Correct cache

.github/workflows/self-push.yml

@@ -11,63 +11,17 @@ on:
   # pull_request:
   repository_dispatch:
 
-
 jobs:
-  run_tests_torch_and_tf_gpu:
+  run_tests_tf_gpu:
     runs-on: [self-hosted, single-gpu]
-    steps:
-    - uses: actions/checkout@v2
-    - name: Python version
-      run: |
-        which python
-        python --version
-        pip --version
-    - name: Current dir
-      run: pwd
-    - run: nvidia-smi
-
-    - name: Loading cache.
-      uses: actions/cache@v2
-      id: cache
-      with:
-        path: .env
-        key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
-
-    - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-      run: |
-        python -m venv .env
-        source .env/bin/activate
-        which python
-        python --version
-        pip --version
-    - name: Install dependencies
-      run: |
-        source .env/bin/activate
-        pip install --upgrade pip
-        pip install torch!=1.6.0
-        pip install .[sklearn,testing,onnxruntime]
-        pip install git+https://github.com/huggingface/datasets
-
-    - name: Are GPUs recognized by our DL frameworks
-      run: |
-        source .env/bin/activate
-        python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-        python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
-
-    - name: Run all non-slow tests on GPU
-      env:
-        TF_FORCE_GPU_ALLOW_GROWTH: "true"
-        # TF_GPU_MEMORY_LIMIT: 4096
-        OMP_NUM_THREADS: 1
-        USE_CUDA: yes
-      run: |
-        source .env/bin/activate
-        python -m pytest -n 2 --dist=loadfile -s ./tests/
-
-  run_tests_torch_and_tf_multiple_gpu:
-    runs-on: [self-hosted, multi-gpu]
     steps:
       - uses: actions/checkout@v2
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
       - name: Python version
         run: |
           which python
@@ -76,15 +30,8 @@ jobs:
       - name: Current dir
         run: pwd
       - run: nvidia-smi
-
-      - name: Loading cache.
-        uses: actions/cache@v2
-        id: cache
-        with:
-          path: .env
-          key: v0-tests_tf_torch_multiple_gpu-${{ hashFiles('setup.py') }}
-
       - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
         run: |
           python -m venv .env
           source .env/bin/activate
@@ -98,19 +45,153 @@ jobs:
           pip install torch!=1.6.0
           pip install .[sklearn,testing,onnxruntime]
           pip install git+https://github.com/huggingface/datasets
-
       - name: Are GPUs recognized by our DL frameworks
         run: |
           source .env/bin/activate
           python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
           python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
-
-      - name: Run all non-slow tests on GPU
+      - name: Run all tests on GPU
         env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
-          # TF_GPU_MEMORY_LIMIT: 4096
           OMP_NUM_THREADS: 1
           USE_CUDA: yes
         run: |
           source .env/bin/activate
-          python -m pytest -n 2 --dist=loadfile -s ./tests/
+          python -m pytest -n 1 --dist=loadfile -s ./tests/
+
+  run_tests_torch_gpu:
+    runs-on: [self-hosted, single-gpu]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+      - name: Install dependencies
+        run: |
+          source .env/bin/activate
+          pip install --upgrade pip
+          pip install torch!=1.6.0
+          pip install .[sklearn,testing,onnxruntime]
+          pip install git+https://github.com/huggingface/datasets
+      - name: Are GPUs recognized by our DL frameworks
+        run: |
+          source .env/bin/activate
+          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
+          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+      - name: Run all tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+          USE_CUDA: yes
+        run: |
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s ./tests/
+
+  run_tests_tf_multiple_gpu:
+    runs-on: [self-hosted, multi-gpu]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+      - name: Install dependencies
+        run: |
+          source .env/bin/activate
+          pip install --upgrade pip
+          pip install torch!=1.6.0
+          pip install .[sklearn,testing,onnxruntime]
+          pip install git+https://github.com/huggingface/datasets
+      - name: Are GPUs recognized by our DL frameworks
+        run: |
+          source .env/bin/activate
+          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
+          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+      - name: Run all tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+          USE_CUDA: yes
+        run: |
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s ./tests/
+
+  run_tests_torch_multiple_gpu:
+    runs-on: [self-hosted, multi-gpu]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v0-tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+      - name: Install dependencies
+        run: |
+          source .env/bin/activate
+          pip install --upgrade pip
+          pip install torch!=1.6.0
+          pip install .[sklearn,testing,onnxruntime]
+          pip install git+https://github.com/huggingface/datasets
+      - name: Are GPUs recognized by our DL frameworks
+        run: |
+          source .env/bin/activate
+          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
+          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+      - name: Run all tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+          USE_CUDA: yes
+        run: |
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s ./tests/

