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Remove old th06 universal training images #978
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@@ -33,14 +33,14 @@ When `DOWNSTREAM=true`: | |
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| | Flavor | Directory | Use Case | | ||
| |--------|-----------|----------| | ||
| | **CUDA** | `th<VERSION>-cuda<VERSION>-torch<VERSION>-py<VERSION>/` | NVIDIA GPU training | | ||
| | **ROCm** | `th<VERSION>-rocm<VERSION>-torch<VERSION>-py<VERSION>/` | AMD GPU training | | ||
| | **CPU** | `th<VERSION>-cpu-torch<VERSION>-py<VERSION>/` | CPU-only training | | ||
| | **CUDA** | `th-torch-cuda-py312/` | NVIDIA GPU training | | ||
| | **ROCm** | `th-torch-rocm-py312/` | AMD GPU training | | ||
| | **CPU** | `th-torch-cpu-py312/` | CPU-only training | | ||
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| All flavors include: | ||
| - **Training Hub** with LoRA support | ||
| - **PyTorch <VERSION>** | ||
| - **Python <VERSION>** | ||
| - **PyTorch** | ||
| - **Python 3.12** | ||
| - **JupyterLab** (workbench mode) | ||
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| ### CUDA Image | ||
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@@ -163,9 +163,9 @@ Update to dependencies **other than** CUDA, ROCm, PyTorch, or Training Hub. | |
| 2. Coordinate with AIPCC team for the new index with updated major packages | ||
| 3. Coordinate with Notebooks team for the new base image | ||
| 4. Create new directory following naming convention: | ||
| - CUDA: `th<TH_VERSION>-cuda<CUDA_VERSION>-torch<TORCH_VERSION>-py<PYTHON_VERSION>/` | ||
| - ROCm: `th<TH_VERSION>-rocm<ROCM_VERSION>-torch<TORCH_VERSION>-py<PYTHON_VERSION>/` | ||
| - CPU: `th<TH_VERSION>-cpu-torch<TORCH_VERSION>-py<PYTHON_VERSION>/` | ||
| - CUDA: `th-torch-cuda-py<PYTHON_VERSION>/` | ||
| - ROCm: `th-torch-rocm-py<PYTHON_VERSION>/` | ||
| - CPU: `th-torch-cpu-py<PYTHON_VERSION>/` | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 🗄️ Data Integrity & Integration | 🟠 Major | 🏗️ Heavy lift Resolve the major-update naming conflict. Scenario 2 requires a new directory for major CUDA, ROCm, PyTorch, or Training Hub updates. These names vary only by Python version. A major dependency update with Python 3.12 would reuse Either add the relevant dependency version to the directory name, or change Scenario 2 to update the existing stable directory and define immutable image tags, digests, and rollback rules. Update As per path instructions, architectural issues and bug-prone patterns are review priorities. Possible documentation fix for stable directories-**New folder required:**
+**Existing flavor directory update:**
...
-4. Create new directory following naming convention:
+4. Update the existing `th-torch-<flavor>-py<PYTHON_VERSION>/` directory in place.
+ Publish an immutable image tag or digest for each major dependency update.🤖 Prompt for AI AgentsSource: Path instructions |
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| 5. Copy files from previous version and update: | ||
| - `Dockerfile` - update base image, package versions | ||
| - `pyproject.toml` - update dependencies to match `training-hub` requirements | ||
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🗄️ Data Integrity & Integration | 🟠 Major | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
Repository: opendatahub-io/distributed-workloads
Length of output: 2165
🏁 Script executed:
Repository: opendatahub-io/distributed-workloads
Length of output: 265
🏁 Script executed:
Repository: opendatahub-io/distributed-workloads
Length of output: 2385
Update the stale runtime image references in
tests/trainer/utils/utils_runtimes.go.The renamed
th-torch-*image directories exist, but this file still maps runtimes to deletedth06-*image names. CWE-1104 (Cross-module Inconsistency).🤖 Prompt for AI Agents
Source: Path instructions