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Docs: Add warning against installing Ray in base environment #3267

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Mar 14, 2024
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Docs: Add warning against installing Ray in base environment #3267

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Docs: Add warning against installing Ray in base environment
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Docs: Add warning against installing Ray in base environment
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Update docs/source/running-jobs/distributed-jobs.rst
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50 changes: 50 additions & 0 deletions docs/source/running-jobs/distributed-jobs.rst
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Expand Up @@ -137,3 +137,53 @@ This allows you directly to SSH into the worker nodes, if required.
# Worker nodes.
$ ssh mycluster-worker1
$ ssh mycluster-worker2


Executing a Distributed Ray Program
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For discussion: We may want to add a dedicated guide on how to use Ray as part of the user job. That page can use a tip like this. A full YAML example #3195 may help promote this recommendation more directly.

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Yes, let’s add a yaml example of starting a distributed job with ray as mentioned above. We can try to move it to a separate page later after this pr is merged.

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So for confirmation: I should add the command to run the example in distributed_ray_train, the one that uses FashionMNIST and add the command for tha yaml file and its output as example in the documentation ?

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Yes, we can add the yaml file inline with the command to run that yaml : )

------------------------------------
To execute a distributed Ray program on many VMs, you can download the `training script <https://github.com/skypilot-org/skypilot/blob/master/examples/distributed_ray_train/train.py>`_ and launch the `task yaml <https://github.com/skypilot-org/skypilot/blob/master/examples/distributed_ray_train/ray_train.yaml>`_:

.. code-block:: console

$ wget https://raw.githubusercontent.com/skypilot-org/skypilot/master/examples/distributed_ray_train/train.py
$ sky launch ray_train.yaml

.. code-block:: yaml

resources:
accelerators: L4:2
memory: 64+

num_nodes: 2

workdir: .

setup: |
conda activate ray
if [ $? -ne 0 ]; then
conda create -n ray python=3.10 -y
conda activate ray
fi

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pip install "ray[train]"
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pip install tqdm
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

run: |
sudo chmod 777 -R /var/tmp
head_ip=`echo "$SKYPILOT_NODE_IPS" | head -n1`
num_nodes=`echo "$SKYPILOT_NODE_IPS" | wc -l`
if [ "$SKYPILOT_NODE_RANK" == "0" ]; then
ps aux | grep ray | grep 6379 &> /dev/null || ray start --head --disable-usage-stats --port 6379
sleep 5
python train.py --num-workers $num_nodes
else
sleep 5
ps aux | grep ray | grep 6379 &> /dev/null || ray start --address $head_ip:6379 --disable-usage-stats
fi

.. warning::
**Avoid Installing Ray in Base Environment**: Before proceeding with the execution of a distributed Ray program, it is crucial to ensure that Ray is **not** installed in the *base* environment. Installing a different version of Ray in the base environment can lead to abnormal cluster status.

It is highly recommended to **create a dedicated virtual environment** (as above) for Ray and its dependencies, and avoid calling `ray stop` as that will also cause issue with the cluster.

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