.github/workflows/self-scheduled.yml

@@ -9,82 +9,16 @@ on:
     - cron: "0 0 * * *"
 
 jobs:
-  run_all_tests_torch_and_tf_gpu:
+  run_all_tests_torch_gpu:
     runs-on: [self-hosted, single-gpu]
-    steps:
-    - uses: actions/checkout@v2
-
-    - name: Loading cache.
-      uses: actions/cache@v2
-      id: cache
-      with:
-        path: .env
-        key: v0-slow_tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
-
-    - name: Python version
-      run: |
-        which python
-        python --version
-        pip --version
-    - name: Current dir
-      run: pwd
-    - run: nvidia-smi
-    - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
-      if: steps.cache.outputs.cache-hit != 'true'
-      run: |
-        python -m venv .env
-        source .env/bin/activate
-        which python
-        python --version
-        pip --version
-    - name: Install dependencies
-      run: |
-        source .env/bin/activate
-        pip install --upgrade pip
-        pip install torch!=1.6.0
-        pip install .[sklearn,testing,onnxruntime]
-        pip install git+https://github.com/huggingface/datasets
-
-    - name: Are GPUs recognized by our DL frameworks
-      run: |
-        source .env/bin/activate
-        python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
-        python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
-
-
-    - name: Run all tests on GPU
-      env:
-        TF_FORCE_GPU_ALLOW_GROWTH: "true"
-        OMP_NUM_THREADS: 1
-        RUN_SLOW: yes
-        USE_CUDA: yes
-      run: |
-        source .env/bin/activate
-        python -m pytest -n 1 --dist=loadfile -s ./tests/
-
-    - name: Run examples tests on GPU
-      env:
-        TF_FORCE_GPU_ALLOW_GROWTH: "true"
-        OMP_NUM_THREADS: 1
-        RUN_SLOW: yes
-        USE_CUDA: yes
-      run: |
-        source .env/bin/activate
-        pip install -r examples/requirements.txt
-        python -m pytest -n 1 --dist=loadfile -s examples
-
-  run_all_tests_torch_and_tf_multiple_gpu:
-    runs-on: [self-hosted, multi-gpu]
     steps:
       - uses: actions/checkout@v2
-
       - name: Loading cache.
         uses: actions/cache@v2
         id: cache
         with:
           path: .env
-          key: v0-slow_tests_tf_torch_multi_gpu-${{ hashFiles('setup.py') }}
-
+          key: v0-slow_tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
       - name: Python version
         run: |
           which python
@@ -108,16 +42,13 @@ jobs:
           pip install torch!=1.6.0
           pip install .[sklearn,testing,onnxruntime]
           pip install git+https://github.com/huggingface/datasets
-
       - name: Are GPUs recognized by our DL frameworks
         run: |
           source .env/bin/activate
           python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
           python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
-
-      - name: Run all tests on GPU
+      - name: Run examples tests on GPU
         env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
           OMP_NUM_THREADS: 1
           RUN_SLOW: yes
           USE_CUDA: yes
@@ -125,9 +56,235 @@ jobs:
           source .env/bin/activate
           python -m pytest -n 1 --dist=loadfile -s ./tests/
 
