automatic import of python-coinstac-dinunetopeneuler20.03

3 files changed, 377 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..808c409 100644
--- a/.gitignore
+++ b/.gitignore
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+/coinstac-dinunet-2.5.3.tar.gz
diff --git a/python-coinstac-dinunet.spec b/python-coinstac-dinunet.spec
new file mode 100644
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--- /dev/null
+++ b/python-coinstac-dinunet.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-coinstac-dinunet
+Version:	2.5.3
+Release:	1
+Summary:	Distributed Neural Network implementation on COINSTAC.
+License:	MIT
+URL:		https://github.com/trendscenter/coinstac-dinunet
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/dc/98/4c90d4ea832696e1c396119f4165c4cdfa736749df621296f7ff015a3662/coinstac-dinunet-2.5.3.tar.gz
+BuildArch:	noarch
+
+
+%description
+## coinstac-dinunet
+#### Distributed Neural Network implementation  on COINSTAC.
+
+![PyPi version](https://img.shields.io/pypi/v/coinstac-dinunet)
+[![YourActionName Actions Status](https://github.com/trendscenter/coinstac-dinunet/workflows/build/badge.svg)](https://github.com/trendscenter/coinstac-dinunet/actions)
+![versions](https://img.shields.io/pypi/pyversions/pybadges.svg)
+
+```
+pip install coinstac-dinunet
+```
+#### Specify supported packages like pytorch & torchvision in a requirements.txt file
+#### Highlights:
+```
+1. Handles multi-network/complex training schemes.
+2. Automatic data splitting/k-fold cross validation.
+3. Automatic model checkpointing.
+4. GPU enabled local sites.
+5. Customizable metrics(w/Auto serialization between nodes) to work with any schemes.
+6. We can integrate any custom reduction and learning mechanism by extending coinstac_dinunet.distrib.reducer/learner.
+7. Realtime profiling each sites by specifying in compspec file(see dinune_fsv example below for details). 
+...
+```
+
+
+<hr />
+
+![DINUNET](assets/dinunet.png)
+
+
+### Working examples:
+1. **[FreeSurfer volumes classification.](https://github.com/trendscenter/dinunet_implementations/)**
+2. **[VBM 3D images classification.](https://github.com/trendscenter/dinunet_implementations_gpu)**
+
+### [Running an analysis](https://github.com/trendscenter/coinstac-instructions/blob/master/coinstac-how-to-run-analysis.md) in the coinstac App.
+### Add a new NN computation to COINSTAC (Development guide):
+#### imports
+```python
+from coinstac_dinunet import COINNDataset, COINNTrainer, COINNLocal
+from coinstac_dinunet.metrics import COINNAverages, Prf1a
+```
+
+#### 1. Define Data Loader
+```python
+class MyDataset(COINNDataset):
+    def __init__(self, **kw):
+        super().__init__(**kw)
+        self.labels = None
+
+    def load_index(self, id, file):
+        data_dir = self.path(id, 'data_dir') # data_dir comes from inputspecs.json
+        ...
+        self.indices.append([id, file])
+
+    def __getitem__(self, ix):
+        id, file = self.indices[ix]
+        data_dir = self.path(id, 'data_dir') # data_dir comes from inputspecs.json
+        label_dir = self.path(id, 'label_dir') # label_dir comes from inputspecs.json
+        ...
+        # Logic to load, transform single data item.
+        ...
