experiments/STT
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Merge pull request #1698 from j-a-h-i-r/master

Browse Source 
Updated making mmap-able model part of README.md
This commit is contained in:
lissyx 2018-11-05 20:02:05 +01:00 committed by GitHub
commit 4a520e8b57
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 1 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
README.md
View File

@ -327,7 +327,7 @@ If you want to experiment with the TF Lite engine, you need to export a model th
The `output_graph.pb` model file generated in the above step will be loaded in memory to be dealt with when running inference.
This will result in extra loading time and memory consumption. One way to avoid this is to directly read data from the disk.
TensorFlow has tooling to achieve this: it requires building the target `//tensorflow/contrib/util:convert_graphdef_memmapped_format` (binaries are produced by our TaskCluster for some systems including Linux/amd64 and macOS/amd64), use `util/taskcluster.py` tool to download, specifying `tensorflow` as a source.
TensorFlow has tooling to achieve this: it requires building the target `//tensorflow/contrib/util:convert_graphdef_memmapped_format` (binaries are produced by our TaskCluster for some systems including Linux/amd64 and macOS/amd64), use `util/taskcluster.py` tool to download, specifying `tensorflow` as a source and `convert_graphdef_memmapped_format` as artifact.
Producing a mmap-able model is as simple as:
```
$ convert_graphdef_memmapped_format --in_graph=output_graph.pb --out_graph=output_graph.pbmm