Difference between revisions of "Using the DevBox"
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Test tensorflow: | Test tensorflow: | ||
python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))" | python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))" | ||
| + | |||
| + | ==Sample CNN Projects=== | ||
| + | |||
| + | There are some nice Tensorflow CNN tutorials online: | ||
| + | *https://www.tensorflow.org/tutorials/estimators/cnn | ||
| + | *https://www.tensorflow.org/alpha/tutorials/images/intro_to_cnns | ||
| + | *https://www.tensorflow.org/tutorials/images/deep_cnn | ||
| + | |||
| + | Some of which use standard datasets: | ||
| + | *https://medium.com/tensorflow/introducing-tensorflow-datasets-c7f01f7e19f3 | ||
| + | |||
| + | The code for MNIST.py is in E:\projects\tensorflow. | ||
Revision as of 19:37, 15 May 2019
Accessing the DevBox
Connect to the DevBox by SSH over the internal network. It is on:
- 192.168.2.202
- Username: researcher
- Password hint: littleamount
It has a /bulk samba share that can be mounted from the RDP. Follow the instructions on Help:Access RDP Server.
Specification
Our DIGITS DevBox, affectionately named after Lois McMaster Bujold's fifth God, has a XEON e5-2620v3 processor, 256GB of DDR4 RAM, two GPUs - one Titan RTX and one Titan Xp - with room for two more, a 500GB SSD hard drive (mounting /), and an 8TB RAID5 array bcached with a 512GB m.2 drive (mounting the /bulk share, which is available over samba). It runs Ubuntu 18.04, CUDA 10.0, cuDNN 7.6.1, Anaconda3-2019.03, python 3.7, tensorflow 1.13, digits 6, and other useful machine learning tools/libraries.
Working in Tensorflow
After you've connected to the box as researcher, you should be in /home/researcher. If not, cd there. Then load the virtual environment:
source ./venv/bin/activate
Test tensorflow:
python -c "import tensorflow as tf; tf.enable_eager_execution(); print(tf.reduce_sum(tf.random_normal([1000, 1000])))"
Sample CNN Projects=
There are some nice Tensorflow CNN tutorials online:
- https://www.tensorflow.org/tutorials/estimators/cnn
- https://www.tensorflow.org/alpha/tutorials/images/intro_to_cnns
- https://www.tensorflow.org/tutorials/images/deep_cnn
Some of which use standard datasets:
The code for MNIST.py is in E:\projects\tensorflow.
