![]() A takeaway for maintainers of open source Python packages.Solving problem with CPU emulation, some of the downsides of this solution, and future improvements to look forward to.Solving the problem by ensuring the code is installable or compilable.The cause of the problem: a different CPU instruction set.Common symptoms of the problem when using Python.The symptoms can be non-obvious, though, so in this article we’ll cover: The problem is that the promise of reproducibility relies on certain invariants that don’t apply on newer Macs. What used to work before-on an older Mac, or on a Linux machine-fails in completely unexpected ways. So it can be a little confusing when you try to build your Python-based Dockerfile on a new Mac, and everything starts failing. Make sure you have virtual environment installed and activated, and then type the following command to compile tokenizers pip install setuptools_rustĪnd finally, install tokenizers python setup.py install 3.One of the promises of Docker is reproducibility: you can build an image on a different machine, and assuming you’ve done the appropriate setup, get the same result. Go to the python bindings folder cd tokenizers/bindings/python Once we have Rust, we can download the source for tokenizers git clone You can configure your current shell as per the instruction on the command prompt after installation, or you could just open another session to see it in effect. You can do so by typing the following command curl -proto '=https' -tlsv1.2 -sSf | sh We need to have Rust installed to install tokenizers from source. There are a few steps here so be precise when you follow along. We will be building Tokenizers from source to avoid any interruptions, which I am sure will be there if we decide to go otherwise. If that’s the case for you, learn more about it here. ![]() ![]() Note - You can leverage M1 to accelerate training of your machine learning model with tensorflow-metal. You can also let it create another one, but in my opinion it is always better to create the one we intend to use, or the one that we have already. It will ask you for the path to your virtual environment folder, provide the one that we created. Once you have the script, go to the download directory and run the following command. Yes, it is tensorflow-macos, and not tensorflow. At the time of writing this article, the latest release is tensorflow-macos 0.1 alpha 3. Make sure you download the tar.gz file which contains the installation script. To enable the virtual env source venv/bin/activateĭownload the latest release from here. Install the virtualenv package if you don’t already have one pip install virtualenvĬreate virtualenv by typing the following command python virtualenv venv -python=python3 ![]() Installing tensorflow is easy, you just have to point to your existing virtual env, or you can create a new one to play around, or it will create one for you if you are just not feeling over the moon to create a new one. Install Tokenizers Package (with Rust Compilers)Īlthough it works, please consider researching for more reliable ways to install transformers - written on 2021.10.25 1.There are three steps to get transformers up and running. And no, it is not pip install transformers. If you are an Apple M1 user, and work closely with NLP, there are chances that you’ve encountered this before or even found a solution, but if not, or you recently joined the M1 party, this article provide a way you can install Hugging Face transformer on your MacBook with M1 chip. Even though I love the speed, I hate going to have to find non-traditional ways to install traditional libraries that otherwise would have been just one line on command prompt. The transition to Apple M1 has a similar story to tell. Photo by david latorre romero on Unsplash
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |