Please consult the detailed usage in the help of each command
--help argument to display the manual).
Listing currently running sessions
This command is actually an alias of the following command:
backend.ai admin sessions
Running simple sessions
The following command spawns a Python session and executes
the code passed as
-c argument immediately.
--rm option states that the client automatically terminates
the session after execution finishes.
backend.ai run --rm -c 'print("hello world")' python
The following command spawns a Python session and execute
the code passed as
./myscript.py file, using the shell command
specified in the
backend.ai run --rm --exec 'python myscript.py arg1 arg2' \ python ./myscript.py
Running sessions with accelerators
The following command spawns a Python TensorFlow session using a half
of virtual GPU device and executes
./mygpucode.py file inside it.
backend.ai run --rm -r gpu=0.5 \ python-tensorflow ./mygpucode.py
Terminating running sessions
--rm option, your session remains alive for a configured
amount of idle timeout (default is 30 minutes).
You can see such sessions using the
backend.ai ps command.
Use the following command to manually terminate them via their session
IDs. You may specifcy multiple session IDs to terminate them at once.
backend.ai rm <sessionID>
Starting a session and connecting to its Jupyter Notebook
The following command first spawns a Python session named "mysession"
without running any code immediately, and then executes a local proxy which connects
to the "jupyter" service running inside the session via the local TCP port 9900.
start command shows application services provided by the created compute
session so that you can choose one in the subsequent
backend.ai start -t mysession python backend.ai app -p 9900 mysession jupyter
Once executed, the
app command waits for the user to open the displayed
address using appropriate application.
For the jupyter service, use your favorite web browser just like the
way you use Jupyter Notebooks.
To stop the
app command, press
Ctrl+C or send the
Running sessions with vfolders
The following command creates a virtual folder named "mydata1", and then
./bigdata.csv file into it.
backend.ai vfolder create mydata1 backend.ai vfolder upload mydata1 ./bigdata.csv
The following command spawns a Python session where the virtual folder "mydata1"
is mounted. The execution options are omitted in this example.
Then, it downloads
./bigresult.txt file (generated by your code) from the
"mydata1" virtual folder.
backend.ai run --rm -m mydata1 python ... backend.ai vfolder download mydata1 ./bigresult.txt
In your code, you may access the virtual folder via
(where the default current working directory is
/home/work) just like
a normal directory.
Running parallel experiment sessions