Noah's feedback

content/guides/integrations/add-wandb-to-any-library.md

@@ -115,6 +115,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="python-notebook" %}}
+
 ```notebook
 !pip install wandb
 

content/guides/integrations/deepchem.md

@@ -23,11 +23,11 @@ model.fit(…)
 
 Explore the Using [W&B with DeepChem: Molecular Graph Convolutional Networks](https://wandb.ai/kshen/deepchem_graphconv/reports/Using-W-B-with-DeepChem-Molecular-Graph-Convolutional-Networks--Vmlldzo4MzU5MDc?galleryTag=) article for an example charts generated using the W&B DeepChem integration.
 
-If you'd rather dive straight into working code, check out this [**Google Colab**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/deepchem/W%26B_x_DeepChem.ipynb).
+To dive straight into working code, check out this [**Google Colab**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/deepchem/W%26B_x_DeepChem.ipynb).
 
 ## Track experiments
 
-Setup Weights & Biases for DeepChem models of type [KerasModel](https://deepchem.readthedocs.io/en/latest/api_reference/models.html#keras-models) or [TorchModel](https://deepchem.readthedocs.io/en/latest/api_reference/models.html#pytorch-models).
+Set up W&B for DeepChem models of type [KerasModel](https://deepchem.readthedocs.io/en/latest/api_reference/models.html#keras-models) or [TorchModel](https://deepchem.readthedocs.io/en/latest/api_reference/models.html#pytorch-models).
 
 ### Sign up and create an API key
 
@@ -68,6 +68,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="python-notebook" %}}
+
 ```notebook
 !pip install wandb
 
@@ -76,11 +88,12 @@ wandb.login()
 ```
 
 {{% /tab %}}
+
 {{< /tabpane >}}
 
 ### Log your training and evaluation data to W&B
 
-Training loss and evaluation metrics can be automatically logged to Weights & Biases. Optional evaluation can be enabled using the DeepChem [ValidationCallback](https://github.com/deepchem/deepchem/blob/master/deepchem/models/callbacks.py), the `WandbLogger` will detect ValidationCallback callback and log the metrics generated.
+Training loss and evaluation metrics can be automatically logged to W&B. Optional evaluation can be enabled using the DeepChem [ValidationCallback](https://github.com/deepchem/deepchem/blob/master/deepchem/models/callbacks.py), the `WandbLogger` will detect ValidationCallback callback and log the metrics generated.
 
 {{< tabpane text=true >}}
 

content/guides/integrations/fastai/_index.md

@@ -49,6 +49,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="python-notebook" %}}
+
 ```notebook
 !pip install wandb
 
@@ -148,7 +160,7 @@ $ torchrun --nproc_per_node 2 train.py
 in this case, the machine has 2 GPUs.
 
 {{% /tab %}}
-{{% tab header="Notebook" value="notebook" %}}
+{{% tab header="Python notebook" value="notebook" %}}
 
 You can now run distributed training directly inside a notebook.
 
@@ -225,7 +237,7 @@ $ torchrun --nproc_per_node 2 train.py
 ```
 
 {{% /tab %}}
-{{% tab header="Notebook" value="notebook" %}}
+{{% tab header="Python notebook" value="notebook" %}}
 
 ```python
 import wandb

content/guides/integrations/huggingface.md

@@ -68,6 +68,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="python" %}}
+
 ```notebook
 !pip install wandb
 
@@ -95,21 +107,24 @@ WANDB_PROJECT=amazon_sentiment_analysis
 ```
 
 {{% /tab %}}
-{{% tab header="Notebook" value="notebook" %}}
 
-```notebook
-%env WANDB_PROJECT=amazon_sentiment_analysis
+{{% tab header="Python" value="python" %}}
+
+```python
+import os
+os.environ["WANDB_PROJECT"]="amazon_sentiment_analysis"
 ```
 
 {{% /tab %}}
-{{% tab header="Python" value="python" %}}
+
+{{% tab header="Python notebook" value="notebook" %}}
 
 ```notebook
-import os
-os.environ["WANDB_PROJECT"]="amazon_sentiment_analysis"
+%env WANDB_PROJECT=amazon_sentiment_analysis
 ```
 
 {{% /tab %}}
+
 {{< /tabpane >}}
 
 {{% alert %}}
@@ -180,25 +195,25 @@ Use `WANDB_LOG_MODEL` along with `load_best_model_at_end` to upload the best mod
 
 {{< tabpane text=true >}}
 
-{{% tab header="Python" value="python" %}}
-
-```python
-import os
+{{% tab header="Command Line" value="cli" %}}
 
-os.environ["WANDB_LOG_MODEL"] = "checkpoint"
+```bash
+WANDB_LOG_MODEL="checkpoint"
 ```
 
 {{% /tab %}}
 
-{{% tab header="Command Line" value="cli" %}}
+{{% tab header="Python" value="python" %}}
 
