Setting up Knet¶
Knet.jl is a deep learning package implemented in Julia, so you should be able to run it on any machine that can run Julia. It has been extensively tested on Linux machines with NVIDIA GPUs and CUDA libraries, but most of it works on vanilla Linux and OSX machines as well (currently cpu-only support for some operations is incomplete). If you would like to try it on your own computer, please follow the instructions on Installation. If you would like to try working with a GPU and do not have access to one, take a look at Using Amazon AWS. If you find a bug, please open a GitHub issue. If you would like to contribute to Knet, see Tips for developers. If you need help, or would like to request a feature, please consider joining the knet-users mailing list.
First download and install the latest version of Julia from
http://julialang.org/downloads. As of this writing the latest
version is 0.4.6 and I have tested Knet using 64-bit Generic Linux
binaries and the Mac OS X package (dmg). Once Julia is installed,
julia at the command prompt to start the Julia interpreter.
Pkg.add("Knet") to install Knet.
|__/ | x86_64-apple-darwin13.4.0
By default Knet only installs the minimum requirements. Some examples
use extra packages like ArgParse, GZip and JLD. GPU support requires
the packages CUDArt, CUBLAS, CUDNN and CUSPARSE (0.3). These extra
packages can be installed using additional
Pkg.add() commands. If
you have a GPU machine, you may need to type
compile the Knet GPU kernels. If you do not have a GPU machine, you
Pkg.build but you may get some warnings indicating the
lack of GPU support. Usually, these can be safely ignored. To make
sure everything has installed correctly, type
which should take a couple of minutes kicking the tires. If all is
OK, continue with the next section, if not you can get help at the
knet-users mailing list.
Tips for developers¶
Knet is an open-source project and we are always open to new contributions: bug fixes, new machine learning models and operators, inspiring examples, benchmarking results are all welcome. If you’d like to contribute to the code base, here are some tips:
- Please get an account at github.com.
- Fork the Knet repository.
- Point Julia to your fork using
Pkg.build("Knet"). You may want to remove any old versions with
- Make sure your fork is up-to-date.
- Retrieve the latest version of the master branch using
- Implement your contribution.
- Test your code using
- Please submit your contribution using a pull request.
Using Amazon AWS¶
If you don’t have access to a GPU machine, but would like to experiment with one, Amazon Web Services is a possible solution. I have prepared a machine image (AMI) with everything you need to run Knet. Here are step by step instructions for launching a GPU instance with a Knet image:
1. First, you need to sign up and create an account following the instructions on Setting Up with Amazon EC2. Once you have an account, open the Amazon EC2 console at https://console.aws.amazon.com/ec2 and login. You should see the following screen:
2. Make sure you select the “N. California” region in the upper right corner, then click on AMIs on the lower left menu. At the search box, choose “Public images” and search for “Knet”. Click on the latest Knet image (Knet-0.7.2d as of this writing). You should see the following screen with information about the Knet AMI. Click on the “Launch” button on the upper left.
3. You should see the “Step 2: Choose an Instance Type” page. Next to “Filter by:” change “All instance types” to “GPU instances”. This should reduce the number of instance types displayed to a few. Pick the “g2.2xlarge” instance (“g2.8xlarge” has multiple GPUs and is more expensive) and click on “Review and Launch”.
4. This should take you to the “Step 7: Review Instance Launch” page. You can just click “Launch” here:
5. You should see the “key pair” pop up menu. In order to login to your instance, you need an ssh key pair. If you have created a pair during the initial setup you can use it with “Choose an existing key pair”. Otherwise pick “Create a new key pair” from the pull down menu, enter a name for it, and click “Download Key Pair”. Make sure you keep the downloaded file, we will use it to login. After making sure you have the key file (it has a .pem extension), click “Launch Instances” on the lower right.
6. We have completed the request. You should see the “Launch Status” page. Click on your instance id under “Your instances are launching”:
7. You should be taken to the “Instances” screen and see the address of your instance where it says something like “Public DNS: ec2-54-153-5-184.us-west-1.compute.amazonaws.com”.
Open up a terminal (or Putty if you are on Windows) and type:
ssh -i knetkey.pem firstname.lastname@example.org
knetkey.pem with the path to your key file and
ec2-54-153-5-184 with the address of your machine. If all goes
well you should get a shell prompt on your machine instance.
9. There you can type
julia, and at the julia prompt
Pkg.build("Knet") to get the latest versions
of the packages, as the versions in the AMI may be out of date:
[ec2-user@ip-172-31-6-90 ~]$ julia _ _ _ _(_)_ | A fresh approach to technical computing (_) | (_) (_) | Documentation: http://docs.julialang.org _ _ _| |_ __ _ | Type "?help" for help. | | | | | | |/ _` | | | | |_| | | | (_| | | Version 0.4.2 (2015-12-06 21:47 UTC) _/ |\__'_|_|_|\__'_| | Official http://julialang.org/ release |__/ | x86_64-unknown-linux-gnu WARNING: Terminal not fully functional julia> Pkg.update() julia> Pkg.build("Knet")
Finally you can run
Pkg.test("Knet") to make sure all is good.
This should take about a minute. If all tests pass, you are ready to
work with Knet:
julia> Pkg.test("Knet") INFO: Testing Knet INFO: Simple linear regression example ... INFO: Knet tests passed julia>