If you want to do this tutorial with active reflections in the style of a assignment see this: https://docs.google.com/document/d/1k3d3_AOp-Y22L-GbgHivDqKe9r9LC5nscaIzbm3LWYc/edit?usp=sharing
Always use the original documentation or wiki for each cluster: https://ilwiki.stanford.edu/doku.php?id=start -- your snap bible. Other useful resources:
- Support IT for snap: il-action@cs.stanford.edu (don't be shy to ask them question or help for SNAP.)
- compute instructions from Professor Koyejo's (Sanmi's) lab (STAIR Lab): https://docs.google.com/document/d/1PSTLJdtG3AymDGKPO-bHtzSnDyPmJPpJWXLmnJKzdfU/edit?usp=sharing
- advanced video from Rylan and Brando (made for the STAIR/Koyejo Lab): https://www.youtube.com/watch?v=XEB79C1yfgE&feature=youtu.be
- our CS 197 section channel
- join the snap slack & ask questions there too: https://join.slack.com/t/snap-group/shared_invite/zt-1lokufgys-g6NOiK3gQi84NjIK_2dUMQ
First create a CSID here and please make your CSID the same as your Stanford SUNET id. Request it here: https://webdb.cs.stanford.edu/csid and If you are a CS197 (only!) then:
- Put Michael Bernstein as your CSID sponsor/supervisor and e-mail you are doing cs197 with me.
- Put Sanmi Koyejo as your CSID sponor/supervisor and e-mail them you are planning to collaborate with me in research. Note: this is different from SNAP cluster sponso (which you email il-action@stanford.edu), but CSID is needed first.
To get access to snap write an e-mail with this subject:
Access Request SNAP Cluster Working With Brando Miranda CS197 for <full_name>
For example:
Access Request SNAP Cluster Working With Brando Miranda CS197 for Brando Miranda brando9 brando9$
and sent it to
- Eric Pineda: eric.pineda@stanford.edu
- Brando Miranda: brando9@stanford.edu
- SNAP cluster IT: il-action@cs.stanford.edu
Once you get access to it you will be able to login to the cluster via the ssh command in the terminal (cli) and vscode.
SSH into a SNAP server/node owned by Sanmi's Lab (skampere1, mercury1, mercury2) directly with your csid/sunet e.g.,:
# Example
ssh brando9@skampere1.stanford.edu
# Yours
ssh CSID@skampere1.stanford.edutype your password and login.
Then use the ls command and pwd and realpath ~ and realpath $HOME and realpath . commands to see the contents of your root ~ directory.
Do the cd .. and pwd to be familiar with the file system/directory structure in snap.
Then run the reauth command, this command is needed due to the (kerberos) security settings in SNAP (hote: Brando strongly disagrees with how IT set up this part and apologizes for the complexity for you).
For more info on reauth read the wiki here.
Tip: see the wiki for other nodes that exist on snap!
Sample output if it worked:
(base) brandomiranda~ ❯ ssh brando9@skampere1.stanford.edu
brando9@skampere1.stanford.edu's password:
Last login: Wed Apr 3 12:07:20 2024 from 172.24.69.154
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
skampere1.stanford.edu
Ubuntu 20.04 (5.4.0-162-generic)
128 x AMD EPYC 7543 32-Core Processor, 1.96 TiB RAM, 8.00 GiB swap
enp44s0f0: 172.24.75.194
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-> For help or to report problems, go to http://support.cs.stanford.edu
ln: failed to create symbolic link '/lfs/skampere1/0/brando9/iit-term-synthesis': File exists
(evals_af) brando9@skampere1~ $
(evals_af) brando9@skampere1~ $ realpath ~
/lfs/skampere1/0/brando9
(evals_af) brando9@skampere1~ $ realpath $HOME
/lfs/skampere1/0/brando9
(evals_af) brando9@skampere1~ $ realpath .
/lfs/skampere1/0/brando9
(evals_af) brando9@skampere1~ $ ls
beyond-scale-language-data-diversity massive-autoformalization-maf ultimate-anatome
data miniconda ultimate-pycoq
diversity-for-predictive-success-of-meta-learning miniconda.sh ultimate-utils
evals-for-autoformalization nltk_data vscode.beyond-scale-language-data-diversity.skampere1.code-workspace
evaporate proverbot9001 vscode.evaporate.skampere1.code-workspace
iit-term-synthesis putnam-math vscode.lean4ai.skampere1.code-workspace
keys pycoq vscode.maf_data.creating_data_math_training.skamapere1.code-workspace
_KoyejoLab-Predictable-LLM-Evals snap-cluster-setup vscode.maf_data.training_af_model.skampere1.code-workspace
KoyejoLab-Predictable-LLM-Evals tempt.txt vscode.snap-cluster-setup.code-workspace
KoyejoLab-Predictable-LLM-Evals.skampere1.code-workspace test_folder wandb
lean4ai the-data-quality-edge-for-top-llms
(evals_af) brando9@skampere1~ $ pwd
/lfs/skampere1/0/brando9
(evals_af) brando9@skampere1~ $ cd ..
(evals_af) brando9@skampere1/lfs/skampere1/0 $ pwd
/lfs/skampere1/0
(evals_af) brando9@skampere1/lfs/skampere1/0 $ cd ~
(evals_af) brando9@skampere1~ $
(evals_af) brando9@skampere1~ $ reauth
Password for brando9:
Background process pid is: 4145964Note: this might look slightly different if you already set up your .bashrc file.
Tip: If the reauth command doesn't work do or/and e-mail the SNAP cluster IT il-action@cs.stanford.edu:
export PATH="/afs/cs/software/bin:$PATH"TIP: Ask ChatGPT what export PATH="/afs/cs/software/bin:$PATH" does. ChatGPT is excellent at the terminal and bash commands. Note consider adding this to your .bashrc for example see this.
TIP: you might have to ssh into your node again outside of vscode for this to work if vscode is giving you permission issues or e-mail snap IT.
Setup your .bashrc, redirect the $HOME (~) envi variable from afs to lfs and create a soft link for .bashrc (afs/.bashrc -> lfs/.bashrc)
Rationale: SNAP has 3 file systems afs, lfs, dfs (folders where your files, data and code could potentially be stored).
We will only be using afs and lfs.
dsf stands for distributed file system and it makes your files available with all nodes/servers/computers in the snap cluster but it's too slow to be usable (IT should have set dfs up properly but they did not and this is why this tutorial is long).
So what we will do is put your code afs and create a soft link to it in lfs.
lfs stands for local file system and it's where your actual data (trainning data, models, python conda environment will reside).
We will soft link (with ln -s <src> <target>) your code/github repos from afs (src) to lfs (target) later.
TIP: anything you don't understand we encourage you to discuss it with GPT4/Claude/your favorite LLM + paste the entire tutorial to in your conversation with it!
e.g., ask it what an environment variable is, what a git command does, nvidia-smi command does, what vim is or what git clone is, pip install, pip instlal -e . , what conda is and python envs, what $PATH or $HOME is, tmux, basically bash related things it's great for!.
Now that you understand the rationale, so background on how starting a terminal (cli) works.
Every time one logins into a snap node/server (or creates a new linux terminal with the bash cli command), the terminal (cli) needs to configure your unix/linux environment (the set of variables that hold important paths so that the terminal/cli "works as expected" e.g., standard commands/execuatable binaries are found like python, ls, cd, echo, pwd, cat, vim etc. or environment variables like $HOME, $PATH, etc. have the right content)
Usually the linux terminal (e.g. bash) runs ("sources") .bash_profile to set up your linux environment before providing the bash/cli/terminal session (where you write commands).
In the SNAP cluster, the system admins have their .bash_profile located at your root's afs.
More precisely, you will have a (sym) link called .bash_profile that points to the system adimns file at /afs/cs/etc/skel/.bash_profile.
This file is ran each time you ssh/log in or you run bash in SNAP.
This command usually sets up your environment variables for your terminal (e.g., current bash session) by running commands you specify in that file.
In particular, it sets up environment variables, which are terminal/cli variables that point to important locations in the node/computer so that everything works!
Now, let's inspect your .bash_profile.
For that you need to /afs/cs.stanford.edu/u/<CSID> with the cd command and then we can display the contents of the file with cat (always make sure you understand the commands you're running & read the output):
# ... AFTER sshing to a SNAP node
(evals_af) brando9@skampere1~ $
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ cd /afs/cs.stanford.edu/u/brando9
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ cat .bash_profile
# DO NOT MODIFY THIS FILE! Your changes will be overwritten!
# For user-customization, please edit ~/.bashrc.user
#
# $ROOT$
#
# CSD-CF miles 2003-01-24
# Don't do anything in this file -- do it in .bashrc
# Get the aliases and functions
if [ -f ~/.bashrc ]; then
. ~/.bashrc
fiIn this specific .bash_profile runs ("sources") another file called .bashrc.
If you yours runs ~/.bashrc.user (which is a very non-standard setup), then change your .bash_profile to be the same as the one above with vim. If you need to do that do:
vim .bash_profileThis open the file. Then move to the line you want to edit.
Then type i in vim. Then you are now in insert mode.
Then change the line . ~/.bashrc to . ~/.bashrc (note the indentation).
Then do :w followed by :q (or :wq or :x) which tells vim to save your changes and exit vim.
Make sure you know the basics of vim even if you use vscode with the remote ssh extension (we will cover the basic of vim bellow and we do recommend vscode with the ssh extension).
Make sure you understand why you are doing this.
You are doing this so that .bash_profile runs your configurations of bash each time you start a bash session in the terminal.
Now, since .bash_profile runs/sources your .bashrc file each time you ssh/login to snap we will put our personal configurations for SNAP in our .bashrc located at ~/.bashrc (note: ~ is the same as $HOME and points to your local path) i.e., that is the meaning of . ~/.bashrc.
The plan is to:
- put
.bashrcinafssinceafsis accessible via all nodes (to manage a single version for all SNAP nodes) - have the environment variable
$HOME(~) point from the root ofafstolfsautomatically everytime you login to any node. - create a soft link in your node's lfs home root
$HOME(~) pointing to the path atafsfor the.bashrcfile (otherwise when.bash_profileis ran/sourced your terminal won't be set up as you expect/want) This culminates in you putting your.bashrcfile in exactly this path:
/afs/cs.stanford.edu/u/<YOUR_CSID>/.bashrcLet's do setps 1, 2, 3.
Now we are doing 1 and 2 in one shot in .bashrc.
Remove the sym link to the system admins .bashrc (always understand the commands you're running and read the output):
rm .bashrcThis should resolve any permissions issues.
Then create a file .bashrc at /afs/cs.stanford.edu/u/<YOUR_CSID>/ with (if it doesn't exist already, if it does exit remove it's contents, you will change it):
touch /afs/cs.stanford.edu/u/<YOUR_CSID>/.bashrcopen it with a terminal text editor.
I suggest vim (read this for a quick ref for vim) since that is what I use:
vim /afs/cs.stanford.edu/u/<YOUR_CSID>/.bashrcthen in vim press i to get into insert mode.
Then copy paste the contents of our base .bashrc file but change everywhere the string brando9 appears and put your CSID (so read the file carefully before copy pasting).
Then press esc to :w enter to save the file. Then press :q enter to exist (or :x enter for for save & exit).
In particular, note this command in your .bashrc file:
# - The defaul $HOME is /afs/cs.stanford.edu/u/brando9 but since you want to work in a specific server due to otherwise conda being so hard you need to reset what home is, see lfs: https://ilwiki.stanford.edu/doku.php?id=hints:storefiles#lfs_local_server_storage
export LOCAL_MACHINE_PWD=$(python3 -c "import socket;hostname=socket.gethostname().split('.')[0];print('/lfs/'+str(hostname)+'/0/brando9');")
mkdir -p $LOCAL_MACHINE_PWD
export WANDB_DIR=$LOCAL_MACHINE_PWD
export HOME=$LOCAL_MACHINE_PWDwhich changes where your $HOME directory points to every time you login to a node in SNAP (since .bashrc is sourced via .bash_profile everytime you login or run the bash terminal command).
It changes your default home/root location from afs (some weird Stanford file system with limited disk space) to the local node's lfs file system/directories.
We do this because afs does not have a lot of disk compared to lfs (so that you don't run into disk space issues, if you do however, you will need to clean your disk or e-mail snap's IT).
However, afs is fast but has little space (so it can only hold small text file e.g., usually git hub repos with code only, so not with data or gitlfs).
In addition, lfs is also quicker (since it is "a local computer" i.e., you are logged in to a compute node directly, which is an unusual set up for a cluster).
I repeat, we won't be using dfs.
The eventual goal is that your code will be at afs and that soft link to your code will be at lfs.
Note: the python conda environment, data and big files or data that needs fast access will be at lfs.
Now let's do 3 manually and needs to be done manually every time you want to use a new node in SNAP.
This is only SNAP specific.
Note: if you think you know how to not set up this manually each time, open a gitissue and propose a solution!
So let's make sure there is a pointer/reference to the your .bashrc file so .bash_profile can actually find it and run it when you create a terminal/cli or run bash.
For that create the soft link but change brando9 to your CSID:
ln -s /afs/cs.stanford.edu/u/brando9/.bashrc ~/.bashrccheck that this worked by inspecting if the link exists at your root in lfs. Run these commands:
cd /lfs/skampere1/0/<CSID>
ls -lahSample output:
(evals_af) brando9@skampere1~ $ cd /lfs/skampere1/0/brando9
(evals_af) brando9@skampere1~ $ ls -lah
total 99M
drwxr-sr-x 22 brando9 root 4.0K Apr 3 17:07 .
drwxrwsrwt 23 root root 4.0K Mar 27 19:21 ..
-rw------- 1 brando9 root 48K Apr 3 11:20 .bash_history
lrwxrwxrwx 1 brando9 root 38 Oct 27 12:57 .bashrc -> /afs/cs.stanford.edu/u/brando9/.bashrc
...
lrwxrwxrwx 1 brando9 root 49 Apr 3 11:02 snap-cluster-setup -> /afs/cs.stanford.edu/u/brando9/snap-cluster-setup
...Notice how we have .bashrc -> /afs/cs.stanford.edu/u/brando9/.bashrc, which menas the name .bashrc actually points to /afs/cs.stanford.edu/u/brando9/.bashrc.
Now that your .bashrc is in the afs location and $HOME (~) points to your node's lfs home path, we should restart your bash terminal in SNAP to test that your changes take effect.
Recall, your terminal is now set up as in described in 1,2, 3 because .bash_profile runs .bashrc.
So let's test it.
Run in your terminal, one at a time and read the output of each command
(never run any command blindly, always read/understand the command you're running and it's output):
bash
echo $HOME
realpath ~/.bashrc
pwd ~/.bashrcSample ouput:
# Or log out and relogin to snap with your preferred node, so bash isn't needed anymore
(evals_af) brando9@skampere1~ $ bash
ln: failed to create symbolic link '/lfs/skampere1/0/brando9/iit-term-synthesis': File exists
(evals_af) brando9@skampere1~ $ echo $HOME
/lfs/skampere1/0/brando9
(evals_af) brando9@skampere1~ $ realpath ~/.bashrc
/afs/cs.stanford.edu/u/brando9/.bashrc
(evals_af) brando9@skampere1~ $ pwd ~/.bashrc
/lfs/skampere1/0/brando9this demonstrates $HOME (~) points to your node's lfs and that the real path of .bashrc is actually in afs.
Now repeat the above (without the bash command) but log out the snap node you are into and re login via ssh, and check your .bashrc is in afs and $HOME points to lfs (never run any command blindly, always read/understand the command you're running and it's output)::
sh brando9@skampere1.stanford.edu
echo $HOME
realpath ~/.bashrc
pwd ~/.bashrcsample output:
(evals_af) brando9@skampere1~ $ echo $HOME
/lfs/skampere1/0/brando9
(evals_af) brando9@skampere1~ $ realpath ~/.bashrc
/afs/cs.stanford.edu/u/brando9/.bashrc
(evals_af) brando9@skampere1~ $ pwd ~/.bashrc
/lfs/skampere1/0/brando9make sure you understand the difference between realpath ~/.bashrc and pwd ~/.bashrc.
Bonus Note: this is (most likely) correct even though the wiki/docs for snap say to update .bash.user (but .bash.user is never sourced, I asked the it and I strongly recommend you ask too, see wrong/confusing docs if you want https://ilwiki.stanford.edu/doku.php?id=hints:enviroment but that's not what .bash_profile is sourcing!?).
You already created a .bashrc
Vim is for editing file in the terminal (cli).
These are the commands you might need https://coderwall.com/p/adv71w/basic-vim-commands-for-getting-started.
Mostly knowing that:
-> pressing i puts you in insert mode
-> pressing w writes/save the files
-> pressing q quits
that's all you need.
If you want more experience with vim do this:
Certainly! Below is a small, self-contained markdown tutorial focusing on editing files in the terminal using Vim. This tutorial will cover the basics of opening a file, entering insert mode for editing, exiting insert mode, and saving your changes.
#### Vim Editing Basics Tutorial
Vim is a highly efficient text editor that operates in various modes. The most commonly used modes are "Normal" and "Insert" modes. This tutorial will guide you through opening a file in Vim, editing it, and saving your changes.
## Opening a File in Vim
To open a file with Vim, use the following command in your terminal:
```bash
vim filenameReplace filename with the name of the file you wish to edit. If the file does not exist, Vim will create it for you.
Entering Insert Mode:
Once you have your file opened in Vim, you'll start in Normal mode. To start editing your file, you need to enter Insert mode.
iPressing i will switch you from Normal mode to Insert mode. Now you can start typing and editing the file as you would in a conventional text editor.
Exiting Insert Mode:
To stop editing and return to Normal mode, press:
EscThe Esc key brings you back to Normal mode, where you can execute Vim commands.
Saving Changes: To save your changes in Normal mode, type:
:wThis command writes your modifications to the file but keeps it open in Vim.
Exiting Vim: If you want to exit Vim after saving your changes, type:
:qHowever, to both save your changes and exit Vim in one command, you can use:
:xor
:wqBoth commands save any changes and then close the file.
Conclusion: This tutorial covered the basics of file editing with Vim in the terminal, including opening files, switching between Insert and Normal modes, saving changes, and exiting Vim. With these commands, you can start editing files efficiently in the terminal. Couresty of ChatGPT (GPT4).
At this point, you know what the environment variable $HOME is and that yours should be pointing to your node’s lfs home directory -- as suggested by this .bahrc file (but using your CSID).
Now the goal will be to:
- show you how to
git clone(from your git fork of this repo) a small sized git project (so not usinggitlfs, that would most likely create issues with AFS's storage limit) like this one (snap-cluster-setup) - learn what a python environment is and create one using conda
For step 1 we will first go to your afs home root path.
We do this because afs is the only "distributed" file system that isn't very slow and that is accessible at all SNAP nodes/servers.
It will be similar to how we created our .bashrc file previously at your afs root directory and then soft link it at your lfs directory (which should be your $HOME path).
The main difference is that instead of soft linking a file, we will soft linke a directory.
So cd to your afs root directory.
If you correctly edited your .bashrc then you should be able to do to move to your afs root:
cd $AFStip: do echo $AFS or to any environment variable to see the contents of them!).
If that didn't work, fix your .bashrc and do the previous command (e.g., make sure .bashrc exports $AFS, change all brando9 instances with your CSID, and potentially see the original .bashrc if something doesn't work or even start with a new .bashrc again.
Note, cd $AFS should be equivalent to doing (with your CSID):
cd /afs/cs.stanford.edu/u/brando9Since AFS should be point to your afs due to this command export AFS=/afs/cs.stanford.edu/u/brando9 in the .bashrc.
Sample output (understand the commands):
# after sshing into a snap node or running bash on your terminal (cli)
(evals_af) brando9@skampere1~ $
(evals_af) brando9@skampere1~ $ realpath .
/lfs/skampere1/0/brando9
(evals_af) brando9@skampere1~ $ pwd
/lfs/skampere1/0/brando9
(evals_af) brando9@skampere1~ $ cd $AFS
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ pwd
/afs/cs.stanford.edu/u/brando9
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ realpath .
/afs/cs.stanford.edu/u/brando9Now that we are at our afs home root directory, let's git clone your git fork.
First git fork this repo using the github UI (likely best option is to Google this or ChatGPT it).
This step is really showing you to git clone any github repo to your afs (npte: later you will git clone your actual project's repo and repeat cloning it to your afs every time you have a new repo you want to use for snap.
Note: if you're following this setup, since we are using lfs due to storage issues with afs you will need to set up a new conda env each time use a new node, although the repo might be available already).
Git clone this repo with using ssh path from the github repo option and check that it was downloaded with ls -lah and realpath
e.g.,:
cd $AFS
git clone git@github.com:brando90/snap-cluster-setup.git
ls -lah
realpath snap-cluster-setupSample output:
(evals_af) brando9@skampere1~ $ cd $AFS
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ git clone git@github.com:brando90/snap-cluster-setup.git
Cloning into 'snap-cluster-setup'...
remote: Enumerating objects: 906, done.
remote: Counting objects: 100% (360/360), done.
remote: Compressing objects: 100% (134/134), done.
remote: Total 906 (delta 284), reused 291 (delta 225), pack-reused 546
Receiving objects: 100% (906/906), 74.63 MiB | 7.03 MiB/s, done.
Resolving deltas: 100% (503/503), done.
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ ls -lah
total 74M
drwxrwxrwx 29 brando9 users 4.0K Apr 3 13:41 .
drwxr-xr-x 2 root users 520K Apr 1 11:10 ..
...
-rw-r--r-- 1 brando9 users 8.8K Jan 29 16:33 .bashrc
...
drwxr-xr-x 5 brando9 users 2.0K Apr 3 16:50 snap-cluster-setup
...
