This is a TensorFlow wrapper for VA Smalltalk
Report a defect
|
Request feature
TensorFlow is a Google open source machine learning library for research and production. And this is a wrapper to be used from a higher level language like VA Smalltalk.
Currently we tested this wrapper on Linux and Windows, both on x86 and x64. In addition, we tested on ARM (Raspberry Pi 3B+ and Raspbian Buster), ARM64 (Rock64 and Armbian Buster) and ARM64 with GPU support (Nvidia Jetson Nano).
VA Smalltalk needed version is 9.2.2 or above and we have only tested on TensorFlow versions 1.13.x and 1.14.x.
.so
or .dll
) is findable by OS lookup procedure or reference full path in VAST ini file.TENSORFLOW_LIB
key/value under [PlatformLibrary Name Mappings]
section in abt.ini
file. Some examples:
TENSORFLOW_LIB=tensorflow
TENSORFLOW_LIB=/usr/local/lib/libtensorflow_framework.so.1.14.0
TENSORFLOW_LIB=/home/mpeck/Instantiations/TensorFlow/libtensorflow-cpu-linux-x86_64-1.14.0/lib/libtensorflow.so.1.14.0
TENSORFLOW_LIB=c:\Users\mpeck\Documents\Instantiations\tensorflow.dll
TENSORFLOW_LIB=z:\Instantiations\TensorFlow\libtensorflow-cpu-windows-x86_64-1.13.1\lib\tensorflow.dll
| loader path |
path := (CfsPath named: '<insert path to root tensorflow-vast local repo here>').
loader := TonelLoader readFromPath: path.
loader
beUnattended; "do not prompt and use all defaults"
useGitVersion.
loader loadAllMapsWithRequiredMaps.
Or you can load the Configuration Map TensorFlow
from the context menu of the Configuration Maps Browser: "Import"
-> "Load Configuration Maps from Tonel repository..."
-> select path to root tensorflow-vast
local repo. This will open a dialog and will use convenient defaults for the load. Refer to its documentation for more details.
TensorFlow
to ensure correct installation. One easy way is to right-click on the TensorFlow
map name in the Name pane (as opposed to version pane) and then select Test Loaded Applications
.We will be submitting more and more examples in TensorFlowExamplesApp
. So far the only working example is LabelImage
which loads a pre-trained TensorFlow network and use it to recognize objects in images. You can read its class comments for details, instructions and possible uses.
There is also a full detailed blog post about this example.
You can also run a Inception V3 like what is described here:
We also have a more advanced Object Detection example with bounding boxes, labels and scores. This blog post goes over the glory details of this example:
We believe that running TensorFlow and VA Smalltalk on ARM-based boards is really interesting. From limited devices such as a Raspberry Pi to a Nvidia Jetson.
We have compiled TensorFlow C library for ARM and ARM 64 and have been playing with different operating systems and boards such as Raspberry Pi 3B+ (Raspbian), Rock64 (Armbian) and soon Nvidia Jetson Nano and XT2.
Building TensorFlow from scratch on ARM is a bit complicated so we try to document this process on blog posts as we learn. For the moment, you can checkout these:
Check the Contribution Guidelines