The purpose of this docker image is to make it easy to run Jupyter Notebooks authored on Google Colab or Amazon SageMaker Studio on the OVH AI Notebook infrastructure. For example, this allows you to try out those Huggingface Transformers Examples in a private european Cloud.
In your target environment, run
!apt list --installed
and copy the result into package-list.txt.
Afterwards, run
!pip-chill
and copy the result into requirements.txt.
Move large requirements such as torch or TensorFlow
into requirements-large.txt so that you
don't have to reinstall them in case of version conflicts.
Also exclude
- en_core_web_sm
- GDAL
- fbprophet
because they have to be installed separately.
In the Dockerfile, update the CUDA and cudnn versions on top, as needed to match the package list.
Run packagelist2apt.py
to create updated apt-install-*.sh scripts
out of your new package list.
Copyright 2022 by Hajo Nils Krabbenhöft licensed under Apache 2.0
Dockerfile and jupyter.sh are based on files Copyright 2021 by OVH SAS licensed under Apache 2.0
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.