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ realpath snap-cluster-setup
/afs/cs.stanford.edu/u/brando9/snap-cluster-setupNote that since you are at your root afs, the .bashrc file is not a soft link.
Your snap-cluster-setup should also not be a soft link at your root afs directory.
Verify that with realpath (and not pwd):
pwd .bashrc
realpath .bashrc
pwd snap-cluster-setup/
realpath snap-cluster-setup/Sample output:
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ pwd .bashrc
/afs/cs.stanford.edu/u/brando9
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ realpath .bashrc
/afs/cs.stanford.edu/u/brando9/.bashrc
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ pwd snap-cluster-setup/
/afs/cs.stanford.edu/u/brando9
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ realpath snap-cluster-setup/
/afs/cs.stanford.edu/u/brando9/snap-cluster-setupWe will create a soft link as we previously did in your home lfs path for the github project you just cloned, and santiy check it is a soft link (similar to your .bashrc file).
Run one command at a time and see output (we strongly suggest against running command blindly, e.g., if one doesn't work, then how will you fix it if you don't know what you've run so far?):
echo $HOME
# cd ~
cd $HOME
# ln -s /afs/cs.stanford.edu/u/brando9/snap-cluster-setup $HOME/snap-cluster-setup
ln -s $AFS/snap-cluster-setup $HOME/snap-cluster-setup
ls -lah
pwd $HOME/snap-cluster-setup
realpath $HOME/snap-cluster-setup(Tip: to learn about the equivalence of ~ and $HOME, re-run the pwd command with ~ instead of $HOME.
What happens?)
Sample output:
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ echo $HOME
/lfs/skampere1/0/brando9
(evals_af) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ cd $HOME
(evals_af) brando9@skampere1~ $ ln -s $AFS/snap-cluster-setup $HOME/snap-cluster-setup
(evals_af) brando9@skampere1~ $ ln -s $AFS/snap-cluster-setup $HOME/snap-cluster-setup
(evals_af) brando9@skampere1~ $ ls -lah
total 99M
drwxr-sr-x 22 brando9 root 4.0K Apr 3 19:39 .
drwxrwsrwt 24 root root 4.0K Apr 3 18:33 ..
...
lrwxrwxrwx 1 brando9 root 38 Oct 27 12:57 .bashrc -> /afs/cs.stanford.edu/u/brando9/.bashrc
...
lrwxrwxrwx 1 brando9 root 49 Apr 3 11:02 snap-cluster-setup -> /afs/cs.stanford.edu/u/brando9/snap-cluster-setup
...
(evals_af) brando9@skampere1~ $ pwd $HOME/snap-cluster-setup
/lfs/skampere1/0/brando9
(evals_af) brando9@skampere1~ $ realpath $HOME/snap-cluster-setup
/afs/cs.stanford.edu/u/brando9/snap-cluster-setupTip: ls is a very useful command. What is is? Try ls vs ls -l vs ls -la vs ls -lah.
What is the difference? Use the man command or help flag to learn more about it.
This sanity checks that your snap-cluster-setup indeed is locacted at your root afs but a soft link is at your root lfs path.
This should convince you by looking where .bashrc points too and where your github repo called snap-cluster-setup points too.
Note: you have to repeat the above ach time you set up a new github repo in SNAP.
This is because you have not cloned the repo to afs to be shared accross all the SNAP nodes.
In addition afs is small (but fast) and dfs is useless in this cluster sadly, so we put the code in afs so it's shared accross all nodes and not use dfs.
When you have python projects, they usually have libraries they depend on. These are called depedencies (Google it to learn more!). So that these depedency installations for different projects in the same machine do not conflict we have different "https://csguide.cs.princeton.edu/software/virtualenv#:~:text=A%20Python%20virtual%20environment%20(venv,installed%20in%20the%20specific%20venv.) is and create one using conda" (also called conda environments or virtual environments). Approximately, these are different locations in your system will your the installations for each project and setting up usually "secret" enviornment variable depending what depedency management system you are using e.g., conda or virtual env or poetry etc. This is usually specific for each programming langauge you use (e.g., pip, conda for python, opam for coq, etc.) so it's often hard to remember the details and often people learn one setup and re-use it all the time.
For snap we will use conda. First check if you have conda in your cli by doing
which conda Pro Tip: if you are using vscode make sure your vscode is pointing to the python or conda env you want for your project, usually you set it up at the bottom right in vscode. Sample output:
(base) brando9@skampere1~ $ which conda
/lfs/skampere1/0/brando9/miniconda/bin/condaTip: a nice sanity check is check which binary the default/system is using. Usually by doing conda deactivate and then which python in the cli.
If the previous command doesn't work then we will have to locally install conda (i.e., you will install conda in lfs by putting it's binary in a location you have permission to use).
When you have a cluster usually the system admins install software you need and usually you do not have sudo priviledges (google that or do man sudo t learn!).
Otherwise a trick to go around it is to install locally as we are about to do (but it's not usually recommended).
Install conda (and update pip) if it's missing by running the following commands (do not run them blindly, see the output before proceeding to the next one):
echo $HOME
cd $HOME
# -- Install miniconda
# get conda from the web
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
# source conda with the bash command and put the installation at $HOME/miniconda
bash $HOME/miniconda.sh -b -p $HOME/miniconda
# activate conda
source $HOME/miniconda/bin/activate
# - Set up conda
conda init
conda init bash
conda install conda-build
conda update -n base -c defaults conda
conda update conda
# - Make sure pip is up to date
pip install --upgrade pip
pip3 install --upgrade pip
which pip
which pip3
# - Restart your bash terminal
bashThe previous commands take a while so instead I will only demonstrate two commands to check if conda was installed right.
Make sure you have a fresh bash terminal (understand the commands):
# which conda binary are we using?
which conda
# which python binary are we using?
which python
# which conda envs do we have?
conda info -esample output:
(base) brando9@skampere1~ $ which conda
/lfs/skampere1/0/brando9/miniconda/bin/conda
(base) brando9@skampere1~ $ which python
/lfs/skampere1/0/brando9/miniconda/bin/python
(base) brando9@skampere1~ $ conda info -e
# conda environments:
#
base * /lfs/skampere1/0/brando9/miniconda
beyond_scale /lfs/skampere1/0/brando9/miniconda/envs/beyond_scale
evaporate /lfs/skampere1/0/brando9/miniconda/envs/evaporate
lean4ai /lfs/skampere1/0/brando9/miniconda/envs/lean4ai
maf /lfs/skampere1/0/brando9/miniconda/envs/maf
my_env /lfs/skampere1/0/brando9/miniconda/envs/my_env
olympus /lfs/skampere1/0/brando9/miniconda/envs/olympus
pred_llm_evals_env /lfs/skampere1/0/brando9/miniconda/envs/pred_llm_evals_env
putnam_math /lfs/skampere1/0/brando9/miniconda/envs/putnam_math
snap_cluster_setup /lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setupIt would be ideal to create a single conda env for each project and put it in afs so that all nodes can use all the code for your github project and it's depedencies too.
Unfortuantely, installing depedencies usually takes a lot of space.
Thus we will install conda and create a conda env for each github project with it's installed depedencies in each node's lfs.
Thus, create a new conda env for this snap-cluster-setup tutorial:
# - check envs
conda info -e
# - activate conda
conda update
pip install --upgrade pip
# - create conda env (note: vllm has issues with 3.10 so we are using 3.9)
conda create -n snap_cluster_setup python=3.9
# - activate your conda env
conda activate snap_cluster_setupNote: I try to use pip as much as I can instead of conda to install my packages (or be consistent with which one I use, but sometimes you will have to combine each one since package management and installation can be tricky and hacky. Welcome to the real world!). Sample output:
(base) brando9@skampere1~ $ conda create -n snap_cluster_setup python=3.9
Retrieving notices: ...working... done
Channels:
- defaults
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setup
added / updated specs:
- python=3.9
The following NEW packages will be INSTALLED:
_libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main
_openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu
ca-certificates pkgs/main/linux-64::ca-certificates-2024.3.11-h06a4308_0
ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.38-h1181459_1
libffi pkgs/main/linux-64::libffi-3.4.4-h6a678d5_0
libgcc-ng pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1
libgomp pkgs/main/linux-64::libgomp-11.2.0-h1234567_1
libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1
ncurses pkgs/main/linux-64::ncurses-6.4-h6a678d5_0
openssl pkgs/main/linux-64::openssl-3.0.13-h7f8727e_0
pip pkgs/main/linux-64::pip-23.3.1-py39h06a4308_0
python pkgs/main/linux-64::python-3.9.19-h955ad1f_0
readline pkgs/main/linux-64::readline-8.2-h5eee18b_0
setuptools pkgs/main/linux-64::setuptools-68.2.2-py39h06a4308_0
sqlite pkgs/main/linux-64::sqlite-3.41.2-h5eee18b_0
tk pkgs/main/linux-64::tk-8.6.12-h1ccaba5_0
tzdata pkgs/main/noarch::tzdata-2024a-h04d1e81_0
wheel pkgs/main/linux-64::wheel-0.41.2-py39h06a4308_0
xz pkgs/main/linux-64::xz-5.4.6-h5eee18b_0
zlib pkgs/main/linux-64::zlib-1.2.13-h5eee18b_0
Proceed ([y]/n)? y
Downloading and Extracting Packages:
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate snap_cluster_setup
#
# To deactivate an active environment, use
#
# $ conda deactivate
(base) brando9@skampere1~ $ conda activate snap_cluster_setup
(snap_cluster_setup) brando9@skampere1~ $ which python
/lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setup/bin/python
(snap_cluster_setup) brando9@skampere1~ $ pip install --upgrade pip
Requirement already satisfied: pip in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (24.0)
(snap_cluster_setup) brando9@skampere1~ $ pip install wandb
Requirement already satisfied: wandb in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (0.16.6)
Requirement already satisfied: Click!=8.0.0,>=7.1 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (8.1.7)
Requirement already satisfied: GitPython!=3.1.29,>=1.0.0 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (3.1.43)
Requirement already satisfied: requests<3,>=2.0.0 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (2.31.0)
Requirement already satisfied: psutil>=5.0.0 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (5.9.8)
Requirement already satisfied: sentry-sdk>=1.0.0 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (1.44.1)
Requirement already satisfied: docker-pycreds>=0.4.0 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (0.4.0)
Requirement already satisfied: PyYAML in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (6.0.1)
Requirement already satisfied: setproctitle in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (1.3.3)
Requirement already satisfied: setuptools in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (68.2.2)
Requirement already satisfied: appdirs>=1.4.3 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (1.4.4)
Requirement already satisfied: typing-extensions in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (4.11.0)
Requirement already satisfied: protobuf!=4.21.0,<5,>=3.15.0 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from wandb) (4.25.3)
Requirement already satisfied: six>=1.4.0 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from docker-pycreds>=0.4.0->wandb) (1.16.0)
Requirement already satisfied: gitdb<5,>=4.0.1 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from GitPython!=3.1.29,>=1.0.0->wandb) (4.0.11)
Requirement already satisfied: charset-normalizer<4,>=2 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from requests<3,>=2.0.0->wandb) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from requests<3,>=2.0.0->wandb) (3.6)
Requirement already satisfied: urllib3<3,>=1.21.1 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from requests<3,>=2.0.0->wandb) (2.2.1)
Requirement already satisfied: certifi>=2017.4.17 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from requests<3,>=2.0.0->wandb) (2024.2.2)
Requirement already satisfied: smmap<6,>=3.0.1 in ./miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb) (5.0.1)Note: to remove a conda env do conda remove --name snap_cluster_setup --all, careful!
