Updation for inference docs page: migration to R2 (#2374)

* Updation for documentation page: migration to R2

* fix for llama2 assets download instructions

Author: anandhu-eng

docs/benchmarks/automotive/3d_object_detection/get-pointpainting-data.md

@@ -13,16 +13,16 @@ The benchmark implementation run command will automatically download the preproc
 
 === "Validation"
 
-    ### Get Validation Dataset
+    ### Get Validation and Calibration Dataset
     ```
-    mlcr get,dataset,waymo -j
+    mlcr get,dataset,waymo,_r2-downloader,_mlc -j
     ```
 
 === "Calibration"
 
-    ### Get Calibration Dataset
+    ### Get Calibration Dataset only
     ```
-    mlcr get,dataset,waymo,calibration -j
+    mlcr get,dataset,waymo,calibration,_r2-downloader,_mlc -j
     ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_WAYMO_DATASET>` could be provided to download the dataset to a specific location.
@@ -33,7 +33,7 @@ The benchmark implementation run command will automatically download the preproc
 The benchmark implementation run command will automatically download the model. In case you want to download only the PointPainting model, you can use the below command.
 
 ```bash
-mlcr get,ml-model,pointpainting -j
+mlcr get,ml-model,pointpainting,_r2-downloader,_mlc -j
 ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_POINTPAINTING_MODEL>` could be provided to download the model files to a specific location.

docs/benchmarks/graph/get-rgat-data.md

@@ -46,7 +46,7 @@ Get the Official MLPerf R-GAT Model
 
     ### PyTorch
     ```
-    mlcr get,ml-model,rgat -j
+    mlcr get,ml-model,rgat,_r2-downloader,_mlcommons -j
     ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_RGAT_MODEL>` could be provided to download the model to a specific location.

docs/benchmarks/language/get-deepseek-r1-data.md

@@ -21,4 +21,16 @@ The benchmark implementation run command will automatically download the validat
     ### Get Calibration Dataset
     ```
     mlcr get,preprocessed,dataset,deepseek-r1,_calibration,_mlc,_rclone --outdirname=<path to download> -j
-    ```
+    ```
+
+## Model
+The benchmark implementation run command will automatically download the required model and do the necessary conversions. In case you want to only download the official model, you can use the below commands.
+
+=== "Pytorch"
+
+    === "From MLCOMMONS Storage"
+
+        ### Get the Official MLPerf DeekSeek-R1 model from MLCOMMONS Storage
+        ```
+        mlcr get,ml-model,deepseek-r1,_r2-downloader,_mlc,_dry-run -j
+        ```

docs/benchmarks/language/get-gptj-data.md

@@ -36,7 +36,7 @@ Get the Official MLPerf GPT-J Model
 
     ### Pytorch
     ```
-    mlcr get,ml-model,gptj,_pytorch -j
+    mlcr get,ml-model,gptj,_fp32,_pytorch,_r2-downloader -j
     ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_GPTJ_MODEL>` could be provided to download the model to a specific location.

docs/benchmarks/language/get-llama2-70b-data.md

@@ -33,14 +33,15 @@ The benchmark implementation run command will automatically download the validat
 
         ### Get Preprocessed Validation Dataset
         ```
-        mlcr get,dataset,preprocessed,openorca,_validation -j
+        mlcr get,dataset,preprocessed,openorca,_validation,_r2-downloader,_mlc -j
         ```
 
     === "Calibration"
 
         ### Get Preprocessed Calibration dataset
         ```
-        mlcr get,dataset,preprocessed,openorca,_calibration -j
+        mlcr get,dataset,preprocessed,openorca,_calibration,_r2-downloader,_mlc -j
+        ```
         ```
 
 === "Unprocessed Dataset"
@@ -69,13 +70,13 @@ The benchmark implementation run command will automatically download the require
 
     === "Pytorch"
 