-```bash
-WANDB_LOG_MODEL="checkpoint"
+```python
+import os
+
+os.environ["WANDB_LOG_MODEL"] = "checkpoint"
 ```
 
 {{% /tab %}}
 
-{{% tab header="Notebook" value="notebook" %}}
+{{% tab header="Python notebook" value="notebook" %}}
 
 ```notebook
 %env WANDB_LOG_MODEL="checkpoint"

content/guides/integrations/kubeflow-pipelines-kfp.md

@@ -54,6 +54,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="notebook" %}}
+
 ```notebook
 !pip install wandb
 

content/guides/integrations/lightning.md

@@ -92,6 +92,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="notebook" %}}
+
 ```notebook
 !pip install wandb
 

content/guides/integrations/metaflow.md

@@ -57,6 +57,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install -Uqqq metaflow fastcore wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="notebook" %}}
+
 ```notebook
 !pip install -Uqqq metaflow fastcore wandb
 
@@ -150,7 +162,7 @@ class WandbExampleFlow(FlowSpec):
 
 You can access the information we've captured in three ways: inside the original Python process being logged using the [`wandb` client library]({{< relref "/ref/python/" >}}), with the [web app UI]({{< relref "/guides/models/track/workspaces.md" >}}), or programmatically using [our Public API]({{< relref "/ref/python/public-api/" >}}). `Parameter`s are saved to W&B's [`config`]({{< relref "/guides/models/track/config.md" >}}) and can be found in the [Overview tab]({{< relref "/guides/models/track/runs/#overview-tab" >}}). `datasets`, `models`, and `others` are saved to [W&B Artifacts]({{< relref "/guides/core/artifacts/" >}}) and can be found in the [Artifacts tab]({{< relref "/guides/models/track/runs/#artifacts-tab" >}}). Base python types are saved to W&B's [`summary`]({{< relref "/guides/models/track/log/" >}}) dict and can be found in the Overview tab. See our [guide to the Public API]({{< relref "/guides/models/track/public-api-guide.md" >}}) for details on using the API to get this information programmatically from outside .
 
-### Cheat sheet
+### Quick reference
 
 | Data                                            | Client library                            | UI                    |
 | ----------------------------------------------- | ----------------------------------------- | --------------------- |

content/guides/integrations/paddledetection.md

@@ -57,6 +57,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="python" %}}
+
 ```notebook
 !pip install wandb
 

content/guides/integrations/paddleocr.md

@@ -52,6 +52,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="notebook" %}}
+
 ```notebook
 !pip install wandb
 
@@ -96,6 +108,6 @@ Once you run your `train.py` file with Weights & Biases turned on, a link will b
 
 {{< img src="/images/integrations/paddleocr_wb_dashboard3.png" alt="W&B Dashboard for the Text Detection Model" >}}
 
-## Feedback or Issues?
+## Feedback or issues
 
 If you have any feedback or issues about the Weights & Biases integration please open an issue on the [PaddleOCR GitHub](https://github.com/PaddlePaddle/PaddleOCR) or email <a href="mailto:[email protected]">[email protected]</a>.

content/guides/integrations/pytorch-geometric.md

@@ -49,6 +49,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="notebook" %}}
+
 ```notebook
 !pip install wandb
 

content/guides/integrations/scikit.md

@@ -49,6 +49,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="notebook" %}}
+
 ```notebook
 !pip install wandb
 

content/guides/integrations/spacy.md

@@ -47,6 +47,18 @@ To install the `wandb` library locally and log in:
 
 {{% tab header="Python" value="python" %}}
 
+```bash
+pip install wandb
+```
+```python
+import wandb
+wandb.login()
+```
+
+{{% /tab %}}
+
+{{% tab header="Python notebook" value="notebook" %}}
+
 ```notebook
 !pip install wandb
 
@@ -101,10 +113,10 @@ python -m spacy train \
 
 {{% /tab %}}
 
-{{% tab header="Notebook" value="notebook" %}}
+{{% tab header="Python" value="python" %}}
 
 ```python
-!python -m spacy train \
+python -m spacy train \
     config.cfg \
     --output ./output \
     --paths.train ./train \
@@ -113,6 +125,17 @@ python -m spacy train \
 
 {{% /tab %}}
 
+{{% tab header="Python notebook" value="notebook" %}}
+
+```notebook
+!python -m spacy train \
+    config.cfg \
+    --output ./output \
+    --paths.train ./train \
+    --paths.dev ./dev
+```
+
+{{% /tab %}}
 {{< /tabpane >}}
 
 When training begins, a link to your training run's [W&B page]({{< relref "/guides/models/track/runs/" >}}) will be output which will take you to this run's experiment tracking [dashboard]({{< relref "/guides/models/track/workspaces.md" >}}) in the Weights & Biases web UI.