diff --git a/.jenkins/build.sh b/.jenkins/build.sh
index 9705ea53a..92fc1fe52 100755
--- a/.jenkins/build.sh
+++ b/.jenkins/build.sh
@@ -72,7 +72,7 @@ if [[ "$DISTRIB_RELEASE" == 14.04 ]]; then
     source activate tc-env
     conda install -y pyyaml mkl-include
     conda install -yc conda-forge pytest
-    conda install -y pytorch-nightly=2018.04.17 -c pytorch
+    conda install -y pytorch -c pytorch
     WITH_PYTHON_C2=OFF CORES=$(nproc) CLANG_PREFIX=/usr/local/clang+llvm-tapir5.0 BUILD_TYPE=Release ./build.sh --all
   else
     echo "Building TC in non-conda env"
@@ -87,7 +87,7 @@ if [[ "$DISTRIB_RELEASE" == 16.04 ]]; then
     source activate tc-env
     conda install -y pyyaml mkl-include
     conda install -yc conda-forge pytest
-    conda install -y pytorch-nightly=2018.04.17 cuda90 -c pytorch
+    conda install -y pytorch cuda90 -c pytorch
     WITH_PYTHON_C2=OFF CORES=$(nproc) CLANG_PREFIX=/usr/local/clang+llvm-tapir5.0 BUILD_TYPE=Release ./build.sh --all
   else
     echo "Building TC in non-conda env"
diff --git a/docker/README.md b/docker/README.md
index 2300379ca..d05339cae 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -78,7 +78,7 @@ git submodule update --init --recursive
 # build TC
 conda install -y mkl-include pyyaml
 conda install -yc conda-forge pytest
-conda install -y pytorch-nightly=2018.04.17 -c pytorch # OR  conda install -y pytorch-nightly=2018.04.17 cuda90 -c pytorch
+conda install -y pytorch -c pytorch # OR  conda install -y pytorch cuda90 -c pytorch
 CORES=$(nproc) WITH_CAFFE2=ON CLANG_PREFIX=/usr/local/clang+llvm-tapir5.0 BUILD_TYPE=Release ./build.sh --all
 # Test the TC build is fine
 ./test.sh
diff --git a/docker/common/install_base.sh b/docker/common/install_base.sh
index 571f2334e..fc1dc1d14 100755
--- a/docker/common/install_base.sh
+++ b/docker/common/install_base.sh
@@ -34,11 +34,15 @@ apt-get install -y --no-install-recommends \
 apt-get clean
 rm -rf /var/lib/apt/lists/*
 # setup gcc
-add-apt-repository ppa:ubuntu-toolchain-r/test
-apt-get update
-apt-get install -y --no-install-recommends libcilkrts5 gcc-$GCC_VERSION g++-$GCC_VERSION
-update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-$GCC_VERSION 50
-update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-$GCC_VERSION 50
+if [[ "$GCC_VERSION" == 4.9 ]]; then
+  add-apt-repository ppa:ubuntu-toolchain-r/test
+  apt-get update
+  apt-get install -y --no-install-recommends libcilkrts5 gcc-$GCC_VERSION g++-$GCC_VERSION
+  update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-$GCC_VERSION 50
+  update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-$GCC_VERSION 50
+else
+  apt-get install -y --no-install-recommends libcilkrts5 gcc g++
+fi
 
 # Install ccache from source. Needs 3.4 or later for ccbin support
 # Needs specific branch to work with nvcc (ccache/ccache#145)
diff --git a/tensor_comprehensions/tc_unit.py b/tensor_comprehensions/tc_unit.py
index eb30f751d..c479fa1ad 100644
--- a/tensor_comprehensions/tc_unit.py
+++ b/tensor_comprehensions/tc_unit.py
@@ -66,7 +66,7 @@ def set_gflags(
 def check_cache_file_exists(cache_file):
     # for autotuning, we save two files: .cuda and .options, we will check that
     # these two files exists for the validity of cache
-    if os.path.exists(cache_file + ".options") and os.path.exists(cache_file + ".cuda"):
+    if os.path.exists(cache_file + ".options"):
         return True
     return False
 
@@ -254,7 +254,7 @@ def autotune(self, *inputs, **kwargs):
                 cache_file = "/tmp/{}_{}".format(hash_key, str(uuid.uuid4()))
             elif isinstance(kwargs["cache"], str):
                 cache_file = kwargs["cache"]
-            logger.info('Autotuning cache will be saved to: {}.cuda/options'.format(cache_file))
+            logger.info('Autotuning cache will be saved to: {}.options'.format(cache_file))
         else:
             logger.warning("Autotuning results won't be cached. 'cache' option is not set")
 
@@ -297,7 +297,7 @@ def autotune(self, *inputs, **kwargs):
 
         if cache_file:
             cache_file = cache_file + "_backward"
-            logger.info('Backwards autotuning cache will be saved to: {}.cuda/options'.format(cache_file))
+            logger.info('Backwards autotuning cache will be saved to: {}.options'.format(cache_file))
         kwargs["type"] = "backward"
         options = get_options_from_kwargs_and_tuner_cache(backward_name, cache_file, options_cache, *inputs, **kwargs)
         backward_best_options = self.tune_and_store(
diff --git a/test_python/test_tc.py b/test_python/test_tc.py
index 134b32311..e1a8e22fb 100644
--- a/test_python/test_tc.py
+++ b/test_python/test_tc.py
@@ -79,7 +79,9 @@ def matmul(float(M,N) A, float(N,K) B) -> (output) {
         inputs = [mat1, mat2]
         handle = cu.compile("matmul", [mat1, mat2], options="mlp")
         outputs = cu.run(handle, "matmul", inputs)
+        torch.cuda.synchronize()
         expected = torch.mm(mat1, mat2)
+        torch.cuda.synchronize()
         diff = outputs[0] - expected
         self.assert_almost_equal(diff, inputs, 4)
 
diff --git a/test_python/test_tc_torch.py b/test_python/test_tc_torch.py
index 9ddc86e0b..d4048fbc2 100644
--- a/test_python/test_tc_torch.py
+++ b/test_python/test_tc_torch.py
@@ -181,7 +181,7 @@ def test_autotuner_cachefile_first(self):
     def test_autotuner_cachefile_load_automatic(self):
         lang = MATMUL_LANG
         cache_file = "{}/matmul_100_400_500".format(PATH_PREFIX)    # use argparse if input from command line
-        assert os.path.isfile("{}.cuda".format(cache_file)), "looks like the cache_file doesn't exist"
+        assert os.path.isfile("{}.options".format(cache_file)), "looks like the cache_file doesn't exist"
 
         matmul = tc.define(lang, name="matmul")
         mat1, mat2 = torch.randn(100, 400).cuda(), torch.randn(400, 500).cuda()