We have developing an app that uses Linux VM virtualization and com.apple.security.virtualization entitlement. Question: do we need any approval from Apple to release this app that uses com.apple.security.virtualization entitlement?

Hi there, I have got a question but I don't know if this is the right place. I am using an M1 Mac mini and have installed Fedora Asahi Remix for certain developing Tasks next to MacOS. How is the fan speed controlled? Does Fedora turn on the fan when the CPU gets warm or does the hardware firmware set the fan speed? Thank you for your answers! Laurin

I am running on Sonoma 14.X and I need to download and install VM of older OS (Ventura). Whatever the value I change in deployment target it downloads VM of Sonoma OS. I am from QA and need older OS to test app compatibility. https://developer.apple.com/documentation/virtualization/running_macos_in_a_virtual_machine_on_apple_silicon_macs

I've tried to implement the steps suggested for configuring code signing https://docs.github.com/en/actions/deployment/deploying-xcode-applications/installing-an-apple-certificate-on-macos-runners-for-xcode-development on runners However, I could not sign my app build when I was using Virtual Runner. Is it a limitation coming from the Apple virtualization framework restriction (for signing in with Apple ID), or did I miss some configuration for Xcode? I was trying both manual and automatic signing but never succeeded (

I used to run VirtualBox on macOS to run Windows guests for some reasons. Recently I bought a new Mac mini M1, now I have a problem - VB does not have a stable release for arm (yet). What other options do I have? BTW, I came across this doc article (https://developer.apple.com/documentation/virtualization/running_macos_in_a_virtual_machine_on_apple_silicon). I read thru it, but could not conclude if it offers the same functionalities as a full-blown VM suite; and more specifically I want to run Windows guests.

With Apple Virtualization Framework, we can create virtual machines and run macOS and Linux-based operating systems and pass thru USB devices like mice and keyboards. Are there any plans to be able to pass thru bluetooth or bluetooth devices to the Virtual Machine so that the VM OS could connect to bluetooth devices? Related docs: https://developer.apple.com/documentation/virtualization

I'm trying to use the host camera from inside a virtual machine created using the Virtualization framework in Swift. I can't seem to figure out how to achieve this though, and unlike audio devices, keyboards, displays etc there doesn't seem to be a corresponding class and docs page for cameras or generic usb devices. Is there any way to connect a built in Apple camera to a mac virtual machine created with the Virtualization framework?

When I call either of VZVirtualMachineConfiguration.validateSaveRestoreSupport(..) VZVirtualMachine.saveMachineStateTo(URL) I get the error: NSLocalizedFailure=Invalid virtual machine configuration. NSLocalizedFailureReason=Unsupported graphics device in configuration For Linux there seems to be only vanilla size configuration (at least 1 scanout is required): let graphicsDevice = VZVirtioGraphicsDeviceConfiguration() graphicsDevice.scanouts = [ VZVirtioGraphicsScanoutConfiguration(widthInPixels: 1920, heightInPixels: 1080) ] I've tried valid sizes and aspect ratios. I've otherwise removed console and audio devices provoking similar errors, but I can't find an acceptable graphics configuration. I believe it should work because my code is substantially similar to the open-source tart application that supports suspend. I'm running Sonoma 14.2.1 on an M1 Studio with an HP24mh external monitor via a third-party HDMI/USB-C adapter. I see nothing in the Console (except some ...wakeups_resource.diag common to other applications). So I'm looking for help with How to get more information for this failure How to configure suspendable/resumable devices: graphics for sure, but also console, audio, keyboard, etc. Thanks! GUI Linux guest: https://developer.apple.com/documentation/virtualization/running_gui_linux_in_a_virtual_machine_on_a_mac macos guest: https://developer.apple.com/documentation/virtualization/running_macos_in_a_virtual_machine_on_apple_silicon wwdc-2022: https://developer.apple.com/videos/play/wwdc2022/10002/ wwdc-2023: https://developer.apple.com/videos/play/wwdc2023/10007/