-        === "From MLCOMMONS storage"
-
-            > **Note:**  One has to accept the [MLCommons Llama 2 License Confidentiality Notice](https://llama2.mlcommons.org/) to access the model files in MLCOMMONS storage. 
-
-            ### Get the Official MLPerf LLAMA2-70B model from MLCOMMONS storage
+        === "From MLCOMMONS Storage"
+    
+            > **Note:**  One has to accept the [MLCommons Llama 2 License Confidentiality Notice](https://llama2.mlcommons.org/) to access the model files in MLCOMMONS Storage. 
+    
+            ### Get the Official MLPerf LLAMA2-70B model from MLCOMMONS Storage
             ```
-            mlcr get,ml-model,llama2-70b,_fp32,_pytorch -j
+            mlcr get,ml-model,llama2-70b,_pytorch,_r2-downloader,_70b,_mlc -j
             ```
 
         === "From Hugging Face repo"

docs/benchmarks/language/get-llama3_1-405b-data.md

@@ -13,14 +13,14 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    mlcr get,dataset,mlperf,inference,llama3,_validation --outdirname=<path to download> -j
+    mlcr get,dataset,mlperf,inference,llama3,_validation,_r2-downloader --outdirname=<path to download> -j
     ```
 
 === "Calibration"
 
     ### Get Calibration Dataset
     ```
-    mlcr get,dataset,mlperf,inference,llama3,_calibration --outdirname=<path to download> -j
+    mlcr get,dataset,mlperf,inference,llama3,_calibration,_r2-downloader --outdirname=<path to download> -j
     ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_LLAMA3_405B_DATASET>` could be provided to download the dataset to a specific location.
@@ -30,13 +30,13 @@ The benchmark implementation run command will automatically download the require
 
 === "Pytorch"
 
-    === "From MLCOMMONS Google Drive"
+    === "From MLCOMMONS Storage"
 
-        > **Note:**  One has to accept the [MLCommons Llama 3.1 License Confidentiality Notice](http://llama3-1.mlcommons.org/) to access the model files in MLCOMMONS Google Drive. 
+        > **Note:**  One has to accept the [MLCommons Llama 3.1 License Confidentiality Notice](http://llama3-1.mlcommons.org/) to access the model files in MLCOMMONS Storage. 
 
         ### Get the Official MLPerf LLAMA3.1-405B model from MLCOMMONS Google Drive
         ```
-        mlcr get,ml-model,llama3 -j
+        mlcr get,ml-model,llama3,_mlc,_r2-downloader,_405b --outdirname=<path to download> -j
         ```
 
     === "From Hugging Face repo"

docs/benchmarks/language/get-llama3_1-8b-data.md

@@ -15,21 +15,21 @@ The benchmark implementation run command will automatically download the validat
 
         ### Get Validation Dataset
         ```
-        mlcr get,dataset,cnndm,_validation,_datacenter,_llama3,_mlc,_rclone --outdirname=<path to download> -j
+        mlcr get,dataset,cnndm,_validation,_datacenter,_llama3,_mlc,_r2-downloader --outdirname=<path to download> -j
         ```
 
     === "5000 samples (Edge)"
 
         ### Get Validation Dataset
         ```
-        mlcr get,dataset,cnndm,_validation,_edge,_llama3,_mlc,_rclone --outdirname=<path to download> -j
+        mlcr get,dataset,cnndm,_validation,_edge,_llama3,_mlc,_r2-downloader --outdirname=<path to download> -j
         ```
 
 === "Calibration"
 
     ### Get Calibration Dataset
     ```
-    mlcr get,dataset,cnndm,_calibration,_llama3,_mlc,_rclone --outdirname=<path to download> -j
+    mlcr get,dataset,cnndm,_calibration,_llama3,_mlc,_r2-downloader --outdirname=<path to download> -j
     ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_LLAMA3_405B_DATASET>` could be provided to download the dataset to a specific location.
@@ -39,13 +39,13 @@ The benchmark implementation run command will automatically download the require
 
 === "Pytorch"
 
-    === "From MLCOMMONS Google Drive"
+    === "From MLCOMMONS Storage"
 
-        > **Note:**  One has to accept the [MLCommons Llama 3.1 License Confidentiality Notice](http://llama3-1.mlcommons.org/) to access the model files in MLCOMMONS Google Drive. 
+        > **Note:**  One has to accept the [MLCommons Llama 3.1 License Confidentiality Notice](http://llama3-1.mlcommons.org/) to access the model files in MLCOMMONS Storage. 
 
