181 lines
14 KiB
Markdown
181 lines
14 KiB
Markdown
# Google Cloud Usage Tutorial
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This document has been created to help you setup a google cloud instance to be used for the MLP course using the student credit the course has acquired.
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This document is non-exhaustive and many more useful information is available on the [google cloud documentation page](https://cloud.google.com/docs/).
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For any question you might have, that is not covered here, a quick google search should get you what you need. Anything in the official google cloud docs should be very helpful.
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| WARNING: Read those instructions carefully. You will be given 50$ worth of credits and you will need to manage them properly. We will not be able to provide more credits. |
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### To create your account and start a project funded by the student credit
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1. Login with your preferred gmail id to [google cloud console](https://cloud.google.com/). Click on `Console` (upper right corner), which would lead you to a new page and once there, click on Select a Project on the left hand side of the search bar on top of the page and then click on New Project on the right hand side of the Pop-Up.
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Name your project sxxxxxxx-MLPractical - replacing the sxxxxxxx with your student number. **Make sure you are on this project before following the next steps**.
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2. Get your coupon by following the instructions in the coupon retrieval link that you received.
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3. Once you receive your coupon, follow the email instructions to add your coupon to your account.
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4. Once you have added your coupon, join the [MLPractical GCP Google Group](https://groups.google.com/forum/#!forum/mlpractical_gcp) using the same Google account you used to redeem your coupon. This ensures access to the shared disk images.
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5. Make sure that the financial source for your project is the MLPractical credit. You can check this by going to the [Google Cloud Console](https://console.cloud.google.com/) and selecting your project. Then, click on the `Billing` tile. Once on the `Billing` page, you should be prompted to add the billing account if you haven't yet done so. Choose `Billing Account for Education` as your billing account. Then, under the billing account, click `account management` on the left-hand side tab. You should see your project under `Projects linked to this billing account`. If not, you can add it by clicking on `Add projects` and selecting your project from the list of available projects.
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### To create an instance
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1. On the console page, click the button with the three lines at the top left corner.
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2. In the ```Compute Engine``` sub-menu select ```VM Instances```.
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3. Enable ```Compute Engine API``` if prompted.
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4. Click the ```CREATE INSTANCE``` button at the top of the window.
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5. Click on ```VM FROM INSTANCE TEMPLATE```, and create your VM template for this coursework:
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6. Name the template ```mlpractical-1```.
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7. Select ```Regional``` as the location type and ```us-west1(Oregon)``` as the region.
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8. Under ```Machine Configuration```, select ```GPU``` machine family. Select one NVIDIA T4. Those are the cheapest one, be careful as others can cost up to 8 times more to run.
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9. Below, in ```Machine type```, under ```PRESET``` select ```n1-standard-2 (2 vCPU, 1 core, 7.5Gb memory)```.
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10. Under ```Boot disk```, click change.
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11. On the right-hand new menu that appears (under ```PUBLIC IMAGES```), select
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* ```Deep Learning on Linux``` operating system,
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* ```Deep Learning VM for PyTorch 2.0 with CUDA 11.8 M125```
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* **Note**: If the above version is not available, you can use any ```Deep Learning VM for PyTorch 2.0 with CUDA 11.8 M***``` instead.
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* ```Balanced persistent disk``` as boot disk type,
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* ```100```GB as disk size, and then click select at the bottom.
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12. Under ```Availability policies```, in the ```VM provisioning model``` drop down menu, select ```Spot```. Using this option will be helpful if you're running low on credits.
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13. You can ```Enable display device``` if you want to use a GUI. This is not necessary for the coursework.
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14. Leave other options as default and click ```CREATE```.
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15. Tick your newly created template and click ```CREATE VM``` (top centre).
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16. Click ```CREATE```. Your instance should be ready in a minute or two.
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15. If your instance failed to create due to the following error - ```The GPUS-ALL-REGIONS-per-project quota maximum has been exceeded. Current limit: 0.0. Metric: compute.googleapis.com/gpus_all_regions.```, click on ```REQUEST QUOTA``` in the notification.
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16. Tick ```Compute Engine API``` and then click ```EDIT QUOTAS``` (top right).
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17. This will open a box in the right side corner. Put your ```New Limit``` as ```1``` and in the description you can mention you need GPU for machine learning coursework.
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18. Click ```NEXT```, fill in your details and then click ```SUBMIT REQUEST```.