Hi, everyone! I want to know whether I can check my app is running on a virtual machine or not. If so, how can I do that in C++ or Objective-C? The code needs to support both Intel processors and Apple Silicon. Thanks! Leo

I am following the Running Linux in a Virtual Machine guide on MacOS 14 w/ ARM. Fedora kernel and RAM disk images were acquired for aarch64. I opened LinuxVirtualMachine.xcodeproj from the example; selecting 'LinuxVirtualMachine' under 'Targets', and navigating to 'Signing and Capabilities', I linked my Personal Team (needed to connect my Apple ID) under 'Signing->Team'. Clicking 'run' outputs the following: Usage: /Users/shea/Library/Developer/Xcode/DerivedData/LinuxVirtualMachine-baalsbzvoxxbspgicozzllxbyqmh/Build/Products/Debug/LinuxVirtualMachine <kernel-path> <initial-ramdisk-path> Program ended with exit code: 64 So I execute the following in Terminal from a directory containing the two images from Fedora: % /Users/shea/Library/Developer/Xcode/DerivedData/LinuxVirtualMachine-baalsbzvoxxbspgicozzllxbyqmh/Build/Products/Debug/LinuxVirtualMachine ./vmlinuz ./initrd.img And receive the output: Failed to start the virtual machine. Error Domain=VZErrorDomain Code=1 "The virtual machine failed to start." UserInfo={NSLocalizedFailure=Internal Virtualization error., NSLocalizedFailureReason=The virtual machine failed to start.} An old thread seems to imply this may be because the kernel image used to be compressed but is no longer? Another commenter suggests it is because vmlinuzis a gz file and to extract it, but file vmlinuzoutputs vmlinuz: PE32+ executable (EFI application) Aarch64 (stripped to external PDB), for MS Windows and gzip won't work even with the .gz extension. Did I miss a step?

Hello! I am watching this video: https://developer.apple.com/videos/play/wwdc2022/10002/ and I wonder if there is a repo where i can find full code for the project? Please let me know. Thank you! Vicki

