Getting Started¶
Let’s fire up a minimal working example to verify that the installation succeeded and you can hit the ground running. Before trying these steps, make sure you’ve set up everything according to the Requirements.
Tensorflow-GPU¶
First of all, if you are using tensorflow-gpu, you should verify that your installation has been successful:
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
This should result in the constant being printed. Besides that, the debug output should include something along these lines:
2019-01-04 09:48:22.560571: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 980 major: 5 minor: 2 memoryClockRate(GHz): 1.304
2019-01-04 09:48:23.258135: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3042 MB memory) -> physical GPU (device: 0, name: GeForce GTX 980, pci bus id: 0000:01:00.0, compute capability: 5.2)
Minimal Working Example¶
I recommend to set up the NSL_KDD dataset, as it is considerably
small and quick to download / prepare.
You will need this version of NSL_KDD
in the MLT/MLT/datasets/NSL_KDD
folder:
cd MLT/datasets
git clone https://github.com/defcom17/NSL_KDD NSL_KDD
The next step is the preparation of the pickles MLT uses:
python run.py --pnsl
This will result in the creation of kdd_train_data.pkl
and kdd_test_data.pkl
amongst other supplemental caching and index files.
From here on, you can call your implementations with the --nsl6
flag to
run them on the NSL_KDD dataset.
A quick first test is to run a single benchmark run on NSL_KDD with XGBoost:
python run.py --nsl6 --single --xgb 10 10 0.1
Afterwards, you can find all stats for the test run either on the console
or serialised in the folder MLT/results/NSL_6class_fb/DATE_TIME
.