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19. You will receive a confirmation email with your Quota Limit increased. This may take some minutes.
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20. After the confirmation email, you can recheck the GPU(All Regions) Quota Limit being set to 1. This usually shows up in 10-15 minutes after the confirmation email.
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21. Retry making the VM instance again as before, by choosing your template, and you should have your instance now.
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#### Note
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Be careful to select 1 x T4 GPU (Others can be much more expensive).
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You only have $50 dollars worth of credit, which should be about 6 days of GPU usage on a T4.
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### To login into your instance via terminal:
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1. In a DICE terminal window (Or your local environment) ```conda activate mlp```
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2. Download the `gcloud` toolkit using ```curl -O https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-linux-x86_64.tar.gz```
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3. Install the `gcloud` toolkit using ```tar zxvf google-cloud-cli-linux-x86_64.tar.gz; bash google-cloud-sdk/install.sh```.
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**Note**: You might be asked to provide a passphrase to generate your local key, simply use a password of your choice. There might be some Yes/No style questions as well, choose yes, when that happens.
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4. Reset your terminal using ```reset; source ~/.bashrc```. Then authorize the current machine to access your nodes run ```gcloud auth login```. This will authenticate your google account login.
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5. Follow the prompts to get a token for your current machine.
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6. Run ```gcloud config set project PROJECT_ID``` where you replace `PROJECT-ID` with your project ID. You can find that in the projects drop down menu on the top of the Google Compute Engine window; this sets the current project as the active one. If you followed the above instructions, your project ID should be `sxxxxxxx-mlpractical`, where `sxxxxxxx` is your student number.
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7. In your compute engine window, in the line for the instance that you have started (`mlpractical-1`), click on the downward arrow next to ```SSH```. Choose ```View gcloud command```. Copy the command to your terminal and press enter. Make sure your VM is up and running before doing this.
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8. Add a password for your ssh-key (and remember it!).
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9. Re-enter password (which will unlock your ssh-key) when prompted.
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10. On your first login, you will be asked if you want to install nvidia drivers, **DO NOT AGREE** and follow the nvidia drivers installation below.
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11. Install the R470 Nvidia driver by running the following commands:
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* Add "contrib" and "non-free" components to /etc/apt/sources.list
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```bash
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sudo tee -a /etc/apt/sources.list >/dev/null <<'EOF'
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deb http://deb.debian.org/debian/ bullseye main contrib non-free
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deb-src http://deb.debian.org/debian/ bullseye main contrib non-free
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EOF
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```
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* Check that the lines were well added by running:
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```bash
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sudo -e /etc/apt/sources.list
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```
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* Update the list of available packages and install the nvidia-driver package:
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```bash
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sudo apt update
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sudo apt install nvidia-driver firmware-misc-nonfree
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```
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12. Run ```nvidia-smi``` to confirm that the GPU can be found. This should report 1 Tesla T4 GPU. if not, the driver might have failed to install.
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13. To test that PyTorch has access to the GPU you can type the commands below in your terminal. You should see `torch.cuda_is_available()` return `True`.
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```
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python
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```
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```
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import torch
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torch.cuda.is_available()
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```
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```
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exit()
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```
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14. Well done, you are now in your instance and ready to use it for your coursework.
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15. Clone a fresh mlpractical repository, and checkout branch `mlp2024-25/mlp_compute_engines`:
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```
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git clone https://github.com/VICO-UoE/mlpractical.git ~/mlpractical
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cd ~/mlpractical
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git checkout mlp2024-25/mlp_compute_engines
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```
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Then, to test PyTorch running on the GPU, run this script that trains a small convolutional network on EMNIST dataset:
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```
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python train_evaluate_emnist_classification_system.py --filepath_to_arguments_json_file experiment_configs/emnist_tutorial_config.json
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```
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You should be able to see an experiment running, using the GPU. It should be doing about 26-30 it/s (iterations per second). You can stop it when ever you like using `ctrl-c`.
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If all the above matches what’s stated then you should be ready to run your experiments.
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To log out of your instance, simply type ```exit``` in the terminal.
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### Remember to ```stop``` your instance when not using it. You pay for the time you use the machine, not for the computational cycles used.
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To stop the instance go to `Compute Engine -> VM instances` on the Google Cloud Platform, slect the instance and click ```Stop```.
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#### Future ssh access:
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To access the instance in the future simply run the `gcloud` command you copied from the google compute engine instance page.