My environment is a router with OpenWRT, a laptop connected to the router via patch cable and an untagged VLAN with VID 5 configured on that port in the router, and a MacBook M1 Pro connected via Wi-Fi to the router. On the macbook, I created a VLAN interface by specifying en0 (AirPort) as the parent interface, then created a VM using QEMU with hvf accelerator or lima/UTM with VZ and I observe a strange situation: using arping, packets from the laptop sometimes reach the VM connected to the VLAN interface on the macbook, but arping running on the VM only sends requests but does not receive replies from the laptop. If I assign an IP address to the VLAN interface in macOS, packets are transmitted over either protocol without any problems. The problem is delivering packets to the virtual machine. With tcpdump on the laptop I can see that it reply to arp requests, I can also see on the router that replies to arp requests passed through the Wi-Fi interface, but I don't see them in tcpdump on en0 on the macbook, which is also strange. But as I wrote before, if I assign an address from the same network on the macbook's VLAN interface, any packets are delivered between the laptop and macbook. Laptop: 192.168.24.50 MacBook: 192.168.24.20 (vlan5 interface). VM: 192.168.24.10 With lima-vm I use socket_vmnet in bridge mode. With QEMU I use vmnet-bridged. ➜ ~ ifconfig vlan5 vlan5: flags=8943&lt;UP,BROADCAST,RUNNING,PROMISC,SIMPLEX,MULTICAST&gt; mtu 1496 options=6063&lt;RXCSUM,TXCSUM,TSO4,TSO6,PARTIAL_CSUM,ZEROINVERT_CSUM&gt; ether f0:2f:4b:xx:xx:xx inet 192.168.24.20 netmask 0xffffff00 broadcast 192.168.24.255 vlan: 5 parent interface: en0 media: autoselect status: active ➜ ~ ping 192.168.24.50 -c2 PING 192.168.24.50 (192.168.24.50): 56 data bytes 64 bytes from 192.168.24.50: icmp_seq=0 ttl=64 time=5.241 ms 64 bytes from 192.168.24.50: icmp_seq=1 ttl=64 time=5.429 ms --- 192.168.24.50 ping statistics --- 2 packets transmitted, 2 packets received, 0.0% packet loss round-trip min/avg/max/stddev = 5.241/5.335/5.429/0.094 ms ➜ ~ sudo arping -c 5 -i vlan5 192.168.24.50 ARPING 192.168.24.50 60 bytes from 10:e7:c6:xx:xx:xx (192.168.24.50): index=0 time=6.061 msec 60 bytes from 10:e7:c6:xx:xx:xx (192.168.24.50): index=1 time=6.084 msec 60 bytes from 10:e7:c6:xx:xx:xx (192.168.24.50): index=2 time=5.945 msec 60 bytes from 10:e7:c6:xx:xx:xx (192.168.24.50): index=3 time=3.092 msec 60 bytes from 10:e7:c6:xx:xx:xx (192.168.24.50): index=4 time=3.848 msec --- 192.168.24.50 statistics --- 5 packets transmitted, 5 packets received, 0% unanswered (0 extra) rtt min/avg/max/std-dev = 3.092/5.006/6.084/1.278 ms ➜ ~ ifconfig bridge100 bridge100: flags=8863&lt;UP,BROADCAST,SMART,RUNNING,SIMPLEX,MULTICAST&gt; mtu 1496 options=3&lt;RXCSUM,TXCSUM&gt; ether f2:2f:4b:xx:xx:xx Configuration: id 0:0:0:0:0:0 priority 0 hellotime 0 fwddelay 0 maxage 0 holdcnt 0 proto stp maxaddr 100 timeout 1200 root id 0:0:0:0:0:0 priority 0 ifcost 0 port 0 ipfilter disabled flags 0x0 member: vlan5 flags=3&lt;LEARNING,DISCOVER&gt; ifmaxaddr 0 port 22 priority 0 path cost 0 member: vmenet0 flags=3&lt;LEARNING,DISCOVER&gt; ifmaxaddr 0 port 23 priority 0 path cost 0 Address cache: 10:e7:c6:xx:xx:xx Vlan1 vlan5 326 flags=0&lt;&gt; 52:55:55:ae:36:b4 Vlan1 vmenet0 1172 flags=0&lt;&gt; nd6 options=201&lt;PERFORMNUD,DAD&gt; media: autoselect status: active [root@VM1 ~]# arping 192.168.24.50 -c5 ARPING 192.168.24.50 from 192.168.24.10 lima0 Sent 5 probes (5 broadcast(s)) Received 0 response(s) [root@VM1 ~]# [root@laptop ~]# arping 192.168.24.10 -c5 ARPING 192.168.24.15 from 192.168.24.10 lima0 Unicast reply from 192.168.24.10 [52:55:55:AE:36:B4] 2.492ms Unicast reply from 192.168.24.10 [52:55:55:AE:36:B4] 1.791ms Unicast reply from 192.168.24.10 [52:55:55:AE:36:B4] 3.059ms Sent 5 probes (1 broadcast(s)) Received 3 response(s) [root@laptop ~]# What could be the reason for this behavior?

I have an M3 MacBook Pro, running macOS Sonoma 14.1.1. A tester of our app is running Monterrey, and is experiencing something we can't reproduce, so I need to set up a Monterrey environment. I searched for information here about VMs, and found an article about using VM Frameworks and Xcode, but I am not a programmer in any way. I used to use VMware years ago to host Windows and Linux machines on my Mac, but Google searching leads me to a 2 year old post about Parallels https://www.reddit.com/r/MacOS/comments/tuxfzq/how_can_i_run_macos_monterrey_in_a_virtual/ Can I get a suggestion for how to create and use VMs on Apple Silicon to test older OSs? Thank you.