-        ### Get the Official MLPerf LLAMA3.1-405B model from MLCOMMONS Cloudfare R2
+        ### Get the Official MLPerf LLAMA3.1-8B model from MLCOMMONS Cloudfare R2
         ```
-        TBD
+        mlcr get,ml-model,llama3,_mlc,_8b,_r2-downloader --outdirname=<path to download> -j
         ```
 
     === "From Hugging Face repo"

docs/benchmarks/language/get-mixtral-8x7b-data.md

@@ -12,14 +12,14 @@ The benchmark implementation run command will automatically download the preproc
 
     ### Get Validation Dataset
     ```
-    mlcr get,dataset-mixtral,openorca-mbxp-gsm8k-combined -j
+    mlcr get,dataset-mixtral,openorca-mbxp-gsm8k-combined,_r2-downloader,_validation -j
     ```
 
 === "Calibration"
 
     ### Get Calibration Dataset
     ```
-    mlcr get,dataset-mixtral,openorca-mbxp-gsm8k-combined,_calibration -j
+    mlcr get,dataset-mixtral,openorca-mbxp-gsm8k-combined,_r2-downloader,_calibration -j
     ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_MIXTRAL_DATASET>` could be provided to download the dataset to a specific location.
@@ -33,7 +33,7 @@ Get the Official MLPerf MIXTRAL-8x7b Model
 
     ### Pytorch
     ```
-    mlcr get,ml-model,mixtral -j
+    mlcr get,ml-model,mixtral,_r2-downloader,_mlc -j
     ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_MIXTRAL_MODEL>` could be provided to download the model to a specific location.

docs/benchmarks/recommendation/get-dlrm-v2-data.md

@@ -14,7 +14,7 @@ The benchmark implementation run command will automatically download the validat
 
     ### Get Validation Dataset
     ```
-    mlcr get,dataset,criteo,_validation -j
+    mlcr get,preprocessed,dataset,criteo,_r2-downloader,_mlc -j
     ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_CRITEO_DATASET>` could be provided to download the dataset to a specific location.
@@ -28,7 +28,7 @@ Get the Official MLPerf DLRM v2 Model
 
     ### Pytorch
     ```
-    mlcr get,ml-model,dlrm,_pytorch,_weight_sharded,_rclone -j
+    mlcr get,ml-model,dlrm,_pytorch,_fp32,_weight_sharded,_r2-downloader -j
     ```
 
 

docs/benchmarks/speech_to_text/get-whisper-data.md

@@ -15,7 +15,7 @@ The benchmark implementation run command will automatically download the validat
 
         ### Get Preprocessed Validation Dataset
         ```
-        mlcr get,dataset,whisper,_preprocessed,_mlc,_rclone --outdirname=<path to download> -j
+        mlcr get,dataset,whisper,_preprocessed,_mlc,_r2-downloader --outdirname=<path to download> -j
         ```
 
     === "Unprocessed"
@@ -34,7 +34,7 @@ The benchmark implementation run command will automatically download the require
 
         ### Get the Official MLPerf Whisper model from MLCOMMONS Cloudflare R2
         ```
-        mlcr get,ml-model,whisper,_rclone,_mlc s-j
+        mlcr get,ml-model,whisper,_r2-downloader,_mlc -j
         ```
 
 - `--outdirname=<PATH_TO_DOWNLOAD_WHISPER_MODEL>` could be provided to download the model to a specific location.