1 name="punch_all"&gt;&lt;/param1&gt;         &lt;param2 name="load"&gt;&lt;/param2&gt; &lt;param3 name="threads"&gt;&lt;/param3&gt; &lt;param4 name="4"&gt;&lt;/param4&gt;     &lt;/config&gt; &lt;/um-configuration&gt; well got autoupdates on here is a lldb -f /usr/local/bin/enigma output ... Process 4230 stopped * thread #1, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x0)     frame #0: 0x00000001882cb700 libsystem_pthread.dylib`_pthread_create + 1084 libsystem_pthread.dylib`: -&gt;  0x1882cb700 &lt;+1084&gt;: str    x25, [x22]     0x1882cb704 &lt;+1088&gt;: mov    x0, x21     0x1882cb708 &lt;+1092&gt;: ldp    x29, x30, [sp, #0x70]     0x1882cb70c &lt;+1096&gt;: ldp    x20, x19, [sp, #0x60]   thread #2, stop reason = EXC_BAD_ACCESS (code=2, address=0x10000b26e)     frame #0: 0x00000001882f8090 libsystem_platform.dylib`_platform_memmove + 448 libsystem_platform.dylib`: -&gt;  0x1882f8090 &lt;+448&gt;: strb   w6, [x3], #0x1     0x1882f8094 &lt;+452&gt;: subs   x2, x2, #0x1     0x1882f8098 &lt;+456&gt;: b.ne   0x1882f808c               ; &lt;+444&gt;     0x1882f809c &lt;+460&gt;: ret     Target 0: (enigma) stopped. (lldb) bt * thread #1, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x0)   * frame #0: 0x00000001882cb700 libsystem_pthread.dylib`_pthread_create + 1084     frame #1: 0x0000000100009f5c enigma`sbfParams + 1448     frame #2: 0x000000010000aa48 enigma`main + 1456     frame #3: 0x0000000187f9fe50 dyld`start + 2544

I cannot find the bug ... but run this code (python) on torch device mps0 is slow quicker and cpu0 or cpu1 ... but where is the bug? or run it on neural engine with cpu1? you need a setup like this: #!/bin/bash export HOMEBREW_BREW_GIT_REMOTE="https://github.com/Homebrew/brew" # put your Git mirror of Homebrew/brew here export HOMEBREW_CORE_GIT_REMOTE="https://github.com/Homebrew/homebrew-core" # put your Git mirror of Homebrew/homebrew-core here /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)" eval "$(/opt/homebrew/bin/brew shellenv)" brew update --force --quiet chmod -R go-w "$(brew --prefix)/share/zsh" export OPENBLAS=$(/opt/homebrew/bin/brew --prefix openblas) export CFLAGS="-falign-functions=8 ${CFLAGS}" brew install wget brew install unzip conda init --all conda create -n torch-gpu python=3.10 conda activate torch-gpu conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 -c pytorch conda install -c conda-forge jupyter jupyterlab python3 -m pip install --upgrade pip python3 -m pip install insightface==0.2.1 onnx imageio scikit-learn scikit-image moviepy python3 -m pip install googledrivedownloader python3 -m pip install imageio==2.4.1 python3 -m pip install Cython python3 -m pip install --no-use-pep517 numpy python3 -m pip install torch python3 -m pip install image python3 -m pip install timm python3 -m pip install PlL python3 -m pip install h5py for i in `seq 1 6`; do python3 test.py done conda deactivate exit 0 test.py: import torch import math # this ensures that the current MacOS version is at least 12.3+ print(torch.backends.mps.is_available()) # this ensures that the current current PyTorch installation was built with MPS activated. print(torch.backends.mps.is_built()) dtype = torch.float device = torch.device("cpu",0) #device = torch.device("cpu",1) #device = torch.device("mps",0) # Create random input and output data x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype) y = torch.sin(x) # Randomly initialize weights a = torch.randn((), device=device, dtype=dtype) b = torch.randn((), device=device, dtype=dtype) c = torch.randn((), device=device, dtype=dtype) d = torch.randn((), device=device, dtype=dtype) learning_rate = 1e-6 for t in range(2000): # Forward pass: compute predicted y y_pred = a + b * x + c * x ** 2 + d * x ** 3 # Compute and print loss loss = (y_pred - y).pow(2).sum().item() if t % 100 == 99: print(t, loss) # Backprop to compute gradients of a, b, c, d with respect to loss grad_y_pred = 2.0 * (y_pred - y) grad_a = grad_y_pred.sum() grad_b = (grad_y_pred * x).sum() grad_c = (grad_y_pred * x ** 2).sum() grad_d = (grad_y_pred * x ** 3).sum() # Update weights using gradient descent a -= learning_rate * grad_a b -= learning_rate * grad_b c -= learning_rate * grad_c d -= learning_rate * grad_d print(f'Result: y = {a.item()} + {b.item()} x + {c.item()} x^2 + {d.item()} x^3')