Hello, I am working with Virtualization framework in an Xcode swift project. I have also installed Parallels Desktop on my mac (Apple Silicon). I would like to convert a Parallels Desktop hard drive (hds file) to an image compatible with my Virtualization framework project. My goal is to run my Parallels Desktop virtual machine in my Xcode project. Is there a way to do that ? Thanks

Hello, I am downloading macOS restore image with VZMacOSRestoreImage, in order to deploy virtual machines. I have just upgraded my host mac to last Sonoma version. So, macOS restore image has just been downloaded on my computer during upgrade procedure. Is there a way to avoid a second download and ask VZMacOSRestoreImage to fetch my last local macOS image ? I think this image is still stored somewhere on my computer... Thanks

I created a macOS 14 VM using https://github.com/s-u/macosvm which uses the Virtualization Framework. I want to check if I can use paravirtualized graphics for tensorflow workloads. I followed the steps from https://developer.apple.com/metal/tensorflow-plugin/ but when I run the script from step 4. Verify, I get a segmentation fault (see below). Did anyone try to get this kind of GPU compute in a VM and succeed? /Users/teuf/venv-metal/lib/python3.9/site-packages/urllib3/__init__.py:34: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020 warnings.warn( 2023-11-20 07:41:11.723578: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple Paravirtual device 2023-11-20 07:41:11.723620: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 10.00 GB 2023-11-20 07:41:11.723626: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 0.50 GB 2023-11-20 07:41:11.723700: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2023-11-20 07:41:11.723968: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) zsh: segmentation fault python3 ./tensorflow-test.py Thread 0 Crashed:: Dispatch queue: metal gpu stream 0 MPSCore 0x1999598f8 MPSDevice::GetMPSLibrary_DoNotUse(MPSLibraryInfo const*) + 92 1 MPSCore 0x19995c544 0x199927000 + 218436 2 MPSCore 0x19995c908 0x199927000 + 219400 3 MetalPerformanceShadersGraph 0x1fb696a58 0x1fb583000 + 1129048 4 MetalPerformanceShadersGraph 0x1fb6f0cc8 0x1fb583000 + 1498312 5 MetalPerformanceShadersGraph 0x1fb6ef2dc 0x1fb583000 + 1491676 6 MetalPerformanceShadersGraph 0x1fb717ea0 0x1fb583000 + 1658528 7 MetalPerformanceShadersGraph 0x1fb717ce4 0x1fb583000 + 1658084 8 MetalPerformanceShadersGraph 0x1fb6edaac 0x1fb583000 + 1485484 9 MetalPerformanceShadersGraph 0x1fb7a85e0 0x1fb583000 + 2250208 10 MetalPerformanceShadersGraph 0x1fb7a79f0 0x1fb583000 + 2247152 11 MetalPerformanceShadersGraph 0x1fb6602b4 0x1fb583000 + 905908 12 MetalPerformanceShadersGraph 0x1fb65f7b0 0x1fb583000 + 903088 13 libmetal_plugin.dylib 0x1156dfdcc invocation function for block in metal_plugin::runMPSGraph(MetalStream*, MPSGraph*, NSDictionary*, NSDictionary*) + 164 14 libdispatch.dylib 0x18e79b910 _dispatch_client_callout + 20 15 libdispatch.dylib 0x18e7aacc4 _dispatch_lane_barrier_sync_invoke_and_complete + 56 16 libmetal_plugin.dylib 0x1156dfd14 metal_plugin::runMPSGraph(MetalStream*, MPSGraph*, NSDictionary*, NSDictionary*) + 108 17 libmetal_plugin.dylib 0x115606634 metal_plugin::MPSStatelessRandomUniformOp<float>::ProduceOutput(metal_plugin::OpKernelContext*, metal_plugin::Tensor*) + 