Note: sometimes you might have to do pip3, not sure why pip is inconsistent.
At this point, you know what the environment variable $HOME is, and it is pointing to your/any node’s lfs path as suggested by this .bahrc file (but using your CSID instead of Brando’s). In addition, you have created a fork of the snap-cluster-setup github repo in your afs root, and made sure you have a soft link in your lfs pointing to the afs path of the github repo. You also created a conda environment in your lfs (NOT your afs). You also know why we put large storage things (like conda installations and data or models) in your working node’s lfs and not lfs or afs (if not review it here).
Every programming language has it's own way to organize installations of the project itself and it's depedencies.
In python usually you have one folder called src where the python code goes and you tell setup.py that it is the projects root package.
For details see the comments in this project setup.py.
In particular see the package_dir={'': 'src'} line and packages=find_packages('src') and the accompanying documentation and the setuptools website to get a feel of how python packages are installed.
Note: one reason that file is heavily documented is because I often forget how python packages have to be install (since it's programming language depdent e.g., Coq and Lean are different).
Now that you know how the setup.py has to be set up for this projects source code to work and with an example of the dependencies, let’s install this code's python repo.
For this we will pip install this project in editable (development) mode, usually done with pip install -e . or pip install -e <path_2_setup.py> or pip install -e $HOME/setup.py.
Usually a python project is installed with pip install <package_name> and that install a fixed usually non-editable version of it.
If we do pip install <package_name> it won't work since our project has not been pushed to the internat.
Instead we will install locally an editable version -- so that whenever you make code edits you run the new code.
For that you will run one of the following editable install commands:
cd <path_2_your_project_root>
pip install -e .
# OR
pip install -e $HOME/setup.py
# check it installed
pip list | grep <name_github_repo>
# check all installations
pip listThis will install this package and all it's depedencies in the location where you installed conda (for us ~/minicoda or sometimes in other places like ~/opt or even ~/.minicoda but it is conda installation dedepdent!).
Note: you can also check the installations conda has done so far with conda list (but, perhaps confusingly, we are avoiding conda to install).
Sample output (note: I pasted the whole thing to emphasize I am reading the output, because if something does NOT isntall you usually have to fix it! e.g., a common issue is the version of pytorch or vllm and it has to be the right version according to the hardware you use and sometimes you have to manually install and unistall packages. So don't ever assume the installation instructions work for your setting!):
(snap_cluster_setup) brando9@skampere1~ $ cd ~/snap-cluster-setup
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ pip install -e .
Obtaining file:///afs/cs.stanford.edu/u/brando9/snap-cluster-setup
Preparing metadata (setup.py) ... done
Collecting dill (from snap-cluster-setup==0.0.1)
Using cached dill-0.3.8-py3-none-any.whl.metadata (10 kB)
Collecting networkx>=2.5 (from snap-cluster-setup==0.0.1)
Using cached networkx-3.2.1-py3-none-any.whl.metadata (5.2 kB)
Collecting scipy (from snap-cluster-setup==0.0.1)
Using cached scipy-1.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)
Collecting scikit-learn (from snap-cluster-setup==0.0.1)
Using cached scikit_learn-1.4.1.post1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
Collecting lark-parser (from snap-cluster-setup==0.0.1)
Using cached lark_parser-0.12.0-py2.py3-none-any.whl.metadata (1.7 kB)
Collecting tensorboard (from snap-cluster-setup==0.0.1)
Using cached tensorboard-2.16.2-py3-none-any.whl.metadata (1.6 kB)
Collecting pandas (from snap-cluster-setup==0.0.1)
Using cached pandas-2.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)
Collecting progressbar2 (from snap-cluster-setup==0.0.1)
Using cached progressbar2-4.4.2-py3-none-any.whl.metadata (17 kB)
Collecting requests (from snap-cluster-setup==0.0.1)
Using cached requests-2.31.0-py3-none-any.whl.metadata (4.6 kB)
Collecting aiohttp (from snap-cluster-setup==0.0.1)
Using cached aiohttp-3.9.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.4 kB)
Collecting numpy (from snap-cluster-setup==0.0.1)
Using cached numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)
Collecting plotly (from snap-cluster-setup==0.0.1)
Using cached plotly-5.20.0-py3-none-any.whl.metadata (7.0 kB)
Collecting wandb (from snap-cluster-setup==0.0.1)
Using cached wandb-0.16.6-py3-none-any.whl.metadata (10 kB)
Collecting matplotlib (from snap-cluster-setup==0.0.1)
Using cached matplotlib-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.8 kB)
Collecting twine (from snap-cluster-setup==0.0.1)
Using cached twine-5.0.0-py3-none-any.whl.metadata (3.3 kB)
Collecting torch==2.1.2 (from snap-cluster-setup==0.0.1)
Using cached torch-2.1.2-cp39-cp39-manylinux1_x86_64.whl.metadata (25 kB)
Collecting transformers==4.39.2 (from snap-cluster-setup==0.0.1)
Using cached transformers-4.39.2-py3-none-any.whl.metadata (134 kB)
Collecting datasets==2.18.0 (from snap-cluster-setup==0.0.1)
Using cached datasets-2.18.0-py3-none-any.whl.metadata (20 kB)
Collecting filelock (from datasets==2.18.0->snap-cluster-setup==0.0.1)
Using cached filelock-3.13.3-py3-none-any.whl.metadata (2.8 kB)
Collecting pyarrow>=12.0.0 (from datasets==2.18.0->snap-cluster-setup==0.0.1)
Using cached pyarrow-15.0.2-cp39-cp39-manylinux_2_28_x86_64.whl.metadata (3.0 kB)
Collecting pyarrow-hotfix (from datasets==2.18.0->snap-cluster-setup==0.0.1)
Using cached pyarrow_hotfix-0.6-py3-none-any.whl.metadata (3.6 kB)
Collecting tqdm>=4.62.1 (from datasets==2.18.0->snap-cluster-setup==0.0.1)
Using cached tqdm-4.66.2-py3-none-any.whl.metadata (57 kB)
Collecting xxhash (from datasets==2.18.0->snap-cluster-setup==0.0.1)
Using cached xxhash-3.4.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)
Collecting multiprocess (from datasets==2.18.0->snap-cluster-setup==0.0.1)
Using cached multiprocess-0.70.16-py39-none-any.whl.metadata (7.2 kB)
Collecting fsspec<=2024.2.0,>=2023.1.0 (from fsspec[http]<=2024.2.0,>=2023.1.0->datasets==2.18.0->snap-cluster-setup==0.0.1)
Using cached fsspec-2024.2.0-py3-none-any.whl.metadata (6.8 kB)
Collecting huggingface-hub>=0.19.4 (from datasets==2.18.0->snap-cluster-setup==0.0.1)
Using cached huggingface_hub-0.22.2-py3-none-any.whl.metadata (12 kB)
Collecting packaging (from datasets==2.18.0->snap-cluster-setup==0.0.1)
Using cached packaging-24.0-py3-none-any.whl.metadata (3.2 kB)
Collecting pyyaml>=5.1 (from datasets==2.18.0->snap-cluster-setup==0.0.1)
Using cached PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.1 kB)
Collecting typing-extensions (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached typing_extensions-4.11.0-py3-none-any.whl.metadata (3.0 kB)
Collecting sympy (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached sympy-1.12-py3-none-any.whl.metadata (12 kB)
Collecting jinja2 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached Jinja2-3.1.3-py3-none-any.whl.metadata (3.3 kB)
Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cublas-cu12==12.1.3.1 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cufft-cu12==11.0.2.54 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-curand-cu12==10.3.2.106 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-nccl-cu12==2.18.1 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_nccl_cu12-2.18.1-py3-none-manylinux1_x86_64.whl.metadata (1.8 kB)
Collecting nvidia-nvtx-cu12==12.1.105 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)
Collecting triton==2.1.0 (from torch==2.1.2->snap-cluster-setup==0.0.1)
Using cached triton-2.1.0-0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.3 kB)
Collecting regex!=2019.12.17 (from transformers==4.39.2->snap-cluster-setup==0.0.1)
Using cached regex-2023.12.25-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (40 kB)
Collecting tokenizers<0.19,>=0.14 (from transformers==4.39.2->snap-cluster-setup==0.0.1)
Using cached tokenizers-0.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)
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Installing collected packages: pytz, nh3, mpmath, lark-parser, appdirs, zipp, xxhash, urllib3, tzdata, typing-extensions, tqdm, threadpoolctl, tensorboard-data-server, tenacity, sympy, smmap, six, setproctitle, safetensors, rfc3986, regex, pyyaml, pyparsing, Pygments, pycparser, pyarrow-hotfix, psutil, protobuf, pkginfo, pillow, packaging, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, multidict, more-itertools, mdurl, MarkupSafe, kiwisolver, joblib, jeepney, idna, grpcio, fsspec, frozenlist, fonttools, filelock, docutils, dill, cycler, Click, charset-normalizer, certifi, backports.tarfile, attrs, async-timeout, absl-py, yarl, werkzeug, triton, sentry-sdk, scipy, requests, readme-renderer, python-utils, python-dateutil, pyarrow, plotly, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, markdown-it-py, jinja2, jaraco.functools, jaraco.context, jaraco.classes, importlib-resources, importlib-metadata, gitdb, docker-pycreds, contourpy, cffi, aiosignal, scikit-learn, rich, requests-toolbelt, progressbar2, pandas, nvidia-cusolver-cu12, matplotlib, markdown, huggingface-hub, GitPython, cryptography, aiohttp, wandb, torch, tokenizers, tensorboard, SecretStorage, transformers, keyring, datasets, twine, snap-cluster-setup
