# 1. Use TensorBoard

![](https://612292586-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FstSX6fpES491F1pGCECr%2Fuploads%2F14omBCaO24uRxCw6paKK%2Fimage.png?alt=media\&token=b04830a8-f540-4648-8ba5-f223dbeb18fb)

TensorBoard provides visualizations and tools for machine learning experiments. Try it out with the guide below.

This guideline was written based on the GPU Jupyter container.

### How to use

![](https://612292586-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FstSX6fpES491F1pGCECr%2Fuploads%2FsQ6dStAYUl6nZFKw6J60%2Fmatlab1.png?alt=media\&token=62db2c1a-51e6-4461-881f-07ad18777c2d)

1. On the Yennefer Studio project details page, click **"Activate Server > Run Server"** to run the IDE.
2. After running the server, click **Terminal** in the Launcher.
3. Enter the following in the terminal in order.\
   \&#xNAN;*<mark style="color:blue;">`pip install tensorflow`</mark>* \
   *<mark style="color:blue;">`pip install jupyter-server-proxy`</mark>* \
   *<mark style="color:blue;">`npm install -g n export N_PREFIX=/opt/conda/`</mark>* \
   *<mark style="color:blue;">`n stable`</mark>* \
   *<mark style="color:blue;">`pip install git+https://github.com/twalcari/jupyterlab_tensorboard.git`</mark>*<br>

![](https://612292586-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FstSX6fpES491F1pGCECr%2Fuploads%2FsuzfAaPUFCChwjmXN1Ao%2Fhtu2.png?alt=media\&token=6a730dce-63f2-4a09-9689-f9b3514d26cc)

&#x20;   4\. When the installation is complete, close the window and deactivate the server.\
&#x20;   5\. **Activate the server** again and run the server.\
&#x20;  &#x20;

![](https://612292586-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FstSX6fpES491F1pGCECr%2Fuploads%2FdEt8X2wYTMswPjDkjpBa%2Fimage.png?alt=media\&token=5ee95da4-87e2-4824-80c6-377db6f6c2be)

&#x20;    6.Create a notebook to use TensorBoard

&#x20;  &#x20;

![](https://612292586-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FstSX6fpES491F1pGCECr%2Fuploads%2FYNpDvDaMBQSoWftSk270%2Fimage.png?alt=media\&token=280d56d5-e398-4153-8bb9-88e4b8ec1078)

&#x20;     7.After writing the code as above, proceed with learning. <br>

![](https://612292586-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FstSX6fpES491F1pGCECr%2Fuploads%2FVNC7fbEcrkc1rrkZ9m7m%2Fimage.png?alt=media\&token=8c62c2ea-a285-483b-a9d5-ec21a7c3427a)

&#x20;8\. When training, **logdir** and **tensorboard\_callback** variables must be added.                         (Reference code: <https://www.tensorflow.org/tensorboard/tensorboard\\_in\\_notebooks>)

![](https://612292586-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FstSX6fpES491F1pGCECr%2Fuploads%2FhdH0jvCD0aDGb38uVB5r%2Fimage.png?alt=media\&token=de35323d-180e-4ba1-bd1e-5c2ee793e69c)

&#x20;  9\. When learning is completed normally, **a logs** folder is created.

![](https://612292586-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FstSX6fpES491F1pGCECr%2Fuploads%2FBtBtLTlyipxSZrO2MT0l%2Fimage.png?alt=media\&token=feb7622f-3938-49b2-911c-c28ed0c7b1e1)

&#x20;   10\. Check hub0/use&#x72;**/X/Y/** in the address bar at the top.

![](https://612292586-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FstSX6fpES491F1pGCECr%2Fuploads%2FP3tnEchvAd6livDPGfQk%2Fimage.png?alt=media\&token=87b52258-7599-4d0e-b82c-6df649fa02e9)

&#x20;    11\. Execute the code referring to **hub0/User/X/Y** in the address bar at the top.

&#x20;         Enter numbers in X and Y, and you can write as it is otherwise.

![](https://612292586-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FstSX6fpES491F1pGCECr%2Fuploads%2FKRr8wvyNjc2CO9VUt9ho%2Fimage.png?alt=media\&token=001fb49c-3ceb-4f45-aed4-fb503827f8fa)

&#x20;       12\. Enter **`%tensorboard --logdir logs`** code to run TensorBoard..