Edgebrook Farm Curated Seed Co. and Stems Flower Farm
This husband and wife Ontario seed supplier duo offer up lots of options for growing flowers and vegetables. Their flower farm supplies local florists and lucky for us, we can get in on the online seed orders too. Tip: if you live in the Cookstown area, their fresh-cut blooms will be available for contactless pickup and porch delivery starting early summer.
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Whether as a form of stress-relief or to create a reliable source of food, gardening has perhaps never seemed so appealing to so many. In a year where browsing your gardening centre may not be possible, seeds play an even more critical part — and they are in high demand. Although these little packets are incredibly popular these days, there are so many seed suppliers still shipping the products needed to get a garden growing at home this spring. In fact, their catalogues contain just about everything you could ever want to grow in your backyard or on your balcony — plus the tools to tackle it. In search of flowers, fruit and veggies to sow, we rounded up ten Canadian companies that are still accepting online orders right now.
W. H. Perron
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It’s our mission to make more seeds more accessible to more people; we believe everyone should have access to quality seeds and a diversity of varieties at a fair price.
PO Box 7400, Upper Ferntree Gully, VIC 3156, Australia.
The cuDNN library, used by CUDA convolution operations, can be a source of nondeterminism across multiple executions of an application. When a cuDNN convolution is called with a new set of size parameters, an optional feature can run multiple convolution algorithms, benchmarking them to find the fastest one. Then, the fastest algorithm will be used consistently during the rest of the process for the corresponding set of size parameters. Due to benchmarking noise and different hardware, the benchmark may select different algorithms on subsequent runs, even on the same machine.
For example, running the nondeterministic CUDA implementation of torch.Tensor.index_add_() will throw an error:
Random number generators in other libraries¶
However, if you do not need reproducibility across multiple executions of your application, then performance might improve if the benchmarking feature is enabled with torch.backends.cudnn.benchmark = True .
You can use torch.manual_seed() to seed the RNG for all devices (both CPU and CUDA):
If you or any of the libraries you are using rely on NumPy, you can seed the global NumPy RNG with: