![]() In 2017, Boston Scientific was one of the first medical device manufacturers to pledge carbon neutrality for scope 1 and 2 greenhouse gas (GHG) emissions in all manufacturing and key distribution sites by 2030. Then came the GEMS framework in 2013 which was quickly followed by a range of milestone years. The company received ISO 14001 certification for Galway in 2001 – ensuring an effective environmental system that could be measured and improved. To achieve its ambitious goals, Boston Scientific has been making a number of investments not only in Ireland but also across the full breadth of its global operations. The common goal was clear: the companies were all paying a high energy rate – and this began the first driver to cut fossil fuel use and to transition towards less expensive and more efficient options where available.Įileen Sharpe, Executive Vice President, IDA Ireland Turning theory into action Because of this, energy conservation fast became a very important element of running operations in Ireland and it ultimately started to bring the manufacturing industry together – and began what would become a core element of operating in Ireland for many organisations: collaboration.Ī leading example of what this collaboration can achieve was demonstrated by a government-sponsored technology centre, known as I2E2, which was formed to enable the Irish manufacturing industry to improve competitiveness via breakthroughs in energy efficiency and cost reduction. With the company shipping well in excess of four million products from Ireland to the rest of the world – including defibrillators, pacemakers, heart stents and valves, vascular balloons, deep brain stimulators, catheters, coils and oesophageal stents – there was a heightened pressure for operational efficiency and high performance.īut twenty years ago, high-electricity costs in Ireland were a major obstacle for U.S.-headquartered companies – with a kilowatt-hour costing double in Ireland compared to mainland U.S. Memory efficiency was one of gensim’s design goals, and is a central feature of gensim, rather than something bolted on as an afterthought.Boston Scientific has had a presence in Ireland for nearly 30 years – establishing its first site in Galway in 1994. Memory-wise, gensim makes heavy use of Python’s built-in generators and iterators for streamed data processing. So while gensim-the-top-level-code is pure Python, it actually executes highly optimized Fortran/C under the hood, including multithreading (if your BLAS is so configured). Gensim taps into these low-level BLAS libraries, by means of its dependency on NumPy. Many scientific algorithms can be expressed in terms of large matrix operations (see the BLAS note above). How come gensim is so fast and memory efficient? Isn’t it pure Python, and isn’t Python slow and greedy? Support for Python 2.7 was dropped in gensim 4.0.0 – install gensim 3.8.3 if you must use Python 2.7. Gensim is being continuously tested under all supported Python versions. Or, if you have instead downloaded and unzipped the source tar.gz package: python setup.py installįor alternative modes of installation, see the documentation. Install the latest version of gensim: pip install -upgrade gensim On OSX, NumPy picks up its vecLib BLAS automatically, so you don’t need to do anything special. This is optional, but using an optimized BLAS such as MKL, ATLAS or OpenBLAS is known to improve performance by as much as an order of magnitude. It is also recommended you install a fast BLAS library before installing NumPy. You must have them installed prior to installing gensim. This software depends on NumPy and Scipy, two Python packages for scientific computing. If this feature list left you scratching your head, you can first read more about the Vectorĭocument analysis on Wikipedia. Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning.ĭistributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers.Įxtensive documentation and Jupyter Notebook tutorials. the corpus size (can process input larger than RAM, streamed, out-of-core)Įasy to plug in your own input corpus/datastream (simple streaming API)Įasy to extend with other Vector Space algorithms (simple transformation API)Įfficient multicore implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD), FeaturesĪll algorithms are memory-independent w.r.t. Target audience is the natural language processing (NLP) and information retrieval (IR) community. Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora.
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