Running setup.py develop for snap-cluster-setup
Successfully installed Click-8.1.7 GitPython-3.1.43 MarkupSafe-2.1.5 Pygments-2.17.2 SecretStorage-3.3.3 absl-py-2.1.0 aiohttp-3.9.3 aiosignal-1.3.1 appdirs-1.4.4 async-timeout-4.0.3 attrs-23.2.0 backports.tarfile-1.0.0 certifi-2024.2.2 cffi-1.16.0 charset-normalizer-3.3.2 contourpy-1.2.1 cryptography-42.0.5 cycler-0.12.1 datasets-2.18.0 dill-0.3.8 docker-pycreds-0.4.0 docutils-0.20.1 filelock-3.13.3 fonttools-4.51.0 frozenlist-1.4.1 fsspec-2024.2.0 gitdb-4.0.11 grpcio-1.62.1 huggingface-hub-0.22.2 idna-3.6 importlib-metadata-7.1.0 importlib-resources-6.4.0 jaraco.classes-3.4.0 jaraco.context-5.3.0 jaraco.functools-4.0.0 jeepney-0.8.0 jinja2-3.1.3 joblib-1.4.0 keyring-25.1.0 kiwisolver-1.4.5 lark-parser-0.12.0 markdown-3.6 markdown-it-py-3.0.0 matplotlib-3.8.4 mdurl-0.1.2 more-itertools-10.2.0 mpmath-1.3.0 multidict-6.0.5 multiprocess-0.70.16 networkx-3.2.1 nh3-0.2.17 numpy-1.26.4 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.18.1 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 packaging-24.0 pandas-2.2.1 pillow-10.3.0 pkginfo-1.10.0 plotly-5.20.0 progressbar2-4.4.2 protobuf-4.25.3 psutil-5.9.8 pyarrow-15.0.2 pyarrow-hotfix-0.6 pycparser-2.22 pyparsing-3.1.2 python-dateutil-2.9.0.post0 python-utils-3.8.2 pytz-2024.1 pyyaml-6.0.1 readme-renderer-43.0 regex-2023.12.25 requests-2.31.0 requests-toolbelt-1.0.0 rfc3986-2.0.0 rich-13.7.1 safetensors-0.4.2 scikit-learn-1.4.1.post1 scipy-1.13.0 sentry-sdk-1.44.1 setproctitle-1.3.3 six-1.16.0 smmap-5.0.1 snap-cluster-setup-0.0.1 sympy-1.12 tenacity-8.2.3 tensorboard-2.16.2 tensorboard-data-server-0.7.2 threadpoolctl-3.4.0 tokenizers-0.15.2 torch-2.1.2 tqdm-4.66.2 transformers-4.39.2 triton-2.1.0 twine-5.0.0 typing-extensions-4.11.0 tzdata-2024.1 urllib3-2.2.1 wandb-0.16.6 werkzeug-3.0.2 xxhash-3.4.1 yarl-1.9.4 zipp-3.18.1
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ pip list | grep snap-cluster-setup
snap-cluster-setup 0.0.1 /afs/cs.stanford.edu/u/brando9/snap-cluster-setup/src
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ pip list
Package Version Editable project location
------------------------ ----------- -----------------------------------------------------
absl-py 2.1.0
accelerate 0.29.1
aiohttp 3.9.3
aiosignal 1.3.1
appdirs 1.4.4
async-timeout 4.0.3
attrs 20.3.0
bitsandbytes 0.43.0
bnb 0.3.0
certifi 2024.2.2
charset-normalizer 3.3.2
click 7.1.2
contourpy 1.2.1
cycler 0.12.1
datasets 2.18.0
dill 0.3.8
docker-pycreds 0.4.0
filelock 3.13.3
fonttools 4.51.0
frozenlist 1.4.1
fsspec 2024.2.0
gitdb 4.0.11
GitPython 3.1.43
grpcio 1.62.1
huggingface-hub 0.22.2
idna 3.6
importlib_metadata 7.1.0
importlib_resources 6.4.0
jax 0.4.26
Jinja2 3.1.3
joblib 1.4.0
kiwisolver 1.4.5
Markdown 3.6
MarkupSafe 2.1.5
matplotlib 3.8.4
ml-dtypes 0.4.0
mpmath 1.3.0
multidict 6.0.5
multiprocess 0.70.16
networkx 3.2.1
numpy 1.26.4
nvidia-cublas-cu12 12.1.3.1
nvidia-cuda-cupti-cu12 12.1.105
nvidia-cuda-nvrtc-cu12 12.1.105
nvidia-cuda-runtime-cu12 12.1.105
nvidia-cudnn-cu12 8.9.2.26
nvidia-cufft-cu12 11.0.2.54
nvidia-curand-cu12 10.3.2.106
nvidia-cusolver-cu12 11.4.5.107
nvidia-cusparse-cu12 12.1.0.106
nvidia-nccl-cu12 2.19.3
nvidia-nvjitlink-cu12 12.4.127
nvidia-nvtx-cu12 12.1.105
opt-einsum 3.3.0
packaging 24.0
pandas 2.2.1
patsy 0.5.6
pillow 10.3.0
pip 24.0
plotly 5.20.0
progressbar2 4.4.2
prompt-toolkit 3.0.43
protobuf 4.25.3
psutil 5.9.8
pyarrow 15.0.2
pyarrow-hotfix 0.6
pyparsing 3.1.2
python-dateutil 2.9.0.post0
python-utils 3.8.2
pytz 2024.1
PyYAML 5.4.1
questionary 1.10.0
regex 2023.12.25
requests 2.31.0
safetensors 0.4.2
scikit-learn 1.4.1.post1
scipy 1.13.0
seaborn 0.13.2
sentencepiece 0.2.0
sentry-sdk 1.44.1
setproctitle 1.3.3
setuptools 68.2.2
six 1.16.0
smart-getenv 1.1.0
smmap 5.0.1
snap-cluster-setup 0.0.1 /afs/cs.stanford.edu/u/brando9/snap-cluster-setup/src
statsmodels 0.14.1
sympy 1.12
tenacity 8.2.3
tensorboard 2.16.2
tensorboard-data-server 0.7.2
threadpoolctl 3.4.0
tokenizers 0.15.2
torch 2.2.2
torchaudio 2.2.2
torchvision 0.17.2
tqdm 4.66.2
transformers 4.39.3
triton 2.2.0
typing_extensions 4.11.0
tzdata 2024.1
urllib3 2.2.1
wandb 0.16.6
wcwidth 0.2.13
Werkzeug 3.0.2
wheel 0.41.2
xxhash 3.4.1
yarl 1.9.4
zipp 3.18.1
zstandard 0.22.0Note, no errors! But the version of pytorch chaged and it is good to be aware of them or anything funny that might have happened.
Now that we pip installed this project in editable, we will play around with imports and make sure we installed our project in editable mode. Fire up a python interactive shell or REPL (REPL stands for Read-Eval-Print).
pythonthen import the function to print hello from the hello_world.py module in src:
from hello_world import print_hello_snapSample output;
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ python
Python 3.9.19 (main, Mar 21 2024, 17:11:28)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from hello_world import print_hello_snap
>>> print_hello_snap()
Hello, World from the Snap Cluster Setup src!Now check that your imports work! i.e., that you can use code form another file/package:
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ python
Python 3.9.19 (main, Mar 21 2024, 17:11:28)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from hello_world import print_from_another
>>> print_from_another()
anotherNow let's try the same but by running a file explicitly: For that we will do:
python /lfs/skampere1/0/brando9/snap-cluster-setup/src/hello_world.py
# Or when in your projects root folder usually
python src/hello_world.pySample output:
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ python /lfs/skampere1/0/brando9/snap-cluster-setup/src/hello_world.py
Hello, World from the Snap Cluster Setup src!
EDIT THIS
another
Time taken: 0.00 seconds, or 0.00 minutes, or 0.00 hours.
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ python src/hello_world.py
Hello, World from the Snap Cluster Setup src!
EDIT THIS
another
Time taken: 0.00 seconds, or 0.00 minutes, or 0.00 hours.This is where you learn that imports "run" the code they import and that __name__ == "__main__": is the code that is run only when you explicitly run this file.
This python specific.
I personally use __name__ == "__main__": to put code for unit tests (but really you can do whatever you want).
Usually installing pytorch can be non-trivial.
For example, depending on other depedencies or specific python version you might need, it can lead to taking a few hours downgrading pytorch versions, cuda version, python versions etc. until the fragile depedency set up is done.
In addition, if the node or cluster is not set up the way you expect, you mind need to e-mail the system admins until the version of cuda (gpu lib for pytorch) you need is installed with the right version and the right environment variables.
In this case because you likely will use the vllm library for fast inference with llms, it means we are forced to use python 3.9.
In this case I already figure out the right version of pytorch from this vllm git issue. and put the right versions of everything in the setup.py file.
Therefore, you only need to run these commands to check that pytorch with a gpu works:
python -c "import torch; print(torch.__version__); print(torch.version.cuda); print((torch.randn(2, 4).cuda() @ torch.randn(4, 1).cuda()))"
python -c "import torch; print(torch.version.cuda); print((torch.randn(2, 4).cuda() @ torch.randn(4, 1).cuda()))"
python -c "import torch; print(f'{torch.cuda.device_count()=}'); print(f'Device: {torch.cuda.get_device_name(0)=}')"Note: the -c is just the quick way to open a python shell and run a command without going manually into the python shell.
Sample output:
(snap_cluster_setup) brando9@skampere1~ $ python -c "import torch; print(torch.__version__); print(torch.version.cuda); print((torch.randn(2, 4).cuda() @ torch.randn(4, 1).cuda()))"
"import torch; print(torch.version.cuda); print((torch.randn(2, 4).cuda() @ torch.randn(4, 1).cuda()))"
python -c "import torch; print(f'{torch.cuda.device_count()=}'); print(f'Device: {torch.cuda.get_device_name(0)=}')"
2.1.2+cu121
12.1
tensor([[1.5192],
[2.3463]], device='cuda:0')
(snap_cluster_setup) brando9@skampere1~ $ python -c "import torch; print(torch.version.cuda); print((torch.randn(2, 4).cuda() @ torch.randn(4, 1).cuda()))"
12.1
tensor([[-0.5750],
[ 0.2061]], device='cuda:0')
(snap_cluster_setup) brando9@skampere1~ $ python -c "import torch; print(f'{torch.cuda.device_count()=}'); print(f'Device: {torch.cuda.get_device_name(0)=}')"
torch.cuda.device_count()=1
Device: torch.cuda.get_device_name(0)='NVIDIA A100-SXM4-80GB'Note: read the commands and understand what they are. Also, let's test vllm:
python ~/snap-cluster-setup/src/test_vllm.pyNote: if you ever use ~ inside python you need to use your some library to expand the user path e.g., os.path.expanduser(path_string).
Sample output:
(snap_cluster_setup) brando9@skampere1~ $ python /lfs/skampere1/0/brando9/snap-cluster-setup/src/test_vllm.py
INFO 04-08 19:52:46 llm_engine.py:74] Initializing an LLM engine (v0.4.0.post1) with config: model='facebook/opt-125m', tokenizer='facebook/opt-125m', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=auto, tensor_parallel_size=1, disable_custom_all_reduce=True, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, seed=0)
INFO 04-08 19:52:47 selector.py:51] Cannot use FlashAttention because the package is not found. Please install it for better performance.
INFO 04-08 19:52:47 selector.py:25] Using XFormers backend.
