Bug fixes

nbs/02_datasets.ipynb

@@ -45,7 +45,8 @@
     "from datasets import load_dataset,load_dataset_builder\n",
     "\n",
     "import torchvision.transforms.functional as TF\n",
-    "from fastcore.test import test_close"
+    "from fastcore.test import test_close\n",
+    "from rapidai.init import clean_mem"
    ]
   },
   {
@@ -1049,6 +1050,16 @@
     "        return cls(*get_dls(*dd.values(), bs=batch_size, collate_fn=f, **kwargs))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dac3b07f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_mem()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ff61b773",

nbs/03_convolutions.ipynb

@@ -1817,6 +1817,16 @@
     "We have `ch_out` filters like this, so in the end, the result of our convolutional layer will be a batch of images with `ch_out` channels."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rapidai.init import clean_mem\n",
+    "clean_mem()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

nbs/04_learner.ipynb

@@ -123,7 +123,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "bs = 1024\n",
+    "bs = 256\n",
     "tds = dsd.with_transform(transformi)"
    ]
   },
@@ -758,7 +758,7 @@
     "    order = MetricsCB.order+1\n",
     "    def __init__(self, plot=False): self.plot = plot\n",
     "    def before_fit(self, learn):\n",
-    "        learn.epochs = self.mbar = master_bar(range(learn.epochs))\n",
+    "        learn.epochs = self.mbar = master_bar(learn.epochs)\n",
     "        self.first = True\n",
     "        if hasattr(learn, 'metrics'): learn.metrics._log = self._log\n",
     "        self.losses = []\n",
@@ -1391,6 +1391,17 @@
     "MomentumLearner(get_model(), dls, F.cross_entropy, cbs=cbs).lr_find()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b182795",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rapidai.init import clean_mem\n",
+    "clean_mem()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7bfb9bd2",

nbs/05_activations.ipynb

@@ -79,7 +79,7 @@
     "x,y = 'image','label'\n",
     "name = \"fashion_mnist\"\n",
     "dsd = load_dataset(name)\n",
-    "bs = 1024\n",
+    "bs = 256\n",
     "\n",
     "@inplace\n",
     "def transformi(b): b[x] = [TF.to_tensor(o) for o in b[x]]\n",
@@ -1479,6 +1479,16 @@
     "astats.plot_stats()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rapidai.init import clean_mem\n",
+    "clean_mem()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

nbs/06_initializing.ipynb

@@ -87,7 +87,7 @@
     "@inplace\n",
     "def transformi(b): b[xl] = [TF.to_tensor(o) for o in b[xl]]\n",
     "\n",
-    "bs = 1024\n",
+    "bs = 256\n",
     "tds = dsd.with_transform(transformi)\n",
     "\n",
     "dls = DataLoaders.from_dd(tds, bs, num_workers=4)\n",
@@ -2606,6 +2606,16 @@
     "learn.fit(2)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rapidai.init import clean_mem\n",
+    "clean_mem()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

nbs/07_accel_sgd.ipynb

@@ -89,7 +89,7 @@
     "name = \"fashion_mnist\"\n",
     "dsd = load_dataset(name)\n",
     "\n",
-    "bs = 1024\n",
+    "bs = 256\n",
     "xmean,xstd = 0.28, 0.35\n",
     "\n",
     "@inplace\n",
@@ -1603,6 +1603,16 @@
     "rec.plot()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rapidai.init import clean_mem\n",
+    "clean_mem()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

nbs/08_resnet.ipynb

@@ -74,7 +74,7 @@
    "source": [
     "xl,yl = 'image','label'\n",
     "name = \"fashion_mnist\"\n",
-    "bs = 1024\n",
+    "bs = 256\n",
     "xmean,xstd = 0.28, 0.35\n",
     "\n",
     "@inplace\n",
@@ -723,6 +723,16 @@
     "learn.fit(epochs)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rapidai.init import clean_mem\n",
+    "clean_mem()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

nbs/09_augment.ipynb

@@ -98,7 +98,7 @@
    "source": [
     "xl,yl = 'image','label'\n",
     "name = \"fashion_mnist\"\n",
-    "bs = 1024\n",
+    "bs = 256\n",
     "xmean,xstd = 0.28, 0.35\n",
     "\n",
     "@inplace\n",
@@ -4236,6 +4236,16 @@
     "torch.save(learn.model, 'models/data_aug2.pkl')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rapidai.init import clean_mem\n",
+    "clean_mem()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

nbs/sidebar.yml

@@ -12,7 +12,3 @@ website:
       - 07_accel_sgd.ipynb
       - 08_resnet.ipynb
       - 09_augment.ipynb
-      - 10_DDPM_v2.ipynb
-      - 11_fid.ipynb
-      - 12_diffusion-attn-cond.ipynb
-      - consistency.ipynb

rapidai/__init__.py

@@ -1 +1 @@
-__version__ = "0.0.2"
+__version__ = "0.0.3"

settings.ini

@@ -1,29 +1,22 @@
 [DEFAULT]
-### Python library 
 repo = rapidai
 lib_name = rapidai
-version = 0.0.2
+version = 0.0.3
 min_python = 3.9
 license = apache2
-
-### Nbdev 
 black_formatting = False
 doc_path = _docs
 lib_path = rapidai
 nbs_path = nbs
 recursive = True
 tst_flags = notest
 put_version_in_init = True
-
-### Docs
 branch = main
 custom_sidebar = False
 doc_host = https://m-gopichand.github.io
 doc_baseurl = /rapidai
 git_url = https://github.com/m-gopichand/rapidai
 title = rapidai
-
-### PyPI
 audience = Developers
 author = Gopichand Madala
 author_email = [email protected]
@@ -40,9 +33,8 @@ jupyter_hooks = False
 clean_ids = True
 clear_all = False
 cell_number = True
-
-### Optional
-requirements = matplotlib datasets fastprogress fastcore 
+requirements = matplotlib datasets fastprogress fastcore
 pip_requirements = torch>=1.7,<=2.4 torcheval diffusers einops timm
 conda_requirements = pytorch>=1.7,<1.14
-dev_requirements = nbdev 
+dev_requirements = nbdev
+