Conda install ipython notebook11/7/2022 The following will create a fastai env with python-3. For the sake of this example we will use an environment name fastai, but you can name it whatever you’d like it to be. Once you followed the instructions and installed anaconda, you’re ready to build you first environment. You will find the instructions for installing conda on each platform here. conda doesn’t have all python packages available, so when that’s the case we use pip to install whatever is missing. There are several implementations of python virtual environment, and the one we recommend is conda (anaconda), because we release our packages for this environment and pypi, as well. bleeding edge git version), but also because it’s usually a bad idea to install various python package into the system-wide python, because it’s so easy to break the system, if it relies on python and its 3rd party packages for its functionality. It’s highly recommended to use a virtual python environment for the fastai project, first because you could experiment with different versions of it (e.g. conda install somepackage: Installs a Python package (replace somepackage by the name of the package you want to install). The currently active one is marked by a star. conda env list: Displays the list of environments installed. To accomplish the same for the cutting edge master git version: conda list: Lists all packages installed in the current environment. To install the latest released version of fastai with developer dependencies, do: In addition to the ways explained in the aforementioned document, you can also install fastai with developer dependencies without needing to check out the fastai repo. Python setup.py -q deps -dep-groups=core,visionĪs explained in Development Editable Install, if you want to work on contributing to fastai you will also need to install the optional development dependencies. # print dependency list for specified groups to run the fastai course lessons and you haven’t already setup the jupyter environment, here is how you can do it. So if you are planning on using fastai in the jupyter notebook environment, e.g. The fastai library doesn’t require the jupyter environment to work, therefore those dependencies aren’t included. Whereas older versions of Conda automatically installed a. So follow the instructions there, but replace pytorch with pytorch-cpu, and torchvision with torchvision-cpu.Īlso, please note, that if you have an old GPU and pytorch fails because it can’t support it, you can still use the normal (GPU) pytorch build, by setting the env var CUDA_VISIBLE_DEVICES="", in which case pytorch will not try to check if you even have a GPU. Conda/Virtual environments must be installed on JupyterLab or Jupyter Notebook prior to use. Since we have only a single fastai package that relies on the default pytorch package working with and without GPU environment, if you want to install something custom you will have to manually tweak the dependencies. Just make sure to pick the correct torch wheel url, according to the needed platform, python and CUDA version, which you will find here.
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