INFO 04-08 19:52:48 weight_utils.py:177] Using model weights format ['*.bin']
INFO 04-08 19:52:49 model_runner.py:104] Loading model weights took 0.2389 GB
INFO 04-08 19:52:49 gpu_executor.py:94] # GPU blocks: 127981, # CPU blocks: 7281
INFO 04-08 19:52:51 model_runner.py:791] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
INFO 04-08 19:52:51 model_runner.py:795] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
INFO 04-08 19:52:53 model_runner.py:867] Graph capturing finished in 2 secs.
Processed prompts: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 53.56it/s]
Prompt: 'Hello, my name is', Generated text: ' Joel, my dad is my friend and we are in a relationship. I am'
Prompt: 'The president of the United States is', Generated text: ' speaking out against the release of some State Department documents which show the Russians are trying'
Prompt: 'The capital of France is', Generated text: ' known as the “Proud French capital”. What is this city'
Prompt: 'The future of AI is', Generated text: ' literally in danger of being taken by any other company.\nAgreed. '
Time taken: 8.17 seconds, or 0.14 minutes, or 0.00 hours.Note: inspect the test_vllm.py file, and note vscode might complain it's not installed. I think this happens because it's such optimized code that it's not quite installed in the "standard pytorch way" e.g., the binaries are stored instead of the stnadard pytorch bytes (not sure).
It is often that you are sharing GPUs with other.
Usually people share GPUs (or compute) with the slurm workload manger.
But in the SNAP cluster we loging to nodes directly.
Therefore, you might need to make sure the right GPU is available e.g., "righ" meaning one with enough memory for example.
For that one can do nvia-smi.
Do nvidia-smi:
nvidia-smi
nvcc --versionSample output:
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ nvidia-smi
Mon Apr 8 20:02:00 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.54.03 Driver Version: 535.54.03 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA A100-SXM4-80GB On | 00000000:07:00.0 Off | 0 |
| N/A 31C P0 64W / 350W | 4MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
| 1 NVIDIA A100-SXM4-80GB On | 00000000:0A:00.0 Off | 0 |
| N/A 29C P0 67W / 350W | 4MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
| 2 NVIDIA A100-SXM4-80GB On | 00000000:44:00.0 Off | 0 |
| N/A 29C P0 66W / 350W | 4MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
| 3 NVIDIA A100-SXM4-80GB On | 00000000:4A:00.0 Off | 0 |
| N/A 33C P0 65W / 350W | 4MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
| 4 NVIDIA A100-SXM4-80GB On | 00000000:84:00.0 Off | 0 |
| N/A 33C P0 64W / 350W | 4MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
| 5 NVIDIA A100-SXM4-80GB On | 00000000:8A:00.0 Off | 0 |
| N/A 28C P0 61W / 350W | 4MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
| 6 NVIDIA A100-SXM4-80GB On | 00000000:C0:00.0 Off | 0 |
| N/A 29C P0 64W / 350W | 4MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
| 7 NVIDIA A100-SXM4-80GB On | 00000000:C3:00.0 Off | 0 |
| N/A 31C P0 62W / 350W | 4MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage uname -a|
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Tue_May__3_18:49:52_PDT_2022
Cuda compilation tools, release 11.7, V11.7.64
Build cuda_11.7.r11.7/compiler.31294372_0The last command is for:
The command nvcc --version is used to check the version of the NVIDIA CUDA Compiler (NVCC) installed on your system. To change gpu you can do it with the following env variable:
export $CUDA_VISIBLE_DEVICES=1for multiple:
CUDA_VISIBLE_DEVICES=0,7Tip: as reference, I always suggest to at least skim the suggested ways to use resources according to the admins/"official wiki" of the compute cluster you're using -- in this case skim the suggested way to use GPUs in SNAP.
Usually one of the reasons to use a compute cluster, is to dispatch/run long running jobs.
In a standard cluster (note SNAP's set up is not standard) one usually uses a workload manager like slurm is more standard.
This workload manger (e.g., slurm) usually has commands like srun or sbatch to run an interactive job and a long running background job respectively.
We will mimic these slurm commands and run them manually.
The standard workflow in slurm is:
- ssh/login into the ("non compute") head node (where your code lies in). You do not run real jobs here, it's only for scheduling the jobs with slurm.
- get your script, data, command etc. ready and ask slurm to run it (
srunif you need a cli terminal to interact with your job e.g., to get a gpu for debugging orsbatchto run a long running job)
In SNAP we will do the following:
- Since we can ssh directly into compute nodes (e.g., with GPUS) -- we don't need
srun - For long running jobs we will use (kerberos)
tmuxto run (because that is the recommended way to do it in SNAP)
Note: usually the system admins set up slurm so that you minimally have to worry about the file system (afs, lfs, dfs). It usually "just works". If not, they tell you how to take care of it and it's only difficult/extra work if you use a lot of storage or many files (or file descriptors).
You should be logged in to a (compute) node already. In this case when you ran GPU commands in the python shell previously is the essence of a interactive job! You get a node + GPU and you are aple to interact with it. Now we will expand that so that you run GPT2 fine tunning with Hugging Face (HF). For that make sure you check which GPU is free and then set up which GPU you want:
nvidia-smi
# I saw the output and all gpus were free so I choose any gpu
export CUDA_VISIBLE_DEVICES=0Tip: never run commands blindly or they will fail and you won't be able to debug them.
Tip: do you know what CUDA_VISIBLE_DEVICES is for?
Now let's run a small training run for GPT2 small. We are doing small to avoid memory issues, which is a common problem in LLM projects and one that eventually you need to know how to solve (according to the reqs for your project! e.g., many many options, some open problems; larger gpu, use fsdp, use lora, use qlora, hf accelerate, deep speed. Feel free to Google them if you are curious, but you will use them as your requirement for your project need them). Using the code from your fork, run:
python ~/snap-cluster-setup/src/train/simple_train.py
# or CUDA_VISIBLE_DEVICES=0 python ~/snap-cluster-setup/src/train/simple_train.py Sample output:
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ python ~/snap-cluster-setup/src/train/simple_train.py
tokenizer.pad_token='<|endoftext|>'
block_size=1024
Number of parameters: 124439808
/lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead:
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)
warnings.warn(
Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
{'train_runtime': 4.4686, 'train_samples_per_second': 41.4, 'train_steps_per_second': 10.294, 'train_loss': 2.902917944866678, 'epoch': 0.99}
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/lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead:
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)
warnings.warn(
Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24/24 [00:00<00:00, 28.52it/s]
Eval metrics hoskinson-center_proofnet test Unknown_Eval_Max_Samples: metrics={'eval_loss': 2.3169755935668945, 'eval_runtime': 0.8879, 'eval_samples_per_second': 209.491, 'eval_steps_per_second': 27.031, 'perplexity': 10.144945422979891}
***** eval_hoskinson-center_proofnet__test_Unknown_Eval_Max_Samples metrics *****
eval_loss = 2.317
eval_runtime = 0:00:00.88
eval_samples_per_second = 209.491
eval_steps_per_second = 27.031
perplexity = 10.1449
path='hoskinson-center/proofnet' split=test results={'eval_loss': 2.3169755935668945, 'eval_runtime': 0.8879, 'eval_samples_per_second': 209.491, 'eval_steps_per_second': 27.031, 'perplexity': 10.144945422979891}
Time taken: 12.64 seconds, or 0.21 minutes, or 0.00 hours.Now edit your gpt2 small file to use wandb and to train for more epochs:
CUDA_VISIBLE_DEVICES=0 python ~/snap-cluster-setup/src/train/simple_train.pySample output:
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ python ~/snap-cluster-setup/src/train/simple_train.py
tokenizer.pad_token='<|endoftext|>'
block_size=1024
Number of parameters: 124439808
/lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead:
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)
warnings.warn(
Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
wandb: Currently logged in as: brando. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.16.6
wandb: Run data is saved locally in /lfs/skampere1/0/brando9/wandb/run-20240410_180758-7tns0d5y
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run morning-music-5
wandb: ⭐️ View project at https://wandb.ai/brando/huggingface
wandb: 🚀 View run at https://wandb.ai/brando/huggingface/runs/7tns0d5y
{'train_runtime': 9.9081, 'train_samples_per_second': 18.672, 'train_steps_per_second': 4.643, 'train_loss': 2.902917944866678, 'epoch': 0.99}
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 46/46 [00:04<00:00, 10.46it/s]
/lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead:
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)
warnings.warn(
Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24/24 [00:00<00:00, 28.45it/s]
Eval metrics hoskinson-center_proofnet test Unknown_Eval_Max_Samples: metrics={'eval_loss': 2.3169755935668945, 'eval_runtime': 0.886, 'eval_samples_per_second': 209.933, 'eval_steps_per_second': 27.088, 'perplexity': 10.144945422979891}
***** eval_hoskinson-center_proofnet__test_Unknown_Eval_Max_Samples metrics *****
eval_loss = 2.317
eval_runtime = 0:00:00.88
eval_samples_per_second = 209.933
eval_steps_per_second = 27.088
perplexity = 10.1449
path='hoskinson-center/proofnet' split=test results={'eval_loss': 2.3169755935668945, 'eval_runtime': 0.886, 'eval_samples_per_second': 209.933, 'eval_steps_per_second': 27.088, 'perplexity': 10.144945422979891}
Time taken: 18.53 seconds, or 0.31 minutes, or 0.01 hours.
wandb: | 0.037 MB of 0.037 MB uploaded
wandb: Run history:
wandb: eval/loss ▁
wandb: eval/runtime ▁
wandb: eval/samples_per_second ▁
wandb: eval/steps_per_second ▁
wandb: train/epoch ▁
wandb: train/global_step █▁
wandb:
wandb: Run summary:
wandb: eval/loss 2.31698
wandb: eval/runtime 0.886
wandb: eval/samples_per_second 209.933
wandb: eval/steps_per_second 27.088
wandb: total_flos 48077733888000.0
wandb: train/epoch 0.99
wandb: train/global_step 0
wandb: train_loss 2.90292
wandb: train_runtime 9.9081
wandb: train_samples_per_second 18.672
wandb: train_steps_per_second 4.643
wandb:
wandb: 🚀 View run morning-music-5 at: https://wandb.ai/brando/huggingface/runs/7tns0d5y
wandb: ⭐️ View project at: https://wandb.ai/brando/huggingface
wandb: Synced 7 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: /lfs/skampere1/0/brando9/wandb/run-20240410_180758-7tns0d5y/logs
(snap_cluster_setup) brando9@skampere1~/snap-cluster-setup $ python ~/snap-cluster-setup/src/train/simple_train.py
tokenizer.pad_token='<|endoftext|>'
block_size=1024
Number of parameters: 124439808
/lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead:
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)
warnings.warn(
Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
wandb: Currently logged in as: brando. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.16.6
wandb: Run data is saved locally in /lfs/skampere1/0/brando9/wandb/run-20240410_181121-deik32un
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run comic-bird-6
wandb: ⭐️ View project at https://wandb.ai/brando/huggingface
wandb: 🚀 View run at https://wandb.ai/brando/huggingface/runs/deik32un
{'train_runtime': 24.3208, 'train_samples_per_second': 38.033, 'train_steps_per_second': 9.457, 'train_loss': 2.0359664253566576, 'epoch': 4.95}
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/lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead:
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)
warnings.warn(
Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24/24 [00:00<00:00, 28.38it/s]
Eval metrics hoskinson-center_proofnet test Unknown_Eval_Max_Samples: metrics={'eval_loss': 1.7542803287506104, 'eval_runtime': 0.9027, 'eval_samples_per_second': 206.051, 'eval_steps_per_second': 26.587, 'perplexity': 5.779287058236344}
***** eval_hoskinson-center_proofnet__test_Unknown_Eval_Max_Samples metrics *****
eval_loss = 1.7543
eval_runtime = 0:00:00.90
eval_samples_per_second = 206.051
eval_steps_per_second = 26.587
perplexity = 5.7793
path='hoskinson-center/proofnet' split=test results={'eval_loss': 1.7542803287506104, 'eval_runtime': 0.9027, 'eval_samples_per_second': 206.051, 'eval_steps_per_second': 26.587, 'perplexity': 5.779287058236344}
Time taken: 33.09 seconds, or 0.55 minutes, or 0.01 hours.