+        return {'inputs':.., 'labels': ...}
+```
+
+#### 2. Define Trainer
+```python
+class MyTrainer(COINNTrainer):
+    def __init__(self, **kw):
+        super().__init__(**kw)
+
+    def _init_nn_model(self):
+        self.nn['model'] = MYModel(in_size=self.cache['input_size'], out_size=self.cache['num_class'])
+
+    def iteration(self, batch):
+        inputs, labels = batch['inputs'].to(self.device['gpu']).float(), batch['labels'].to(self.device['gpu']).long()
+
+        out = F.log_softmax(self.nn['model'](inputs), 1)
+        loss = F.nll_loss(out, labels)
+        _, predicted = torch.max(out, 1)
+        score = self.new_metrics()
+        score.add(predicted, labels)
+        val = self.new_averages()
+        val.add(loss.item(), len(inputs))
+        return {'out': out, 'loss': loss, 'averages': val,
+                'metrics': score, 'prediction': predicted}
+```
+
+#### 3. Add entries to:
+* Local node entry point [CPU](https://github.com/trendscenter/dinunet_implementations/blob/master/local.py), [GPU](https://github.com/trendscenter/dinunet_implementations_gpu/blob/master/local.py)
+* Aggregator node point [CPU](https://github.com/trendscenter/dinunet_implementations/blob/master/remote.py), [GPU](https://github.com/trendscenter/dinunet_implementations_gpu/blob/master/remote.py)
+* compspec.json file [CPU](https://github.com/trendscenter/dinunet_implementations/blob/master/compspec.json), [GPU](https://github.com/trendscenter/dinunet_implementations_gpu/blob/master/compspec.json)
+
+<hr />
+
+#### Advanced use cases:
+
+* **Define custom metrics:**
+  - Extend [coinstac_dinunet.metrics.COINNMetrics](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/metrics/metrics.py)
+  - Example: [coinstac_dinunet.metrics.Prf1a](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/metrics/metrics.py) for Precision, Recall, F1, and Accuracy
+
+* **Define [custom DataHandle](https://github.com/trendscenter/dinunet_implementations/blob/8411bb95a0bef86bf6451b39f580f79c3c74eb94/comps/fs/__init__.py#L75)**
+* **Define [Custom Learner](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/distrib/learner.py) / [custom Aggregator](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/distrib/reducer.py) (Default is Distributed SGD)**
+
+
+%package -n python3-coinstac-dinunet
+Summary:	Distributed Neural Network implementation on COINSTAC.
+Provides:	python-coinstac-dinunet
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-coinstac-dinunet
+## coinstac-dinunet
+#### Distributed Neural Network implementation  on COINSTAC.
+
+![PyPi version](https://img.shields.io/pypi/v/coinstac-dinunet)
+[![YourActionName Actions Status](https://github.com/trendscenter/coinstac-dinunet/workflows/build/badge.svg)](https://github.com/trendscenter/coinstac-dinunet/actions)
+![versions](https://img.shields.io/pypi/pyversions/pybadges.svg)
+
+```
+pip install coinstac-dinunet
+```
+#### Specify supported packages like pytorch & torchvision in a requirements.txt file
+#### Highlights:
+```
+1. Handles multi-network/complex training schemes.
+2. Automatic data splitting/k-fold cross validation.
+3. Automatic model checkpointing.
+4. GPU enabled local sites.
+5. Customizable metrics(w/Auto serialization between nodes) to work with any schemes.
+6. We can integrate any custom reduction and learning mechanism by extending coinstac_dinunet.distrib.reducer/learner.
+7. Realtime profiling each sites by specifying in compspec file(see dinune_fsv example below for details). 
+...
+```
+
+
+<hr />
+
+![DINUNET](assets/dinunet.png)
+
+
+### Working examples:
+1. **[FreeSurfer volumes classification.](https://github.com/trendscenter/dinunet_implementations/)**
+2. **[VBM 3D images classification.](https://github.com/trendscenter/dinunet_implementations_gpu)**
+
+### [Running an analysis](https://github.com/trendscenter/coinstac-instructions/blob/master/coinstac-how-to-run-analysis.md) in the coinstac App.
+### Add a new NN computation to COINSTAC (Development guide):
+#### imports
+```python
+from coinstac_dinunet import COINNDataset, COINNTrainer, COINNLocal
+from coinstac_dinunet.metrics import COINNAverages, Prf1a
+```
+
+#### 1. Define Data Loader
+```python
+class MyDataset(COINNDataset):
+    def __init__(self, **kw):
+        super().__init__(**kw)
+        self.labels = None
+
+    def load_index(self, id, file):
+        data_dir = self.path(id, 'data_dir') # data_dir comes from inputspecs.json
+        ...
