3e02c97140
* [SCRIPT] Refactor grep for multiple patterns Tidy up the use of grep. Use -E rather than run multiple grep instances. * [SCRIPT] Refactor grep use in pipeline. Prefer to use stdin redirection rather than create a pipeline. * [SCRIPT] Refactor placement and cleanup of temporary files. Place temporary files in the conventional /tmp location. Avoid poisoning file name space by using $$. Ensure the temporary files get cleaned up, even when the script fails / exits early. * [SCRIPT] Improve robustness of task_lint.sh error handling. Ensure script failures are caught and propagated. Rather than trying to explicitly catch and propagate failures with explicit "|| exit" annotations, use the "set -e" idom from docker/install scripts and have the shell catch and propagate errors in the general case and special case the grep instances where non zero exit is permitted and should be ignored. |
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| version.py | ||
README.md
Open Deep Learning Compiler Stack
Documentation | Contributors | Community | Release Notes
TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends. Checkout the tvm stack homepage for more information.
License
© Contributors Licensed under an Apache-2.0 license.
Contribute to TVM
TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Checkout the Contributor Guide
Acknowledgement
We learnt a lot from the following projects when building TVM.
- Halide: TVM uses HalideIR as data structure for arithmetic simplification and low level lowering. We also learnt and adapted some part of lowering pipeline from Halide.
- Loopy: use of integer set analysis and its loop transformation primitives.
- Theano: the design inspiration of symbolic scan operator for recurrence.