And for a deeper dive into JAX: Common gotchas and sharp edges The Autodiff Cookbook, Part 1: easy and powerful automatic differentiation in JAX Directly using XLA in Python MAML Tutorial with JAX Generative Modeling by Estimating Gradeints of Data Distribution in JAX. ...
NumPy also does a lot of work to cast any array-like function arguments to arrays, as in np.sum([x, y]), while jax.numpy typically requires explicit casting of array arguments, like np.sum(np.array([x, y])). For automatic differentiation with grad, JAX has the same restrictions as...
OCT4-hKEpC can be long-term expanded in the dedifferentiated state that is primed for renal differentiation. Thus, when expanded OCT4-hKEpC are grown as kidney spheroids (OCT4-kSPH), they reactivate the HNF1B gene signature, redifferentiate, and efficiently generate renal structures in vivo. ...
NumPy also does a lot of work to cast any array-like function arguments to arrays, as in np.sum([x, y]), while jax.numpy typically requires explicit casting of array arguments, like np.sum(np.array([x, y])). For automatic differentiation with grad, JAX has the same restrictions as...
The Python version must match your Python interpreter. There are prebuilt wheels for Python 2.7, 3.6, and 3.7; for anything else, you must build from source. Running the tests To run all the JAX tests, we recommend using pytest-xdist, which can run tests in parallel. First, install pytes...
The Python version must match your Python interpreter. There are prebuilt wheels for Python 2.7, 3.6, and 3.7; for anything else, you must build from source. To run all the JAX tests, we recommend usingpytest-xdist, which can run tests in parallel. First, installpytest-xdistby runningpip...
python build/build.py --enable_cuda pip install -e build # install jaxlib (includes XLA) pip install -e . # install jax (pure Python) See python build/build.py --help for configuration options, including ways to specify the paths to CUDA and CUDNN, which you must have installed. The...
And for a deeper dive into JAX: Common gotchas and sharp edges The Autodiff Cookbook, Part 1: easy and powerful automatic differentiation in JAX Directly using XLA in Python How JAX primitives work MAML Tutorial with JAX Generative Modeling by Estimating Gradients of Data Distribution in JAX. ...
The Python version must match your Python interpreter. There are prebuilt wheels for Python 2.7, 3.6, and 3.7; for anything else, you must build from source. Running the tests To run all the JAX tests, we recommend using pytest-xdist, which can run tests in parallel. First, install pytes...
NumPy also does a lot of work to cast any array-like function arguments to arrays, as in np.sum([x, y]), while jax.numpy typically requires explicit casting of array arguments, like np.sum(np.array([x, y])). For automatic differentiation with grad, JAX has the same restrictions as...