SKIL is a tool to help you train, track and deploy Deep Learning models to production
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The Docker edition gives developers an easy way to train and deploy powerful deep learning models to production quickly and easily.
To pull the SKIL image from docker hub, just use the following command:
docker pull skymind/skil
This image is the CentOS version of SKIL running Python 2 with Spark 1.6 and Hadoop 2.7.
To run the SKIL server, make sure your docker host has at least 8GB of RAM available. After that, run the following command:
docker run --rm -it -p 9008:9008 -p 8080:8080 skymind/skil
SKIL server will be ready to use in 5-10 minutes. If you are running it on your local Linux machine you can access SKIL UI by navigating to http://localhost:9008. On Mac or Windows the URL will need to be changed to the IP of the VM running docker (typically http://192.168.99.100:9008).
For the first startup, you will need to do the two initialization steps below:
For first time users, SKIL is going to ask you for license verification. To get that done, you will need to create a license for the community edition by visiting https://skymind.ai/activate. Fill up your details and after you receive your license details at the provided email address, paste it into SKIL and navigate to the next step.
After setting up the license, you can setup a password for the user admin for future logins into the SKIL server.
If you already have a license file and want to persist data, see the "Persisting Data" section below.
To persist your notebooks and models servers when using SKIL, you need to use Docker's data management features.
You can do this with the following commands (See Managing Data in Containers for additional information):
docker volume create --name skil-data
docker volume create --name skil-conf
docker volume create --name skil-root
docker run -it --rm -v skil-root:/opt/skil -v skil-data:/var/skil -v skil-conf:/etc/skil -p 9008:9008 -p 8080:8080 -d skymind/skil
If you already have a license file, you can link it with docker in the following way:
docker volume create --name skil-data
docker volume create --name skil-root
docker run -it --rm -v skil-root:/opt/skil -v skil-data:/var/skil -v /path/to/license:/etc/skil/license.txt -p 9008:9008 -p 8080:8080 -d skymind/skil
Afterwards you can stop SKIL with:
docker stop <container_name> # You can find it with "docker ps"
and start it back again with (depending on your configuration):
docker run -it --rm -v skil-root:/opt/skil -v skil-data:/var/skil -v skil-conf:/etc/skil -p 9008:9008 -p 8080:8080 -d skymind/skil
# OR
docker run -it --rm -v skil-root:/opt/skil -v skil-data:/var/skil -v /path/to/license:/etc/skil/license.txt -p 9008:9008 -p 8080:8080 -d skymind/skil
When SKIL configures itself for the first time it performs some installation steps and can take up to 10 minutes before it is ready to use. The subsequent starts will be ready within 2-5 minutes.
Apart from the default CentOS image, other variations (depending on CPU/GPU support as well as OS, spark, hadoop and python versions) of the SKIL Docker Image are:
Content type
Image
Digest
Size
9.8 GB
Last updated
over 7 years ago
docker pull skymind/skil