@allow-large-files [vissl][PR] Update tutorials (facebookresearch#462)

Summary: Changing tutorials as per tutorial reviews. I couldn't find an easier way to version with Github than what we already have. There also is not anyway to hide specific github branches w/ the collab links. The easiest thing to do would be to have main tutorials mirror v0.1.6 tutorials and have all links in docs/website to the v0.1.6 collab link. You could potentially remove all tutorials from main, but I don't think it's really worth it -- I don't love diverging v0.1.6 branch from main. **Note**: Immediately after merging this, I must build and launch the website, as per https://github.com/facebookresearch/vissl/tree/main/dev/website_docs. Then must test tutorial links in docs, readmes, and "Go to Collab" links. Pull Request resolved: facebookresearch#462 Reviewed By: prigoyal Differential Revision: D31935844 Pulled By: iseessel fbshipit-source-id: 64ebb36a308c1eec6684723ce02b815ed7152b1b
johnnynunez · Oct 27, 2021 · a8e92f4 · a8e92f4
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diff --git a/GETTING_STARTED.md b/GETTING_STARTED.md
@@ -60,7 +60,7 @@ python3 tools/run_distributed_engines.py \
 Users need to set the dataset and obtain the builtin tool for training. Follow the steps:
 
 #### Step1: Setup ImageNet1K dataset
-If you installed pre-built VISSL packages, we will set the ImageNet1K dataset following our [data documentation](https://vissl.readthedocs.io/en/latest/vissl_modules/data.html) and [tutorial](https://colab.research.google.com/drive/1CCuZ50BN99JcOB6VEPytVi_i2tSMd7A3#scrollTo=KPGCiTsXZeW3). NOTE that we need to register
+If you installed pre-built VISSL packages, we will set the ImageNet1K dataset following our [data documentation](https://vissl.readthedocs.io/en/latest/vissl_modules/data.html) and [tutorial](https://colab.research.google.com/github/facebookresearch/vissl/blob/v0.1.6/tutorials/Benchmark_Linear_Image_Classification_on_ImageNet_1K_V0_1_6.ipynb). NOTE that we need to register
 the dataset with VISSL.
 
 In your python interpretor:

diff --git a/INSTALL.md b/INSTALL.md
@@ -58,7 +58,7 @@ git checkout -b v0.1.6
 # install vissl dependencies
 pip install --progress-bar off -r requirements.txt
 pip install opencv-python
-# update classy vision install to current main branch
+# update classy vision install to current main branch.
 pip uninstall -y classy_vision
 pip install classy-vision@https://github.com/facebookresearch/ClassyVision/tarball/main
 # install vissl dev mode (e stands for editable)

diff --git a/README.md b/README.md
@@ -39,7 +39,7 @@ See [`INSTALL.md`](https://github.com/facebookresearch/vissl/blob/main/INSTALL.m
 ## Getting Started
 
 Install VISSL by following the [installation instructions](https://github.com/facebookresearch/vissl/blob/main/INSTALL.md).
-After installation, please see [Getting Started with VISSL](https://github.com/facebookresearch/vissl/blob/main/GETTING_STARTED.md) and the [Colab Notebook](https://colab.research.google.com/drive/1iigQmKL_DUuBLT6BqjrGXlW9ZIqKIFmt?usp=sharing) to learn about basic usage.
+After installation, please see [Getting Started with VISSL](https://github.com/facebookresearch/vissl/blob/main/GETTING_STARTED.md) and the [Colab Notebook](https://colab.research.google.com/github/facebookresearch/vissl/blob/v0.1.6/tutorials/Understanding_VISSL_Training_and_YAML_Config_V0_1_6.ipynb) to learn about basic usage.
 
 ## Documentation
 
@@ -49,12 +49,12 @@ Learn more about VISSL at our [documentation](https://vissl.readthedocs.io). And
 
 Get started with VISSL by trying one of the **Colab tutorial notebooks**.
 
