The tool isn't made for editing, so you can't particularly consider it as a professional video editor.VLC video metadata editor isn't the best for editing your file's metadata.If you want to know more about the VLC metadata editor on Android, iOS, Windows, & macOS, it is best to keep on reading its pros and cons. Now that we have an idea about the metadata editor ability of VLC, let's see if it is an ideal tag editor that you should use. Even though many features and functions are added to the tool, it is still not enough to compete with the other dedicated metadata editor. After its initial release in 2001, its developer improved the overall media player function and features to compete with the other software. Instead of downloading codecs on your file, this tool makes the job hassle-free by adding the file and enjoying watching it. This tool plays a wide-range format that our native media player isn't supported. The primary purpose of the VLC metadata editor isn't as an editor but as a player. VLC Metadata Editor Review/Pros & Cons/ Best Alternatives for VLC Metadata Editor Introduction to the Metadata Editing Function of VLC Media Player Keep reading this article to know more about the VLC metadata editor and its other competitive alternatives. In addition to its ability to play multiple formats, this tool has various features and functions that you would love to use. ![]() With its supported formats and codecs, playing videos here became as swiftly as it is. VLC Media Player became one of the known video players you can download on any device such as Windows, Mac, Android, and iOS. Performance tests are conducted using specific computer systems and reflect the approximate performance of Mac Studio.VLC Metadata Editor & Its Best Alternatives for Editing Metadata at Ease Tested with macOS Monterey 12.3, prerelease PyTorch 1.12, ResNet50 (batch size=128), HuggingFace BERT (batch size=64), and VGG16 (batch size=64). * Testing conducted by Apple in April 2022 using production Mac Studio systems with Apple M1 Ultra, 20-core CPU, 64-core GPU 128GB of RAM, and 2TB SSD. You can also learn more about Metal and MPS on Apple’s Metal page. To get started, just install the latest Preview (Nightly) build on your Apple silicon Mac running macOS 12.3 or later with a native version (arm64) of Python. In the graphs below, you can see the performance speedup from accelerated GPU training and evaluation compared to the CPU baseline:Īccelerated GPU training and evaluation speedups over CPU-only (times faster) The Unified Memory architecture also reduces data retrieval latency, improving end-to-end performance. This reduces costs associated with cloud-based development or the need for additional local GPUs. This makes Mac a great platform for machine learning, enabling users to train larger networks or batch sizes locally. Training Benefits on Apple SiliconĮvery Apple silicon Mac has a unified memory architecture, providing the GPU with direct access to the full memory store. ![]() The new device maps machine learning computational graphs and primitives on the MPS Graph framework and tuned kernels provided by MPS. MPS optimizes compute performance with kernels that are fine-tuned for the unique characteristics of each Metal GPU family. The MPS backend extends the PyTorch framework, providing scripts and capabilities to set up and run operations on Mac. This unlocks the ability to perform machine learning workflows like prototyping and fine-tuning locally, right on Mac.Īccelerated GPU training is enabled using Apple’s Metal Performance Shaders (MPS) as a backend for PyTorch. Until now, PyTorch training on Mac only leveraged the CPU, but with the upcoming PyTorch v1.12 release, developers and researchers can take advantage of Apple silicon GPUs for significantly faster model training. ![]() In collaboration with the Metal engineering team at Apple, we are excited to announce support for GPU-accelerated PyTorch training on Mac.
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