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Update docs to remove tools link, additional jupyter mentions (#1184)
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* remove jupyter and tools from installation doc

Signed-off-by: Paul S. Schweigert <[email protected]>

* fix link formatting

Signed-off-by: Paul S. Schweigert <[email protected]>

* update readme to remove container jupyter

Signed-off-by: Paul S. Schweigert <[email protected]>

---------

Signed-off-by: Paul S. Schweigert <[email protected]>
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psschwei authored Jan 23, 2024
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40 changes: 25 additions & 15 deletions README.md
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# Quantum serverless

Quantum Serverless is a user-friendly tool that enables you to easily run complex quantum computing tasks.
With this software, you can execute Qiskit programs as long running jobs and distribute them across multiple CPUs, GPUs, and QPUs.
This means you can take on more complex quantum-classical programs and run them with ease.
You don't have to worry about configuration or scaling up computational resources, as Quantum Serverless takes care of everything for you.
Quantum Serverless is a user-friendly tool that enables you to easily run complex quantum computing tasks.
With this software, you can execute Qiskit programs as long running jobs and distribute them across multiple CPUs, GPUs, and QPUs.
This means you can take on more complex quantum-classical programs and run them with ease.
You don't have to worry about configuration or scaling up computational resources, as Quantum Serverless takes care of everything for you.

![diagram](./docs/images/qs_diagram.png)

Expand All @@ -35,34 +35,44 @@ For user convenience, this section assumes that users will deploy the infrastruc
1. Prepare local QuantumServerless infrastructure
1. Install Docker
If Docker is not installed on your system, follow the directions on the [Docker website](https://docs.docker.com/engine/install/) to install Docker on your system.
1. Install quantum-serverless on your local system (we recommend using a [virtual environment](https://docs.python.org/3/library/venv.html)).
```shell
pip install quantum-serverless
```
Optional: install [Jupyter Lab](https://jupyter.org/)
```shell
pip install jupyterlab
```
1. Clone the Quantum Serverless repository
```shell
git clone https://github.com/Qiskit-Extensions/quantum-serverless.git
```
1. Run QuantumServerless infrastructure
Execute Docker Compose using the following commands. (Note: Make sure to stop any running Jupyter Notebook servers before proceeding.)
Execute Docker Compose using the following commands.
```shell
cd quantum-serverless/
sudo docker compose --profile jupyter up
sudo docker compose up
```

The output should resemble the following.
```
~/quantum-serverless$ sudo docker compose --profile jupyter up
[+] Running 6/0
[+] Running 5/0
✔ Network public-quantum-serverless_safe-tier Created 0.0s
✔ Container ray-head Created 0.0s
✔ Container qs-jupyter Created 0.0s
✔ Container public-quantum-serverless-postgres-1 Created 0.0s
✔ Container gateway Created 0.0s
✔ Container scheduler Created 0.0s
Attaching to gateway, public-quantum-serverless-postgres-1, qs-jupyter, ray-head, scheduler
```

Now you can access the deployed QuantumServerless infrastructure using the built-in JupyterLab.

1. Access the JupyterLab environment
Open `localhost:8888` in your web browser. The default token for the JupyterLab is `123`

1. Launch JupyterLab environment.
```shell
cd docs/getting_started/ # the directory with sample notebooks
jupyter lab
```
This will open the Jupyter Lab environment in your web browser.
1. Write your first example Qiskit Pattern.
In the JupyterLab, create a new file, `pattern.py`, in the `work` directory. You can include any arbitrary Python code in your program, or you can use the
[example Python file in this tutorial](https://github.com/Qiskit-Extensions/quantum-serverless/blob/main/docs/getting_started/basic/01_running_program.ipynb).
Expand All @@ -75,7 +85,7 @@ For user convenience, this section assumes that users will deploy the infrastruc
```
job.status()
# 'DONE'

job.logs()
# 2023-09-21 03:48:40,286\tINFO worker.py:1329 -- Using address 172.18.0.4:6379 set in the environment variable RAY_ADDRESS\n2023-09-21 03:48:40,286\tINFO worker.py:1458 -- Connecting to existing Ray cluster at address: 172.18.0.4:6379...\n2023-09-21 03:48:40,295\tINFO worker.py:1633 -- Connected to Ray cluster. View the dashboard at \x1b[1m\x1b[32m172.18.0.4:8265 \x1b[39m\x1b[22m\n
```
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```
That's all!
For more detailed examples and explanations refer to the [Guide](https://qiskit-extensions.github.io/quantum-serverless/index.html):
1. [Getting Started](https://qiskit-extensions.github.io/quantum-serverless/getting_started/index.html#)
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9 changes: 2 additions & 7 deletions docs/deployment/cloud.rst
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Expand Up @@ -28,8 +28,6 @@ To deploy the infrastructure required for ``Quantum Serverless`` you need to hav

Each of these tools' webpages contain instructions for installing on Windows, MacOS, and Linux.

* **Important**: before you download the above packages, check the `versions listed in our Github <https://github.com/Qiskit-Extensions/quantum-serverless/tree/main/infrastructure#tools>`_ of each tool to ensure that you download a compatible version.

Once you have these tools installed, you can check the installation by running the following commands in a terminal:

.. code-block::
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approach is to use the ``port-forward`` command:

.. code-block::
:caption: get gateway and jupyter pods
:caption: get gateway pod
$ kubectl get service
$ > ...
$ > jupyter ClusterIP 10.43.74.253 <none> 80/TCP
$ > gateway ClusterIP 10.43.86.146 <none> 8000/TCP
$ > ...
Expand All @@ -110,14 +107,12 @@ Now that we have the desired services, we can expose their ports:
:caption: ports 8265 and 8888 are the the default ports for each service
$ kubectl port-forward service/gateway 3333:8000
$ kubectl port-forward jupyter-<POD_ID> 4444:80
Now you may access your cluster services from localhost.

For development this is more than enough, but if you are considering deploying it remotely you will need to
configure the various ``ingress`` properties in `values.yaml <https://github.com/Qiskit-Extensions/quantum-serverless/blob/main/charts/quantum-serverless/values.yaml>`_
with the configuration of your domain and provider. In the ``Jupyter configs`` section you have a
configuration example to expose through ``ingress`` in ``localhost`` the Jupyter service (disabled by default).
with the configuration of your domain and provider.

* **Important**: ``nginx-ingress-controller`` is disabled by default because third party providers should provide its own Ingress controller. To use it locally you need to activate it too.

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2 changes: 1 addition & 1 deletion docs/deployment/local.rst
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Expand Up @@ -54,5 +54,5 @@ monitorying systems.
Step 4: Run a program in the test environment

Once the containers are running, you can simulate a remote cluster with the resources on your
local machine. To create and run programs in this simulated cluster, we recommend using [Jupyter Lab](https://jupyter.org/install). Refer to the :ref:`getting_started` guides
local machine. To create and run programs in this simulated cluster, we recommend using `Jupyter Lab <https://jupyter.org/install>`_. Refer to the :ref:`getting_started` guides
for details about running your program remotely.

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