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## Copying data to and from an instance
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Please look at the [transfering files to VMs from Linux, macOS and Windows](https://cloud.google.com/compute/docs/instances/transfer-files?hl=en) and [google docs page on copying data](https://cloud.google.com/filestore/docs/copying-data). Note also the link on the page for [seting up your SSH keys (Linux or MacOS)](https://cloud.google.com/compute/docs/instances/access-overview?hl=en).
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To copy from local machine to a google instance, have a look at this [stackoverflow post](https://stackoverflow.com/questions/27857532/rsync-to-google-compute-engine-instance-from-jenkins).
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## Running experiments over ssh:
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If ssh fails while running an experiment, then the experiment is normally killed.
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To avoid this use the command ```screen```. It creates a process of the current session that keeps running whether a user is signed in or not.
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The basics of using screen is to use ```screen``` to create a new session, then to enter an existing session you use:
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```screen -ls```
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To get a list of all available sessions. Then once you find the one you want use:
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```screen -d -r screen_id```
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Replacing screen_id with the id of the session you want to enter.
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While in a session, you can use:
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- ```ctrl+a+esc``` To pause process and be able to scroll.
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- ```ctrl+a+d``` to detach from session while leaving it running (once you detach you can reattach using ```screen -r```).
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- ```ctrl+a+n``` to see the next session.
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- ```ctrl+a+c``` to create a new session.
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You are also free to use other tools such as `nohup` or `tmux`. Use online tutorials and learn it yourself.
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## Troubleshooting:
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| Error| Fix|
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| --- | --- |
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| ```ERROR: (gcloud.compute.ssh) [/usr/bin/ssh] exited with return code [255].``` | Delete the ssh key files and try again: ```rm ~/.ssh/google_compute_engine*``` |
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|"Mapping" error after following step 3 (```tar zxvf google-cloud-sdk-365.0.0-linux-x86_64.tar.gz; bash google-cloud-sdk/install.sh```) | This is due to conflicts and several packages not being installed properly according to your Python version when creating your Conda environment. Run ```conda create --name mlp python=3.9``` to recreate the environment supported with Python 3.9. Then, activate the environment ```conda activate mlp``` and follow the instructions from step 3 again. |
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|"Mapping" error even after successfully completing steps 3 and 4 when using the ```gcloud``` command | Restart your computer and run the following command: ```export CLOUDSDK_PYTHON="/usr/bin/python3"``` |
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| ```gcloud command not found``` | Restart your computer and run the following command: ```export CLOUDSDK_PYTHON="/usr/bin/python3"``` |
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| ```module 'collections' has no attribute 'Mapping'``` when installing the Google Cloud SDK | Install Google Cloud SDK with brew: ```brew install --cask google-cloud-sdk```|
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| ```Access blocked: authorisation error``` in your browser after running ```gcloud auth login``` | Run ```gcloud components update``` and retry to login again. |
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| ```ModuleNotFoundError: No module named 'GPUtil'``` | Install the GPUtil package and you should be able to run the script afterwards: ```pip install GPUtil``` |
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| ```module mlp not found``` | Install the mlp package in your environment: ```python setup.py develop``` |
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| ```NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.``` | Remove the current driver by running: ```cd /``` and ```sudo apt purge nvidia-*``` Follow step 11 of the instructions or the following commands: (1) download the R470 driver ```wget https://us.download.nvidia.com/XFree86/Linux-x86_64/470.223.02/NVIDIA-Linux-x86_64-470.223.02.run```, (2) change the file permissions to make it executable with ```chmod +x NVIDIA-Linux-x86_64-470.223.02.run``` and (3) install the driver ```sudo ./NVIDIA-Linux-x86_64-470.223.02.run``` |
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| ```module 'torch' has no attribute 'cuda'``` | You most probably have a file named ```torch.py``` in your current directory. Rename it to something else and try again. You might need to run the setup again. Else ```import torch``` will be calling this file instead of the PyTorch library and thus causing a conflict. |
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| ```Finalizing NVIDIA driver installation. Error! Your kernel headers for kernel 5.10.0-26-cloud-amd64 cannot be found. Please install the linux-headers-5.10.0-26-cloud-amd64 package, or use the --kernelsourcedir option to tell DKMS where it's located. Driver updated for latest kernel.``` | Install the header package with ```sudo apt install linux-headers-5.10.0-26-cloud-amd64``` |
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