fix: fix pytorch in the cuda11 image

doc/model/show-model-info.md

@@ -11,7 +11,6 @@ dp --pt show <INPUT> <ATTRIBUTES...>
 
 - `<INPUT>`: Path to the model checkpoint file or frozen model file.
 - `<ATTRIBUTES>`: One or more information categories to display. Supported values are:
-
   - `model-branch`: Shows available branches for multi-task models.
   - `type-map`: Shows the type mapping used by the model.
   - `descriptor`: Displays the model descriptor parameters.
@@ -34,35 +33,28 @@ dp show frozen_model.pth type-map descriptor fitting-net size
 Depending on the provided attributes and the model type, the output includes:
 
 - **Model Type**
-
   - Logs whether the loaded model is a _singletask_ or _multitask_ model.
 
 - **model-branch**
-
   - _Only available for multitask models._
   - Lists all available model branches and the special `"RANDOM"` branch, which refers to a randomly initialized fitting net.
 
 - **type-map**
-
   - For multitask models: Shows the type map for each branch.
   - For singletask models: Shows the model's type map.
 
 - **descriptor**
-
   - For multitask models: Displays the descriptor parameter for each branch.
   - For singletask models: Displays the descriptor parameter.
 
 - **fitting-net**
-
   - For multitask models: Shows the fitting network parameters for each branch.
   - For singletask models: Shows the fitting network parameters.
 
 - **size**
-
   - Prints the number of parameters for each component (`descriptor`, `fitting-net`, etc.), as well as the total parameter count.
 
 - **observed-type**
-
   - Displays the count and list of observed element types of the model during data statistics.
   - For multitask models, it shows the observed types for each branch.
   - Note: This info shows the types observed during training data statistics, which may differ from the type map.

doc/train/multi-task-training.md

@@ -48,7 +48,6 @@ Specifically, there are several parts that need to be modified:
 - {ref}`model/model_dict <model/model_dict>`: The core definition of the model part and the explanation of sharing rules,
   starting with user-defined model name keys `model_key`, such as `my_model_1`.
   Each model part needs to align with the components of the single-task training {ref}`model <model>`, but with the following sharing rules:
-
   - If you want to share the current model component with other tasks, which should be part of the {ref}`model/shared_dict <model/shared_dict>`,
     you can directly fill in the corresponding `part_key`, such as
     `"descriptor": "my_descriptor", `

source/install/docker/Dockerfile

@@ -8,7 +8,7 @@ ENV PATH="/opt/deepmd-kit/bin:$PATH"
 ENV VIRTUAL_ENV="/opt/deepmd-kit"
 # Install package
 COPY dist /dist
-RUN if [ "${CUDA_VERSION}" = 11 ]; then uv pip install torch --index-url https://download.pytorch.org/whl/cu118; fi \
+RUN if [ "${CUDA_VERSION}" = 11 ]; then export UV_TORCH_BACKEND=cu118; fi \
     && uv pip install "$(ls /dist/deepmd_kit${VARIANT}-*manylinux*_x86_64.whl)[gpu,cu${CUDA_VERSION},lmp,ipi,torch]" \
     && dp -h \
     && lmp -h \