Pike, and E. Stackebrandt. 1992. The polymerase chain reaction: an epidemiological tool to differentiate between two clusters of pathogenic Yersinia enterocolitica strains. FEMS Microbiol. Lett. 97 : 63-66.Ibrahim, A. , Liesack, W. , Pike, S. , Stackebrandt, E. 1992a The polymerase ...
Using jit puts constraints on the kind of Python control flow the function can use; see the tutorial on Control Flow and Logical Operators with JIT for more. Auto-vectorization with vmap vmap is the vectorizing map. It has the familiar semantics of mapping a function along array axes, but ...
Most wrappers have an option to specify automatic UTF-8 decoding for all raw bytes, but that is wrong because it will apply to ALL raw bytes, while you might have a mixture of text strings and binary bytes within the same messagepack. It's not at all uncommon. As I said, this problem...
First the random.normal call is compiled and the array referred to by x is generated on the GPU. Next, each function called on x (namely transpose, dot, and divide) is individually JIT-compiled and executed, each keeping its results on the device. It’s only when a value needs to be...
vmapis the vectorizing map. It has the familiar semantics of mapping a function along array axes, but instead of keeping the loop on the outside, it pushes the loop down into a function’s primitive operations for better performance.
First the random.normal call is compiled and the array referred to by x is generated on the GPU. Next, each function called on x (namely transpose, dot, and divide) is individually JIT-compiled and executed, each keeping its results on the device. It’s only when a value needs to be...
First the random.normal call is compiled and the array referred to by x is generated on the GPU. Next, each function called on x (namely transpose, dot, and divide) is individually JIT-compiled and executed, each keeping its results on the device. It’s only when a value needs to be...
First the random.normal call is compiled and the array referred to by x is generated on the GPU. Next, each function called on x (namely transpose, dot, and divide) is individually JIT-compiled and executed, each keeping its results on the device. It’s only when a value needs to be...
Those ops have a precision parameter which can be used to simulate true 32-bit, with a cost of possibly slower runtime. Some of NumPy's dtype promotion semantics involving a mix of Python scalars and NumPy types aren't preserved, namely np.add(1, np.array([2], np.float32)).dtype ...
vmapis the vectorizing map. It has the familiar semantics of mapping a function along array axes, but instead of keeping the loop on the outside, it pushes the loop down into a function’s primitive operations for better performance.