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On the next page, click 'Generate API Key'. Save this key to a text file for easy access on your machine. If you lose your key you can generate a new one on this page again. Next you will install the Nvidia CLI
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Using the Nvidia CLI
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Next, download the CLI according to your system specifications. To find your system specifications, press the Windows key and type 'System Information'.
Your system information on a Windows machine will look like this. In this case, this is a 64 bit installation of Windows since it is x64-based.
Next select the appropriate download of the CLI and install it.
Once the install is complete, you will configure the terminal and check if it has installed correctly.
Configuring your Terminal
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via LMM
Access LMM via SSHing to lmm.its.albany.edu. The NGC command is available here and can be checked by invoking:
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ngc --version |
You should see the following output to your terminal.
Configuring your Terminal on LMM
Next you will need to configure your terminal such that you can upload your data in a neat and usable format. Press the windows key and type 'Windows PowerShell' to open a powershell terminal. Powershell has many similarities to a linux terminal that you may be familiar with from accessing exiting University resources but looks different due to syntax. You can still use commands such as 'cd' and 'ls' just like you can in linux.In your terminal, call the following to check if Nvidia CLI has installed correctlyChange directories to your data.
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cd #brings you home
cd /path/to/my/data |
Next, you will configure your ngc such that you can upload your data to your workspace and access it within a job. To begin, invoke:
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ngc --version |
This should return the version of the CLI that you installed. Your username should appear here as well. You can change directories by invoking:
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cd C:\Users\your_username\ |
To list the contents of your directory, invoke the command 'ls'.
Next, you will configure your ngc such that you can upload your data to your workspace and access it within a job. To begin, invoke:
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ngc config set |
You will be prompted for your API key that you generated earlier. You can copy (ctrl+c) and then paste (ctrl+v) in your powershell terminal to submit your API key.
Next it will prompt you for your CLI output type, select ascii by typing in:
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ascii |
If you entered a different option or accidentally skipped this entry, you can invoke 'ngc config set' again to pick your choices again. Hitting enter without any input will skip the prompt, so you do not need to re-enter your API key unless you need to.
Next you will be asked to enter your organization, enter the following:
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University at Albany (SUNY) |
Next it will ask you to enter your team name. This should be your lab team in which you will be working in. Lastly the terminal will prompt you for 'ace'. Enter the following in order to set your Accelerated Computing Environment:
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univ-of-albany-iad2-ace |
If done successfully, you will see something similar to the following:
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config set |
You will be prompted for your API key that you generated earlier. You can copy (ctrl+c) and then right click in your terminal to paste. You will need this API key to upload your data.
Next it will prompt you for your CLI output type, select ascii by typing in:
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ascii |
If you entered a different option or accidentally skipped this entry, you can invoke 'ngc config set' again to pick your choices again. Hitting enter without any input will skip the prompt, so you do not need to re-enter your API key unless you need to.
Next you will be asked to enter your organization, enter the following:
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tt6xxv6at61b |
Next it will ask you to enter your team name. This should be your lab team in which you will be working in. Lastly the terminal will prompt you for 'ace'. Enter the following in order to set your Accelerated Computing Environment:
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univ-of-albany-iad2-ace |
If done successfully, you will see something similar to the following:
You should see your username and lab name in the appropriate spaces.If you mis-enred a value, you can use invoke 'ngc config set' to go through each step. Pressing enter without any input will not overwrite previously inputted information, thus you can hit enter to skip portions you entered correctly. To clear the entire config, invoke 'ngc config clear'.
Congrats on setting up the terminal! You are now ready to upload your data.
Uploading Data
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In your linux terminal or powershell terminal, navigate to where your data is stored. To do this you can change directories via:
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cd \Users\your_username\path\to\your\data\/path/to/your/data |
You can also obtain this path by opening a file explorer and copy-pasting the address at the top into your terminal for Windows.
Make sure your data is not zipped, tarred, or archived. If your data is zipped or in .tar format, it will upload as such is and will not be as accessible on the cloud. Unzip/untar your data before uploading. The option under --source should be the file or folder that contains your files. The --desc option has a descriptor and name section. Lastly the --share option will designate to which team you want to upload the data to if you are a part of multiple teams. Add a 2nd --share option if you want to upload to another team at the same time.
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import pandas as pd
df = pd.read_csv("/mount/data/folder/name_of_your_data.csv")
print(df) |
In this fashion, your original data is never truly changed, ensuring reproducibility of your work. If you are doing data cleaning, you can do so in a workspace and save it as a new csv, or clean the data locally if that is easier.
From your lab folder:
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.csv")
print(df) |
In this fashion, your original data is never truly changed, ensuring reproducibility of your work. If you are doing data cleaning, you can do so in a workspace and save it as a new csv, or clean the data locally if that is easier.
Starting a Job
The default time limit of a job is 30 days. There are 2 ways to start a job on DGX cloud, via web interface or via CLI. The web interface is graphical, easy to use, and also generates a CLI prompt for you to use if you wish. In this example, we will submit a job to launch a jupyternotebook instance where we can access our data from inside the notebook.
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