In order to get started with deep learning, you need access to GPU.
Here are some several options to get access to a free GPU.
My favorite options are:
Those options are very limited in how much you can customize the image. Eventually, you will need to set up your own image.
Setting up a deep learning VM from scratch involves installing below ideally on a ubuntu machine
- nvidia gpu drivers
- nvidia cuda library
- nvidia cudadnn
- python /anaconda
To help aid in this process, Jeremy Horward created a bash script that automates this process.
wget http://files.fast.ai/setup/paperspace
bash paperspace
Google Cloud Recently created a base image that contains all these libraries installed.
To get started
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Navigate to Google Cloud Console https://console.cloud.google.com/
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Search for “Deep Learning VM”
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Click “Launch on Compute Engine”
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Choose a zone to launch the machine Since I live in the US and east coast, I choose us-east1-c. Not all GPUs a are avaialble in all regions. Refer to this link for uptodate GPU availability
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Choose Machine Type Click on Cutomize to choose your machine specs. Nvidia Tesla K80 is the chepest GPU. Choose Atleast 4CPU and 15GB if you are doing anything semi serious
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Note: Google Allows you to add upto 8GPUS to one machine
- Choose Base Framework There are several base images. Choose Tensorflow, pytorch if you need a specific Deep Learning Framwork.
If you are interested, you could also use the base image to include your custom libraries.
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- Verify your quota Click on the quoata link, to verify if you have quoata to launch the gpu.
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If you don’t have a quoata, choose your metric and location. Select the gpu and click edit quota.
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Note: You will need to add your billing info to get approved.
- Click Deploy
Congrats, you have launched your deep learning vm.
To connect to the jupyter notebook, run
ZONE=us-east1-c
gcloud compute ssh $INSTANCE_NAME --zone ${ZONE} -- -L 8888:localhost:8080
This is the set of commands I run to provision and update this machine
export IMAGE_FAMILY="pytorch-latest-gpu"
export ZONE="us-east1-c"
export INSTANCE_TYPE="n1-highmem-8"
export NUM_GPUS=1
export GCP_PROJECT=np-training
export INSTANCE_NAME="dl2"
gcloud compute instances create $INSTANCE_NAME \
--zone=$ZONE \
--image-family=$IMAGE_FAMILY \
--image-project=deeplearning-platform-release \
--maintenance-policy=TERMINATE \
--accelerator="type=nvidia-tesla-k80,count=${NUM_GPUS}" \
--machine-type=$INSTANCE_TYPE \
--boot-disk-size=30GB \
--metadata='install-nvidia-driver=True,jupyter-user=ubuntu' \
--tags=deep-learning,gpu,jupyter \
--project ${GCP_PROJECT}
gcloud compute --project ${GCP_PROJECT} ssh --zone=$ZONE ubuntu@$INSTANCE_NAME -- -L 8080:localhost:8080
conda install -c fastai fastai -y