wandb: | 0.024 MB of 0.045 MB uploaded
wandb: Run history:
wandb: eval/loss ▁
wandb: eval/runtime ▁
wandb: eval/samples_per_second ▁
wandb: eval/steps_per_second ▁
wandb: train/epoch ▁
wandb: train/global_step █▁
wandb:
wandb: Run summary:
wandb: eval/loss 1.75428
wandb: eval/runtime 0.9027
wandb: eval/samples_per_second 206.051
wandb: eval/steps_per_second 26.587
wandb: total_flos 239343501312000.0
wandb: train/epoch 4.95
wandb: train/global_step 0
wandb: train_loss 2.03597
wandb: train_runtime 24.3208
wandb: train_samples_per_second 38.033
wandb: train_steps_per_second 9.457
wandb:
wandb: 🚀 View run comic-bird-6 at: https://wandb.ai/brando/huggingface/runs/deik32un
wandb: ⭐️ View project at: https://wandb.ai/brando/huggingface
wandb: Synced 7 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: /lfs/skampere1/0/brando9/wandb/run-20240410_181121-deik32un/logsFirst read long lived jobs in SNAP.
Now we will train small GPT2 for a longer time using krbtmux/tmux. First open krbtmux and run reauth and type your password:
krbtmux
reauthreauth is a SNAP specific command so that any process (inside bash or a cli or tmux) has the kerberos ticket renewed so that it can it's not killed. Sample output:
brando9@skampere1:/afs/cs.stanford.edu/u/brando9/snap-cluster-setup$ Since this opens a fresh like (multiplexed) cli we need to re activate your bash settings (see why SNAP is annoying?!):
source $AFS/.bash_profile
# or type bashSample output:
brando9@skampere1:/afs/cs.stanford.edu/u/brando9/snap-cluster-setup$ bash
EnvironmentNameNotFound: Could not find conda environment: evals_af
You can list all discoverable environments with `conda info --envs`.
ln: failed to create symbolic link '/lfs/skampere1/0/brando9/iit-term-synthesis': File exists
(base) brando9@skampere1~ $ reauth
Password for brando9:
Background process pid is: 1198383If you notice I made a main_krbtmux.sh file.
This is so that it's easier to run long lived jobs.
Now use it to run a long lived job training GPT2 (for say 10 epochs, edit your github's fork to change that):
bash ~/snap-cluster-setup/main_krbtmux.sh
# or source ~/snap-cluster-setup/main_krbtmux.sh
# or python ~/snap-cluster-setup/src/train/simple_train.py after you set up bash and conda and your gpu, see the main_krbtmux.sh file! that's why I made it to automate the borning thingsTip: you might have to set the cuda visible devices manually depending on what is free or what you need.
Sample output:
(base) brando9@skampere1~ $ bash ~/snap-cluster-setup/main_krbtmux.sh
EnvironmentNameNotFound: Could not find conda environment: evals_af
You can list all discoverable environments with `conda info --envs`.
ln: failed to create symbolic link '/lfs/skampere1/0/brando9/iit-term-synthesis': File exists
CUDA_VISIBLE_DEVICES = 0
tokenizer.pad_token='<|endoftext|>'
block_size=1024
Number of parameters: 124439808
/lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead:
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)
warnings.warn(
Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
wandb: Currently logged in as: brando. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.16.6
wandb: Run data is saved locally in /lfs/skampere1/0/brando9/wandb/run-20240410_192505-t5pg9q3o
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run jolly-forest-8
wandb: ⭐️ View project at https://wandb.ai/brando/huggingface
wandb: 🚀 View run at https://wandb.ai/brando/huggingface/runs/t5pg9q3o
{'loss': 4.0951, 'grad_norm': 15.34875202178955, 'learning_rate': 4.9782608695652176e-05, 'epoch': 0.02}
{'loss': 2.7984, 'grad_norm': 9.243730545043945, 'learning_rate': 4e-05, 'epoch': 0.99}
{'loss': 2.0728, 'grad_norm': 11.679845809936523, 'learning_rate': 2.9782608695652175e-05, 'epoch': 2.0}
{'loss': 1.8794, 'grad_norm': 7.380716800689697, 'learning_rate': 1.9782608695652176e-05, 'epoch': 2.99}
{'loss': 1.7264, 'grad_norm': 10.552717208862305, 'learning_rate': 9.565217391304349e-06, 'epoch': 4.0}
{'loss': 1.6644, 'grad_norm': 8.1737060546875, 'learning_rate': 0.0, 'epoch': 4.95}
{'train_runtime': 24.0418, 'train_samples_per_second': 38.475, 'train_steps_per_second': 9.567, 'train_loss': 2.035966329989226, 'epoch': 4.95}
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 230/230 [00:18<00:00, 12.31it/s]
/lfs/skampere1/0/brando9/miniconda/envs/snap_cluster_setup/lib/python3.9/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead:
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)
warnings.warn(
Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24/24 [00:00<00:00, 27.82it/s]
Eval metrics hoskinson-center_proofnet test Unknown_Eval_Max_Samples: metrics={'eval_loss': 1.7542803287506104, 'eval_runtime': 0.8845, 'eval_samples_per_second': 210.294, 'eval_steps_per_second': 27.135, 'perplexity': 5.779287058236344}
***** eval_hoskinson-center_proofnet__test_Unknown_Eval_Max_Samples metrics *****
eval_loss = 1.7543
eval_runtime = 0:00:00.88
eval_samples_per_second = 210.294
eval_steps_per_second = 27.135
perplexity = 5.7793
path='hoskinson-center/proofnet' split=test results={'eval_loss': 1.7542803287506104, 'eval_runtime': 0.8845, 'eval_samples_per_second': 210.294, 'eval_steps_per_second': 27.135, 'perplexity': 5.779287058236344}
Time taken: 32.63 seconds, or 0.54 minutes, or 0.01 hours.
wandb: \ 0.023 MB of 0.048 MB uploaded
wandb: Run history:
wandb: eval/loss ▁
wandb: eval/runtime ▁
wandb: eval/samples_per_second ▁
wandb: eval/steps_per_second ▁
wandb: train/epoch ▁▂▄▅▇██
wandb: train/global_step ▁▂▄▅▇██▁
wandb: train/grad_norm █▃▅▁▄▂
wandb: train/learning_rate █▇▅▄▂▁
wandb: train/loss █▄▂▂▁▁
wandb:
wandb: Run summary:
wandb: eval/loss 1.75428
wandb: eval/runtime 0.8845
wandb: eval/samples_per_second 210.294
wandb: eval/steps_per_second 27.135
wandb: total_flos 239343501312000.0
wandb: train/epoch 4.95
wandb: train/global_step 0
wandb: train/grad_norm 8.17371
wandb: train/learning_rate 0.0
wandb: train/loss 1.6644
wandb: train_loss 2.03597
wandb: train_runtime 24.0418
wandb: train_samples_per_second 38.475
wandb: train_steps_per_second 9.567
wandb:
wandb: 🚀 View run jolly-forest-8 at: https://wandb.ai/brando/huggingface/runs/t5pg9q3o
wandb: ⭐️ View project at: https://wandb.ai/brando/huggingface
wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)
wandb: Find logs at: ./wandb/run-20240410_192505-t5pg9q3o/logsTip: todo: allows mouse scrolling tput rmcupa in tmux, help, can't remember why I use this command!
Pros of SNAP's setup:
- you can run jobs for months without having to worry about the sys admins limits on
sbatchcommands- but no guarantees it won't fail for some random (hardware) reason! Cons:
- very complicated e.g., afs vs lfs vs dfs and too many things you have to manually do like:
- gpu selection, manually running background jobs with (hacked) tmux sessions, reauth shouldn't exist etc.
Pros of slurm setup:
- sys admins do all the dirty work Cons of slurm setup:
- only one I personally know of is that it's hard to by pass the "let me run a job for X months"
- but your labmates might get mad anyway...and ping you.
In either case the sys admins have control of what you can install, so SNAP's set up doesn't even provide that advantage e.g., use the module avail command and Google what it is and why sys admins want it like that.
Sometimes you might need to kill vscode processes so that vscode does not lose the kerberos authentication it needs. Some useful commands I run very carefully e.g., if you have a job running, it might kill it!
# kill vscode
# https://chat.openai.com/c/a114f637-cfb7-4515-afe8-6590d0ce9c78
ps -f -u brando9
pkill -f 'code-server'
pkill -f 'vscode-remote'
pkill -f code-insiders-f
pgrep -f 'code|code-insiders|vscode' | xargs -r kill
kills all my processes:
pkill -f 'brando9' || pgrep -f 'brando9' | xargs -r killTo see the disk space of the current path/dir you can do:
du -shsample output:
(base) brando9@skampere1/afs/cs.stanford.edu/u/brando9 $ du -sh .
4.6G .To find size of all files and dirs at . do:
du -sh * .[!.]*
Note: .[!.]*: This is a pattern that matches hidden files and directories in the current directory, excluding the special directories . and ...
Ref: handy claude Opus 2024 april 14.
First if you are in SNAP, to make sure afs is not overwhelmed do:
git config --global lfs.storage ~/.cache/git-lfsThis makes sure that the large files (e.g., the image files) to be tracked by git lfs are not tracked in your github repo, where we usually store the git repo in SNAP.
We do this since your afs directory is available on all nodes and it's fast so only the code can be there.
But the large files might still be needed. For that the previous command puts the git lfs files in lfs and since we made sure $HOME (~) points to your lfs home directory path where is storage.
Now to initialize git lfs do:
git lfs installThis will trakc the large files in a remote directory in the remove and in the local at ~/.cache/git-lfs.
