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

default password

Browse Source
This commit is contained in:
Chris
2019-11-15 10:39:10 +01:00
committed by GitHub
parent 995a10ff28
commit 41bbfe814e
1 changed files with 3 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
README.md
View File

@@ -1,5 +1,5 @@
# gpu-jupyter
#### Leverage the power of Jupyter and use your NVIDEA GPU and use Tensorflow and Pytorch in collaborative notebooks.
#### Leverage Jupyter Notebooks with the power of your NVIDEA GPU and perform GPU calculations using Tensorflow and Pytorch in collaborative notebooks.
![Jupyterlab Overview](/extra/jupyterlab-overview.png)
@@ -30,7 +30,7 @@ As soon as you have access to your GPU locally (it can be tested via a Tensorflo
./start-local.sh
```
This will run *gpu-jupyter* on the default port [localhost:8888](http://localhost:8888). The general usage is:
This will run *gpu-jupyter* on the default port [localhost:8888](http://localhost:8888) with the default password `asdf`. The general usage is:
```bash
./start-local.sh -p [port] # port must be an integer with 4 or more digits.
```
@@ -111,7 +111,7 @@ where:
* port specifies the port on which the service will be available.
* and docker-network is the name of the attachable network from the previous step, e.g., here it is **elk_datastack**.
Now, *gpu-jupyter* will be accessable on [localhost:port](http://localhost:8888) and shares the network with the other data-source. I.e, all ports of the data-source will be accessable within *gpu-jupyter*, even if they aren't routed it the source's `docker-compose` file.
Now, *gpu-jupyter* will be accessable on [localhost:port](http://localhost:8888) with the default password `asdf` and shares the network with the other data-source. I.e, all ports of the data-source will be accessable within *gpu-jupyter*, even if they aren't routed it the source's `docker-compose` file.
Check if everything works well using:
```bash