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Also, if your server is an EC2 instance, we need to edit the security group appropriately to allow inbound TCP connection in port 3389. Note the port written, it is important for making connection. A minimal configuration might look like this: bitmap_cache=yes bitmap_compression=yes port=3389 crypt_level=low channel_code=1 max_bpp=24 name=sesman-Xvnc lib=libvnc.so username=ask password=ask ip=127.0.0.1 port=ask-1 Edit the RDP configuration file, /etc/xrdp/xrdp.ini, on the server. You may also need to do so to downgrade the version of xrdp if the xrdp version downloaded has some bugs or incompatibility with the OS.Ĭ. Chrome ipynb viewer install#PS: If you don’t have a sudo permission in the server, alternatively, you may install the package from Github using wget and build it from source. Chrome ipynb viewer update#Install some packages sudo apt update sudo apt install -y ubuntu-desktop xrdp This step is probably the trickiest step of all, especially when the server’s OS version is outdated.ī. You may skip this step if the server has had a remote desktop configured. The script stays running even when I’m disconnected.ġ.Configure a remote desktop on the server ![]() Chrome ipynb viewer code#It allows me to see the progress of the code as well as the output of each cell. TLDR, it handles all the disadvantages of the previous methods. When I used this approach, I utilize nvidia-smi and htop bash command to try to guess the current state of the program. Thus, even though the script will stop the execution in error, it may be hard to track the progress of our code. However, one drawback I found from this approach is the lack of logging displayed to the user. To keep this command running in a remote server even when we disconnect from the remote server, we can configure screen or tmux and run the Jupyter’s command inside either one of them. ![]() There are several other configuration options, such as timeout, report generation, and output files generation which can be found in these two sites, for runipy and nbconvert respectively. If we use nbconvert and have it installed, to run a jupyter notebook we can type: jupyter nbconvert -to notebook -execute mynotebook.ipynb To save the output of each cell back to the notebook file, run: runipy MyNotebookFileName.ipynb If we use runipy and have it installed, to run a jupyter notebook we can type: runipy MyNotebookFileName.ipynb # Using pip pip install ipython # Using conda conda install ipython To install runipy or nbconvert, we can use pip/conda. There are two choices of program to use for this purpose, runipy or nbconvert. It will allow us to keep all the logging printed in the jupyter notebook files throughout execution. However, log printed on the Jupyter Notebook files will get lost.Īnother way is to run the jupyter notebook using CLI directly. After that, we can run the output file the way we run a typical python file: python file_name.py Chrome ipynb viewer download#The simplest way is to open the Jupyter Notebook file in a browser, click File > Download as > Python (.py). There are ways to run a Jupyter Notebook file as a python script. Some ways to handle this is to log the outputs into another file or use a specific logger. ![]() This may be an issue when you need to keep all the log written in the Jupyter Notebook file. Lastly, it may lose the log written when the tab is closed. In my experience, this can be prolonged by connecting to a local runtime. Moreover, it is possible to connect to a local runtime in case you need bigger computation usage/resource.Īnother drawback is that you can’t close the browser tab for too long, that is for more than 90 minutes. I found it to be good enough for training a Neural Network on some dataset, such as the Leaf Classification dataset. However, the limit is not that restricting. Once the limit is reached, it will stop running the code and shortly after, the files / variable values are lost. It is well integrated with Google Drive that we can write code to automatically load some dataset from Drive and save the network/result to Drive. Chrome ipynb viewer free#The benefit of this approach is that it is free and it allows importing files/dataset from Drive or Github. Using this, we can even run some bash commands by putting a prefix % before the CLI command. Photo by Andrew Neel on Unsplash Use Google ColabĬolaboratory, or “Colab” for short, allows us to run Python code with zero configuration and computation resources, like GPU / TPU, available. ![]()
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