Getting Started with Machine Learning - Revision history

Dennisc: Explain uv venv --python 3.11 flag and why it is important/why one might want to use it.

2024-05-20T03:33:05Z

Explain uv venv --python 3.11 flag and why it is important/why one might want to use it.

Dennisc: How to install uv on cluster

2024-05-20T03:23:58Z

How to install uv on cluster

Dennisc: fix bold

2024-05-20T03:20:08Z

fix bold

Dennisc: skeleton of uv

2024-05-20T03:19:45Z

skeleton of uv

Dennisc at 03:11, 20 May 2024

2024-05-20T03:11:30Z

Dennisc at 03:11, 20 May 2024

2024-05-20T03:11:12Z

Ben at 01:47, 20 May 2024

2024-05-20T01:47:14Z

Ben at 01:41, 20 May 2024

2024-05-20T01:41:02Z

Ben: Created page with "This is User:Ben's guide to getting started with machine learning. === Dependencies === Here's some useful dependencies that I use: * [https://astral.sh/blog/uv uv] **..."

2024-05-20T01:29:17Z

Created page with "This is User:Ben's guide to getting started with machine learning. === Dependencies === Here's some useful dependencies that I use: * [https://astral.sh/blog/uv uv] **..."

New page

This is [[User:Ben]]'s guide to getting started with machine learning.

=== Dependencies ===

Here's some useful dependencies that I use:

* [https://astral.sh/blog/uv uv]
** This is similar to Pip but written in Rust and is way faster
** It has nice management of virtual environments
** Can use Conda instead but it is much slower
* [https://github.com/features/copilot Github Copilot]
* [https://github.com/kscalelabs/mlfab mlfab]
** This is a Python package I made to help make it easy to quickly try out machine learning ideas in PyTorch

=== Installing Starter Project ===

* Go to [https://github.com/kscalelabs/getting-started this project] and install it

==== Opening the project in VSCode ====

* Create a VSCode config file that looks something like this:

<syntaxhighlight lang="json">
{
"folders": [
{
"name": "Getting Started",
"path": "/home/ubuntu/Github/getting_started"
},
{
"name": "Workspaces",
"path": "/home/ubuntu/.code-workspaces"
}
],
"settings": {
"cmake.configureSettings": {
"CMAKE_CUDA_COMPILER": "/usr/bin/nvcc",
"CMAKE_PREFIX_PATH": [
"/home/ubuntu/.virtualenvs/getting-started/lib/python3.11/site-packages/torch/share/cmake"
],
"PYTHON_EXECUTABLE": "/home/ubuntu/.virtualenvs/getting-started/bin/python",
"TORCH_CUDA_ARCH_LIST": "'8.0'"
},
"python.defaultInterpreterPath": "/home/ubuntu/.virtualenvs/getting-started/bin/python",
"ruff.path": [
"/home/ubuntu/.virtualenvs/getting-started/bin/ruff"
]
}
}
</syntaxhighlight>

* Install the [https://code.visualstudio.com/docs/remote/ssh VSCode SSH extension]
* SSH into the cluster (see [[K-Scale Cluster]] for instructions)
* Open the workspace that you created in VSCode

@@ Line 30: / Line 30: @@
 uv venv
 </syntaxhighlight>
 To activate your virtual environment, run
 <syntaxhighlight lang="bash">
 source .venv/bin/activate
 </syntaxhighlight>

← Older revision		Revision as of 03:23, 20 May 2024
Line 18:		Line 18:

	==== uv ====		==== uv ====
		+
		+	To install <code>uv</code> on the K-Scale clusters, run
		+
		+	<syntaxhighlight lang="bash">
		+	curl -LsSf https://astral.sh/uv/install.sh \| sh
		+	</syntaxhighlight>

	To get started with <code>uv</code>, pick a directory you want your virtual environment to live in. (<code>$HOME</code> is not recommended.) Once you have <code>cd</code>ed there, run		To get started with <code>uv</code>, pick a directory you want your virtual environment to live in. (<code>$HOME</code> is not recommended.) Once you have <code>cd</code>ed there, run

@@ Line 31: / Line 31: @@
 </syntaxhighlight>
-*while in the directory you created your <code>.venv</code> in*.
+'''while in the directory you created your <code>.venv</code> in'''.
 === Installing Starter Project ===

@@ Line 16: / Line 16: @@
 ** [https://github.com/psf/black black] code formatter
 ** [https://docs.astral.sh/ruff/ ruff] alternative to flake8
 === Installing Starter Project ===

@@ Line 63: / Line 63: @@
 * Check out the [https://github.com/kscalelabs/mlfab/tree/master/examples mlfab examples directory] for some ideas
 * It is a good idea to try to write the full training loop yourself to figure out what's going on
-* Run `nvidia-smi` to see the GPUs and their statuses/any active processes
+* Run <code>nvidia-smi</code> to see the GPUs and their statuses/any active processes

← Older revision		Revision as of 03:11, 20 May 2024
Line 63:		Line 63:
	* Check out the [https://github.com/kscalelabs/mlfab/tree/master/examples mlfab examples directory] for some ideas		* Check out the [https://github.com/kscalelabs/mlfab/tree/master/examples mlfab examples directory] for some ideas
	* It is a good idea to try to write the full training loop yourself to figure out what's going on		* It is a good idea to try to write the full training loop yourself to figure out what's going on
		+	* Run `nvidia-smi` to see the GPUs and their statuses/any active processes

← Older revision		Revision as of 01:47, 20 May 2024
Line 61:		Line 61:

	* Use <code>breakpoint()</code> to debug code		* Use <code>breakpoint()</code> to debug code
		+	* Check out the [https://github.com/kscalelabs/mlfab/tree/master/examples mlfab examples directory] for some ideas
		+	* It is a good idea to try to write the full training loop yourself to figure out what's going on

@@ Line 12: / Line 12: @@
 * [https://github.com/kscalelabs/mlfab mlfab]
 ** This is a Python package I made to help make it easy to quickly try out machine learning ideas in PyTorch
 === Installing Starter Project ===
@@ Line 53: / Line 57: @@
 * SSH into the cluster (see [[K-Scale Cluster]] for instructions)
 * Open the workspace that you created in VSCode