Every file type you want to associate with Git LFS will need to be added with git lfs track.
To track all image files with Git LFS, you can use the git lfs track command followed by the file patterns for common image file extensions. Here's an example command that tracks several popular image file formats:
git lfs track "*.jpg" "*.jpeg" "*.png" "*.gif" "*.psd" "*.tiff" "*.bmp" "*.svg" "*.eps" "*.webp"It creates a .gitattributes file that tracks git lfs tracked files.
If you want to then commit them you do:
git commit -m "Convert image files to Git LFS"But if you did a "mistake" and had already pushed them, you can do:
git lfs migrate import --include="*.jpg,*.jpeg,*.png,*.gif,*.psd,*.tiff,*.bmp,*.svg,*.eps,*.webp"
# if it worked, then do:
git pushcheck if this fixed it with:
git lfs status
git statusthis should show no issues with either normal code vs the git lfs tracked files.
Ref:
To undo the last git commit that hasn't been pushed to a remote repository, you can use the git reset command. Here's how you can do it:
git reset --soft HEAD~1Explanation:
git resetis the command used to undo commits.- The
--softoption moves the branch pointer back to the previous commit, but keeps the changes from the last commit in the staging area. This means that the changes from the last commit will still be present in your working directory and staged for the next commit. -HEAD~1refers to the commit that is one before the currentHEAD. This effectively undoes the last commit. Sample output:
(base) brando9@skampere1/afs/cs.stanford.edu/u/brando9/gold-ai-olympiad $ git reset --soft HEAD~1
(base) brando9@skampere1/afs/cs.stanford.edu/u/brando9/gold-ai-olympiad $ Ref: handy claude reference in the context of git lfs.
To undo last git add . do:
git restore --staged .You can put your vscode files in AFS and symlink them to all nodes e.g.,
# Set the AFS environment variable if it is not already set
export AFS="/afs/cs.stanford.edu/u/brando9"
# Move and rename the VS Code workspaces to the AFS directory, and create symbolic links in the home directory
mv "$HOME/vscode.beyond-scale-language-data-diversity.ampere1.code-workspace" "$AFS/vscode.beyond-scale-language-data-diversity.code-workspace"
ln -s "$AFS/vscode.beyond-scale-language-data-diversity.code-workspace" "$HOME/vscode.beyond-scale-language-data-diversity.code-workspace"
mv "$HOME/vscode.gold-ai-olympiad.afs_snap.code-workspace" "$AFS/vscode.gold-ai-olympiad.code-workspace"
ln -s "$AFS/vscode.gold-ai-olympiad.code-workspace" "$HOME/vscode.gold-ai-olympiad.code-workspace"
mv "$HOME/vscode.evaporate.skampere1.code-workspace" "$AFS/vscode.evaporate.code-workspace"
ln -s "$AFS/vscode.evaporate.code-workspace" "$HOME/vscode.evaporate.code-workspace"
mv "$HOME/vscode.lean4ai.skampere1.code-workspace" "$AFS/vscode.lean4ai.code-workspace"
ln -s "$AFS/vscode.lean4ai.code-workspace" "$HOME/vscode.lean4ai.code-workspace"
mv "$HOME/vscode.snap-cluster-setup.code-workspace" "$AFS/vscode.snap-cluster-setup.code-workspace"
ln -s "$AFS/vscode.snap-cluster-setup.code-workspace" "$HOME/vscode.snap-cluster-setup.code-workspace"
mv "$HOME/vscode.maf_data.creating_data_math_training.skamapere1.code-workspace" "$AFS/vscode.maf_data.creating_data_math_training.code-workspace"
ln -s "$AFS/vscode.maf_data.creating_data_math_training.code-workspace" "$HOME/vscode.maf_data.creating_data_math_training.code-workspace"
mv "$HOME/vscode.maf_data.training_af_model.skampere1.code-workspace" "$AFS/vscode.maf_data.training_af_model.code-workspace"
ln -s "$AFS/vscode.maf_data.training_af_model.code-workspace" "$HOME/vscode.maf_data.training_af_model.code-workspace"
mv "$HOME/vscode.math_evaporate.skampere1.code-workspace" "$AFS/vscode.math_evaporate.code-workspace"
ln -s "$AFS/vscode.math_evaporate.code-workspace" "$HOME/vscode.math_evaporate.code-workspace"
mv "$HOME/vscode.beyond-scale-language-data-diversity.skampere1.code-workspace" "$AFS/vscode.beyond-scale-language-data-diversity.code-workspace"
ln -s "$AFS/vscode.beyond-scale-language-data-diversity.code-workspace" "$HOME/vscode.beyond-scale-language-data-diversity.code-workspace"this is because the vscode file looks as follows:
(gold_ai_olympiad) (base) brando9@ampere1:~$ cat vscode.beyond-scale-language-data-diversity.ampere1.code-workspace
{
"folders": [
{
"path": "beyond-scale-language-data-diversity"
},
{
"path": "massive-autoformalization-maf"
},
{
"path": "data/maf_data"
},
{
"path": "evals-for-autoformalization"
},
{
"path": "ultimate-anatome"
},
{
"path": "ultimate-utils"
},
{
"path": "data"
}
],
"settings": {}which means it's relative to $HOME most likely.
Official reference for installing manually: https://python-poetry.org/docs/#installing-manually
So that poetry's own depedencies don't get accidentally overwritten, you need to install poetry in a virtual env. Thus, to install Poetry manually in a virtual env using pip and the venv module with python, do:
# check your %HOME (~) points to lfs
echo $HOME
# If the virtual env folder doesn't exist create it (note $HOME should point to your lfs)
mkdir $HOME/.virtualenvs
# Set the python virtual env that will hold poetry
export VENV_PATH=$HOME/.virtualenvs/venv_for_poetry
# Create a virtual environment at the specified path (python3 -m uses python's way to execute modules without needing to know their location, in this case we use the venv, virtualenv, one to create a virtual env at $VENV_PATH)
python3 -m venv $VENV_PATH
# Upgrade pip and setuptools within the virtual environment
$VENV_PATH/bin/pip install -U pip setuptools
# Install poetry within the virtual environment
$VENV_PATH/bin/pip install poetryPoetry will be available at $VENV_PATH/bin/poetry, but to have the poetry command available without using the full path, add the virtual env's path to you $PATH env:
# WARNING: put this export command before conda does the same thing, so that this path is not used by accident over your conda's python env
export PATH="$VENV_PATH/bin:$PATH"Ideally (especially if you are using conda), the following commands have to be placed carefully in your .bashrc file:
# create env for the virtual env that will have the poetry command (note: assumes `mkdir $HOME/.virtualenvs` has been ran)
export VENV_PATH=$HOME/.virtualenvs/venv_for_poetry
# careful running the export bellow, it will put it in front of your conda path if you are using conda and you won't use your conda env's python
export PATH="$VENV_PATH/bin:$PATH"In particular, you need to put it before your export for your conda env (see our .bashrc for an example)
In particular note how export PATH="$VENV_PATH/bin:$PATH" is before any of the following commands:
# dummy .bashrc file
# exports to make the poetry command available goes first
export VENV_PATH=$HOME/.virtualenvs/venv_for_poetry
export PATH="$VENV_PATH/bin:$PATH"
# export to make conda work (assuming that's what you want to use)
export PATH="$HOME/miniconda/bin:$PATH"
source $HOME/miniconda/bin/activate
conda activate snap_cluster_setupTo uninstall Poetry, simply delete the entire $VENV_PATH directory:
# uninstall Poetry
rm -rf $VENV_PATHref: venv ref: SNAP cluster tutorial for venv https://ilwiki.stanford.edu/doku.php?id=hints:virtualenv ref: chat that got us here: https://chat.openai.com/c/9f4f25f6-fbce-445c-8955-ce0b9bc5ab6f
TODO
ref: https://chat.openai.com/c/e01336a7-6f67-4cd2-b6cd-09b8ee8aef5a
TODO
ref: https://chat.openai.com/c/e01336a7-6f67-4cd2-b6cd-09b8ee8aef5a
poetry build: Packages the project into distributable formats (like .whl and .tar.gz) based on the configurations set in pyproject.toml. poetry install: Installs the project and its dependencies into the virtual environment, creating the environment if it doesn't exist, in editable mode. poetry shell: Spawns a new shell with the project's virtual environment activated, making it the primary Python environment. poetry env use: Specifies which Python interpreter to use for the project's virtual environment, creating a new environment if the specified interpreter doesn't match any existing one. poetry env list: Lists all virtual environments associated with the current project, indicating which is currently activated. poetry show: lists and provides detailed information about each package installed in the current project's Poetry-managed virtual environment, including version numbers and descriptions. poetry show --tree: displays a hierarchical view of all packages installed in the project's environment, showing dependencies in a tree-like structure to illustrate their relationships
If you have not added the submodules to your .gitmodules files, you can do it as follows:
# -- add the following modules to your .gitsubmodule file
git submodule add -f -b hdb --name meta-dataset git@github.com:brando90/meta-dataset.git meta-dataset/Then to initialize the git submodules so that git can keep track of the submodules being used do:
# - initialize the git submodules by preparing the git repository, but it does not clone or fetch them, just init's git's internal configs
git submodule initThen to clone, fetch & update the submodules code (and also initilize anything you might have forgotten that is specificed in the .gitmodules file):
# - initialize the git submodules so that git can track them and then the update clone/fetches & updates the submodules
git submodule update --initYou will need a small tool called elan will handle updating Lean according to the needs of your current project.
Install it with:
# this special version is needed in SNAP because of $HOME points to lfs and elan detects that it seems
curl -sSf https://raw.githubusercontent.com/leanprover/elan/master/elan-init.sh | sh -s -- -ySample output:
(base) brando9@ampere1~/PyPantograph $ curl -sSf https://raw.githubusercontent.com/leanprover/elan/master/elan-init.sh | sh -s -- -y
info: downloading installer
warning: $HOME differs from euid-obtained home directory: you may be using sudo
info: syncing channel updates for 'stable'
info: latest update on stable, lean version v4.7.0
info: downloading component 'lean'
info: installing component 'lean'
info: default toolchain set to 'stable'
stable installed - Lean (version 4.7.0, x86_64-unknown-linux-gnu, commit 6fce8f7d5cd1, Release)Let's sanity check that the hidden that Lean's elan tool is where we expect it to be:
(base) brando9@ampere1~/PyPantograph $ ls $HOME/.elan
bin env settings.toml tmp toolchains update-hashesThe previous curl install will also try to put put the $HOME/.elan hidden path in your PATH env variable, so that the elan becomes available by creating a .profile with the following contents:
(base) brando9@ampere1~/PyPantograph $ cat $HOME/.profile
export PATH="$HOME/.elan/bin:$PATH"However, you will need to put the above in your .bashrc file (e.g., at the end) if it's not already there.
Note: the default .bashrc I provide should now already have it.
Then you can check if it worked by installing PyPentograph.
ref: https://leanprover-community.github.io/install/linux.html