Hello_User/gpu-jupyter
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

made docker-compose descr. more compact

Browse Source
This commit is contained in:
Christoph Schranz 2020-02-22 20:34:39 +01:00
parent c9efe13c33
commit aa9e9c499c
1 changed files with 9 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
README.md
View File

@ -12,6 +12,7 @@ toolstack on top of it to enable GPU calculations in the Jupyter notebooks.
1. [Requirements](#requirements)
2. [Quickstart](#quickstart)
2. [Tracing](#tracing)
3. [Deployment](#deployment-in-the-docker-swarm)
4. [Configuration](#configuration)
@ -43,19 +44,19 @@ docker-compose (internally):
docker run -d -p [port]:8888 gpu-jupyter
```
Alternatively, you can configure the environment in `docker-compose.yml` and run this to deploy
the `GPU-Jupyter` via docker-compose (under-the-hood):
Alternatively, you can configure the environment in `docker-compose.yml` and run
this to deploy the `GPU-Jupyter` via docker-compose (under-the-hood):
```bash
./generate_Dockerfile.sh
./start-local.sh
./start-local.sh -p 8888 # where -p stands for the port of the service
```
Both options will run *GPU-Jupyter* by default on [localhost:8888](http://localhost:8888) with the default
password `asdf`. The general usage of the `docker-compose` variant is:
```bash
./start-local.sh -p [port] # port must be an integer with 4 or more digits.
```
**Both options will run *GPU-Jupyter* by default on [localhost:8888](http://localhost:8888) with the default
password `asdf`**.
## Tracing
With these commands we can see if everything worked well:
```bash