+        self.indices.append([id, file])
+
+    def __getitem__(self, ix):
+        id, file = self.indices[ix]
+        data_dir = self.path(id, 'data_dir') # data_dir comes from inputspecs.json
+        label_dir = self.path(id, 'label_dir') # label_dir comes from inputspecs.json
+        ...
+        # Logic to load, transform single data item.
+        ...
+        return {'inputs':.., 'labels': ...}
+```
+
+#### 2. Define Trainer
+```python
+class MyTrainer(COINNTrainer):
+    def __init__(self, **kw):
+        super().__init__(**kw)
+
+    def _init_nn_model(self):
+        self.nn['model'] = MYModel(in_size=self.cache['input_size'], out_size=self.cache['num_class'])
+
+    def iteration(self, batch):
+        inputs, labels = batch['inputs'].to(self.device['gpu']).float(), batch['labels'].to(self.device['gpu']).long()
+
+        out = F.log_softmax(self.nn['model'](inputs), 1)
+        loss = F.nll_loss(out, labels)
+        _, predicted = torch.max(out, 1)
+        score = self.new_metrics()
+        score.add(predicted, labels)
+        val = self.new_averages()
+        val.add(loss.item(), len(inputs))
+        return {'out': out, 'loss': loss, 'averages': val,
+                'metrics': score, 'prediction': predicted}
+```
+
+#### 3. Add entries to:
+* Local node entry point [CPU](https://github.com/trendscenter/dinunet_implementations/blob/master/local.py), [GPU](https://github.com/trendscenter/dinunet_implementations_gpu/blob/master/local.py)
+* Aggregator node point [CPU](https://github.com/trendscenter/dinunet_implementations/blob/master/remote.py), [GPU](https://github.com/trendscenter/dinunet_implementations_gpu/blob/master/remote.py)
+* compspec.json file [CPU](https://github.com/trendscenter/dinunet_implementations/blob/master/compspec.json), [GPU](https://github.com/trendscenter/dinunet_implementations_gpu/blob/master/compspec.json)
+
+<hr />
+
+#### Advanced use cases:
+
+* **Define custom metrics:**
+  - Extend [coinstac_dinunet.metrics.COINNMetrics](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/metrics/metrics.py)
+  - Example: [coinstac_dinunet.metrics.Prf1a](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/metrics/metrics.py) for Precision, Recall, F1, and Accuracy
+
+* **Define [custom DataHandle](https://github.com/trendscenter/dinunet_implementations/blob/8411bb95a0bef86bf6451b39f580f79c3c74eb94/comps/fs/__init__.py#L75)**
+* **Define [Custom Learner](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/distrib/learner.py) / [custom Aggregator](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/distrib/reducer.py) (Default is Distributed SGD)**
+
+
+%package help
+Summary:	Development documents and examples for coinstac-dinunet
+Provides:	python3-coinstac-dinunet-doc
+%description help
+## coinstac-dinunet
+#### Distributed Neural Network implementation  on COINSTAC.
+
+![PyPi version](https://img.shields.io/pypi/v/coinstac-dinunet)
+[![YourActionName Actions Status](https://github.com/trendscenter/coinstac-dinunet/workflows/build/badge.svg)](https://github.com/trendscenter/coinstac-dinunet/actions)
+![versions](https://img.shields.io/pypi/pyversions/pybadges.svg)
+
+```
+pip install coinstac-dinunet
+```
+#### Specify supported packages like pytorch & torchvision in a requirements.txt file
+#### Highlights:
+```
+1. Handles multi-network/complex training schemes.
+2. Automatic data splitting/k-fold cross validation.
+3. Automatic model checkpointing.
+4. GPU enabled local sites.
+5. Customizable metrics(w/Auto serialization between nodes) to work with any schemes.
+6. We can integrate any custom reduction and learning mechanism by extending coinstac_dinunet.distrib.reducer/learner.
+7. Realtime profiling each sites by specifying in compspec file(see dinune_fsv example below for details). 
+...