-- [Train SimCLR on 1-gpu](https://colab.research.google.com/drive/1Rt3Plt3ph84i1A-eolLFafybwjrBFxYe?usp=sharing)
-- [Extracting Features from a pretrained model](https://colab.research.google.com/drive/1EXZyW65lXBryWE2ZmC0bar2996KEiBZb?usp=sharing)
-- [Benchmark task: Full finetuning on ImageNet-1K](https://colab.research.google.com/drive/1m1LUa-3vIR-rxwcm0QCrefc5S6PAY874?usp=sharing)
-- [Benchmark task: Linear image classification on ImageNet-1K](https://colab.research.google.com/drive/1CCuZ50BN99JcOB6VEPytVi_i2tSMd7A3?usp=sharing)
-- [Large scale training (fp16, LARC, ZeRO)](https://colab.research.google.com/drive/1fvZdRNUyHxMOaxuEO34x7XeGndzTUfIW?usp=sharing)
-- [Using a pre-trained model in inference mode](https://colab.research.google.com/drive/1DkqGi9L47eWjA5j77EVEmbrktWBGoynN?usp=sharing)
+- [Train SimCLR on 1-gpu](https://colab.research.google.com/github/facebookresearch/vissl/blob/v0.1.6/tutorials/Train_SimCLR_on_1_gpu_V0_1_6.ipynb)
+- [Extracting Features from a pretrained model](https://colab.research.google.com/github/facebookresearch/vissl/blob/v0.1.6/tutorials/Using_a_pretrained_model_for_inference_V0_1_6.ipynb)
+- [Benchmark task: Full finetuning on ImageNet-1K](https://colab.research.google.com/github/facebookresearch/vissl/blob/v0.1.6/tutorials/Benchmark_Full_Finetuning_on_ImageNet_1K_V0_1_6.ipynb)
+- [Benchmark task: Linear image classification on ImageNet-1K](https://colab.research.google.com/github/facebookresearch/vissl/blob/v0.1.6/tutorials/Benchmark_Linear_Image_Classification_on_ImageNet_1K_V0_1_6.ipynb)
+- [Large scale training (fp16, LARC, ZeRO)](https://colab.research.google.com/github/facebookresearch/vissl/blob/v0.1.6/tutorials/Large_Scale_Training_V0_1_6.ipynb)
+- [Using a pre-trained model in inference mode](https://colab.research.google.com/github/facebookresearch/vissl/blob/v0.1.6/tutorials/Using_a_pretrained_model_for_inference_V0_1_6.ipynb)
 
 
 ## Model Zoo and Baselines

diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst
@@ -73,7 +73,7 @@ If using pre-built conda/pip VISSL packages (v0.1.6)
 Users need to set the dataset and obtain the builtin tool for training. Follow the steps:
 
 - **Step1: Setup ImageNet1K dataset**
-If you installed pre-built VISSL packages (v0.1.6), we will set the ImageNet1K dataset following our :ref:`data documentation<Using Data>` and `tutorial <https://colab.research.google.com/drive/1CCuZ50BN99JcOB6VEPytVi_i2tSMd7A3#scrollTo=KPGCiTsXZeW3>`_.
+If you installed pre-built VISSL packages (v0.1.6), we will set the ImageNet1K dataset following our :ref:`data documentation<Using Data>` and tutorial `<https://colab.research.google.com/github/facebookresearch/vissl/blob/v0.1.6/tutorials/Benchmark_Linear_Image_Classification_on_ImageNet_1K_V0_1_6.ipynb>`_.
 **NOTE** that we need to register the dataset with VISSL.
 
 In your python interpretor:
@@ -96,6 +96,7 @@ In your python interpretor:
 
 
 - **Step2: Get the builtin tool and yaml config file**
+
 We will use the pre-built VISSL tool for training `run_distributed_engines.py <https://github.com/facebookresearch/vissl/blob/stable/tools/run_distributed_engines.py>`_ and the config file. Run
 
 .. code-block:: bash