876 18 libmetal_plugin.dylib 0x115607620 metal_plugin::MPSStatelessRandomOpBase::Compute(metal_plugin::OpKernelContext*) + 620 19 libmetal_plugin.dylib 0x1156061f8 void metal_plugin::ComputeOpKernel<metal_plugin::MPSStatelessRandomUniformOp<float>>(void*, TF_OpKernelContext*) + 44 20 libtensorflow_framework.2.dylib 0x10b807354 tensorflow::PluggableDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) + 148 21 libtensorflow_framework.2.dylib 0x10b7413e0 tensorflow::(anonymous namespace)::SingleThreadedExecutorImpl::Run(tensorflow::Executor::Args const&) + 2100 22 libtensorflow_framework.2.dylib 0x10b70b820 tensorflow::FunctionLibraryRuntimeImpl::RunSync(tensorflow::FunctionLibraryRuntime::Options, unsigned long long, absl::lts_20230125::Span<tensorflow::Tensor const>, std::__1::vector<tensorflow::Tensor, std::__1::allocator<tensorflow::Tensor>>*) + 420 23 libtensorflow_framework.2.dylib 0x10b715668 tensorflow::ProcessFunctionLibraryRuntime::RunMultiDeviceSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long long, std::__1::vector<std::__1::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::__1::allocator<std::__1::variant<tensorflow::Tensor, tensorflow::TensorShape>>>*, std::__1::function<absl::lts_20230125::Status (tensorflow::ProcessFunctionLibraryRuntime::ComponentFunctionData const&, tensorflow::ProcessFunctionLibraryRuntime::InternalArgs*)>) const + 1336 24 libtensorflow_framework.2.dylib 0x10b71a8a4 tensorflow::ProcessFunctionLibraryRuntime::RunSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long long, absl::lts_20230125::Span<tensorflow::Tensor const>, std::__1::vector<tensorflow::Tensor, std::__1::allocator<tensorflow::Tensor>>*) const + 848 25 libtensorflow_cc.2.dylib 0x2801b5008 tensorflow::KernelAndDeviceFunc::Run(tensorflow::ScopedStepContainer*, tensorflow::EagerKernelArgs const&, std::__1::vector<std::__1::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::__1::allocator<std::__1::variant<tensorflow::Tensor, tensorflow::TensorShape>>>*, tsl::CancellationManager*, std::__1::optional<tensorflow::EagerFunctionParams> const&, std::__1::optional<tensorflow::ManagedStackTrace> const&, tsl::CoordinationServiceAgent*) + 572 26 libtensorflow_cc.2.dylib 0x28016613c tensorflow::EagerKernelExecute(tensorflow::EagerContext*, absl::lts_20230125::InlinedVector<tensorflow::TensorHandle*, 4ul, std::__1::allocator<tensorflow::TensorHandle*>> const&, std::__1::optional<tensorflow::EagerFunctionParams> const&, tsl::core::RefCountPtr<tensorflow::KernelAndDevice> const&, tensorflow::GraphCollector*, tsl::CancellationManager*, absl::lts_20230125::Span<tensorflow::TensorHandle*>, std::__1::optional<tensorflow::ManagedStackTrace> const&) + 452 27 libtensorflow_cc.2.dylib 0x2801708ec tensorflow::ExecuteNode::Run() + 396 28 libtensorflow_cc.2.dylib 0x2801b0118 tensorflow::EagerExecutor::SyncExecute(tensorflow::EagerNode*) + 244 29 libtensorflow_cc.2.dylib 0x280165ac8 tensorflow::(anonymous namespace)::EagerLocalExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) + 2580 30 libtensorflow_cc.2.dylib 0x2801637a8 tensorflow::DoEagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) + 416 31 libtensorflow_cc.2.dylib 0x2801631e8 tensorflow::EagerOperation::Execute(absl::lts_20230125::Span<tensorflow::AbstractTensorHandle*>, int*) + 132