onnxruntime-tvm/nnvm
ZihengJiang 2e5ca24c76 fix get layout in to_relay (#2610)
(cherry picked from commit c71654277d)
2019-02-18 14:42:45 -08:00
..
amalgamation [RUNTIME] Add fp16/fp32 conversion functions (#1766) 2018-09-24 20:13:34 -07:00
include/nnvm Optimize move semantics of NodeEntry reducing copies of shared_ptr which causes atomic contention (#2576) 2019-02-08 10:18:50 -08:00
make Make choice of archiver configurable (#288) 2018-05-29 08:47:00 -07:00
python fix get layout in to_relay (#2610) 2019-02-18 14:42:45 -08:00
src Optimize move semantics of NodeEntry reducing copies of shared_ptr which causes atomic contention (#2576) 2019-02-08 10:18:50 -08:00
tests [RELAY][FRONTEND] Tensorflow frontend. (#2216) 2019-02-05 15:11:17 -08:00
Makefile Fix dmlc-core path in nnvm Makefile (#1829) 2018-10-05 10:32:57 -07:00
README.md Homepage URL to tvm.ai 2018-05-29 16:24:42 -07:00

README.md

NNVM Compiler Module of TVM Stack

import tvm
from tvm.contrib import graph_runtime, rpc
import nnvm.frontend
import nnvm.compiler

# GET model from frameworks
# change xyz to supported framework name.
graph, params = nnvm.frontend.from_xyz(...)

# OPTIMIZE and COMPILE the graph to get a deployable module
# target can be "opencl", "llvm", "metal" or any target supported by tvm
target = "cuda"
graph, lib, params = nnvm.compiler.build(graph, target, {"data", data_shape}, params=params)

# DEPLOY and run on gpu(0)
module = graph_runtime.create(graph, lib, tvm.gpu(0))
module.set_input(**params)
module.run(data=data_array)
output = tvm.nd.empty(out_shape, ctx=tvm.gpu(0))
module.get_output(0, output)

# DEPLOY to REMOTE mobile/rasp/browser with minimum tvm rpc runtime
# useful for quick experiments on mobile devices
remote = rpc.connect(remote_host, remote_port)
lib.export_library("mylib.so")
remote.upload("mylib.so")
rlib = rpc.load_module("mylib.so")
# run on remote device
rmodule = graph_runtime.create(graph, rlib, remote.gpu(0))
rmodule.set_input(**params)
rmodule.run()