[example] update auto_parallel img path (#1910)

examples/tutorial/README.md

@@ -29,14 +29,14 @@ quickly deploy large AI model training and inference, reducing large AI model tr
    - Try sequence parallelism with BERT
    - Combination of data/pipeline/sequence parallelism
    - Faster training and longer sequence length
+  - Large Batch Training Optimization
+   - Comparison of small/large batch size with SGD/LARS optimizer
+   - Acceleration from a larger batch size
  - Auto-Parallelism
    - Parallelism with normal non-distributed training code
    - Model tracing + solution solving + runtime communication inserting all in one auto-parallelism system
    - Try single program, multiple data (SPMD) parallel with auto-parallelism SPMD solver on ResNet50
- - Large Batch Training Optimization
-   - Comparison of small/large batch size with SGD/LARS optimizer
-   - Acceleration from a larger batch size
- - Fine-tuning and Serving for OPT from Hugging Face
+ - Fine-tuning and Serving for OPT
    - Try OPT model imported from Hugging Face with Colossal-AI
    - Fine-tuning OPT with limited hardware using ZeRO, Gemini and parallelism
    - Deploy the fine-tuned model to inference service

examples/tutorial/auto_parallel/README.md

@@ -66,10 +66,10 @@ python demo_gpt2_medium.py
 There are some results for your reference
 
 ### ResNet 50
-![](./imgs/resnet50_benchmark.png)
+![](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/tutorial/resnet50_benchmark.png)
 
 ### GPT2 Medium
-![](./imgs/gpt2_benchmark.png)
+![](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/tutorial/gpt2_benchmark.png)
 
 We also prepare the demo `demo_resnet152.py` to manifest the benefit of auto activation with large batch, the usage is listed as follows
 ```bash