+```
+
+
+<hr />
+
+![DINUNET](assets/dinunet.png)
+
+
+### Working examples:
+1. **[FreeSurfer volumes classification.](https://github.com/trendscenter/dinunet_implementations/)**
+2. **[VBM 3D images classification.](https://github.com/trendscenter/dinunet_implementations_gpu)**
+
+### [Running an analysis](https://github.com/trendscenter/coinstac-instructions/blob/master/coinstac-how-to-run-analysis.md) in the coinstac App.
+### Add a new NN computation to COINSTAC (Development guide):
+#### imports
+```python
+from coinstac_dinunet import COINNDataset, COINNTrainer, COINNLocal
+from coinstac_dinunet.metrics import COINNAverages, Prf1a
+```
+
+#### 1. Define Data Loader
+```python
+class MyDataset(COINNDataset):
+    def __init__(self, **kw):
+        super().__init__(**kw)
+        self.labels = None
+
+    def load_index(self, id, file):
+        data_dir = self.path(id, 'data_dir') # data_dir comes from inputspecs.json
+        ...
+        self.indices.append([id, file])
+
+    def __getitem__(self, ix):
+        id, file = self.indices[ix]
+        data_dir = self.path(id, 'data_dir') # data_dir comes from inputspecs.json
+        label_dir = self.path(id, 'label_dir') # label_dir comes from inputspecs.json
+        ...
+        # Logic to load, transform single data item.
+        ...
+        return {'inputs':.., 'labels': ...}
+```
+
+#### 2. Define Trainer
+```python
+class MyTrainer(COINNTrainer):
+    def __init__(self, **kw):
+        super().__init__(**kw)
+
+    def _init_nn_model(self):
+        self.nn['model'] = MYModel(in_size=self.cache['input_size'], out_size=self.cache['num_class'])
+
+    def iteration(self, batch):
+        inputs, labels = batch['inputs'].to(self.device['gpu']).float(), batch['labels'].to(self.device['gpu']).long()
+
+        out = F.log_softmax(self.nn['model'](inputs), 1)
+        loss = F.nll_loss(out, labels)
+        _, predicted = torch.max(out, 1)
+        score = self.new_metrics()
+        score.add(predicted, labels)
+        val = self.new_averages()
+        val.add(loss.item(), len(inputs))
+        return {'out': out, 'loss': loss, 'averages': val,
+                'metrics': score, 'prediction': predicted}
+```
+
+#### 3. Add entries to:
+* Local node entry point [CPU](https://github.com/trendscenter/dinunet_implementations/blob/master/local.py), [GPU](https://github.com/trendscenter/dinunet_implementations_gpu/blob/master/local.py)
+* Aggregator node point [CPU](https://github.com/trendscenter/dinunet_implementations/blob/master/remote.py), [GPU](https://github.com/trendscenter/dinunet_implementations_gpu/blob/master/remote.py)
+* compspec.json file [CPU](https://github.com/trendscenter/dinunet_implementations/blob/master/compspec.json), [GPU](https://github.com/trendscenter/dinunet_implementations_gpu/blob/master/compspec.json)
+
+<hr />
+
+#### Advanced use cases:
+
+* **Define custom metrics:**
+  - Extend [coinstac_dinunet.metrics.COINNMetrics](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/metrics/metrics.py)
+  - Example: [coinstac_dinunet.metrics.Prf1a](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/metrics/metrics.py) for Precision, Recall, F1, and Accuracy
+
+* **Define [custom DataHandle](https://github.com/trendscenter/dinunet_implementations/blob/8411bb95a0bef86bf6451b39f580f79c3c74eb94/comps/fs/__init__.py#L75)**
+* **Define [Custom Learner](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/distrib/learner.py) / [custom Aggregator](https://github.com/trendscenter/coinstac-dinunet/blob/main/coinstac_dinunet/distrib/reducer.py) (Default is Distributed SGD)**
+
+
+%prep
+%autosetup -n coinstac-dinunet-2.5.3
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-coinstac-dinunet -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.5.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..593e27f
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+68ee29b055c256a5009388aa62b409c0  coinstac-dinunet-2.5.3.tar.gz