+  run_examples_torch_and_tf_gpu:
+    runs-on: [self-hosted, single-gpu]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v0-slow_tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+      - name: Install dependencies
+        run: |
+          source .env/bin/activate
+          pip install --upgrade pip
+          pip install torch!=1.6.0
+          pip install .[tf,sklearn,testing,onnxruntime]
+          pip install git+https://github.com/huggingface/datasets
+      - name: Are GPUs recognized by our DL frameworks
+        run: |
+          source .env/bin/activate
+          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
+          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+      - name: Run examples tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
+          USE_CUDA: yes
+        run: |
+          source .env/bin/activate
+          pip install -r examples/requirements.txt
+          python -m pytest -n 1 --dist=loadfile -s examples
+
+  run_all_tests_tf_gpu:
+    runs-on: [self-hosted, single-gpu]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v0-slow_tests_tf_torch_gpu-${{ hashFiles('setup.py') }}
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+      - name: Install dependencies
+        run: |
+          source .env/bin/activate
+          pip install --upgrade pip
+          pip install torch!=1.6.0
+          pip install .[sklearn,testing,onnxruntime]
+          pip install git+https://github.com/huggingface/datasets
+      - name: Are GPUs recognized by our DL frameworks
+        run: |
+          source .env/bin/activate
+          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
+          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+      - name: Run examples tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
+          USE_CUDA: yes
+        run: |
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s ./tests/
+
+  run_all_tests_torch_multiple_gpu:
+    runs-on: [self-hosted, multi-gpu]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v0-slow_tests_tf_torch_multi_gpu-${{ hashFiles('setup.py') }}
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+      - name: Install dependencies
+        run: |
+          source .env/bin/activate
+          pip install --upgrade pip
+          pip install torch!=1.6.0
+          pip install .[sklearn,testing,onnxruntime]
+          pip install git+https://github.com/huggingface/datasets
+      - name: Are GPUs recognized by our DL frameworks
+        run: |
+          source .env/bin/activate
+          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
+          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+      - name: Run examples tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
+          USE_CUDA: yes
+        run: |
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s ./tests/
+
+  run_all_tests_tf_multiple_gpu:
+    runs-on: [self-hosted, multi-gpu]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v0-slow_tests_tf_torch_multi_gpu-${{ hashFiles('setup.py') }}
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+      - name: Install dependencies
+        run: |
+          source .env/bin/activate
+          pip install --upgrade pip
+          pip install torch!=1.6.0
+          pip install .[sklearn,testing,onnxruntime]
+          pip install git+https://github.com/huggingface/datasets
+      - name: Are GPUs recognized by our DL frameworks
+        run: |
+          source .env/bin/activate
+          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
+          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
+      - name: Run examples tests on GPU
+        env:
+          OMP_NUM_THREADS: 1
+          RUN_SLOW: yes
+          USE_CUDA: yes
+        run: |
+          source .env/bin/activate
+          python -m pytest -n 1 --dist=loadfile -s ./tests/
+
+  run_examples_torch_and_tf_multiple_gpu:
+    runs-on: [self-hosted, multi-gpu]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: .env
+          key: v0-slow_tests_tf_torch_multi_gpu-${{ hashFiles('setup.py') }}
+      - name: Python version
+        run: |
+          which python
+          python --version
+          pip --version
+      - name: Current dir
+        run: pwd
+      - run: nvidia-smi
+      - name: Create new python env (on self-hosted runners we have to handle isolation ourselves)
+        if: steps.cache.outputs.cache-hit != 'true'
+        run: |
+          python -m venv .env
+          source .env/bin/activate
+          which python
+          python --version
+          pip --version
+      - name: Install dependencies
+        run: |
+          source .env/bin/activate
+          pip install --upgrade pip
+          pip install torch!=1.6.0
+          pip install .[tf,sklearn,testing,onnxruntime]
+          pip install git+https://github.com/huggingface/datasets
+      - name: Are GPUs recognized by our DL frameworks
+        run: |
+          source .env/bin/activate
+          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
+          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
       - name: Run examples tests on GPU
         env:
-          TF_FORCE_GPU_ALLOW_GROWTH: "true"
           OMP_NUM_THREADS: 1
           RUN_SLOW: yes
           USE_CUDA: yes