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Google Colab: clarify language, remove outdated links #451

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Oct 8, 2024
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Google Colab: clarify language, remove outdated links #451

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ce998d8
clarify language in colab docs
jameslamb Oct 7, 2024
1b44bf2
Google Colab: clarify language, remove outdated links
jameslamb Oct 7, 2024
0c6978b
stop hard-coding GPU names
jameslamb Oct 7, 2024
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14 changes: 7 additions & 7 deletions source/platforms/colab.md
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Expand Up @@ -53,13 +53,13 @@ Choose GPU for Hardware Accelerator

### 3. Check GPU type

Check the output of `!nvidia-smi` to make sure you've been allocated a Rapids Compatible GPU, i.e [Tesla T4, P4, or P100].
Check the output of `!nvidia-smi` to make sure you've been allocated a Rapids Compatible GPU ([see the RAPIDS install docs](https://docs.rapids.ai/install/#system-req)).

![Screenshot of nvidia-smi](../images/googlecolab-output-nvidia-smi.png)

### 4. Install RAPIDS on Colab

You can install RAPIDS using pip. The script first checks GPU compatibility with RAPIDS, then installs the latest **stable** versions of RAPIDSAI's core libraries (cuDF, cuML, cuGraph, and xgboost) using `pip`.
You can install RAPIDS using pip. The script first checks GPU compatibility with RAPIDS, then installs the latest **stable** versions of some core RAPIDS libraries (e.g. cuDF, cuML, cuGraph, and xgboost) using `pip`.

```bash
# Colab warns and provides remediation steps if the GPUs is not compatible with RAPIDS.
Expand All @@ -68,12 +68,14 @@ You can install RAPIDS using pip. The script first checks GPU compatibility with
!python rapidsai-csp-utils/colab/pip-install.py
```

### 5. Test Rapids
### 5. Test RAPIDS

Run the following in a Python cell.

```python
import cudf

gdf = cudf.DataFrame({"a":[1,2,3],"b":[4,5,6]})
gdf = cudf.DataFrame({"a":[1,2,3], "b":[4,5,6]})
gdf
a b
0 1 4
Expand All @@ -84,6 +86,4 @@ gdf

### 6. Next steps

Check out this [guide](https://towardsdatascience.com/) for an overview of how to access and work with your own datasets in Colab.

For more RAPIDS examples, check out our RAPIDS [notebooks](https://github.com/rapidsai/notebooks) and [notebooks-contrib](https://github.com/rapidsai/notebooks-contrib) repos
Try a more thorough example of using cuDF on Google Colab, "10 Minutes to RAPIDS cuDF's pandas accelerator mode (cudf.pandas)" ([Google Colab link](https://nvda.ws/rapids-cudf)).